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SGF-768 - Replace all direct URL/links with Asciidoc variables.

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Replace all product names with Asciidoc variables.

Replace all product versions with Asciidoc variables.
This commit is contained in:
John Blum
2018-07-17 14:38:49 -07:00
parent 771ff594e6
commit 6d34d24b2a
28 changed files with 1002 additions and 977 deletions
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1
pom.xml
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@@ -316,7 +316,6 @@
<configuration>
<backend>html5</backend>
<outputDirectory>${project.root}/target/site/reference/html</outputDirectory>
<sectids>false</sectids>
<sourceHighlighter>prettify</sourceHighlighter>
<attributes>
<linkcss>true</linkcss>

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src/main/asciidoc/appendix/appendix-schema.adoc
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@@ -1,6 +1,6 @@
[[appendix-schema]]
[appendix]
= Spring Data for Pivotal GemFire Schema
= Spring Data for {data-store-name} Schema
* http://www.springframework.org/schema/gemfire/spring-gemfire.xsd[Spring Data for Pivotal GemFire Core Schema (`gfe` namespace)]
* http://www.springframework.org/schema/gemfire/spring-data-gemfire.xsd[Spring Data for Pivotal GemFire Data Access Schema (`gfe-data` namespace)]
* http://www.springframework.org/schema/gemfire/spring-gemfire.xsd[Spring Data for {data-store-name} Core Schema (`gfe` namespace)]
* http://www.springframework.org/schema/gemfire/spring-data-gemfire.xsd[Spring Data for {data-store-name} Data Access Schema (`gfe-data` namespace)]

32
src/main/asciidoc/index.adoc
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@@ -1,7 +1,7 @@
= Spring Data for Pivotal GemFire Reference Guide
= Spring Data for {data-store-name} Reference Guide
Costin Leau; David Turanski; John Blum; Oliver Gierke; Jay Bryant
:revnumber: {version}
:revdate: {localdate}
:revnumber: {version}
:linkcss:
:doctype: book
:docinfo: shared
@@ -10,8 +10,30 @@ Costin Leau; David Turanski; John Blum; Oliver Gierke; Jay Bryant
:source-highlighter: prettify
:icons: font
:imagesdir: images
:apache-geode-version: 16
:apache-geode-docs: http://geode.apache.org/docs/guide/{apache-geode-version}
:apache-geode-javadoc: http://geode.apache.org/releases/latest/javadoc
:apache-geode-website: http://geode.apache.org
:apache-geode-wiki: https://cwiki.apache.org/confluence/display/GEODE
:data-store-name: Pivotal GemFire
:data-store-version: 9.5.1
:pivotal-gemfire-version: 95
:pivotal-gemfire-docs: http://gemfire.docs.pivotal.io/{pivotal-gemfire-version}
:pivotal-gemfire-javadoc: http://gemfire-{pivotal-gemfire-version}-javadocs.docs.pivotal.io/
:pivotal-gemfire-website: https://pivotal.io/pivotal-gemfire
:sdg-name: Spring Data for {data-store-name}
:spring-data-commons-docs: https://docs.spring.io/spring-data/commons/docs/current/reference
:spring-data-commons-docs-html: {spring-data-commons-docs}/html
:spring-data-commons-javadoc: https://docs.spring.io/spring-data/commons/docs/current/api
:spring-data-website: https://spring.io/projects/spring-data
:spring-framework-docs: https://docs.spring.io/spring/docs/current/spring-framework-reference
:spring-framework-javadoc: https://docs.spring.io/spring/docs/current/javadoc-api
:spring-framework-website: https://spring.io/projects/spring-framework
:x-data-store-docs: {apache-geode-docs}
:x-data-store-javadoc: {pivotal-gemfire-javadoc}
:x-data-store-website: {pivotal-gemfire-website}
:x-data-store-wiki: {apache-geode-wiki}
ifdef::backend-epub3[:front-cover-image: image:epub-cover.png[Front Cover,1050,1600]]
:spring-data-commons-docs: {basedocdir}/../../../../spring-data-commons/src/main/asciidoc
(C) 2010-2018 The original authors.
@@ -45,8 +67,8 @@ include::{basedocdir}/reference/samples.adoc[leveloffset=+1]
= Resources
In addition to this reference documentation, there are a number of other resources that may help you learn
how to use Pivotal GemFire with the _Spring Framework_. These additional, third-party resources are enumerated
in this section.
how to use {data-store-product-name} with the _Spring Framework_. These additional, third-party resources
are enumerated in this section.
include::{basedocdir}/links.adoc[leveloffset=+1]

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src/main/asciidoc/introduction/introduction.adoc
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@@ -1,6 +1,6 @@
[[introduction]]
= Introduction
The Spring Data for Pivotal GemFire reference guide explains how to use the Spring Framework
to configure and develop applications with Pivotal GemFire. It presents the basic concepts and provides
numerous examples to help you get started.
The Spring Data for {data-store-name} reference guide explains how to use the Spring Framework
to configure and develop applications with {data-store-name}. It presents the basic concepts
and provides numerous examples to help you get started quickly.

104
src/main/asciidoc/introduction/new-features.adoc
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@@ -1,119 +1,119 @@
[[new-features]]
= New Features
NOTE: As of the 1.2.0.RELEASE, this project, formerly known as Spring Pivotal GemFire, has been renamed to
Spring Data for Pivotal GemFire to reflect that it is now a module of the
NOTE: As of the 1.2.0.RELEASE, this project, formerly known as Spring GemFire, has been renamed to
Spring Data for {data-store-name} to reflect that it is now a module of the
http://projects.spring.io/spring-data/[Spring Data] project
and built on https://pivotal.io/pivotal-gemfire[Pivotal GemFire].
and built on {x-data-store-website}[{data-store-name}].
[[new-in-1-2-0]]
== New in the 1.2 Release
* Full support for Pivotal GemFire configuration through the SDG *gfe* namespace. Now Pivotal GemFire components may be configured completely without requiring a native cache.xml file.
* WAN Gateway support for Pivotal GemFire 6.6.x. See <<bootstrap:gateway>>.
* Full support for {data-store-name} configuration through the SDG *gfe* namespace. Now {data-store-name} components may be configured completely without requiring a native cache.xml file.
* WAN Gateway support for {data-store-name} 6.6.x. See <<bootstrap:gateway>>.
* Spring Data Repository support using a dedicated SDG namespace, *gfe-data*. See <<gemfire-repositories>>
* Namespace support for registering Pivotal GemFire Functions. See <<bootstrap:function>>
* Namespace support for registering {data-store-name} Functions. See <<bootstrap:function>>
* A top-level `<disk-store>` element has been added to the SDG *gfe* namespace to allow sharing of persist stores among regions
as well as other components that support persistent backup or overflow. See <<bootstrap-diskstore>>
+
WARNING: The `<*-region>` elements no longer allow a nested `<disk-store>` element.
+
* Pivotal GemFire Sub-Regions are supported by nested `<*-region>` elements.
* {data-store-name} Sub-Regions are supported by nested `<*-region>` elements.
* A `<local-region>` element has been added to configure a Local Region.
* Support for the re-designed WAN Gateway in Pivotal GemFire 7.0.
* Support for the re-designed WAN Gateway in {data-store-name} 7.0.
[[new-in-1-3-0]]
== New in the 1.3 Release
* Annotation support for Pivotal GemFire Functions. It is now possible to declare and register Functions written as POJOs by using annotations. In addition, function executions are defined as
* Annotation support for {data-store-name} Functions. It is now possible to declare and register Functions written as POJOs by using annotations. In addition, function executions are defined as
annotated interfaces, similar to the way Spring Data Repositories work. See <<function-annotations>>.
* Added a `<datasource>` element to the SDG `gfe-data` namespace to simplify establishing a basic <<data-access:datasource,client connection>> to a Pivotal GemFire data grid.
* Added a `<datasource>` element to the SDG `gfe-data` namespace to simplify establishing a basic <<data-access:datasource,client connection>> to a {data-store-name} data grid.
* Added a `<json-region-autoproxy>` element to the SDG `gfe-data` namespace to <<bootstrap:region:json,support JSON>> features introduced
in Pivotal GemFire 7.0, enabling Spring AOP to perform the necessary conversions automatically on Region operations.
* Upgraded to Pivotal GemFire 7.0.1 and added namespace support for new AsyncEventQueue attributes.
in {data-store-name} 7.0, enabling Spring AOP to perform the necessary conversions automatically on Region operations.
* Upgraded to {data-store-name} 7.0.1 and added namespace support for new AsyncEventQueue attributes.
* Added support for setting subscription interest policy on Regions.
* Support for void returns on Function executions. See <<function-annotations>> for complete details.
* Support for persisting Local Regions. See <<bootstrap:region:local>>.
* Support for entry time-to-live and entry idle-time on a Pivotal GemFire Client Cache. See <<bootstrap:cache:client>>.
* Support for multiple Spring Data for Pivotal GemFire web-based applications by using a single Pivotal GemFire cluster, operating concurrently inside tc Server.
* Support for `concurrency-checks-enabled` on all Pivotal GemFire Cache Region definitions by using the SDG `gfe` namespace. See <<bootstrap:region:common:attributes>>.
* Support for entry time-to-live and entry idle-time on a {data-store-name} Client Cache. See <<bootstrap:cache:client>>.
* Support for multiple Spring Data for {data-store-name} web-based applications by using a single {data-store-name} cluster, operating concurrently inside tc Server.
* Support for `concurrency-checks-enabled` on all {data-store-name} Cache Region definitions by using the SDG `gfe` namespace. See <<bootstrap:region:common:attributes>>.
* Support for Cache Loaders and Cache Writers on the Client for Local Regions.
* Support for registering CacheListeners, AsyncEventQueues, and Gateway Senders on Pivotal GemFire Cache Sub-Regions.
* Support for PDX persistent keys in Pivotal GemFire Regions.
* Support for registering CacheListeners, AsyncEventQueues, and Gateway Senders on {data-store-name} Cache Sub-Regions.
* Support for PDX persistent keys in {data-store-name} Regions.
* Support for correct Partition Region bean creation in a Spring context when collocation is specified with the `colocated-with` attribute.
* Full support for Pivotal GemFire Cache Sub-Regions using proper, nested `<*-region>` element syntax in the SDG `gfe` namespace.
* Upgraded Spring Data for Pivotal GemFire to Spring Framework 3.2.8.
* Upgraded Spring Data for Pivotal GemFire to Spring Data Commons 1.7.1.
* Full support for {data-store-name} Cache Sub-Regions using proper, nested `<*-region>` element syntax in the SDG `gfe` namespace.
* Upgraded Spring Data for {data-store-name} to Spring Framework 3.2.8.
* Upgraded Spring Data for {data-store-name} to Spring Data Commons 1.7.1.
[[new-in-1-4-0]]
== New in the 1.4 Release
* Upgraded to Pivotal GemFire 7.0.2.
* Upgraded to {data-store-name} 7.0.2.
* Upgraded to Spring Data Commons 1.8.x.RELEASE.
* Upgraded to Spring Framework 3.2.x.RELEASE.
* Integrated Spring Data for Pivotal GemFire with Spring Boot, which includes both a `spring-boot-starter-data-gemfire` POM
and a Spring Boot sample application that demonstrates Pivotal GemFire Cache Transactions configured with SDG
* Integrated Spring Data for {data-store-name} with Spring Boot, which includes both a `spring-boot-starter-data-gemfire` POM
and a Spring Boot sample application that demonstrates {data-store-name} Cache Transactions configured with SDG
and bootstrapped with Spring Boot.
* Added support for bootstrapping a Spring `ApplicationContext` in a Pivotal GemFire Server when started from `Gfsh`.
* Added support for bootstrapping a Spring `ApplicationContext` in a {data-store-name} Server when started from `Gfsh`.
See <<gemfire-bootstrap>> for more details.
* Added support for persisting application domain object and entities to multiple Pivotal GemFire Cache Regions.
* Added support for persisting application domain object and entities to multiple {data-store-name} Cache Regions.
See <<mapping.entities>> for more details.
* Added support for persisting application domain object and entities to Pivotal GemFire Cache Sub-Regions, avoiding collisions
* Added support for persisting application domain object and entities to {data-store-name} Cache Sub-Regions, avoiding collisions
when Sub-Regions are uniquely identifiable but identically named.
See <<mapping.entities>> for more details.
* Added strict XSD type rules to and full support for Data Policies and Region Shortcuts
on all Pivotal GemFire Cache Region types.
on all {data-store-name} Cache Region types.
* Changed the default behavior of SDG `<*-region>` elements from lookup to always create a new Region
along with an option to restore the old behavior (by using the `ignore-if-exists` attribute).
See <<bootstrap:region:common:attributes, Common Region Attributes>>
and <<bootstrap:region:common:regions-subregions-lookups-caution>>
for more details.
* Spring Data for Pivotal GemFire can now be fully built and ran on JDK 7 and JDK 8.
* Spring Data for {data-store-name} can now be fully built and ran on JDK 7 and JDK 8.
[[new-in-1-5-0]]
== New in the 1.5 Release
* Maintained support for Pivotal GemFire 7.0.2.
* Maintained support for {data-store-name} 7.0.2.
* Upgraded to _Spring Data Commons_ 1.9.x.RELEASE.
* Upgraded to _Spring Framework_ 4.0.x.RELEASE.
* Migrated the Reference Guide to Asciidoc.
* Renewed support for deploying Spring Data for Pivotal GemFire in an OSGi container.
* Removed all default values in the Spring Data for Pivotal GemFire XML namespace Region-type elements to
rely on Pivotal GemFire defaults instead.
* Renewed support for deploying Spring Data for {data-store-name} in an OSGi container.
* Removed all default values in the Spring Data for {data-store-name} XML namespace Region-type elements to
rely on {data-store-name} defaults instead.
* Added convenience to automatically create Disk Store directory locations.
* SDG annotated Function implementations can now be executed from `Gfsh`.
* Enabled Pivotal GemFire `GatewayReceivers` to be started manually.
* Enabled {data-store-name} `GatewayReceivers` to be started manually.
* Added support for Auto Region Lookups. See <<bootstrap:region:auto-lookup>> for further details.
* Added support for Region Templates. See <<bootstrap:region:common:region-templates>> for further details.
[[new-in-1-6-0]]
== New in the 1.6 Release
* Upgraded to Pivotal GemFire 8.0.0.
* Upgraded to {data-store-name} 8.0.0.
* Upgraded to Spring Data Commons 1.10.x.RELEASE.
* Maintained support for Spring Framework 4.0.x.RELEASE.
* Added support for Pivotal GemFire 8's new Cluster-based Configuration.
* Enabled 'auto-reconnect' functionality to be employed in Spring-configured Pivotal GemFire Servers.
* Added support for {data-store-name} 8's new Cluster-based Configuration.
* Enabled 'auto-reconnect' functionality to be employed in Spring-configured {data-store-name} Servers.
* Allowed the creation of concurrent and parallel Async Event Queues and Gateway Senders.
* Added support for Pivotal GemFire 8's Region data compression.
* Added support for {data-store-name} 8's Region data compression.
* Added attributes to set both critical and warning percentages on Disk Store usage.
* Supported the capability to add the new EventSubstitutionFilters to GatewaySenders.
[[new-in-1-7-0]]
== New in the 1.7 Release
* Upgraded to Pivotal GemFire 8.1.0.
* Upgraded to {data-store-name} 8.1.0.
* Upgraded to Spring Data Commons 1.11.x.RELEASE.
* Upgraded to Spring Framework 4.1.x.RELEASE.
* Added early access support for Pivotal GemFire.
* Added support for adding Spring-defined Cache Listeners, Loaders, and Writers on "existing" Pivotal GemFire Regions
configured in Spring XML, `cache.xml`, or even with Pivotal GemFire's Cluster Config.
* Added early access support for {data-store-name}.
* Added support for adding Spring-defined Cache Listeners, Loaders, and Writers on "existing" {data-store-name} Regions
configured in Spring XML, `cache.xml`, or even with {data-store-name}'s Cluster Config.
* Added Spring JavaConfig support to `SpringContextBootstrappingInitializer`.
* Added support for custom `ClassLoaders` in `SpringContextBootstrappingInitializer` to load Spring-defined bean classes.
* Added support for `LazyWiringDeclarableSupport` re-initialization and complete replacement for `WiringDeclarableSupport`.
* Added `locators` and `servers` attributes to the `<gfe:pool>` element, allowing variable Locator and Server
endpoint lists configured with Spring's property placeholders.
* Enables the use of the `<gfe-data:datasource>` element with non-Spring-configured Pivotal GemFire Servers.
* Enables the use of the `<gfe-data:datasource>` element with non-Spring-configured {data-store-name} Servers.
* Added multi-index definition and creation support.
* <<bootstrap:region:expiration:annotation>>
* <<gemfire-repositories:oql-extensions>>
@@ -122,7 +122,7 @@ endpoint lists configured with Spring's property placeholders.
[[new-in-1-8-0]]
== New in the 1.8 Release
* Upgraded to Pivotal GemFire 8.2.0.
* Upgraded to {data-store-name} 8.2.0.
* Upgraded to Spring Data Commons 1.12.x.RELEASE.
* Upgraded to Spring Framework 4.2.x.RELEASE.
* Added Maven POM to build SDG with Maven.
@@ -132,32 +132,32 @@ endpoint lists configured with Spring's property placeholders.
* Added `durable-client-id` and `durable-client-timeout` attributes to `<gfe:client-cache>`.
* Made GemfirePersistentProperty now properly handle other non-entity, scalar-like types (such as `BigDecimal` and `BigInteger`).
* Prevented SDG-defined `Pools` from being destroyed before `Regions` that use those `Pools`.
* Handled case-insensitive Pivotal GemFire OQL queries defined as Repository query methods.
* Changed `Pivotal GemFireCache.evict(key)` to call `Region.remove(key)` in SDG's Spring Cache Abstraction support.
* Handled case-insensitive {data-store-name} OQL queries defined as Repository query methods.
* Changed `GemFireCache.evict(key)` to call `Region.remove(key)` in SDG's Spring Cache Abstraction support.
* Fixed `RegionNotFoundException` with Repository queries on a client `Region` associated with a specific `Pool`
configured for Pivotal GemFire server groups.
* Changed `Gateway Senders/Receivers` to no longer be tied to the _Spring_ container.
configured for {data-store-name} server groups.
* Changed `Gateway Senders/Receivers` to no longer be tied to the Spring container.
[[new-in-1-9-0]]
== New in the 1.9 Release
* Upgraded to Pivotal GemFire 8.2.4.
* Upgraded to {data-store-name} 8.2.4.
* Upgraded to Spring Data Commons 1.13.x.RELEASE.
* Upgraded to Spring Framework 4.3.x.RELEASE.
* Introduced an entirely new Annotation-based configuration model inspired by Spring Boot.
* Added support for suspend and resume in the `GemfireTransactionManager`.
* Added support in Repositories to use the bean `id` property as the Region key when the `@Id` annotation
is not present.
* Used `MappingPdxSerializer` as the default Pivotal GemFire serialization strategy when `@EnablePdx` is used.
* Used `MappingPdxSerializer` as the default {data-store-name} serialization strategy when `@EnablePdx` is used.
* Enabled `GemfireCacheManager` to explicitly list Region names to be used in the Spring's Caching Abstraction.
* Configured Pivotal GemFire Caches, CacheServers, Locators, Pools, Regions, Indexes, DiskStores, Expiration, Eviction,
* Configured {data-store-name} Caches, CacheServers, Locators, Pools, Regions, Indexes, DiskStores, Expiration, Eviction,
Statistics, Mcast, HttpService, Auth, SSL, Logging, System Properties.
* Added repository support with multiple Spring Data modules on the classpath.
[[new-in-2-0-0]]
== New in the 2.0 Release
* Upgraded to Pivotal GemFire 9.0.x.
* Upgraded to {data-store-name} 9.0.x.
* Upgraded to Spring Data Commons 2.0.x.RELEASE.
* Upgraded to Spring Framework 5.0.x.RELEASE.
* Reorganized the SDG codebase by better packaging different classes and components by concern.
@@ -168,4 +168,4 @@ Statistics, Mcast, HttpService, Auth, SSL, Logging, System Properties.
* Renamed `@EnableIndexes` to `@EnableIndexing`.
* Introduced a `InterestsBuilder` class to easily and conveniently express Interests in keys and values between client
and server when using JavaConfig.
* Added support for Off-Heap, Redis Adapter, and Pivotal GemFire's new Security framework to the Annotation configuration model.
* Added support for Off-Heap, Redis Adapter, and {data-store-name}'s new Security framework to the Annotation configuration model.

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@@ -1,5 +1,5 @@
[[requirements]]
= Requirements
Spring Data for Pivotal GemFire requires JDK 8.0, http://projects.spring.io/spring-framework[Spring Framework] 5
and http://geode.apache.org/[Pivotal GemFire] 9.0.x.
Spring Data for {data-store-name} requires Java 8.0, {spring-framework-website}[Spring Framework] 5
and {x-data-store-website}[{data-store-name}] {data-store-version}.

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src/main/asciidoc/links.adoc
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@@ -1,14 +1,14 @@
[[sgf-links]]
= Useful Links
* http://projects.spring.io/spring-data-gemfire[Spring Data for Pivotal GemFire Project Page]
* https://github.com/spring-projects/spring-data-gemfire[Spring Data for Pivotal GemFire source code]
* https://jira.spring.io/browse/SGF[Spring Data for Pivotal GemFire JIRA]
* http://stackoverflow.com/questions/tagged/spring-data-gemfire[Spring Data for Pivotal GemFire on StackOverflow]
* http://forum.spring.io/forum/spring-projects/data/gemfire[Archive of the Spring Data for Pivotal GemFire Forum on Spring IO]
* https://pivotal.io/pivotal-gemfire[Pivotal GemFire Home Page]
* http://gemfire.docs.pivotal.io/latest/gemfire/about_gemfire.html[Pivotal GemFire Documentation]
* http://geode.apache.org/community/[Apache Geode Community]
* http://projects.spring.io/spring-data-gemfire[Spring Data for {data-store-name} Project Page]
* https://github.com/spring-projects/spring-data-gemfire[Spring Data for {data-store-name} source code]
* https://jira.spring.io/browse/SGF[Spring Data for {data-store-name} JIRA]
* http://stackoverflow.com/questions/tagged/spring-data-gemfire[Spring Data for {data-store-name} on StackOverflow]
* http://forum.spring.io/forum/spring-projects/data/gemfire[Archive of the Spring Data for {data-store-name} Forum on Spring IO]
* {x-data-store-website}[{data-store-name} Home Page]
* {x-data-store-docs}/getting_started/book_intro.html[{data-store-name} Documentation]
* {apache-geode-website}/community/[Apache Geode Community]
* https://github.com/apache/geode[Apache Geode source code]
* https://issues.apache.org/jira/projects/GEODE/issues/GEODE-4748?filter=allopenissues[Apache Geode JIRA]
* http://stackoverflow.com/questions/tagged/gemfire[Pivotal GemFire on StackOverflow]
* https://issues.apache.org/jira/projects/GEODE/issues[Apache Geode JIRA]
* http://stackoverflow.com/questions/tagged/gemfire[{data-store-name} on StackOverflow]

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@@ -1,10 +1,10 @@
= Preface
Spring Data for Pivotal GemFire focuses on integrating the Spring Framework's powerful, non-invasive programming model
and concepts with Pivotal GemFire to simplify configuration and development of Java applications when you use Pivotal GemFire.
Spring Data for {data-store-name} focuses on integrating the Spring Framework's powerful, non-invasive programming model
and concepts with {data-store-name} to simplify configuration and development of Java applications when you use {data-store-name}.
This document assumes you already have a basic understanding of and some familiarity with the core Spring Framework
and Pivotal GemFire concepts and APIs.
and {data-store-name} concepts and APIs.
While every effort has been made to ensure this documentation is comprehensive and complete without errors,
some topics are beyond the scope of this document and may require more explanation (for example, data distribution management

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@@ -1,30 +1,34 @@
[[bootstrap]]
= Bootstrapping Pivotal GemFire with the Spring container
= Bootstrapping {data-store-name} with the Spring container
Spring Data for Pivotal GemFire provides full configuration and initialization of the Pivotal GemFire In-Memory Data Grid (IMDG)
by using the Spring IoC container. The framework includes several classes to help simplify the configuration of
Pivotal GemFire components, including: Caches, Regions, Indexes, DiskStores, Functions, WAN Gateways, persistence backup,
and several other Distributed System components to support a variety of use cases with minimal effort.
Spring Data for {data-store-name} provides full configuration and initialization of the {data-store-name} In-Memory Data Grid (IMDG)
using the Spring IoC container. The framework includes several classes to help simplify the configuration of {data-store-name} components,
including: Caches, Regions, Indexes, DiskStores, Functions, WAN Gateways, persistence backup, and several other Distributed System components
to support a variety of application use cases with minimal effort.
NOTE: This section assumes basic familiarity with Pivotal GemFire. For more information,
see the Pivotal GemFire http://geode.apache.org/docs/[product documentation].
NOTE: This section assumes basic familiarity with {data-store-name}. For more information,
see the {data-store-name} {x-data-store-docs}/gemfire/about_gemfire.html[product documentation].
[[bootstrap:namespace:xml]]
== Advantages of Using Spring over Pivotal GemFire `cache.xml`
== Advantages of using Spring over {data-store-name} `cache.xml`
Spring Data for Pivotal GemFire's XML namespace supports full configuration of the Pivotal GemFire In-Memory Data Grid (IMDG).
The XML namespace is one of two ways to configure Pivotal GemFire in a Spring context in order to properly
manage Pivotal GemFire's lifecycle inside the Spring container. The other way to configure Pivotal Gemfire in a Spring context is by using <<bootstrap-annotation-config,annotation-based configuration>>. While support for Pivotal GemFire's native `cache.xml` persists
for legacy reasons, Pivotal GemFire application developers who use XML configuration are encouraged to do everything in Spring XML to take advantage of
the many wonderful things Spring has to offer, such as modular XML configuration, property placeholders and overrides,
SpEL (https://docs.spring.io/spring/docs/current/spring-framework-reference/core.html#expressions[Spring Expression Language]), and environment profiles. Behind the XML namespace, Spring Data for Pivotal GemFire makes extensive use of Spring's
`FactoryBean` pattern to simplify the creation, configuration, and initialization of Pivotal GemFire components.
Spring Data for {data-store-name}'s XML namespace supports full configuration of the {data-store-name} In-Memory Data Grid (IMDG).
The XML namespace is one of two ways to configure {data-store-name} in a Spring context in order to properly
manage {data-store-name}'s lifecycle inside the Spring container. The other way to configure Pivotal Gemfire
in a Spring context is by using <<bootstrap-annotation-config,annotation-based configuration>>. While support for
{data-store-name}'s native `cache.xml` persists for legacy reasons, {data-store-name} application developers
who use XML configuration are encouraged to do everything in Spring XML to take advantage of the many wonderful things
Spring has to offer, such as modular XML configuration, property placeholders and overrides,
SpEL ({spring-framework-docs}/core.html#expressions[Spring Expression Language]), and environment profiles.
Behind the XML namespace, Spring Data for {data-store-name} makes extensive use of Spring's `FactoryBean` pattern
to simplify the creation, configuration, and initialization of {data-store-name} components.
Pivotal GemFire provides several callback interfaces, such as `CacheListener`, `CacheLoader`, and `CacheWriter`,
{data-store-name} provides several callback interfaces, such as `CacheListener`, `CacheLoader`, and `CacheWriter`,
that let developers add custom event handlers. Using Spring's IoC container, you can configure these callbacks
as normal Spring beans and inject them into Pivotal GemFire components. This is a significant improvement over native `cache.xml`,
which provides relatively limited configuration options and requires callbacks to implement Pivotal GemFire's `Declarable`
interface (see <<apis:declarable>> to see how you can still use `Declarables` within Spring's IoC/DI container).
as normal Spring beans and inject them into {data-store-name} components. This is a significant improvement
over native `cache.xml`, which provides relatively limited configuration options and requires callbacks to implement
{data-store-name}'s `Declarable` interface (see <<apis:declarable>> to see how you can still use `Declarables`
within Spring's IoC/DI container).
In addition, IDEs, such as the Spring Tool Suite (STS), provide excellent support for Spring XML namespaces,
including code completion, pop-up annotations, and real-time validation.
@@ -32,17 +36,19 @@ including code completion, pop-up annotations, and real-time validation.
[[bootstrap:namespace]]
== Using the Core Namespace
To simplify configuration, Spring Data for Pivotal GemFire provides a dedicated XML namespace for configuring core Pivotal GemFire
components. It is possible to configure beans directly by using Spring's standard `<bean>` definition. However,
all bean properties are exposed through the XML namespace, so there is little benefit to using raw bean definitions.
For more information about XML Schema-based configuration in Spring, see the
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#xsd-config[appendix]
in the Spring Framework reference documentation.
To simplify configuration, Spring Data for {data-store-name} provides a dedicated XML namespace for configuring
core {data-store-name} components. It is possible to configure beans directly by using Spring's standard
`<bean>` definition. However, all bean properties are exposed through the XML namespace, so there is little benefit
to using raw bean definitions.
NOTE: For more information about XML Schema-based configuration in Spring, see the
{spring-framework-docs}/core.html#appendix[appendix] in the Spring Framework reference documentation.
NOTE: Spring Data Repository support uses a separate XML namespace. See <<gemfire-repositories>> for more information
on how to configure Spring Data for Pivotal GemFire Repositories.
on how to configure Spring Data for {data-store-name} Repositories.
To use the Spring Data for Pivotal GemFire XML namespace, declare it in your Spring XML configuration meta-data, as the following example shows:
To use the Spring Data for {data-store-name} XML namespace, declare it in your Spring XML configuration meta-data,
as the following example shows:
[source,xml]
----
@@ -60,17 +66,17 @@ To use the Spring Data for Pivotal GemFire XML namespace, declare it in your Spr
</beans>
----
<1> Spring Data for Pivotal GemFire XML namespace prefix. Any name works, but, throughout this reference documentation,
<1> Spring Data for {data-store-name} XML namespace prefix. Any name works, but, throughout this reference documentation,
`gfe` is used.
<2> The XML namespace prefix is mapped to the URI.
<3> The XML namespace URI location. Note that, even though the location points to an external address (which does exist
and is valid), Spring resolves the schema locally, as it is included in the Spring Data for Pivotal GemFire library.
and is valid), Spring resolves the schema locally, as it is included in the Spring Data for {data-store-name} library.
<4> Example declaration using the XML namespace with the `gfe` prefix.
[NOTE]
====
You can change the default namespace from `beans` to `gfe`. This is useful for XML configuration
composed mainly of Pivotal GemFire components, as it avoids declaring the prefix. To do so, swap the namespace
composed mainly of {data-store-name} components, as it avoids declaring the prefix. To do so, swap the namespace
prefix declaration shown earlier, as the following example shows:
[source,xml]
@@ -89,7 +95,7 @@ prefix declaration shown earlier, as the following example shows:
</beans>
----
<1> The default namespace declaration for this XML document points to the Spring Data for Pivotal GemFire XML namespace.
<1> The default namespace declaration for this XML document points to the Spring Data for {data-store-name} XML namespace.
<2> The `beans` namespace prefix declaration for Spring's raw bean definitions.
<3> Bean declaration using the `beans` namespace. Notice the prefix.
<4> Bean declaration using the `gfe` namespace. Notice the lack of prefix since `gfe` is the default namespace.

138
src/main/asciidoc/reference/cache.adoc
View File

@@ -1,8 +1,8 @@
[[bootstrap:cache]]
= Configuring a Cache
To use Pivotal GemFire, you need to either create a new `Cache` or connect to an existing one.
With the current version of Pivotal GemFire, you can have only one open Cache per VM (more strictly, per `ClassLoader`).
To use {data-store-name}, you need to either create a new `Cache` or connect to an existing one.
With the current version of {data-store-name}, you can have only one open Cache per VM (more strictly, per `ClassLoader`).
In most cases, the `Cache` should only be created once.
NOTE: This section describes the creation and configuration of a peer cache member, appropriate in
@@ -19,11 +19,11 @@ A peer cache with default configuration can be created with the following simple
----
During Spring container initialization, any application context containing this cache definition registers
a `CacheFactoryBean` that creates a Spring bean named `gemfireCache`, which references a Pivotal GemFire `Cache` instance.
a `CacheFactoryBean` that creates a Spring bean named `gemfireCache`, which references a {data-store-name} `Cache` instance.
This bean refers to either an existing cache or, if one does not already exist, a newly created one. Since no
additional properties were specified, a newly created cache applies the default cache configuration.
All Spring Data for Pivotal GemFire components that depend on the cache respect this naming convention, so you need not
All Spring Data for {data-store-name} components that depend on the cache respect this naming convention, so you need not
explicitly declare the cache dependency. If you prefer, you can make the dependency explicit by using the `cache-ref`
attribute provided by various SDG XML namespace elements. Also, you can override the cache's bean name using
the `id` attribute, as follows:
@@ -33,9 +33,9 @@ the `id` attribute, as follows:
<gfe:cache id="myCache"/>
----
A Pivotal GemFire `Cache` can be fully configured using Spring. However, Pivotal GemFire's native XML configuration file, `cache.xml`,
is also supported. For situations where the Pivotal GemFire cache needs to be configured natively, you can provide a reference
to the Pivotal GemFire XML configuration file by using the `cache-xml-location` attribute, as follows:
A {data-store-name} `Cache` can be fully configured using Spring. However, {data-store-name}'s native XML configuration file, `cache.xml`,
is also supported. For situations where the {data-store-name} cache needs to be configured natively, you can provide a reference
to the {data-store-name} XML configuration file by using the `cache-xml-location` attribute, as follows:
[source,xml]
----
@@ -49,8 +49,8 @@ NOTE: The configuration makes use of Spring's http://docs.spring.io/spring/docs/
abstraction to locate the file. The `Resource` abstraction lets various search patterns be used, depending on the runtime environment
or the prefix specified (if any) in the resource location.
In addition to referencing an external XML configuration file, you can also specify Pivotal GemFire System
http://geode.apache.org/docs/guide/11/reference/topics/gemfire_properties.html[properties]
In addition to referencing an external XML configuration file, you can also specify {data-store-name} System
{x-data-store-docs}/reference/topics/gemfire_properties.html[properties]
that use any of Spring's `Properties` support features.
For example, you can use the `properties` element defined in the `util` namespace to define `Properties`
@@ -127,44 +127,44 @@ or child elements, as the following listing shows:
----
<1> Attributes support various cache options. For further information regarding anything shown in this example,
see the Pivotal GemFire http://docs.pivotal.io/gemfire[product documentation].
see the {data-store-name} http://docs.pivotal.io/gemfire[product documentation].
The `close` attribute determines whether the cache should be closed when the Spring application context is closed.
The default is `true`. However, for use cases in which multiple application contexts use the cache
(common in web applications), set this value to `false`.
<2> Setting the `enable-auto-reconnect` attribute to `true` (the default is `false`) lets a disconnected Pivotal GemFire member
automatically reconnect and rejoin the Pivotal GemFire cluster.
See the Pivotal GemFire http://geode.apache.org/docs/guide/11/managing/autoreconnect/member-reconnect.html[product documentation]
<2> Setting the `enable-auto-reconnect` attribute to `true` (the default is `false`) lets a disconnected {data-store-name} member
automatically reconnect and rejoin the {data-store-name} cluster.
See the {data-store-name} {x-data-store-docs}/managing/autoreconnect/member-reconnect.html[product documentation]
for more details.
<3> Setting the `use-bean-factory-locator` attribute to `true` (it defaults to `false`) applies only when both
Spring (XML) configuration metadata and Pivotal GemFire `cache.xml` is used to configure the Pivotal GemFire cache node
(whether client or peer). This option lets Pivotal GemFire components (such as `CacheLoader`) expressed in `cache.xml`
Spring (XML) configuration metadata and {data-store-name} `cache.xml` is used to configure the {data-store-name} cache node
(whether client or peer). This option lets {data-store-name} components (such as `CacheLoader`) expressed in `cache.xml`
be auto-wired with beans (such as `DataSource`) defined in the Spring application context. This option is typically
used in conjunction with `cache-xml-location`.
<4> Setting the `use-cluster-configuration` attribute to `true` (the default is `false`) enables a Pivotal GemFire member to
<4> Setting the `use-cluster-configuration` attribute to `true` (the default is `false`) enables a {data-store-name} member to
retrieve the common, shared Cluster-based configuration from a Locator.
See the Pivotal GemFire http://geode.apache.org/docs/guide/11/configuring/cluster_config/gfsh_persist.html[product documentation]
See the {data-store-name} {x-data-store-docs}/configuring/cluster_config/gfsh_persist.html[product documentation]
for more details.
<5> Example of a `TransactionListener` callback declaration that uses a bean reference. The referenced bean must implement
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/TransactionListener.html[TransactionListener].
{x-data-store-javadoc}/org/apache/geode/cache/TransactionListener.html[TransactionListener].
A `TransactionListener` can be implemented to handle transaction related events (such as afterCommit and afterRollback).
<6> Example of a `TransactionWriter` callback declaration using an inner bean declaration. The bean must implement
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/TransactionWriter.html[TransactionWriter].
{x-data-store-javadoc}/org/apache/geode/cache/TransactionWriter.html[TransactionWriter].
The `TransactionWriter` is a callback that can veto a transaction.
<7> Example of a `GatewayConflictResolver` callback declaration using a bean reference. The referenced bean
must implement http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/util/GatewayConflictResolver.html
must implement {x-data-store-javadoc}/org/apache/geode/cache/util/GatewayConflictResolver.html
[GatewayConflictResolver].
A `GatewayConflictResolver` is a cache-level plugin that is called upon to decide what to do with events that originate
in other systems and arrive through the WAN Gateway.
<8> Enables Pivotal GemFire's http://geode.apache.org/docs/guide/11/developing/region_options/dynamic_region_creation.html[DynamicRegionFactory],
<8> Enables {data-store-name}'s {x-data-store-docs}/developing/region_options/dynamic_region_creation.html[DynamicRegionFactory],
which provides a distributed Region creation service.
<9> Declares a JNDI binding to enlist an external DataSource in a Pivotal GemFire transaction.
<9> Declares a JNDI binding to enlist an external DataSource in a {data-store-name} transaction.
[[bootstrap:cache:pdx-serialization]]
=== Enabling PDX Serialization
The preceding example includes a number of attributes related to Pivotal GemFire's enhanced serialization framework, PDX.
The preceding example includes a number of attributes related to {data-store-name}'s enhanced serialization framework, PDX.
While a complete discussion of PDX is beyond the scope of this reference guide, it is important to note that PDX
is enabled by registering a `PdxSerializer`, which is specified by setting the `pdx-serializer` attribute. Pivotal GemFire provides
is enabled by registering a `PdxSerializer`, which is specified by setting the `pdx-serializer` attribute. {data-store-name} provides
an implementing class (`org.apache.geode.pdx.ReflectionBasedAutoSerializer`) that uses Java Reflection. However, it is
common for developers to provide their own implementation. The value of the attribute is simply a reference to
a Spring bean that implements the `PdxSerializer` interface.
@@ -176,17 +176,17 @@ More information on serialization support can be found in <<serialization>>
You should be careful when setting the `<gfe:cache enable-auto-reconnect="[true|false*]>` attribute to `true`.
Generally, 'auto-reconnect' should only be enabled in cases where Spring Data for Pivotal GemFire's XML namespace is used to
configure and bootstrap a new, non-application Pivotal GemFire Server to add to a cluster. In other words, 'auto-reconnect'
should not be enabled when Spring Data for Pivotal GemFire is used to develop and build a Pivotal GemFire application that also happens
to be a peer cache member of the Pivotal GemFire cluster.
Generally, 'auto-reconnect' should only be enabled in cases where Spring Data for {data-store-name}'s XML namespace is used to
configure and bootstrap a new, non-application {data-store-name} Server to add to a cluster. In other words, 'auto-reconnect'
should not be enabled when Spring Data for {data-store-name} is used to develop and build a {data-store-name} application that also happens
to be a peer cache member of the {data-store-name} cluster.
The main reason for this restriction is that most Pivotal GemFire applications use references to the Pivotal GemFire cache or Regions in order to
The main reason for this restriction is that most {data-store-name} applications use references to the {data-store-name} cache or Regions in order to
perform data access operations. These references are "`injected`" by the Spring container into application components
(such as DAOs or Repositories) for use by the application. When a peer member is forcefully disconnected from the rest
of the cluster, presumably because the peer member has become unresponsive or a network partition separates one or more
peer members into a group too small to function as an independent distributed system, the peer member shuts down
and all Pivotal GemFire component references (caches, regions, and others) become invalid.
and all {data-store-name} component references (caches, regions, and others) become invalid.
Essentially, the current forced-disconnect processing logic in each peer member dismantles the system from the ground up.
The JGroups stack shuts down, the distributed system is put in a shutdown state and, finally, the cache is closed.
@@ -195,36 +195,36 @@ Effectively, all memory references become stale and are lost.
After being disconnected from the distributed system, a peer member enters a "`reconnecting`" state and periodically
attempts to rejoin the distributed system. If the peer member succeeds in reconnecting, the member rebuilds
its "`view`" of the distributed system from existing members and receives a new distributed system ID. Additionally, all
caches, regions, and other Pivotal GemFire components are reconstructed. Therefore, all old references, which may have been
caches, regions, and other {data-store-name} components are reconstructed. Therefore, all old references, which may have been
injected into application by the Spring container, are now stale and no longer valid.
Pivotal GemFire makes no guarantee (even when using the Pivotal GemFire public Java API) that application cache, region, or other
component references are automatically refreshed by the reconnect operation. As such, Pivotal GemFire applications
{data-store-name} makes no guarantee (even when using the {data-store-name} public Java API) that application cache, region, or other
component references are automatically refreshed by the reconnect operation. As such, {data-store-name} applications
must take care to refresh their own references.
Unfortunately, there is no way to be notified of a disconnect event and, subsequently, a reconnect event.
If that were the case, you would have a clean way to know when to call
`ConfigurableApplicationContext.refresh()`, if it were even applicable for an application to do so, which is why
this "`feature`" of Pivotal GemFire is not recommended for peer cache Pivotal GemFire applications.
this "`feature`" of {data-store-name} is not recommended for peer cache {data-store-name} applications.
For more information about 'auto-reconnect', see Pivotal GemFire's
http://geode.apache.org/docs/guide/11/managing/autoreconnect/member-reconnect.html[product documentation].
For more information about 'auto-reconnect', see {data-store-name}'s
{x-data-store-docs}/managing/autoreconnect/member-reconnect.html[product documentation].
[[bootstrap:cache:cluster-configuration]]
=== Using Cluster-based Configuration
Pivotal GemFire's Cluster Configuration Service is a convenient way for any peer member joining the cluster to get
{data-store-name}'s Cluster Configuration Service is a convenient way for any peer member joining the cluster to get
a "`consistent view`" of the cluster by using the shared, persistent configuration maintained by a locator.
Using the cluster-based Configuration ensures the peer member's configuration is compatible with
the Pivotal GemFire Distributed System when the member joins.
the {data-store-name} Distributed System when the member joins.
This feature of Spring Data for Pivotal GemFire (setting the `use-cluster-configuration` attribute to `true`) works in the same way
as the `cache-xml-location` attribute, except the source of the Pivotal GemFire configuration meta-data comes from the network
This feature of Spring Data for {data-store-name} (setting the `use-cluster-configuration` attribute to `true`) works in the same way
as the `cache-xml-location` attribute, except the source of the {data-store-name} configuration meta-data comes from the network
through a locator, as opposed to a native `cache.xml` file residing in the local file system.
All Pivotal GemFire native configuration metadata, whether from `cache.xml` or from the Cluster Configuration Service,
All {data-store-name} native configuration metadata, whether from `cache.xml` or from the Cluster Configuration Service,
gets applied before any Spring (XML) configuration metadata. As a result, Spring's config serves to "`augment`" the
native Pivotal GemFire configuration metadata and would most likely be specific to the application.
native {data-store-name} configuration metadata and would most likely be specific to the application.
Again, to enable this feature, specify the following in the Spring XML config:
@@ -233,18 +233,18 @@ Again, to enable this feature, specify the following in the Spring XML config:
<gfe:cache use-cluster-configuration="true"/>
----
NOTE: While certain Pivotal GemFire tools, such as `Gfsh`, have their actions "`recorded`" when schema-like changes are made
(for example, `gfsh>create region --name=Example --type=PARTITION`), Spring Data for Pivotal GemFire's configuration metadata
is not recorded. The same is true when using Pivotal GemFire's public Java API directly. It, too, is not recorded.
NOTE: While certain {data-store-name} tools, such as `Gfsh`, have their actions "`recorded`" when schema-like changes are made
(for example, `gfsh>create region --name=Example --type=PARTITION`), Spring Data for {data-store-name}'s configuration metadata
is not recorded. The same is true when using {data-store-name}'s public Java API directly. It, too, is not recorded.
For more information on Pivotal GemFire's Cluster Configuration Service, see the
http://geode.apache.org/docs/guide/11/configuring/cluster_config/gfsh_persist.html[product documentation].
For more information on {data-store-name}'s Cluster Configuration Service, see the
{x-data-store-docs}/configuring/cluster_config/gfsh_persist.html[product documentation].
[[bootstrap:cache:server]]
== Configuring a Pivotal GemFire CacheServer
== Configuring a {data-store-name} CacheServer
Spring Data for Pivotal GemFire includes dedicated support for configuring a
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/server/CacheServer.html[CacheServer],
Spring Data for {data-store-name} includes dedicated support for configuring a
{x-data-store-javadoc}/org/apache/geode/cache/server/CacheServer.html[CacheServer],
allowing complete configuration through the Spring container, as the following example shows:
[source,xml]
@@ -262,7 +262,7 @@ allowing complete configuration through the Spring container, as the following e
<gfe:cache/>
<!-- Example depicting serveral Pivotal GemFire CacheServer configuration options -->
<!-- Example depicting serveral {data-store-name} CacheServer configuration options -->
<gfe:cache-server id="advanced-config" auto-startup="true"
bind-address="localhost" host-name-for-clients="localhost" port="${gemfire.cache.server.port}"
load-poll-interval="2000" max-connections="22" max-message-count="1000" max-threads="16"
@@ -290,21 +290,21 @@ and an http://docs.spring.io/spring/docs/current/spring-framework-reference/html
to support externalization of environment-specific properties from the main codebase, easing deployment
across multiple machines.
NOTE: To avoid initialization problems, the `CacheServer` started by Spring Data for Pivotal GemFire starts *after*
NOTE: To avoid initialization problems, the `CacheServer` started by Spring Data for {data-store-name} starts *after*
the Spring container has been fully initialized. Doing so lets potential regions, listeners, writers or instantiators that are
defined declaratively be fully initialized and registered before the server starts accepting connections.
Keep this in mind when programmatically configuring these elements, as the server might start after your components
and thus not be seen by the clients connecting right away.
[[bootstrap:cache:client]]
== Configuring a Pivotal GemFire ClientCache
== Configuring a {data-store-name} ClientCache
In addition to defining a Pivotal GemFire peer http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/Cache.html[cache],
Spring Data for Pivotal GemFire also supports the definition of a Pivotal GemFire http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/client/ClientCache.html[`ClientCache`]
In addition to defining a {data-store-name} peer {x-data-store-javadoc}/org/apache/geode/cache/Cache.html[cache],
Spring Data for {data-store-name} also supports the definition of a {data-store-name} {x-data-store-javadoc}/org/apache/geode/cache/client/ClientCache.html[`ClientCache`]
in a Spring context. A `ClientCache` definition is similar in configuration and use to
the Pivotal GemFire peer <<bootstrap:cache,cache>> and is supported by the `org.springframework.data.gemfire.client.ClientCacheFactoryBean`.
the {data-store-name} peer <<bootstrap:cache,cache>> and is supported by the `org.springframework.data.gemfire.client.ClientCacheFactoryBean`.
The simplest definition of a Pivotal GemFire cache client using default configuration follows:
The simplest definition of a {data-store-name} cache client using default configuration follows:
[source,xml]
----
@@ -336,14 +336,14 @@ to the cache definition, as the following example shows:
----
The `<client-cache>` element also has a `ready-for-events` attribute. If the attribute is set to `true`, the client cache
initialization includes a call to http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/client/ClientCache.html#readyForEvents[`ClientCache.readyForEvents()`].
initialization includes a call to {x-data-store-javadoc}/org/apache/geode/cache/client/ClientCache.html#readyForEvents[`ClientCache.readyForEvents()`].
<<bootstrap:region:client>> covers client-side configuration in more detail.
[[bootstrap:cache:client:pool]]
=== Pivotal GemFire's DEFAULT Pool and Spring Data for Pivotal GemFire Pool Definitions
=== {data-store-name}'s DEFAULT Pool and Spring Data for {data-store-name} Pool Definitions
If a Pivotal GemFire `ClientCache` is local-only, then no Pool definition is required. For instance, you can define the following:
If a {data-store-name} `ClientCache` is local-only, then no Pool definition is required. For instance, you can define the following:
[source,xml]
----
@@ -353,14 +353,14 @@ If a Pivotal GemFire `ClientCache` is local-only, then no Pool definition is req
----
In this case, the `Example` Region is `LOCAL` and no data is distributed between the client and a server. Therefore,
no pool is necessary. This is true for any client-side, local-only region, as defined by the Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/client/ClientRegionShortcut.html[`ClientRegionShortcut`]
no pool is necessary. This is true for any client-side, local-only region, as defined by the {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/client/ClientRegionShortcut.html[`ClientRegionShortcut`]
(all `LOCAL_*` shortcuts).
However, if a client Region is a (caching) proxy to a server-side Region, a pool is required. In that case, there are several
ways to define and use a Pool.
When a client cache, a pool, and a proxy-based region are all defined but not explicitly identified, Spring Data for Pivotal GemFire
When a client cache, a pool, and a proxy-based region are all defined but not explicitly identified, Spring Data for {data-store-name}
resolves the references automatically, as the following example shows:
[source,xml]
@@ -375,10 +375,10 @@ resolves the references automatically, as the following example shows:
----
In the preceding example, the client cache is identified as `gemfireCache`, the Pool as `gemfirePool`, and the client region
as `Example`. However, the client cache initializes Pivotal GemFire's `DEFAULT` pool from `gemfirePool`, and the client Region
as `Example`. However, the client cache initializes {data-store-name}'s `DEFAULT` pool from `gemfirePool`, and the client Region
uses the `gemfirePool` when distributing data between the client and the server.
Basically, Spring Data for Pivotal GemFire resolves the preceding configuration to the following:
Basically, Spring Data for {data-store-name} resolves the preceding configuration to the following:
[source,xml]
----
@@ -391,7 +391,7 @@ Basically, Spring Data for Pivotal GemFire resolves the preceding configuration
<gfe:client-region id="Example" cache-ref="gemfireCache" pool-name="gemfirePool" shortcut="PROXY"/>
----
Pivotal GemFire still creates a pool called `DEFAULT`. Spring Data for Pivotal GemFire causes the `DEFAULT` pool to be
{data-store-name} still creates a pool called `DEFAULT`. Spring Data for {data-store-name} causes the `DEFAULT` pool to be
initialized from the `gemfirePool`. Doing so is useful in situations where multiple pools are defined and client regions
are using separate Pools.
@@ -416,12 +416,12 @@ Consider the following:
<gfe:client-region id="YetAnotherExample" shortcut="LOCAL"/>
----
In this setup, the Pivotal GemFire client cache's `DEFAULT` pool is initialized from `locatorPool`, as specified by the
`pool-name` attribute. There is no Spring Data for Pivotal GemFire-defined `gemfirePool`, since both pools were explicitly
In this setup, the {data-store-name} client cache's `DEFAULT` pool is initialized from `locatorPool`, as specified by the
`pool-name` attribute. There is no Spring Data for {data-store-name}-defined `gemfirePool`, since both pools were explicitly
identified (named) -- `locatorPool` and `serverPool`, respectively.
The `Example` region explicitly refers to and exclusively uses the `serverPool`. The `AnotherExample` region uses
Pivotal GemFire's `DEFAULT` pool, which was configured from the `locatorPool` based on the client cache bean definition's
{data-store-name}'s `DEFAULT` pool, which was configured from the `locatorPool` based on the client cache bean definition's
`pool-name` attribute.
Finally, the `YetAnotherExample` egion does not use a Pool, because it is `LOCAL`.

43
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View File

@@ -1,28 +1,29 @@
[[apis:continuous-query]]
= Continuous Query (CQ)
A powerful functionality offered by Pivotal GemFire is
http://geode.apache.org/docs/guide/11/developing/continuous_querying/chapter_overview.html[Continuous Query] (or CQ).
In short, CQ allows one to create and register an OQL query, and then automatically be notified when new data
that gets added to Pivotal GemFire matches the query predicate. _Spring Data for Pivotal GemFire_ provides dedicated support for CQs through
the `org.springframework.data.gemfire.listener` package and its *listener container*; very similar in functionality
and naming to the JMS integration in the _Spring Framework_; in fact, users familiar with the JMS support in _Spring_,
should feel right at home.
A powerful functionality offered by {data-store-name} is
{x-data-store-docs}/developing/continuous_querying/chapter_overview.html[Continuous Query] (or CQ).
Basically _Spring Data for Pivotal GemFire_ allows methods on POJOs to become end-points for CQ. Simply define the query
and indicate the method that should be called to be notified when there is a match. _Spring Data for Pivotal GemFire_ takes care
In short, CQ allows a developer to create and register an OQL query, and then automatically be notified when new data
that gets added to {data-store-name} matches the query predicate. Spring Data for {data-store-name} provides dedicated
support for CQs through the `org.springframework.data.gemfire.listener` package and its *listener container*;
very similar in functionality and naming to the JMS integration in the _Spring Framework_; in fact, users familiar with
the JMS support in Spring, should feel right at home.
Basically Spring Data for {data-store-name} allows methods on POJOs to become end-points for CQ. Simply define the query
and indicate the method that should be called to be notified when there is a match. Spring Data for {data-store-name} takes care
of the rest. This is very similar to Java EE's message-driven bean style, but without any requirement for base class
or interface implementations, based on Pivotal GemFire.
or interface implementations, based on {data-store-name}.
NOTE: Currently, Continuous Query is only supported in Pivotal GemFire's client/server topology. Additionally, the client Pool
used is required to have the subscription enabled. Please refer to the Pivotal GemFire
http://geode.apache.org/docs/guide/11/developing/continuous_querying/implementing_continuous_querying.html[documentation]
NOTE: Currently, Continuous Query is only supported in {data-store-name}'s client/server topology. Additionally, the client Pool
used is required to have the subscription enabled. Please refer to the {data-store-name}
{x-data-store-docs}/developing/continuous_querying/implementing_continuous_querying.html[documentation]
for more information.
[[apis:continuous-query:container]]
== Continuous Query Listener Container
_Spring Data for Pivotal GemFire_ simplifies creation, registration, life-cycle and dispatch of CQ events by taking care of
Spring Data for {data-store-name} simplifies creation, registration, life-cycle and dispatch of CQ events by taking care of
the infrastructure around CQ with the use of SDG's `ContinuousQueryListenerContainer`, which does all the heavy lifting
on behalf of the user. Users familiar with EJB and JMS should find the concepts familiar as it is designed
as close as possible to the support provided in the _Spring Framework_ with its Message-driven POJOs (MDPs).
@@ -33,11 +34,11 @@ is responsible for all threading of message reception and dispatches into the li
the intermediary between an EDP (Event-driven POJO) and the event provider and takes care of creation and registration
of CQs (to receive events), resource acquisition and release, exception conversion and the like. This allows you,
as an application developer, to write the (possibly complex) business logic associated with receiving an event
(and reacting to it), and delegate the boilerplate Pivotal GemFire infrastructure concerns to the framework.
(and reacting to it), and delegate the boilerplate {data-store-name} infrastructure concerns to the framework.
The listener container is fully customizable. A developer can chose either to use the CQ thread to perform the dispatch
(synchronous delivery) or a new thread (from an existing pool) for an asynchronous approach by defining the suitable
`java.util.concurrent.Executor` (or _Spring's_ `TaskExecutor`). Depending on the load, the number of listeners
`java.util.concurrent.Executor` (or Spring's `TaskExecutor`). Depending on the load, the number of listeners
or the runtime environment, the developer should change or tweak the executor to better serve her needs. In particular,
in managed environments (such as app servers), it is highly recommended to pick a proper `TaskExecutor`
to take advantage of its runtime.
@@ -45,10 +46,10 @@ to take advantage of its runtime.
[[apis:continuous-query:adapter]]
== The `ContinuousQueryListener` and `ContinuousQueryListenerAdapter`
The `ContinuousQueryListenerAdapter` class is the final component in _Spring Data for Pivotal GemFire_ CQ support. In a nutshell,
The `ContinuousQueryListenerAdapter` class is the final component in Spring Data for {data-store-name} CQ support. In a nutshell,
class allows you to expose almost *any* implementing class as an EDP with minimal constraints.
`ContinuousQueryListenerAdapter` implements the `ContinuousQueryListener` interface, a simple listener interface
similar to Pivotal GemFire's http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/query/CqListener.html[CqListener].
similar to {data-store-name}'s {x-data-store-javadoc}/org/apache/geode/cache/query/CqListener.html[CqListener].
Consider the following interface definition. Notice the various event handling methods and their parameters:
@@ -75,7 +76,7 @@ class DefaultEventDelegate implements EventDelegate {
}
----
In particular, note how the above implementation of the `EventDelegate` interface has *no* Pivotal GemFire dependencies at all.
In particular, note how the above implementation of the `EventDelegate` interface has *no* {data-store-name} dependencies at all.
It truly is a POJO that we can and will make into an EDP via the following configuration.
NOTE: the class does not have to implement an interface; an interface is only used to better showcase the decoupling
@@ -115,7 +116,7 @@ reference and the actual query definition are required. It's possible, however,
the resulting Continuous Query (useful for monitoring) but also the name of the method (the default is `handleEvent`).
The specified method can have various argument types, the `EventDelegate` interface lists the allowed types.
The example above uses the _Spring Data for Pivotal GemFire_ namespace to declare the event listener container
The example above uses the Spring Data for {data-store-name} namespace to declare the event listener container
and automatically register the listeners. The full blown, *beans* definition is displayed below:
[source,xml]
@@ -142,6 +143,6 @@ and automatically register the listeners. The full blown, *beans* definition is
</bean>
----
Each time an event is received, the adapter automatically performs type translation between the Pivotal GemFire event
Each time an event is received, the adapter automatically performs type translation between the {data-store-name} event
and the required method argument(s) transparently. Any exception caused by the method invocation is caught
and handled by the container (by default, being logged).

14
src/main/asciidoc/reference/data-access.adoc
View File

@@ -1,16 +1,16 @@
[[data-access]]
= Using the Data Access Namespace
In addition to the core XML namespace (`gfe`), Spring Data for Pivotal GemFire provides a data access XML namespace (`gfe-data`),
which is primarily intended to simplify the development of Pivotal GemFire client applications. This namespace currently contains
support for Pivotal GemFire <<gemfire-repositories, Repositories>> and function <<function-execution, execution>>,
as well as a `<datasource>` tag that offers a convenient way to connect to a Pivotal GemFire cluster.
In addition to the core XML namespace (`gfe`), Spring Data for {data-store-name} provides a data access XML namespace (`gfe-data`),
which is primarily intended to simplify the development of {data-store-name} client applications. This namespace currently contains
support for {data-store-name} <<gemfire-repositories, Repositories>> and function <<function-execution, execution>>,
as well as a `<datasource>` tag that offers a convenient way to connect to a {data-store-name} cluster.
[[data-access:datasource]]
== An Easy Way to Connect to Pivotal GemFire
== An Easy Way to Connect to {data-store-name}
For many applications, a basic connection to a Pivotal GemFire data grid using default values is sufficient.
Spring Data for Pivotal GemFire's `<datasource>` tag provides a simple way to access data. The data source creates
For many applications, a basic connection to a {data-store-name} data grid using default values is sufficient.
Spring Data for {data-store-name}'s `<datasource>` tag provides a simple way to access data. The data source creates
a `ClientCache` and connection `Pool`. In addition, it queries the cluster servers for all existing root Regions
and creates an (empty) client Region proxy for each one.

156
src/main/asciidoc/reference/data.adoc
View File

@@ -1,19 +1,19 @@
[[apis]]
= Working with Pivotal GemFire APIs
= Working with {data-store-name} APIs
Once the Pivotal GemFire Cache and Regions have been configured, they can be injected and used inside application objects.
This chapter describes the integration with _Spring's_ Transaction Management functionality and DAO exception hierarchy.
This chapter also covers support for dependency injection of Pivotal GemFire managed objects.
Once the {data-store-name} Cache and Regions have been configured, they can be injected and used inside application objects.
This chapter describes the integration with Spring's Transaction Management functionality and DAO exception hierarchy.
This chapter also covers support for dependency injection of {data-store-name} managed objects.
[[apis:template]]
== GemfireTemplate
As with many other high-level abstractions provided by _Spring_, _Spring Data for Pivotal GemFire_ provides a *template*
to simplify Pivotal GemFire data access operations. The class provides several methods containing common Region operations,
but also provides the capability to *execute* code against native Pivotal GemFire APIs without having to deal with
Pivotal GemFire checked exceptions by using a `GemfireCallback`.
As with many other high-level abstractions provided by Spring, Spring Data for {data-store-name} provides a *template*
to simplify {data-store-name} data access operations. The class provides several methods containing common Region operations,
but also provides the capability to *execute* code against native {data-store-name} APIs without having to deal with
{data-store-name} checked exceptions by using a `GemfireCallback`.
The template class requires a Pivotal GemFire `Region`, and once configured, is thread-safe and is reusable
The template class requires a {data-store-name} `Region`, and once configured, is thread-safe and is reusable
across multiple application classes:
[source,xml]
@@ -22,14 +22,14 @@ across multiple application classes:
----
Once the template is configured, a developer can use it alongside `GemfireCallback` to work directly with
the Pivotal GemFire `Region` without having to deal with checked exceptions, threading or resource management concerns:
the {data-store-name} `Region` without having to deal with checked exceptions, threading or resource management concerns:
[source,java]
----
template.execute(new GemfireCallback<Iterable<String>>() {
public Iterable<String> doInGemfire(Region region)
throws Pivotal GemFireCheckedException, Pivotal GemFireException {
throws GemFireCheckedException, GemFireException {
Region<String, String> localRegion = (Region<String, String>) region;
@@ -41,7 +41,7 @@ template.execute(new GemfireCallback<Iterable<String>>() {
});
----
For accessing the full power of the Pivotal GemFire query language, a developer can use the `find` and `findUnique`
For accessing the full power of the {data-store-name} query language, a developer can use the `find` and `findUnique`
methods, which, compared to the `query` method, can execute queries across multiple Regions, execute projections,
and the like.
@@ -63,9 +63,9 @@ As mentioned in _Spring Framework's_ documentation,
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#orm-exception-translation[Exception translation]
can be applied transparently to your Data Access Objects (DAO) through the use of the `@Repository` annotation and AOP
by defining a `PersistenceExceptionTranslationPostProcessor` bean. The same exception translation functionality
is enabled when using Pivotal GemFire as long as the `CacheFactoryBean` is declared, e.g. using either a `<gfe:cache/>`
is enabled when using {data-store-name} as long as the `CacheFactoryBean` is declared, e.g. using either a `<gfe:cache/>`
or `<gfe:client-cache>` declaration, which acts as an exception translator and is automatically detected by
the _Spring_ infrastructure and used accordingly.
the Spring infrastructure and used accordingly.
[[apis:transaction-management]]
== Local, Cache Transaction Management
@@ -73,44 +73,44 @@ the _Spring_ infrastructure and used accordingly.
One of the most popular features of the _Spring Framework_ is
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#transaction[Transaction Management].
If you are not familiar with _Spring's_ transaction abstraction then we strongly recommend
If you are not familiar with Spring's transaction abstraction then we strongly recommend
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#transaction-motivation[reading]
about _Spring's Transaction Management_ infrastructure as it offers a consistent _programming model_ that works
transparently across multiple APIs and can be configured either programmatically or declaratively
(the most popular choice).
For Pivotal GemFire, _Spring Data for Pivotal GemFire_ provides a dedicated, per-cache, `PlatformTransactionManager` that,
once declared, allows Region operations to be executed atomically through _Spring_:
For {data-store-name}, Spring Data for {data-store-name} provides a dedicated, per-cache, `PlatformTransactionManager` that,
once declared, allows Region operations to be executed atomically through Spring:
[source,xml]
----
<gfe:transaction-manager id="txManager" cache-ref="myCache"/>
----
NOTE: The example above can be simplified even further by eliminating the `cache-ref` attribute if the Pivotal GemFire cache
is defined under the default name, `gemfireCache`. As with the other _Spring Data for Pivotal GemFire_ namespace elements,
NOTE: The example above can be simplified even further by eliminating the `cache-ref` attribute if the {data-store-name} cache
is defined under the default name, `gemfireCache`. As with the other Spring Data for {data-store-name} namespace elements,
if the cache bean name is not configured, the aforementioned naming convention will be used.
Additionally, the transaction manager name is "`gemfireTransactionManager`" if not explicitly specified.
Currently, Pivotal GemFire supports optimistic transactions with *read committed* isolation. Furthermore, to guarantee
Currently, {data-store-name} supports optimistic transactions with *read committed* isolation. Furthermore, to guarantee
this isolation, developers should avoid making *in-place* changes that manually modify values present in the cache.
To prevent this from happening, the transaction manager configures the cache to use *copy on read* semantics by default,
meaning a clone of the actual value is created each time a read is performed. This behavior can be disabled if needed
through the `copyOnRead` property.
For more information on the semantics and behavior of the underlying Geode transaction manager, please refer to the Geode
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/CacheTransactionManager.html[CacheTransactionManager Javadoc]
as well as the http://geode.apache.org/docs/guide/11/developing/transactions/chapter_overview.html[documentation].
{x-data-store-javadoc}/org/apache/geode/cache/CacheTransactionManager.html[CacheTransactionManager Javadoc]
as well as the {x-data-store-docs}/developing/transactions/chapter_overview.html[documentation].
[[apis:global-transaction-management]]
== Global, JTA Transaction Management
It is also possible for Pivotal GemFire to participate in a Global, JTA based transaction, such as a transaction managed
It is also possible for {data-store-name} to participate in a Global, JTA based transaction, such as a transaction managed
by an Java EE Application Server (e.g. WebSphere Application Server, a.k.a. WAS) using Container Managed Transactions
(CMT) along with other JTA resources.
However, unlike many other JTA "compliant" resources (e.g. JMS Message Brokers like ActiveMQ), Pivotal GemFire is *not*
an XA compliant resource. Therefore, Pivotal GemFire must be positioned as the "_Last Resource_" in a JTA transaction
However, unlike many other JTA "compliant" resources (e.g. JMS Message Brokers like ActiveMQ), {data-store-name} is *not*
an XA compliant resource. Therefore, {data-store-name} must be positioned as the "_Last Resource_" in a JTA transaction
(_prepare phase_) since it does not implement the 2-phase commit protocol, or rather does not handle
distributed transactions.
@@ -121,23 +121,23 @@ In fact, Red Hat's JBoss project, http://narayana.io/[Narayana] is one such LGPL
refers to this as "_Last Resource Commit Optimization_" (LRCO). More details can be found
http://narayana.io//docs/project/index.html#d0e1859[here].
However, whether you are using Pivotal GemFire in a standalone environment with an Open Source JTA Transaction Management
However, whether you are using {data-store-name} in a standalone environment with an Open Source JTA Transaction Management
implementation that supports "_Last Resource_", or a managed environment (e.g. Java EE AS such as WAS),
_Spring Data Geode_ has you covered.
There are a series of steps you must complete to properly use Pivotal GemFire as a "_Last Resource_" in a JTA transaction
There are a series of steps you must complete to properly use {data-store-name} as a "_Last Resource_" in a JTA transaction
involving more than 1 transactional resource. Additionally, there can only be 1 non-XA compliant resource
(e.g. Pivotal GemFire) in such an arrangement.
(e.g. {data-store-name}) in such an arrangement.
1) First, you must complete Steps 1-4 in Pivotal GemFire's documentation
1) First, you must complete Steps 1-4 in {data-store-name}'s documentation
http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[here].
NOTE: #1 above is independent of your _Spring [Boot] and/or [Data Pivotal GemFire]_ application
NOTE: #1 above is independent of your Spring [Boot] and/or [Data for {data-store-name}] application
and must be completed successfully.
2) Referring to Step 5 in Pivotal GemFire's http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[documentation],
_Spring Data for Pivotal GemFire's_ Annotation support will attempt to set the `Pivotal GemFireCache`, http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/Pivotal GemFireCache.html#setCopyOnRead-boolean-[`copyOnRead`]
property for you when using the `@EnablePivotal GemFireAsLastResource` annotation.
2) Referring to Step 5 in {data-store-name}'s http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[documentation],
_Spring Data for {data-store-name}'s_ Annotation support will attempt to set the `GemFireCache`, {x-data-store-javadoc}/org/apache/geode/cache/GemFireCache.html#setCopyOnRead-boolean-[`copyOnRead`]
property for you when using the `@EnableGemFireAsLastResource` annotation.
However, if SDG's auto-configuration is unsuccessful then you must explicitly set the `copy-on-read` attribute on the
`<gfe:cache>` or `<gfe:client-cache>` element in XML or the `copyOnRead` property of the SDG `CacheFactoryBean` class
@@ -192,18 +192,18 @@ Client Cache JavaConfig:
NOTE: explicitly setting the `copy-on-read` attribute or optionally the `copyOnRead` property
really should not be necessary.
3) At this point, you *skip* Steps 6-8 in Pivotal GemFire's http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[documentation]
and let _Spring Data Geode_ work its magic. All you need do is annotate your _Spring_ `@Configuration` class
with _Spring Data for Pivotal GemFire's_ *new* `@EnablePivotal GemFireAsLastResource` annotation and a combination of _Spring's_
3) At this point, you *skip* Steps 6-8 in {data-store-name}'s http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[documentation]
and let _Spring Data Geode_ work its magic. All you need do is annotate your Spring `@Configuration` class
with Spring Data for {data-store-name}'s *new* `@EnableGemFireAsLastResource` annotation and a combination of Spring's
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#transaction[Transaction Management]
infrastructure and _Spring Data for Pivotal GemFire's_ `@EnablePivotal GemFireAsLastResource` configuration does the trick.
infrastructure and _Spring Data for {data-store-name}'s_ `@EnableGemFireAsLastResource` configuration does the trick.
The configuration looks like this...
[source,java]
----
@Configuration
@EnablePivotal GemFireAsLastResource
@EnableGemFireAsLastResource
@EnableTransactionManagement(order = 1)
class GeodeConfiguration {
@@ -213,13 +213,13 @@ class GeodeConfiguration {
The only requirements are...
3.1) The `@EnablePivotal GemFireAsLastResource` annotation must be declared on the same _Spring_ `@Configuration` class
where _Spring's_ `@EnableTransactionManagement` annotation is also specified.
3.1) The `@EnableGemFireAsLastResource` annotation must be declared on the same Spring `@Configuration` class
where Spring's `@EnableTransactionManagement` annotation is also specified.
3.2) The `order` attribute of the `@EnableTransactionManagement` annotation must be explicitly set to an integer value
that is not `Integer.MAX_VALUE` or `Integer.MIN_VALUE` (defaults to `Integer.MAX_VALUE`).
Of course, hopefully you are aware that you also need to configure _Spring's_ `JtaTransactionManager`
Of course, hopefully you are aware that you also need to configure Spring's `JtaTransactionManager`
when using JTA Transactions like so..
[source,java]
@@ -236,15 +236,15 @@ public JtaTransactionManager transactionManager(UserTransaction userTransaction)
----
NOTE: The configuration in section <<apis:transaction-management>> does *not* apply here.
The use of _Spring Data for Pivotal GemFire's_ `GemfireTransactionManager` is applicable only in "Local", Cache Transactions,
The use of _Spring Data for {data-store-name}'s_ `GemfireTransactionManager` is applicable only in "Local", Cache Transactions,
*not* "Global", JTA Transactions. Therefore, you do *not* configure the SDG `GemfireTransactionManager` in this case.
You configure _Spring's_ `JtaTransactionManager` as shown above.
You configure Spring's `JtaTransactionManager` as shown above.
For more details on using _Spring's Transaction Management_ with JTA,
see http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#transaction-application-server-integration[here].
Effectively, _Spring Data for Pivotal GemFire's_ `@EnablePivotal GemFireAsLastResource` annotation imports configuration containing 2 Aspect
bean definitions that handles the Pivotal GemFire `o.a.g.ra.GFConnectionFactory.getConnection()`
Effectively, _Spring Data for {data-store-name}'s_ `@EnableGemFireAsLastResource` annotation imports configuration containing 2 Aspect
bean definitions that handles the {data-store-name} `o.a.g.ra.GFConnectionFactory.getConnection()`
and `o.a.g.ra.GFConnection.close()` operations at the appropriate points during the transactional operation.
Specifically, the correct sequence of events are...
@@ -260,10 +260,10 @@ Specifically, the correct sequence of events are...
5. Finally, `GFConnection.close()`
This is consistent with how you, as the application developer, would code this manually if you had to use the JTA API
+ Pivotal GemFire API yourself, as shown in the
Pivotal GemFire http://gemfire90.docs.pivotal.io/geode/developing/transactions/jca_adapter_example.html#concept_swv_z2p_wk[example].
+ {data-store-name} API yourself, as shown in the
{data-store-name} http://gemfire90.docs.pivotal.io/geode/developing/transactions/jca_adapter_example.html#concept_swv_z2p_wk[example].
Thankfully, _Spring_ does the heavy lifting for you and all you need do after applying the appropriate configuration
Thankfully, Spring does the heavy lifting for you and all you need do after applying the appropriate configuration
(shown above) is...
[source,java]
@@ -280,11 +280,11 @@ class MyTransactionalService ... {
}
----
#1 & #4 above are appropriately handled for you by _Spring's_ JTA based `PlatformTransactionManager` once the
#1 & #4 above are appropriately handled for you by Spring's JTA based `PlatformTransactionManager` once the
`@Transactional` boundary is entered by your application (i.e. when the `MyTransactionSerivce.someTransactionalMethod()`
is called).
#2 & #3 are handled by _Spring Data for Pivotal GemFire's_ new Aspects enabled with the `@EnablePivotal GemFireAsLastResource` annotation.
#2 & #3 are handled by Spring Data for {data-store-name}'s new Aspects enabled with the `@EnableGemFireAsLastResource` annotation.
#3 of course is the responsibility of your application.
@@ -294,8 +294,8 @@ Indeed, with the appropriate logging configured, you will see the correct sequen
----
2017-Jun-22 11:11:37 TRACE TransactionInterceptor - Getting transaction for [example.app.service.MessageService.send]
2017-Jun-22 11:11:37 TRACE Pivotal GemFireAsLastResourceConnectionAcquiringAspect - Acquiring Pivotal GemFire Connection
from Pivotal GemFire JCA ResourceAdapter registered at [gfe/jca]
2017-Jun-22 11:11:37 TRACE GemFireAsLastResourceConnectionAcquiringAspect - Acquiring {data-store-name} Connection
from {data-store-name} JCA ResourceAdapter registered at [gfe/jca]
2017-Jun-22 11:11:37 TRACE MessageService - PRODUCER [ Message :
[{ @type = example.app.domain.Message, id= MSG0000000000, message = SENT }],
@@ -303,13 +303,13 @@ JSON : [{"id":"MSG0000000000","message":"SENT"}] ]
2017-Jun-22 11:11:37 TRACE TransactionInterceptor - Completing transaction for [example.app.service.MessageService.send]
2017-Jun-22 11:11:37 TRACE Pivotal GemFireAsLastResourceConnectionClosingAspect - Closed Pivotal GemFire Connection @ [Reference [...]]
2017-Jun-22 11:11:37 TRACE GemFireAsLastResourceConnectionClosingAspect - Closed {data-store-name} Connection @ [Reference [...]]
----
For more details on using Pivotal GemFire in JTA transactions,
For more details on using {data-store-name} in JTA transactions,
see http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html[here].
For more details on configuring Pivotal GemFire as a "_Last Resource_",
For more details on configuring {data-store-name} as a "_Last Resource_",
see http://gemfire90.docs.pivotal.io/geode/developing/transactions/JTA_transactions.html#concept_csy_vfb_wk[here].
:leveloffset: +1
@@ -321,33 +321,33 @@ include::{basedocdir}/reference/cq-container.adoc[]
[[apis:declarable]]
== Wiring `Declarable` Components
Pivotal GemFire XML configuration (usually referred to as `cache.xml`) allows *user* objects to be declared
{data-store-name} XML configuration (usually referred to as `cache.xml`) allows *user* objects to be declared
as part of the configuration. Usually these objects are `CacheLoaders` or other pluggable callback components
supported by Pivotal GemFire. Using native Pivotal GemFire configuration, each user type declared through XML must implement
supported by {data-store-name}. Using native {data-store-name} configuration, each user type declared through XML must implement
the `Declarable` interface, which allows arbitrary parameters to be passed to the declared class
through a `Properties` instance.
In this section, we describe how you can configure these pluggable components when defined in `cache.xml`
using _Spring_ while keeping your Cache/Region configuration defined in `cache.xml`. This allows your
using Spring while keeping your Cache/Region configuration defined in `cache.xml`. This allows your
pluggable components to focus on the application logic and not the location or creation of `DataSources`
or other collaborators.
However, if you are starting a green field project, it is recommended that you configure Cache, Region,
and other pluggable Pivotal GemFire components directly in _Spring_. This avoids inheriting from the `Declarable` interface
and other pluggable {data-store-name} components directly in Spring. This avoids inheriting from the `Declarable` interface
or the base class presented in this section.
See the following sidebar for more information on this approach.
.Eliminate `Declarable` components
****
A developer can configure custom types entirely through _Spring_ as mentioned in <<bootstrap:region>>.
A developer can configure custom types entirely through Spring as mentioned in <<bootstrap:region>>.
That way, a developer does not have to implement the `Declarable` interface, and also benefits from
all the features of the _Spring_ IoC container (not just dependency injection but also life-cycle
all the features of the Spring IoC container (not just dependency injection but also life-cycle
and instance management).
****
As an example of configuring a `Declarable` component using _Spring_, consider the following declaration
(taken from the `Declarable` http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/Declarable.html[Javadoc]):
As an example of configuring a `Declarable` component using Spring, consider the following declaration
(taken from the `Declarable` {x-data-store-javadoc}/org/apache/geode/cache/Declarable.html[Javadoc]):
[source,xml]
----
@@ -359,16 +359,16 @@ As an example of configuring a `Declarable` component using _Spring_, consider t
</cache-loader>
----
To simplify the task of parsing, converting the parameters and initializing the object, _Spring Data for Pivotal GemFire_ offers
a base class (`WiringDeclarableSupport`) that allows Pivotal GemFire user objects to be wired through a *template* bean definition
or, in case that is missing, perform auto-wiring through the _Spring_ IoC container. To take advantage of this feature,
To simplify the task of parsing, converting the parameters and initializing the object, Spring Data for {data-store-name} offers
a base class (`WiringDeclarableSupport`) that allows {data-store-name} user objects to be wired through a *template* bean definition
or, in case that is missing, perform auto-wiring through the Spring IoC container. To take advantage of this feature,
the user objects need to extend `WiringDeclarableSupport`, which automatically locates the declaring `BeanFactory`
and performs wiring as part of the initialization process.
.Why is a base class needed?
****
In the current Pivotal GemFire release there is no concept of an *object factory* and the types declared are instantiated
and used as is. In other words, there is no easy way to manage object creation outside Pivotal GemFire.
In the current {data-store-name} release there is no concept of an *object factory* and the types declared are instantiated
and used as is. In other words, there is no easy way to manage object creation outside {data-store-name}.
****
[[apis:declarable:template-wiring]]
@@ -419,8 +419,8 @@ class DBLoader extends WiringDeclarableSupport implements CacheLoader {
----
In the scenario above, as no parameter was specified, a bean with the id/name `com.company.app.DBLoader` was used
as a template for wiring the instance created by Pivotal GemFire. For cases where the bean name uses a different convention,
one can pass in the `bean-name` parameter in the Pivotal GemFire configuration:
as a template for wiring the instance created by {data-store-name}. For cases where the bean name uses a different convention,
one can pass in the `bean-name` parameter in the {data-store-name} configuration:
[source,xml]
----
@@ -465,7 +465,7 @@ However, a developer can also use JDK 5 annotations to provide additional inform
TIP: We strongly recommend reading the dedicated
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#beans-annotation-config[chapter]
in the _Spring_ documentation for more information on the supported annotations and enabling factors.
in the Spring documentation for more information on the supported annotations and enabling factors.
For example, the hypothetical `DBLoader` declaration above can be injected with a Spring-configured `DataSource`
in the following way:
@@ -510,17 +510,17 @@ class DBLoader extends WiringDeclarableSupport implements CacheLoader {
By using the JSR-330 annotations, the `CacheLoader` code has been simplified since the location and creation
of the `DataSource` has been externalized and the user code is concerned only with the loading process.
The `DataSource` might be transactional, created lazily, shared between multiple objects or retrieved from JNDI.
These aspects can easily be configured and changed through the _Spring_ container without touching
These aspects can easily be configured and changed through the Spring container without touching
the `DBLoader` code.
[[apis:spring-cache-abstraction]]
== Support for the Spring Cache Abstraction
_Spring Data for Pivotal GemFire_ provides an implementation of the _Spring_
Spring Data for {data-store-name} provides an implementation of the Spring
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#cache[Cache Abstraction]
to position Pivotal GemFire as a _caching provider_ in Spring's caching infrastructure.
to position {data-store-name} as a _caching provider_ in Spring's caching infrastructure.
To use Pivotal GemFire as a backing implementation, a "_caching provider_" _in Spring's Cache Abstraction_,
To use {data-store-name} as a backing implementation, a "_caching provider_" _in Spring's Cache Abstraction_,
simply add `GemfireCacheManager` to your configuration:
[source,xml]
@@ -550,9 +550,9 @@ NOTE: The `cache-ref` attribute on the `CacheManager` bean definition is not nec
is used (i.e. "gemfireCache"), i.e. `<gfe:cache>` without an explicit ID.
When the `GemfireCacheManager` (Singleton) bean instance is declared and declarative caching is enabled
(either in XML with `<cache:annotation-driven/>` or in JavaConfig with _Spring's_ `@EnableCaching` annotation),
the _Spring_ caching annotations (e.g. `@Cacheable`) identify the "caches" that will cache data in-memory
using Pivotal GemFire Regions.
(either in XML with `<cache:annotation-driven/>` or in JavaConfig with Spring's `@EnableCaching` annotation),
the Spring caching annotations (e.g. `@Cacheable`) identify the "caches" that will cache data in-memory
using {data-store-name} Regions.
These caches (i.e. Regions) must exist before the caching annotations that use them otherwise an error will occur.

8
src/main/asciidoc/reference/diskstore.adoc
View File

@@ -1,7 +1,7 @@
[[bootstrap:diskstore]]
= Configuring a DiskStore
Spring Data for Pivotal GemFire supports `DiskStore` configuration and creation through the `disk-store` element, as the following example shows:
Spring Data for {data-store-name} supports `DiskStore` configuration and creation through the `disk-store` element, as the following example shows:
[source,xml]
----
@@ -13,9 +13,9 @@ Spring Data for Pivotal GemFire supports `DiskStore` configuration and creation
----
`DiskStore` instances are used by regions for file system persistent backup and overflow of evicted entries
as well as persistent backup for WAN Gateways. Multiple Pivotal GemFire components may share the same `DiskStore`.
as well as persistent backup for WAN Gateways. Multiple {data-store-name} components may share the same `DiskStore`.
Additionally, multiple file system directories may be defined for a single `DiskStore`, as shown in the preceding example.
See Pivotal GemFire's documentation for a complete explanation of
http://gemfire.docs.pivotal.io/95/geode/developing/storing_data_on_disk/chapter_overview.html[Persistence and Overflow]
See {data-store-name}'s documentation for a complete explanation of
{x-data-store-docs}/developing/storing_data_on_disk/chapter_overview.html[Persistence and Overflow]
and configuration options on `DiskStore` instances.

126
src/main/asciidoc/reference/function-annotations.adoc
View File

@@ -1,22 +1,22 @@
[[function-annotations]]
= Annotation Support for Function Execution
Spring Data for Pivotal GemFire includes annotation support to simplify working with Pivotal GemFire
http://geode.apache.org/docs/guide/11/developing/function_exec/chapter_overview.html[function execution].
Under the hood, the Pivotal GemFire API provides classes to implement and register Pivotal GemFire
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/Function.html[functions]
that are deployed on Pivotal GemFire servers, which may then be invoked by other peer member applications
Spring Data for {data-store-name} includes annotation support to simplify working with {data-store-name}
{x-data-store-docs}/developing/function_exec/chapter_overview.html[function execution].
Under the hood, the {data-store-name} API provides classes to implement and register {data-store-name}
{x-data-store-javadoc}/org/apache/geode/cache/execute/Function.html[functions]
that are deployed on {data-store-name} servers, which may then be invoked by other peer member applications
or remotely from cache clients.
Functions can execute in parallel, distributed among multiple Pivotal GemFire servers in the cluster, aggregating results
Functions can execute in parallel, distributed among multiple {data-store-name} servers in the cluster, aggregating results
with the map-reduce pattern that are sent back to the caller. Functions can also be targeted to run on a single server
or region. The Pivotal GemFire API supports remote execution of functions targeted by using various predefined scopes:
or region. The {data-store-name} API supports remote execution of functions targeted by using various predefined scopes:
on region, on members (in groups), on servers, and others. The implementation and execution of remote functions,
as with any RPC protocol, requires some boilerplate code.
Spring Data for Pivotal GemFire, true to Spring's core value proposition, aims to hide the mechanics of remote function execution
and let you focus on core POJO programming and business logic. To this end, Spring Data for Pivotal GemFire introduces
annotations to declaratively register the public methods of a POJO class as Pivotal GemFire functions along with the ability to
Spring Data for {data-store-name}, true to Spring's core value proposition, aims to hide the mechanics of remote function execution
and let you focus on core POJO programming and business logic. To this end, Spring Data for {data-store-name} introduces
annotations to declaratively register the public methods of a POJO class as {data-store-name} functions along with the ability to
invoke registered functions (including remotely) by using annotated interfaces.
== Implementation Versus Execution
@@ -24,26 +24,26 @@ invoke registered functions (including remotely) by using annotated interfaces.
There are two separate concerns to address implementation and execution.
The first is function implementation (server-side), which must interact with the
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/FunctionContext.html[`FunctionContext`]
{x-data-store-javadoc}/org/apache/geode/cache/execute/FunctionContext.html[`FunctionContext`]
to access the invocation arguments,
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/ResultSender.html[`ResultsSender`],
{x-data-store-javadoc}/org/apache/geode/cache/execute/ResultSender.html[`ResultsSender`],
and other execution context information. The function implementation typically accesses the cache and regions
and is registered with the
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/FunctionService.html[`FunctionService`]
{x-data-store-javadoc}/org/apache/geode/cache/execute/FunctionService.html[`FunctionService`]
under a unique ID.
A cache client application invoking a function does not depend on the implementation. To invoke a function,
the application instantiates an
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/Execution.html[`Execution`]
{x-data-store-javadoc}/org/apache/geode/cache/execute/Execution.html[`Execution`]
providing the function ID, invocation arguments, and the function target, which defines its scope:
region, server, servers, member, or members. If the function produces a result, the invoker uses a
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/ResultCollector.html[`ResultCollector`]
{x-data-store-javadoc}/org/apache/geode/cache/execute/ResultCollector.html[`ResultCollector`]
to aggregate and acquire the execution results. In certain cases, a custom `ResultCollector` implementation
is required and may be registered with the `Execution`.
NOTE: 'Client' and 'Server' are used here in the context of function execution, which may have a different meaning
than client and server in Pivotal GemFire's client-server topology. While it is common for an application using a `ClientCache`
to invoke a function on one or more Pivotal GemFire servers in a cluster, it is also possible to execute functions
than client and server in {data-store-name}'s client-server topology. While it is common for an application using a `ClientCache`
to invoke a function on one or more {data-store-name} servers in a cluster, it is also possible to execute functions
in a peer-to-peer (P2P) configuration, where the application is a member of the cluster hosting a peer `Cache`.
Keep in mind that a peer member cache application is subject to all the constraints of being a peer member
of the cluster.
@@ -51,7 +51,7 @@ of the cluster.
[[function-implementation]]
== Implementing a Function
Using Pivotal GemFire APIs, the `FunctionContext` provides a runtime invocation context that includes the client's
Using {data-store-name} APIs, the `FunctionContext` provides a runtime invocation context that includes the client's
calling arguments and a `ResultSender` implementation to send results back to the client. Additionally,
if the function is executed on a region, the `FunctionContext` is actually an instance of `RegionFunctionContext`,
which provides additional information, such as the target region on which the function was invoked,
@@ -98,7 +98,7 @@ or the `ResultSender` if you need to control how the results are returned to the
=== Annotations for Function Implementation
The following example shows how SDG's function annotations are used to expose POJO methods
as Pivotal GemFire functions:
as {data-store-name} functions:
[source,java]
----
@@ -117,7 +117,7 @@ public class ApplicationFunctions {
}
----
Note that the class itself must be registered as a Spring bean and each Pivotal GemFire Function is annotated
Note that the class itself must be registered as a Spring bean and each {data-store-name} Function is annotated
with `@GemfireFunction`. In the preceding example, Spring's `@Component` annotation was used, but you can register the bean
by using any method supported by Spring (such as XML configuration or with a Java configuration class when using Spring Boot).
This lets the Spring container create an instance of this class and wrap it in a
@@ -126,15 +126,15 @@ Spring creates a wrapper instance for each method annotated with `@GemfireFuncti
the same target object instance to invoke the corresponding method.
TIP: The fact that the POJO Function class is a Spring bean may offer other benefits, since it shares
the `ApplicationContext` with Pivotal GemFire components, such as the cache and regions. These may be injected into the class
the `ApplicationContext` with {data-store-name} components, such as the cache and regions. These may be injected into the class
if necessary.
Spring creates the wrapper class and registers the functions with Pivotal GemFire's function service. The function ID used
Spring creates the wrapper class and registers the functions with {data-store-name}'s function service. The function ID used
to register each function must be unique. By using convention, it defaults to the simple (unqualified) method name.
The name can be explicitly defined by using the `id` attribute of the `@GemfireFunction` annotation.
The `@GemfireFunction` annotation also provides other configuration attributes, `HA` and `optimizedForWrite`,
which correspond to properties defined by Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/Function.html[`Function`] interface.
which correspond to properties defined by {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/execute/Function.html[`Function`] interface.
If the method's return type is `void`, then the `hasResult` property is automatically set to `false`.
Otherwise, if the method returns a value, the `hasResult` attributes is set to `true`.
@@ -142,7 +142,7 @@ Even for `void` return types, the annotation's `hasResult` attribute can be set
as shown in the `functionWithContext` method show previously. Presumably, the intention is to use the `ResultSender` directly
to send results to the caller.
The `PojoFunctionWrapper` implements Pivotal GemFire's `Function` interface, binds method parameters, and invokes the target method
The `PojoFunctionWrapper` implements {data-store-name}'s `Function` interface, binds method parameters, and invokes the target method
in its `execute()` method. It also sends the method's return value by using the `ResultSender`.
=== Batching Results
@@ -179,11 +179,11 @@ class ApplicationConfiguration { .. }
== Executing a Function
A process that invokes a remote function needs to provide the function's ID, calling arguments, the execution target
(`onRegion`, `onServers`, `onServer`, `onMember`, or `onMembers`) and (optionally) a filter set. By using Spring Data for Pivotal GemFire,
(`onRegion`, `onServers`, `onServer`, `onMember`, or `onMembers`) and (optionally) a filter set. By using Spring Data for {data-store-name},
all you need do is define an interface supported by annotations. Spring creates a dynamic proxy
for the interface, which uses the `FunctionService` to create an `Execution`, invoke the `Execution`, and (if necessary) coerce
the results to the defined return type. This technique is similar to the way
Spring Data for Pivotal GemFire's repository extension works. Thus, some of the configuration and concepts should be familiar.
Spring Data for {data-store-name}'s repository extension works. Thus, some of the configuration and concepts should be familiar.
Generally, a single interface definition maps to multiple function executions, one corresponding to each method
defined in the interface.
@@ -191,11 +191,11 @@ defined in the interface.
To support client-side Function execution, the following SDG Function annotations are provided: `@OnRegion`,
`@OnServer`, `@OnServers`, `@OnMember`, and `@OnMembers`. These annotations correspond to the `Execution` implementations
provided by Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/FunctionService.html[`FunctionService`].
provided by {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/execute/FunctionService.html[`FunctionService`].
Each annotation exposes the appropriate attributes. These annotations also provide an optional
`resultCollector` attribute whose value is the name of a Spring bean implementing the
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/ResultCollector.html[`ResultCollector`]
{x-data-store-javadoc}/org/apache/geode/cache/execute/ResultCollector.html[`ResultCollector`]
to use for the execution.
CAUTION: The proxy interface binds all declared methods to the same execution configuration. Although it is expected
@@ -283,12 +283,12 @@ The filter argument is optional. The remaining arguments are a variable argument
[[function-execution-pdx]]
== Function Execution with PDX
When using Spring Data for Pivotal GemFire's function annotation support combined with Pivotal GemFire's
http://geode.apache.org/docs/guide/11/developing/data_serialization/gemfire_pdx_serialization.html[PDX Serialization],
When using Spring Data for {data-store-name}'s function annotation support combined with {data-store-name}'s
{x-data-store-docs}/developing/data_serialization/gemfire_pdx_serialization.html[PDX Serialization],
there are a few logistical things to keep in mind.
As explained earlier in this section, and by way of example, you should typically define Pivotal GemFire functions by using POJO classes
annotated with Spring Data for Pivotal GemFire
As explained earlier in this section, and by way of example, you should typically define {data-store-name} functions by using POJO classes
annotated with Spring Data for {data-store-name}
http://docs.spring.io/spring-data-gemfire/docs/current/api/org/springframework/data/gemfire/function/annotation/package-summary.html[function annotations],
as follows:
@@ -329,7 +329,7 @@ public enum OrderSource {
}
----
Of course, you can define a function `Execution` interface to call the 'process' Pivotal GemFire server function, as follows:
Of course, you can define a function `Execution` interface to call the 'process' {data-store-name} server function, as follows:
[source,java]
----
@@ -344,16 +344,16 @@ Clearly, this `process(..)` `Order` Function is being called from a client-side
The same is true when invoking peer-to-peer member functions (such as `@OnMember(s)) between peers in the cluster.
Any form of `distribution` requires the data transmitted between client and server (or peers) to be serialized.
Now, if you have configured Pivotal GemFire to use PDX for serialization (instead of Java serialization, for instance)
Now, if you have configured {data-store-name} to use PDX for serialization (instead of Java serialization, for instance)
you can also set the `pdx-read-serialized` attribute to `true` in your configuration
of the Pivotal GemFire server(s), as follows:
of the {data-store-name} server(s), as follows:
[source,xml]
----
<gfe:cache ... pdx-read-serialized="true"/>
----
Alternatively, you can set the `pdx-read-serialized` attribute to `true` for a Pivotal GemFire cache client application, as follows:
Alternatively, you can set the `pdx-read-serialized` attribute to `true` for a {data-store-name} cache client application, as follows:
[source,xml]
----
@@ -363,24 +363,24 @@ Alternatively, you can set the `pdx-read-serialized` attribute to `true` for a P
Doing so causes all values read from the cache (that is, regions) as well as information passed between client and servers
(or peers) to remain in serialized form, including, but not limited to, function arguments.
Pivotal GemFire serializes only application domain object types that you have specifically configured (registered)
either by using Pivotal GemFire's
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`],
or specifically (and recommended) by using a "`custom`" Pivotal GemFire
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/pdx/PdxSerializer.html[`PdxSerializer`]. If you use
Spring Data for Pivotal GemFire's repository extension to Spring Data Common's repository abstraction and infrastructure,
you might even want to consider using Spring Data for Pivotal GemFire's
{data-store-name} serializes only application domain object types that you have specifically configured (registered)
either by using {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`],
or specifically (and recommended) by using a "`custom`" {data-store-name}
{x-data-store-javadoc}/org/apache/geode/pdx/PdxSerializer.html[`PdxSerializer`]. If you use
Spring Data for {data-store-name}'s repository extension to Spring Data Common's repository abstraction and infrastructure,
you might even want to consider using Spring Data for {data-store-name}'s
http://docs.spring.io/spring-data-gemfire/docs/current/api/org/springframework/data/gemfire/mapping/MappingPdxSerializer.html[`MappingPdxSerializer`],
which uses an entity's mapping meta-data to determine data from the application domain object that are serialized
to the PDX instance.
What is less than apparent, though, is that Pivotal GemFire automatically handles Java `Enum` types regardless of whether they are
What is less than apparent, though, is that {data-store-name} automatically handles Java `Enum` types regardless of whether they are
explicitly configured (that is, registered with a `ReflectionBasedAutoSerializer` using a regex pattern
and the `classes` parameter or are handled by a "`custom`" Pivotal GemFire `PdxSerializer`), despite the fact that Java enumerations
and the `classes` parameter or are handled by a "`custom`" {data-store-name} `PdxSerializer`), despite the fact that Java enumerations
implement `java.io.Serializable`.
So, when you set `pdx-read-serialized` to `true` on Pivotal GemFire servers where the Pivotal GemFire functions
(including Spring Data for Pivotal GemFire function-annotated POJO classes) are registered, then you
So, when you set `pdx-read-serialized` to `true` on {data-store-name} servers where the {data-store-name} functions
(including Spring Data for {data-store-name} function-annotated POJO classes) are registered, then you
may encounter surprising behavior when invoking the function `Execution`.
You might pass the following arguments when invoking the function:
@@ -390,7 +390,7 @@ You might pass the following arguments when invoking the function:
orderProcessingFunctions.process(new Order(123, customer, Calendar.getInstance(), items), OrderSource.ONLINE, 400);
----
However, the Pivotal GemFire function on the server gets the following:
However, the {data-store-name} function on the server gets the following:
[source,java]
----
@@ -398,19 +398,19 @@ process(regionData, order:PdxInstance, :PdxInstanceEnum, 400);
----
The `Order` and `OrderSource` have been passed to the function as
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/pdx/PdxInstance.html[PDX instances].
{x-data-store-javadoc}/org/apache/geode/pdx/PdxInstance.html[PDX instances].
Again, this all happens because `pdx-read-serialized` is set to `true`, which may be necessary in cases where
the Pivotal GemFire servers interact with multiple different clients (for example, a combination of Java clients and native clients, such as C++, C#, and others).
the {data-store-name} servers interact with multiple different clients (for example, a combination of Java clients and native clients, such as C++, C#, and others).
This flies in the face of Spring Data for Pivotal GemFire's strongly-typed function-annotated POJO class method signatures,
This flies in the face of Spring Data for {data-store-name}'s strongly-typed function-annotated POJO class method signatures,
as you should reasonably expect application domain object types, not PDX serialized instances.
Consequently, Spring Data for Pivotal GemFire includes enhanced function support to automatically convert method arguments
Consequently, Spring Data for {data-store-name} includes enhanced function support to automatically convert method arguments
type PDX to the desired application domain object types defined by the function method's
parameter types.
However, this also requires you to explicitly register a Pivotal GemFire `PdxSerializer` on the Pivotal GemFire Servers
where Spring Data for Pivotal GemFire function-annotated POJOs are registered and used, as the following example shows:
However, this also requires you to explicitly register a {data-store-name} `PdxSerializer` on the {data-store-name} Servers
where Spring Data for {data-store-name} function-annotated POJOs are registered and used, as the following example shows:
[source,java]
----
@@ -419,13 +419,13 @@ where Spring Data for Pivotal GemFire function-annotated POJOs are registered an
<gfe:cache ... pdx-serializer-ref="customPdxSerializeer" pdx-read-serialized="true"/>
----
Alternatively, you can use Pivotal GemFire's
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`]
Alternatively, you can use {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`]
for convenience. Of course, we recommend that, where possible, you use a custom `PdxSerializer` to maintain
finer-grained control over your serialization strategy.
Finally, Spring Data for Pivotal GemFire is careful not to convert your function arguments if you treat your function arguments
generically or as one of Pivotal GemFire's PDX types, as follows:
Finally, Spring Data for {data-store-name} is careful not to convert your function arguments if you treat your function arguments
generically or as one of {data-store-name}'s PDX types, as follows:
[source,java]
----
@@ -435,9 +435,9 @@ public Object genericFunction(String value, Object domainObject, PdxInstanceEnum
}
----
Spring Data for Pivotal GemFire converts PDX type data to the corresponding application domain types if and only if
Spring Data for {data-store-name} converts PDX type data to the corresponding application domain types if and only if
the corresponding application domain types are on the classpath and the function-annotated POJO method expects it.
For a good example of custom, composed application-specific Pivotal GemFire `PdxSerializers` as well as appropriate
POJO function parameter type handling based on the method signatures, see Spring Data for Pivotal GemFire's
For a good example of custom, composed application-specific {data-store-name} `PdxSerializers` as well as appropriate
POJO function parameter type handling based on the method signatures, see Spring Data for {data-store-name}'s
https://github.com/spring-projects/spring-data-gemfire/blob/2.0.0.M2/src/test/java/org/springframework/data/gemfire/function/ClientCacheFunctionExecutionWithPdxIntegrationTest.java[`ClientCacheFunctionExecutionWithPdxIntegrationTest`] class.

12
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@@ -1,17 +1,17 @@
[[bootstrap:function]]
= Configuring the Function Service
Spring Data for Pivotal GemFire provides <<function-annotations,annotation>> support for implementing and registering
Pivotal GemFire Functions.
Spring Data for {data-store-name} provides <<function-annotations,annotation>> support for implementing and registering
{data-store-name} Functions.
Spring Data for Pivotal GemFire also provides namespace support for registering Pivotal GemFire
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/execute/Function.html[functions]
Spring Data for {data-store-name} also provides namespace support for registering {data-store-name}
{x-data-store-javadoc}/org/apache/geode/cache/execute/Function.html[functions]
for remote function execution.
See Pivotal GemFire's http://geode.apache.org/docs/guide/11/developing/function_exec/chapter_overview.html[documentation]
See {data-store-name}'s {x-data-store-docs}/developing/function_exec/chapter_overview.html[documentation]
for more information on the function execution framework.
Pivotal GemFire Functions are declared as Spring beans and must implement the `org.apache.geode.cache.execute.Function`
{data-store-name} Functions are declared as Spring beans and must implement the `org.apache.geode.cache.execute.Function`
interface or extend `org.apache.geode.cache.execute.FunctionAdapter`.
The namespace uses a familiar pattern to declare functions, as the following example shows:

10
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@@ -1,10 +1,10 @@
[[bootstrap:gateway]]
= Configuring WAN Gateways
WAN Gateways provide a way to synchronize Pivotal GemFire Distributed Systems across geographic areas.
Spring Data for Pivotal GemFire provides namespace support for configuring WAN Gateways as illustrated in the following examples.
WAN Gateways provide a way to synchronize {data-store-name} Distributed Systems across geographic areas.
Spring Data for {data-store-name} provides namespace support for configuring WAN Gateways as illustrated in the following examples.
== WAN Configuration in Pivotal GemFire 7.0
== WAN Configuration in {data-store-name} 7.0
In the following example, `GatewaySenders` are configured for a `PARTITION` region by adding child elements
(`gateway-sender` and `gateway-sender-ref`) to the region.
@@ -60,6 +60,6 @@ The `GatewayReceiver` may also be configured with `EventFilters` and `TransportF
</gfe:gateway-receiver>
----
See the Pivotal GemFire
http://geode.apache.org/docs/guide/11/topologies_and_comm/multi_site_configuration/chapter_overview.html[documentation]
See the {data-store-name}
{x-data-store-docs}/topologies_and_comm/multi_site_configuration/chapter_overview.html[documentation]
for a detailed explanation of all the configuration options.

70
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@@ -1,37 +1,37 @@
[[gemfire-bootstrap]]
= Bootstrapping a Spring ApplicationContext in Pivotal GemFire
= Bootstrapping a Spring ApplicationContext in {data-store-name}
Normally, a Spring based application <<bootstrap,bootstraps Pivotal GemFire>> by using Spring Data for Pivotal GemFire's caching features.
By specifying a `<gfe:cache/>` element that uses the Spring Data for Pivotal GemFire XML namespace, a single embedded Pivotal GemFire
Normally, a Spring based application <<bootstrap,bootstraps {data-store-name}>> by using Spring Data for {data-store-name}'s caching features.
By specifying a `<gfe:cache/>` element that uses the Spring Data for {data-store-name} XML namespace, a single embedded {data-store-name}
peer `Cache` instance is created and initialized with default settings in the same JVM process as your application.
However, it is sometimes necessary (perhaps as a requirement imposed by your IT organization) that Pivotal GemFire be fully managed
and operated by the provided Pivotal GemFire tool suite, perhaps with
http://geode.apache.org/docs/guide/11/tools_modules/gfsh/chapter_overview.html[Gfsh]. By using Gfsh,
Pivotal GemFire bootstraps your Spring application context rather than the other way around. Instead of
an application server or a Java main class that uses Spring Boot, Pivotal GemFire does the bootstrapping and
However, it is sometimes necessary (perhaps as a requirement imposed by your IT organization) that {data-store-name} be fully managed
and operated by the provided {data-store-name} tool suite, perhaps with
{x-data-store-docs}/tools_modules/gfsh/chapter_overview.html[Gfsh]. By using Gfsh,
{data-store-name} bootstraps your Spring application context rather than the other way around. Instead of
an application server or a Java main class that uses Spring Boot, {data-store-name} does the bootstrapping and
hosts your application.
NOTE: Pivotal GemFire is not an application server. In addition, there are limitations to using
this approach where the Pivotal GemFire cache configuration is concerned.
NOTE: {data-store-name} is not an application server. In addition, there are limitations to using
this approach where the {data-store-name} cache configuration is concerned.
[[gemfire-bootstrap-gfsh]]
== Using Pivotal GemFire to Bootstrap a Spring Context Started with Gfsh
== Using {data-store-name} to Bootstrap a Spring Context Started with Gfsh
In order to bootstrap a Spring application context in Pivotal GemFire when starting a Pivotal GemFire Server process by using Gfsh,
you must use Pivotal GemFire's
http://geode.apache.org/docs/guide/11/basic_config/the_cache/setting_cache_initializer.html[initalizer].
An initializer block can declare a callback application that is launched after the cache is initialized by Pivotal GemFire.
In order to bootstrap a Spring application context in {data-store-name} when starting a {data-store-name} Server process by using Gfsh,
you must use {data-store-name}'s
{x-data-store-docs}/basic_config/the_cache/setting_cache_initializer.html[initalizer].
An initializer block can declare a callback application that is launched after the cache is initialized by {data-store-name}.
An initializer is declared within an
http://geode.apache.org/docs/guide/11/reference/topics/cache_xml.html#initializer[initializer] element by
using a minimal snippet of Pivotal GemFire's native `cache.xml`. To bootstrap the Spring application context,
{x-data-store-docs}/reference/topics/cache_xml.html#initializer[initializer] element by
using a minimal snippet of {data-store-name}'s native `cache.xml`. To bootstrap the Spring application context,
the `cache.xml` file is required, in much the same way as a minimal snippet of Spring XML config is needed to bootstrap
a Spring application context configured with component scanning (for example `<context:component-scan base-packages="..."/>`).
Fortunately, such an initializer is already conveniently provided by the framework: the
http://docs.spring.io/spring-data-gemfire/docs/current/api/org/springframework/data/gemfire/support/SpringContextBootstrappingInitializer.html[`SpringContextBootstrappingInitializer`].
The following example shows a typical yet minimal configuration for this class inside Pivotal GemFires's `cache.xml` file:
The following example shows a typical yet minimal configuration for this class inside {data-store-name}'s `cache.xml` file:
[source,xml]
----
@@ -79,7 +79,7 @@ on the classpath, as the following example shows:
----
Then, with a properly configured and constructed `CLASSPATH` and `cache.xml` file (shown earlier) specified as
a command-line option when starting a Pivotal GemFire Server in Gfsh, the command-line would be as follows:
a command-line option when starting a {data-store-name} Server in Gfsh, the command-line would be as follows:
[source]
----
@@ -89,35 +89,35 @@ gfsh>start server --name=Server1 --log-level=config ...
----
The `application-context.xml` can be any valid Spring context configuration metadata, including all of the SDG namespace
elements. The only limitation with this approach is that a Pivotal GemFire cache cannot be configured by using
the Spring Data for Pivotal GemFire namespace. In other words, none of the `<gfe:cache/>` element attributes
elements. The only limitation with this approach is that a {data-store-name} cache cannot be configured by using
the Spring Data for {data-store-name} namespace. In other words, none of the `<gfe:cache/>` element attributes
(such as `cache-xml-location`, `properties-ref`, `critical-heap-percentage`, `pdx-serializer-ref`, `lock-lease`, and others)
can be specified. If used, these attributes are ignored.
The reason for this is that Pivotal GemFire itself has already created and initialized the cache before the initializer
The reason for this is that {data-store-name} itself has already created and initialized the cache before the initializer
gets invoked. As a result, the cache already exists and, since it is a "`singleton`", it cannot be re-initialized
or have any of its configuration augmented.
[[gemfire-bootstrap-lazywiring]]
== Lazy-wiring Pivotal GemFire Components
== Lazy-wiring {data-store-name} Components
Spring Data for Pivotal GemFire already provides support for wiring Pivotal GemFire components (such as `CacheListeners`,
`CacheLoaders`, `CacheWriters` and so on) that are declared and created by Pivotal GemFire in `cache.xml` by using
Spring Data for {data-store-name} already provides support for wiring {data-store-name} components (such as `CacheListeners`,
`CacheLoaders`, `CacheWriters` and so on) that are declared and created by {data-store-name} in `cache.xml` by using
SDG's `WiringDeclarableSupport` class, as described in <<apis:declarable:autowiring>>. However, this works only
when Spring is the one doing the bootstrapping (that is, when Spring bootstraps Pivotal GemFire).
when Spring is the one doing the bootstrapping (that is, when Spring bootstraps {data-store-name}).
When your Spring application context is bootstrapped by Pivotal GemFire, these Pivotal GemFire application components go unnoticed,
When your Spring application context is bootstrapped by {data-store-name}, these {data-store-name} application components go unnoticed,
because the Spring application context does not yet exist. The Spring application context does not get created
until Pivotal GemFire calls the initializer block, which only occurs after all the other Pivotal GemFire components and configuration
until {data-store-name} calls the initializer block, which only occurs after all the other {data-store-name} components and configuration
have already been created and initialized.
To solve this problem, a new `LazyWiringDeclarableSupport` class was introduced. This new class is aware of the
Spring application context. The intention of this abstract base class is that any implementing class
register itself to be configured by the Spring container that is eventually be created by Pivotal GemFire
once the initializer is called. In essence, this gives your Pivotal GemFire application components a chance
register itself to be configured by the Spring container that is eventually be created by {data-store-name}
once the initializer is called. In essence, this gives your {data-store-name} application components a chance
to be configured and auto-wired with Spring beans defined in the Spring application context.
In order for your Pivotal GemFire application components to be auto-wired by the Spring container, you should create an application class
In order for your {data-store-name} application components to be auto-wired by the Spring container, you should create an application class
that extends the `LazyWiringDeclarableSupport` and annotate any class member that needs to be provided as
a Spring bean dependency, similar to the following example:
@@ -134,14 +134,14 @@ public class UserDataSourceCacheLoader extends LazyWiringDeclarableSupport
----
As implied in the `CacheLoader` example above, you might necessarily (though rarely) have defined both
a region and a `CacheListener` component in Pivotal GemFire `cache.xml`. The `CacheLoader` may need access to an application DAO
(or perhaps a JDBC `DataSource` defined in the Spring application context) for loading `Users` into a Pivotal GemFire `REPLICATE` region
a region and a `CacheListener` component in {data-store-name} `cache.xml`. The `CacheLoader` may need access to an application DAO
(or perhaps a JDBC `DataSource` defined in the Spring application context) for loading `Users` into a {data-store-name} `REPLICATE` region
on start.
CAUTION
====
Be careful when mixing the different life-cycles of Pivotal GemFire and the Spring Container together
in this manner. Not all use cases and scenarios are supported. The Pivotal GemFire `cache.xml` configuration would be
Be careful when mixing the different life-cycles of {data-store-name} and the Spring Container together
in this manner. Not all use cases and scenarios are supported. The {data-store-name} `cache.xml` configuration would be
similar to the following (which comes from SDG's test suite):
[source,xml]

88
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@@ -1,19 +1,19 @@
[[bootstrap:indexing]]
= Configuring an Index
Pivotal GemFire allows indexes (also sometimes pluralized as indices) to be created on region data to improve the performance of OQL (Object Query Language) queries.
{data-store-name} allows indexes (also sometimes pluralized as indices) to be created on region data to improve the performance of OQL (Object Query Language) queries.
In Spring Data for Pivotal GemFire (SDG), indexes are declared with the `index` element, as the following example shows:
In Spring Data for {data-store-name} (SDG), indexes are declared with the `index` element, as the following example shows:
[source,xml]
----
<gfe:index id="myIndex" expression="someField" from="/SomeRegion" type="HASH"/>
----
In Spring Data for Pivotal GemFire's XML schema (also called the SDG namespace), `index` bean declarations are not bound to a region,
unlike Pivotal GemFire's native `cache.xml`. Rather, they are top-level elements similar to `&lt;gfe:cache&gt;`. This lets
In Spring Data for {data-store-name}'s XML schema (also called the SDG namespace), `index` bean declarations are not bound to a region,
unlike {data-store-name}'s native `cache.xml`. Rather, they are top-level elements similar to `&lt;gfe:cache&gt;`. This lets
you declare any number of indexes on any Region, whether they were just created or already exist -- a
significant improvement over Pivotal GemFire's native `cache.xml` format.
significant improvement over {data-store-name}'s native `cache.xml` format.
An `Index` must have a name. You can give the `Index` an explicit name by using the `name` attribute.
Otherwise, the bean name (that is, the value of the `id` attribute) of the `index` bean definition is used as
@@ -69,29 +69,29 @@ Therefore, you might want to create an `Index` with a statement similar to the f
----
The `from` clause must refer to a valid, existing region and is how an `index` gets applied to a region.
This is not specific to Spring Data Pivotal GemFir. It is a feature of Pivotal GemFire.
This is not specific to Spring Data Pivotal GemFir. It is a feature of {data-store-name}.
The `index` `type` may be one of three enumerated values defined by Spring Data for Pivotal GemFire's
The `index` `type` may be one of three enumerated values defined by Spring Data for {data-store-name}'s
http://docs.spring.io/spring-data-gemfire/docs/current/api/org/springframework/data/gemfire/IndexType.html[`IndexType`]
enumeration: `FUNCTIONAL`, `HASH`, and `PRIMARY_KEY`.
Each of the enumerated values corresponds to one of the http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html[`QueryService`]
Each of the enumerated values corresponds to one of the {x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html[`QueryService`]
`create[|Key|Hash]Index` methods invoked when the actual `index` is to be created (or "`defined`" -- you can find more on "`defining`"
indexes in the next section). For instance, if the `IndexType` is `PRIMARY_KEY`, then the
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#createKeyIndex-java.lang.String-java.lang.String-java.lang.String-[QueryService.createKeyIndex(..)]
{x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#createKeyIndex-java.lang.String-java.lang.String-java.lang.String-[QueryService.createKeyIndex(..)]
is invoked to create a `KEY` `Index`.
The default is `FUNCTIONAL` and results in one of the `QueryService.createIndex(..)` methods
being invoked.
See the Spring Data for Pivotal GemFire XML schema for a full set of options.
See the Spring Data for {data-store-name} XML schema for a full set of options.
For more information on indexing in Pivotal GemFire, see "`http://gemfire90.docs.pivotal.io/geode/developing/query_index/query_index.html[Working with Indexes]`"
in Pivotal GemFire's User Guide.
For more information on indexing in {data-store-name}, see "`http://gemfire90.docs.pivotal.io/geode/developing/query_index/query_index.html[Working with Indexes]`"
in {data-store-name}'s User Guide.
== Defining Indexes
In addition to creating Indexes up front as `ndex` bean definitions are processed by Spring Data for Pivotal GemFire
In addition to creating Indexes up front as `ndex` bean definitions are processed by Spring Data for {data-store-name}
on Spring container initialization, you may also define all of your application indexes prior to creating
them by using the `define` attribute, as follows:
@@ -102,9 +102,9 @@ them by using the `define` attribute, as follows:
When `define` is set to `true` (it defaults to `false`), it does not actually create the `Index` at that moment.
All "`defined`" Indexes are created all at once, when the Spring `ApplicationContext` is "`refreshed`" or, to put it differently,
when a `ContextRefreshedEvent` is published by the Spring container. Spring Data for Pivotal GemFire registers itself as
an `ApplicationListener` listening for the `ContextRefreshedEvent`. When fired, Spring Data for Pivotal GemFire calls
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#createDefinedIndexes[`QueryService.createDefinedIndexes()`].
when a `ContextRefreshedEvent` is published by the Spring container. Spring Data for {data-store-name} registers itself as
an `ApplicationListener` listening for the `ContextRefreshedEvent`. When fired, Spring Data for {data-store-name} calls
{x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#createDefinedIndexes[`QueryService.createDefinedIndexes()`].
Defining indexes and creating them all at once boosts speed and efficiency when creating indexes.
@@ -113,34 +113,34 @@ for more details.
== `IgnoreIfExists` and `Override`
Two Spring Data for Pivotal GemFire `Index` configuration options warrant special mention: `ignoreIfExists` and `override`.
Two Spring Data for {data-store-name} `Index` configuration options warrant special mention: `ignoreIfExists` and `override`.
These options correspond to the `ignore-if-exists` and `override` attributes on the `&lt;gfe:index&gt;` element
in Spring Data for Pivotal GemFire's XML schema, respectively.
in Spring Data for {data-store-name}'s XML schema, respectively.
WARNING: Make sure you absolutely understand what you are doing before using either of these options. These options can
affect the performance and resources (such as memory) consumed by your application at runtime. As a result, both of
these options are disabled (set to `false`) in SDG by default.
NOTE: These options are only available in Spring Data for Pivotal GemFire and exist to workaround known limitations
with Pivotal GemFire. Pivotal GemFire has no equivalent options or functionality.
NOTE: These options are only available in Spring Data for {data-store-name} and exist to workaround known limitations
with {data-store-name}. {data-store-name} has no equivalent options or functionality.
Each option significantly differs in behavior and entirely depends on the type of Pivotal GemFire `Index` exception thrown.
This also means that neither option has any effect if a Pivotal GemFire Index-type exception is not thrown. These options
are meant to specifically handle Pivotal GemFire `IndexExistsException` and `IndexNameConflictException` instances, which can occur
Each option significantly differs in behavior and entirely depends on the type of {data-store-name} `Index` exception thrown.
This also means that neither option has any effect if a {data-store-name} Index-type exception is not thrown. These options
are meant to specifically handle {data-store-name} `IndexExistsException` and `IndexNameConflictException` instances, which can occur
for various, sometimes obscure reasons. The exceptions have the following causes:
* An http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/IndexExistsException.html[`IndexExistsException`]
* An {x-data-store-javadoc}/org/apache/geode/cache/query/IndexExistsException.html[`IndexExistsException`]
is thrown when there exists another `Index` with the same definition but a different name when attempting to
create an `Index`.
* An http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/IndexNameConflictException.html[`IndexNameConflictException`]
* An {x-data-store-javadoc}/org/apache/geode/cache/query/IndexNameConflictException.html[`IndexNameConflictException`]
is thrown when there exists another `Index` with the same name but possibly different definition when attempting to
create an `Index`.
Spring Data for Pivotal GemFire's default behavior is to fail-fast, always. So, neither `Index` _Exception_ are "`handled`"
Spring Data for {data-store-name}'s default behavior is to fail-fast, always. So, neither `Index` _Exception_ are "`handled`"
by default. These `Index` exceptions are wrapped in a SDG `GemfireIndexException` and rethrown. If you wish
for Spring Data for Pivotal GemFire to handle them for you, you can set either of these `Index` bean definition options to `true`.
for Spring Data for {data-store-name} to handle them for you, you can set either of these `Index` bean definition options to `true`.
`IgnoreIfExists` always takes precedence over `Override`, primarily because it uses fewer resources (because it returns
the "`existing`" `index` in both exceptional cases).
@@ -152,11 +152,11 @@ then the `index` that would have been created by this `index` bean definition or
and the existing `Index` is returned.
There is little consequence in returning the existing `index`, since the `Index` definition is the same,
as determined by Pivotal GemFire itself, not SDG.
as determined by {data-store-name} itself, not SDG.
However, this also means that no `index` with the "`name`" specified in your `Index` bean definition or declaration
actually exists from Pivotal GemFire's perspective (that is, with
http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#getIndexes[`QueryService.getIndexes()`]).
actually exists from {data-store-name}'s perspective (that is, with
{x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#getIndexes[`QueryService.getIndexes()`]).
Therefore, you should be careful when writing OQL query statements that use query hints, especially hints that refer
to the application `Index` being ignored. Those query hints need to be changed.
@@ -173,7 +173,7 @@ this might not be the case. Consequently, you should verify your index names.
NOTE: SDG makes a best effort to inform the user when the `Index` being ignored is significantly different
in its definition from the existing `Index`. However, in order for SDG to accomplish this, it must be able to find
the existing `Index`, which is looked up by using the Pivotal GemFire API (the only means available).
the existing `Index`, which is looked up by using the {data-store-name} API (the only means available).
=== `Override` Behavior
@@ -182,7 +182,7 @@ When an `IndexExistsException` is thrown and `override` is set to `true` (or `&l
the `Index` is effectively renamed. Remember, `IndexExistsExceptions` are thrown when multiple indexes exist that
have the same definition but different names.
Spring Data for Pivotal GemFire can only accomplish this by using Pivotal GemFire's API, by first removing the existing `Index`
Spring Data for {data-store-name} can only accomplish this by using {data-store-name}'s API, by first removing the existing `Index`
and then recreating the `index` with the new name. It is possible that either the remove or subsequent
create invocation could fail. There is no way to execute both actions atomically and rollback this joint operation
if either fails.
@@ -197,7 +197,7 @@ is thrown.
If so, SDG is smart and returns the existing Index as is, even on `override`. There is no harm in this behavior,
since both the name and the definition are exactly the same. Of course, SDG can only accomplish this when
SDG is able to find the existing `Index`, which is dependent on Pivotal GemFire's APIs. If it cannot be found,
SDG is able to find the existing `Index`, which is dependent on {data-store-name}'s APIs. If it cannot be found,
nothing happens and a SDG `GemfireIndexException` is thrown that wraps the `IndexNameConflictException`.
However, when the definition of the existing `Index` is different, SDG attempts to re-create the `Index` by
@@ -207,31 +207,31 @@ and make sure the `Index` definition matches your expectations and application r
=== How Does `IndexNameConflictExceptions` Actually Happen?
It is probably not all that uncommon for `IndexExistsExceptions` to be thrown, especially when
multiple configuration sources are used to configure Pivotal GemFire (Spring Data for Pivotal GemFire, Pivotal GemFire Cluster Config,
Pivotal GemFire native `cache.xml`, the API, and so on). You should definitely prefer one configuration method
multiple configuration sources are used to configure {data-store-name} (Spring Data for {data-store-name}, {data-store-name} Cluster Config,
{data-store-name} native `cache.xml`, the API, and so on). You should definitely prefer one configuration method
and stick with it.
However, when does an `IndexNameConflictException` get thrown?
One particular case is an `Index` defined on a `PARTITION` region (PR). When an `Index` is defined on
a `PARTITION` region (for example, `X`), Pivotal GemFire distributes the `Index` definition (and name) to other peer members
a `PARTITION` region (for example, `X`), {data-store-name} distributes the `Index` definition (and name) to other peer members
in the cluster that also host the same `PARTITION` region (that is, "X"). The distribution of this `Index` definition
to and subsequent creation of this `Index` by peer members on a need-to-know basis (that is, those hosting the same PR)
is performed asynchronously.
During this window of time, it is possible that these pending PR `Indexes` cannot be identified by Pivotal GemFire --
such as with a call to http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#getIndexes[`QueryService.getIndexes()`]
with http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#getIndexes-org.apache.geode.cache.Region[`QueryService.getIndexes(:Region)`],
or even with http://gemfire-90-javadocs.docs.pivotal.io/org/apache/geode/cache/query/QueryService.html#getIndex-org.apache.geode.cache.Region-java.lang.String[`QueryService.getIndex(:Region, indexName:String)`].
During this window of time, it is possible that these pending PR `Indexes` cannot be identified by {data-store-name} --
such as with a call to {x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#getIndexes[`QueryService.getIndexes()`]
with {x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#getIndexes-org.apache.geode.cache.Region[`QueryService.getIndexes(:Region)`],
or even with {x-data-store-javadoc}/org/apache/geode/cache/query/QueryService.html#getIndex-org.apache.geode.cache.Region-java.lang.String[`QueryService.getIndex(:Region, indexName:String)`].
As a result, the only way for SDG or other Pivotal GemFire cache client applications (not involving Spring) to know for sure
As a result, the only way for SDG or other {data-store-name} cache client applications (not involving Spring) to know for sure
is to attempt to create the `Index`. If it fails with either an `IndexNameConflictException`
or even an `IndexExistsException`, the application knows there is a problem. This is because the `QueryService` `Index` creation waits on
pending `Index` definitions, whereas the other Pivotal GemFire API calls do not.
pending `Index` definitions, whereas the other {data-store-name} API calls do not.
In any case, SDG makes a best effort and attempts to inform you what has happened or is happening and tell you
the corrective action. Given that all Pivotal GemFire `QueryService.createIndex(..)` methods are synchronous, blocking operations,
the state of Pivotal GemFire should be consistent and accessible after either of these index-type exceptions are thrown.
the corrective action. Given that all {data-store-name} `QueryService.createIndex(..)` methods are synchronous, blocking operations,
the state of {data-store-name} should be consistent and accessible after either of these index-type exceptions are thrown.
Consequently, SDG can inspect the state of the system and act accordingly, based on your configuration.
In all other cases, SDG embraces a fail-fast strategy.

27
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@@ -1,29 +1,30 @@
[[ref-introduction]]
= Document Structure
The following chapters explain the core functionality offered by Spring Data for Pivotal GemFire:
The following chapters explain the core functionality offered by Spring Data for {data-store-name}:
* <<bootstrap>> describes the configuration support provided for configuring, initializing, and accessing
Pivotal GemFire caches, regions, and related distributed system components.
{data-store-name} Caches, Regions, and related distributed system components.
* <<apis>> explains the integration between the Pivotal GemFire APIs and the various data access features
available in Spring, such as data access, exception translation, transaction management, and caching.
* <<apis>> explains the integration between the {data-store-name} APIs and the various data access features
available in Spring, such as template-based data access, exception translation, transaction management, and caching.
* <<serialization>> describes enhancements to Pivotal GemFire's serialization and deserialization of managed objects.
* <<serialization>> describes enhancements to {data-store-name}'s serialization and deserialization of managed objects.
* <<mapping>> describes persistence mapping for POJOs stored in Pivotal GemFire using Spring Data.
* <<mapping>> describes persistence mapping for POJOs stored in {data-store-name} using Spring Data.
* <<gemfire-repositories>> describes how to create and use Spring Data Repositories to access data
stored in Pivotal GemFire by using basic CRUD and simple query operations.
stored in {data-store-name} by using basic CRUD and simple query operations.
* <<function-annotations>> describes how to create and use Pivotal GemFire functions by using annotations
* <<function-annotations>> describes how to create and use {data-store-name} Functions by using annotations
to perform distributed computations where the data lives.
* <<apis:continuous-query>> describes how to use Pivotal GemFire's Continuous Query (CQ) functionality
to process a stream of events based on interest defined and registered using a Pivotal GemFire OQL (Object Query Language) query.
* <<apis:continuous-query>> describes how to use {data-store-name}'s Continuous Query (CQ) functionality
to process a stream of events based on interest defined and registered using {data-store-name}'s
OQL (Object Query Language) query.
* <<gemfire-bootstrap>> describes how to bootstrap a Spring `ApplicationContext` running in an Pivotal GemFire server
by using `Gfsh`.
* <<gemfire-bootstrap>> describes how to bootstrap a Spring `ApplicationContext` running in an {data-store-name} server
using `Gfsh`.
* <<samples>> describes the examples provided with the distribution to illustrate the various features
available in Spring Data for Pivotal GemFire.
available in Spring Data for {data-store-name}.

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@@ -1,16 +1,16 @@
[[bootstrap:lucene]]
= Apache Lucene Integration
https://pivotal.io/pivotal-gemfire[Pivotal GemFire] integrates with http://lucene.apache.org/[Apache Lucene] to let you
index and search on data stored in Pivotal GemFire by using Lucene queries. Search-based queries also include
{x-data-store-website}[{data-store-name}] integrates with http://lucene.apache.org/[Apache Lucene] to let you
index and search on data stored in {data-store-name} by using Lucene queries. Search-based queries also include
the ability to page through query results.
Additionally, Spring Data for Pivotal GemFire adds support for query projections based on the Spring Data Commons
Additionally, Spring Data for {data-store-name} adds support for query projections based on the Spring Data Commons
projection infrastructure. This feature lets the query results be projected into first-class
application domain types as needed by the application use case.
A Lucene `Index` must be created before any Lucene search-based query can be run. A `LuceneIndex`
can be created in Spring (Data for Pivotal GemFire) XML config as follows:
can be created in Spring (Data for {data-store-name}) XML config as follows:
[source,xml]
----
@@ -40,8 +40,8 @@ and can be configured as shown in the following example:
The `Map` can be specified as a top-level bean definition and referenced by using the `ref` attribute
in the nested `<gfe:field-analyzers>` element, as follows: `<gfe-field-analyzers ref="refToTopLevelMapBeanDefinition"/>`.
Spring Data for Pivotal GemFire's `LuceneIndexFactoryBean` API and SDG's XML namespace also let a
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/cache/lucene/LuceneSerializer.html[`org.apache.geode.cache.lucene.LuceneSerializer`]
Spring Data for {data-store-name}'s `LuceneIndexFactoryBean` API and SDG's XML namespace also let a
{x-data-store-javadoc}/org/apache/geode/cache/lucene/LuceneSerializer.html[`org.apache.geode.cache.lucene.LuceneSerializer`]
be specified when you create the `LuceneIndex`. The `LuceneSerializer` lets you configure the way objects
are converted to Lucene documents for the index when the object is indexed.
@@ -74,7 +74,7 @@ inside a `@Configuration` class, as the following example shows:
----
@Bean(name = "Books")
@DependsOn("bookTitleIndex")
PartitionedRegionFactoryBean<Long, Book> booksRegion(Pivotal GemFireCache gemfireCache) {
PartitionedRegionFactoryBean<Long, Book> booksRegion(GemFireCache gemfireCache) {
PartitionedRegionFactoryBean<Long, Book> peopleRegion =
new PartitionedRegionFactoryBean<>();
@@ -87,7 +87,7 @@ PartitionedRegionFactoryBean<Long, Book> booksRegion(Pivotal GemFireCache gemfir
}
@Bean
LuceneIndexFactoryBean bookTitleIndex(Pivotal GemFireCache gemFireCache,
LuceneIndexFactoryBean bookTitleIndex(GemFireCache gemFireCache,
LuceneSerializer luceneSerializer) {
LuceneIndexFactoryBean luceneIndex = new LuceneIndexFactoryBean();
@@ -106,20 +106,20 @@ CustomLuceneSerializer myLuceneSerialier() {
}
----
There are a few limitations of Pivotal GemFire's, Apache Lucene integration and support.
There are a few limitations of {data-store-name}'s, Apache Lucene integration and support.
First, a `LuceneIndex` can only be created on a Pivotal GemFire `PARTITION` Region.
First, a `LuceneIndex` can only be created on a {data-store-name} `PARTITION` Region.
Second, all `LuceneIndexes` must be created before the region to which the `LuceneIndex` applies.
NOTE: To help ensure that all declared `LuceneIndexes` defined in a Spring context are created before the regions
on which they apply, SDG includes the `org.springframework.data.gemfire.config.support.LuceneIndexRegionBeanFactoryPostProcessor`.
You may register this Spring https://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/beans/factory/config/BeanFactoryPostProcessor.html[`BeanFactoryPostProcessor`]
You may register this Spring {spring-framework-javadoc}/org/springframework/beans/factory/config/BeanFactoryPostProcessor.html[`BeanFactoryPostProcessor`]
in XML config by using `<bean class="org.springframework.data.gemfire.config.support.LuceneIndexRegionBeanFactoryPostProcessor"/>`
The `o.s.d.g.config.support.LuceneIndexRegionBeanFactoryPostProcessor` may only be used when using SDG XML config.
More details about Spring's `BeanFactoryPostProcessors` can be found https://docs.spring.io/spring/docs/current/spring-framework-reference/core.html#beans-factory-extension-factory-postprocessors[here].
More details about Spring's `BeanFactoryPostProcessors` can be found {spring-framework-docs}/core.html#beans-factory-extension-factory-postprocessors[here].
It is possible that these Pivotal GemFire restrictions will not apply in a future release which is why
It is possible that these {data-store-name} restrictions will not apply in a future release which is why
the SDG `LuceneIndexFactoryBean` API takes a reference to the region directly as well, rather than just the region path.
This is more ideal when you want to define a `LuceneIndex` on an existing region
@@ -136,11 +136,11 @@ Now that we have a `LuceneIndex`, we can perform Lucene-based data access operat
== Lucene Template Data Accessors
Spring Data for Pivotal GemFire provides two primary templates for Lucene data access operations, depending on
Spring Data for {data-store-name} provides two primary templates for Lucene data access operations, depending on
how low of a level your application is prepared to deal with.
The `LuceneOperations` interface defines query operations by using Pivotal GemFire
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/cache/lucene/package-summary.html[Lucene types], which are defined in the following interface definition:
The `LuceneOperations` interface defines query operations by using {data-store-name}
{x-data-store-javadoc}/org/apache/geode/cache/lucene/package-summary.html[Lucene types], which are defined in the following interface definition:
[source,java]
----
@@ -170,18 +170,18 @@ public interface LuceneOperations {
NOTE: The `[, int resultLimit]` indicates that the `resultLimit` parameter is optional.
The operations in the `LuceneOperations` interface match the operations provided by the Pivotal GemFire's
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/cache/lucene/LuceneQuery.html[LuceneQuery] interface.
However, SDG has the added value of translating proprietary Pivotal GemFire or Apache Lucene `Exceptions`
The operations in the `LuceneOperations` interface match the operations provided by the {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/lucene/LuceneQuery.html[LuceneQuery] interface.
However, SDG has the added value of translating proprietary {data-store-name} or Apache Lucene `Exceptions`
into Spring's highly consistent and expressive DAO
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#dao-exceptions[exception hierarchy],
particularly as many modern data access operations involve more than one store or repository.
Additionally, SDG's `LuceneOperations` interface can shield your application from interface-breaking changes
introduced by the underlying Pivotal GemFire or Apache Lucene APIs when they occur.
introduced by the underlying {data-store-name} or Apache Lucene APIs when they occur.
However, it would be sad to offer a Lucene Data Access Object (DAO) that only uses Pivotal GemFire
and Apache Lucene data types (such as Pivotal GemFire's `LuceneResultStruct`). Therefore, SDG gives you the
However, it would be sad to offer a Lucene Data Access Object (DAO) that only uses {data-store-name}
and Apache Lucene data types (such as {data-store-name}'s `LuceneResultStruct`). Therefore, SDG gives you the
`ProjectingLuceneOperations` interface to remedy these important application concerns.
The following listing shows the `ProjectingLuceneOperations` interface definition:
@@ -244,7 +244,7 @@ If I define the following `LuceneIndex`...
[source,java]
----
@Bean
LuceneIndexFactoryBean personLastNameIndex(Pivotal GemFireCache gemfireCache) {
LuceneIndexFactoryBean personLastNameIndex(GemFireCache gemfireCache) {
LuceneIndexFactoryBean personLastNameIndex =
new LuceneIndexFactoryBean();
@@ -291,8 +291,8 @@ You can use `setProjectionFactory(:ProjectionFactory)` to set a custom `Projecti
== Annotation Configuration Support
Finally, Spring Data for Pivotal GemFire provides annotation configuration support for `LuceneIndexes`.
Eventually, the SDG Lucene support finds its way into the repository infrastructure extension for Pivotal GemFire
Finally, Spring Data for {data-store-name} provides annotation configuration support for `LuceneIndexes`.
Eventually, the SDG Lucene support finds its way into the repository infrastructure extension for {data-store-name}
so that Lucene queries can be expressed as methods on an application `Repository` interface, in much the same way as the
http://docs.spring.io/spring-data-gemfire/docs/current/reference/html/#gemfire-repositories.executing-queries[OQL support]
works today.
@@ -333,7 +333,7 @@ class ApplicationConfiguration {
}
----
NOTE: `LuceneIndexes` can be created only on Apache Geode Servers since `LuceneIndexes` only apply
NOTE: `LuceneIndexes` can be created only on {data-store-name} Servers since `LuceneIndexes` only apply
to `PARTITION` Regions.
Given our earlier definition of the `Person` class, the SDG annotation configuration support

40
src/main/asciidoc/reference/mapping.adoc
View File

@@ -10,10 +10,10 @@ This section covers:
[[mapping.entities]]
== Entity Mapping
Spring Data for Pivotal GemFire provides support to map entities that are stored in a region in the Geode In-Memory Data Grid.
Spring Data for {data-store-name} provides support to map entities that are stored in a region in the Geode In-Memory Data Grid.
The mapping metadata is defined by using annotations on application domain classes, as the following example shows:
.Mapping a domain class to a Pivotal GemFire Region
.Mapping a domain class to a {data-store-name} Region
====
[source,java]
----
@@ -57,17 +57,17 @@ public class Guest extends User {
}
----
Be sure to use the full path of the Pivotal GemFire region, as defined with the Spring Data for Pivotal GemFire XML namespace by
Be sure to use the full path of the {data-store-name} region, as defined with the Spring Data for {data-store-name} XML namespace by
using the `id` or `name` attributes of the `<*-region>` element.
[[mapping.entities.region]]
=== Entity Mapping by Region Type
In addition to the `@Region` annotation, Spring Data for Pivotal GemFire also recognizes the region type-specific
In addition to the `@Region` annotation, Spring Data for {data-store-name} also recognizes the region type-specific
mapping annotations: `@ClientRegion`, `@LocalRegion`, `@PartitionRegion`, and `@ReplicateRegion`.
Functionally, these annotations are treated exactly the same as the generic `@Region` annotation in the SDG
mapping infrastructure. However, these additional mapping annotations are useful in Spring Data for Pivotal GemFire's
mapping infrastructure. However, these additional mapping annotations are useful in Spring Data for {data-store-name}'s
annotation configuration model. When combined with the `@EnableEntityDefinedRegions` configuration annotation
on a Spring `@Configuration` annotated class, it is possible to generate regions in the local cache, whether
the application is a client or peer.
@@ -86,7 +86,7 @@ As an alternative to specifying the region in which the entity is stored by usin
on the entity class, you can also specify the `@Region` annotation on the entity's `Repository`.
See <<gemfire-repositories>> for more details.
However, suppose you want to store a `Person` record in multiple Pivotal GemFire Regions (for example, `People` and `Customers`).
However, suppose you want to store a `Person` record in multiple {data-store-name} Regions (for example, `People` and `Customers`).
Then you can define your corresponding `Repository` interface extensions as follows:
[source,java]
@@ -102,7 +102,7 @@ public interface CustomerRepository extends GemfireRepository<Person, String> {
}
----
Then, using each Repository individually, you can store the entity in multiple Pivotal GemFire Regions, as the following example shows:
Then, using each Repository individually, you can store the entity in multiple {data-store-name} Regions, as the following example shows:
[source,java]
----
@@ -126,8 +126,8 @@ either as a local cache transaction or a global transaction.
[[mapping.pdx-serializer]]
== Mapping PDX Serializer
Spring Data for Pivotal GemFire provides a custom
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/pdx/PdxSerializer.html[`PdxSerializer`] implementation
Spring Data for {data-store-name} provides a custom
{x-data-store-javadoc}/org/apache/geode/pdx/PdxSerializer.html[`PdxSerializer`] implementation
that uses the mapping information to customize entity serialization.
It also lets you customize entity instantiation by using the Spring Data `EntityInstantiator` abstraction.
@@ -137,7 +137,7 @@ explicitly annotated with `@PersistenceConstructor`).
To provide arguments for constructor parameters, the serializer reads fields with the named constructor parameter,
explicitly specified using Spring's `@Value` annotation, from the supplied
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/pdx/PdxReader.html[`PdxReader`], as shown in the following example:
{x-data-store-javadoc}/org/apache/geode/pdx/PdxReader.html[`PdxReader`], as shown in the following example:
.Using `@Value` on entity constructor parameters
====
@@ -156,10 +156,10 @@ An entity class annotated in this way has the `thing` field read from the `PdxRe
for the constructor parameter, `firstname`. The value for `lastName` is a Spring bean with the name `bean`.
In addition to the custom instantiation logic and strategy provided by `EntityInstantiators`,
the `MappingPdxSerializer` also provides capabilities beyond Pivotal GemFire's own
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`].
the `MappingPdxSerializer` also provides capabilities beyond {data-store-name}'s own
{x-data-store-javadoc}/org/apache/geode/pdx/ReflectionBasedAutoSerializer.html[`ReflectionBasedAutoSerializer`].
While Pivotal GemFire's `ReflectionBasedAutoSerializer` conveniently uses Java reflection to populate entities and
While {data-store-name}'s `ReflectionBasedAutoSerializer` conveniently uses Java reflection to populate entities and
uses regular expressions to identify types that should be handled (serialized and deserialized) by the `ReflectionBasedAutoSerializer`,
it cannot, unlike `MappingPdxSerializer`, perform the following:
@@ -240,10 +240,10 @@ tried to be as explicit as possible about the semantics of registration.
[[mapping.pdx-serializer.id-properties]]
=== Mapping ID Properties
Like Pivotal GemFire's `ReflectionBasedAutoSerializer`, SDG's `MappingPdxSerializer` is also able to determine
Like {data-store-name}'s `ReflectionBasedAutoSerializer`, SDG's `MappingPdxSerializer` is also able to determine
the identifier of the entity. However, `MappingPdxSerializer` does so by using Spring Data's mapping metadata,
specifically by finding the entity property designated as the identifier by using Spring Data's
https://docs.spring.io/spring-data/commons/docs/current/api/org/springframework/data/annotation/Id.html[`@Id`] annotation.
{spring-data-commons-javadoc}/org/springframework/data/annotation/Id.html[`@Id`] annotation.
For example:
@@ -259,7 +259,7 @@ class Customer {
----
In this case, the `Customer` `id` field is marked as the identifier field in the PDX type metadata by using
http://gemfire-95-javadocs.docs.pivotal.io/org/apache/geode/pdx/PdxWriter.html#markIdentityField-java.lang.String-[`PdxWriter.markIdentifierField(:String)`]
{x-data-store-javadoc}/org/apache/geode/pdx/PdxWriter.html#markIdentityField-java.lang.String-[`PdxWriter.markIdentifierField(:String)`]
when the `PdxSerializer.toData(..)` method is called during serialization.
[[mapping.pdx-serializer.read-only-properties]]
@@ -304,12 +304,12 @@ If you want the field or property to always be written, you can define a setter.
Likewise, what happens when your entity defines `transient` properties?
You would expect the `transient` fields or properties of your entity not to be serialized to the stream of PDX bytes
when serializing entity. That is exactly what happens, unlike Pivotal GemFire's own
when serializing entity. That is exactly what happens, unlike {data-store-name}'s own
`ReflectionBasedAutoSerializer`, which serializes everything accessible from the object through Java reflection.
The `MappingPdxSerializer` does not serialize any fields or properties that are qualified as being transient either by using
Java's `transient` keyword (in the case of fields) or by using the
https://docs.spring.io/spring-data/commons/docs/current/api/org/springframework/data/annotation/Transient.html[`@Transient`]
{spring-data-commons-javadoc}/org/springframework/data/annotation/Transient.html[`@Transient`]
Spring Data annotation on either fields or properties.
For example, you might define an entity with transient fields and properties as follows:
@@ -342,10 +342,10 @@ Neither the `Process` `id` field nor the readable `hostname` property are writte
[[mapping.pdx-serializer.type-filtering]]
=== Filtering by Class types
Similar to Pivotal GemFire's `ReflectionBasedAutoSerializer`, SDG's `MappingPdxSerializer` lets you filter
Similar to {data-store-name}'s `ReflectionBasedAutoSerializer`, SDG's `MappingPdxSerializer` lets you filter
the types of objects that the `MappingPdxSerializer` serializes and deserializes.
However, unlike Pivotal GemFire's `ReflectionBasedAutoSerializer`, which uses complex regular expressions to express
However, unlike {data-store-name}'s `ReflectionBasedAutoSerializer`, which uses complex regular expressions to express
which types the serializer handles, SDG's `MappingPdxSerializer` uses the much more robust
https://docs.oracle.com/javase/8/docs/api/java/util/function/Predicate.html[`java.util.function.Predicate`] interface
and API to express type-matching criteria.

188
src/main/asciidoc/reference/region.adoc
View File

@@ -6,7 +6,7 @@ extending `java.util.Map` and enables basic data access using familiar key-value
is wired into application classes that require it so the actual Region type is decoupled from the programming model.
Typically, each Region is associated with one domain object, similar to a table in a relational database.
Pivotal GemFire implements the following types of Regions:
{data-store-name} implements the following types of Regions:
* *REPLICATE* - Data is replicated across all cache members that define the Region. This provides very high
read performance but writes take longer to perform the replication.
@@ -19,13 +19,13 @@ Local updates are synchronized to the cache server. Also, a client Region may su
stay up-to-date (synchronized) with changes originating from remote processes that access the same server Region.
For more information about the various Region types and their capabilities as well as configuration options,
please refer to Pivotal GemFire's documentation on
http://geode.apache.org/docs/guide/11/developing/region_options/region_types.html[Region Types].
please refer to {data-store-name}'s documentation on
{x-data-store-docs}/developing/region_options/region_types.html[Region Types].
[[bootstrap:region:lookup]]
== Using an externally configured Region
To reference Regions already configured in a Pivotal GemFire native `cache.xml` file, use the `lookup-region` element.
To reference Regions already configured in a {data-store-name} native `cache.xml` file, use the `lookup-region` element.
Simply declare the target Region name with the `name` attribute. For example, to declare a bean definition identified
as `ordersRegion` for an existing Region named `Orders`, you can use the following bean definition:
@@ -61,7 +61,7 @@ the region semantics or setup infrastructure.
[[bootstrap:region:lookup:auto]]
== Auto Region Lookup
"`auto-lookup`" lets you import all regions defined in a Pivotal GemFire native `cache.xml` file into a Spring
"`auto-lookup`" lets you import all regions defined in a {data-store-name} native `cache.xml` file into a Spring
application context when you use the `cache-xml-location` attribute on the `<gfe:cache>` element.
For instance, consider the following `cache.xml` file:
@@ -97,12 +97,12 @@ by using the following:
<gfe:auto-region-lookup/>
----
Spring Data for Pivotal GemFire automatically creates beans for all Pivotal GemFire regions defined in `cache.xml` that have not been
Spring Data for {data-store-name} automatically creates beans for all {data-store-name} regions defined in `cache.xml` that have not been
explicitly added to the Spring context with explicit `<gfe:lookup-region>` bean declarations.
It is important to realize that Spring Data for Pivotal GemFire uses a Spring
It is important to realize that Spring Data for {data-store-name} uses a Spring
http://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/beans/factory/config/BeanPostProcessor.html[BeanPostProcessor]
to post-process the cache after it is both created and initialized to determine the regions defined in Pivotal GemFire to add
to post-process the cache after it is both created and initialized to determine the regions defined in {data-store-name} to add
as beans in the Spring application context.
You may inject these "`auto-looked-up`" regions as you would any other bean defined in the Spring application context, with
@@ -137,21 +137,21 @@ If you declarE your components by using Spring XML config, then you would do the
<bean class="example.ApplicationDao" depends-on="gemfireCache"/>
----
Doing so ensures that the Pivotal GemFire cache and all the regions defined in `cache.xml` get created before any components
Doing so ensures that the {data-store-name} cache and all the regions defined in `cache.xml` get created before any components
with auto-wire references when using the new `<gfe:auto-region-lookup>` element.
[[bootstrap:region:overview]]
== Configuring Regions
Spring Data for Pivotal GemFire provides comprehensive support for configuring any type of Region through the following elements:
Spring Data for {data-store-name} provides comprehensive support for configuring any type of Region through the following elements:
* LOCAL Region: `<local-region>`
* PARTITION Region: `<partitioned-region>`
* REPLICATE Region: `<replicated-region>`
* Client Region: `<client-region>`
See the Pivotal GemFire documentation for a comprehensive description of
http://geode.apache.org/docs/guide/11/developing/region_options/region_types.html[region types].
See the {data-store-name} documentation for a comprehensive description of
{x-data-store-docs}/developing/region_options/region_types.html[region types].
[[bootstrap:region:attributes]]
=== Common Region Attributes
@@ -166,8 +166,8 @@ The following table lists the attributes available for all region types:
| Description
| cache-ref
| Pivotal GemFire Cache bean reference
| The name of the bean defining the Pivotal GemFire Cache (by default, 'gemfireCache').
| {data-store-name} Cache bean reference
| The name of the bean defining the {data-store-name} Cache (by default, 'gemfireCache').
| cloning-enabled
| boolean (default: `false`)
@@ -182,7 +182,7 @@ The following table lists the attributes available for all region types:
| Determines whether members perform checks to provide consistent handling for concurrent or out-of-order updates to distributed regions.
| data-policy
| See Pivotal GemFire's http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/DataPolicy.html[data policy].
| See {data-store-name}'s {x-data-store-javadoc}/org/apache/geode/cache/DataPolicy.html[data policy].
| The region's data policy. Note that not all data policies are supported for every Region type.
| destroy
@@ -234,7 +234,7 @@ The following table lists the attributes available for all region types:
| Determines whether the region persists entries to local disk (disk store).
| shortcut
| See http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/RegionShortcut.html
| See {x-data-store-javadoc}/org/apache/geode/cache/RegionShortcut.html
| The `RegionShortcut` for this region. Allows easy initialization of the region based on pre-defined defaults.
| statistics
@@ -255,7 +255,7 @@ The following table lists the attributes available for all region types:
`CacheListener` instances are registered with a region to handle region events, such as when entries are created, updated,
destroyed, and so on. A `CacheListener` can be any bean that implements the
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/CacheListener.html[`CacheListener`] interface.
{x-data-store-javadoc}/org/apache/geode/cache/CacheListener.html[`CacheListener`] interface.
A region may have multiple listeners, declared with the `cache-listener` element nested in the containing
`*-region` element.
@@ -298,7 +298,7 @@ mutually exclusive and using both in the same element results in an exception.
.Bean Reference Conventions
[NOTE]
====
The `cache-listener` element is an example of a common pattern used in the namespace anywhere Pivotal GemFire provides
The `cache-listener` element is an example of a common pattern used in the namespace anywhere {data-store-name} provides
a callback interface to be implemented in order to invoke custom code in response to Cache or Region events.
When you use Spring's IoC container, the implementation is a standard Spring bean. In order to simplify the configuration,
the schema allows a single occurrence of the `cache-listener` element, but, if multiple instances are permitted, it may contain nested bean references
@@ -312,11 +312,11 @@ configuration example.
=== CacheLoaders and CacheWriters
Similar to `cache-listener`, the namespace provides `cache-loader` and `cache-writer` elements to register
these Pivotal GemFire components for a region.
these {data-store-name} components for a region.
A `CacheLoader` is invoked on a cache miss to let an entry be loaded from an external data source, such as a
database. A `CacheWriter` is invoked before an entry is created or updated, to allow the entry to be synchronized to
an external data source. The difference is that Pivotal GemFire supports, at most, a single instance `CacheLoader` and `CacheWriter`
an external data source. The difference is that {data-store-name} supports, at most, a single instance `CacheLoader` and `CacheWriter`
per region. However, either declaration style may be used.
The following example declares a region with both a `CacheLoader` and a `CacheWriter`:
@@ -339,14 +339,14 @@ The following example declares a region with both a `CacheLoader` and a `CacheWr
</beans>
----
See http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/CacheLoader.html[`CacheLoader`]
and http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/CacheWriter.html[`CacheWriter`]
in the Pivotal GemFire documentation for more details.
See {x-data-store-javadoc}/org/apache/geode/cache/CacheLoader.html[`CacheLoader`]
and {x-data-store-javadoc}/org/apache/geode/cache/CacheWriter.html[`CacheWriter`]
in the {data-store-name} documentation for more details.
[[bootstrap:region:compression]]
== Compression
Pivotal GemFire Regions may also be compressed in order to reduce JVM memory consumption and pressure to possibly avoid
{data-store-name} Regions may also be compressed in order to reduce JVM memory consumption and pressure to possibly avoid
stopping the global GCs. When you enable compression for a region, all values stored in memory for the region
are compressed, while keys and indexes remain uncompressed. New values are compressed when put into the region
and all values are decompressed automatically when read back from the region. Values are not compressed when
@@ -365,17 +365,17 @@ The following example shows a region with compression enabled:
</beans>
----
See Pivotal GemFire's documentation for more information on
http://gemfire.docs.pivotal.io/geode/managing/region_compression/region_compression.html[region compression].
See {data-store-name}'s documentation for more information on
{x-data-store-docs}/managing/region_compression/region_compression.html[region compression].
[[bootstrap:region:subregions]]
== Subregions
Spring Data for Pivotal GemFire also supports subregions, allowing regions to be arranged in a hierarchical relationship.
Spring Data for {data-store-name} also supports subregions, allowing regions to be arranged in a hierarchical relationship.
For example, Pivotal GemFire allows for a (for example) `/Customer/Address` region and a different `/Employee/Address` region. Additionally,
For example, {data-store-name} allows for a (for example) `/Customer/Address` region and a different `/Employee/Address` region. Additionally,
a subregion may have its own subregions and its own configuration. A subregion does not inherit attributes from
the parent region. Regions types may be mixed and matched subject to Pivotal GemFire constraints. A subregion is naturally
the parent region. Regions types may be mixed and matched subject to {data-store-name} constraints. A subregion is naturally
declared as a child element of a region. The subregion's name attribute is the simple name. The preceding example
might be configured as follows:
@@ -400,11 +400,11 @@ OQL query strings.
[[bootstrap:region:templates]]
== Region Templates
Spring Data for Pivotal GemFire also supports region templates. This feature allows developers to define common region
Spring Data for {data-store-name} also supports region templates. This feature allows developers to define common region
configuration settings and attributes once and reuse the configuration among many region bean definitions declared
in the Spring application context.
Spring Data for Pivotal GemFire includes five Region template tags in its namespace:
Spring Data for {data-store-name} includes five Region template tags in its namespace:
[cols="1,2", options="header"]
.Region Template Tags
@@ -482,7 +482,7 @@ Attributes and sub-elements defined in subsequent, inherited region bean definit
=== How Templating Works
Spring Data for Pivotal GemFire applies region templates when the Spring application context configuration meta-data is parsed,
Spring Data for {data-store-name} applies region templates when the Spring application context configuration meta-data is parsed,
and therefore, the region templates must be declared in the order of inheritance. In other words, parent templates must be defined
before child templates. Doing so ensures that the proper configuration is applied, especially when element attributes or sub-elements
are overridden.
@@ -496,14 +496,14 @@ NOTE: Region Templates are single-inheritance.
=== Caution concerning Regions, Subregions and Lookups
Previously, one of the underlying properties of the `replicated-region`, `partitioned-region`, `local-region`,
and `client-region` elements in the Spring Data for Pivotal GemFire XML namespace was to perform a lookup first before
and `client-region` elements in the Spring Data for {data-store-name} XML namespace was to perform a lookup first before
attempting to create a Region. This was done in case the region already existed, which would be the case
if the region was defined in an imported Pivotal GemFire native `cache.xml` configuration file. Therefore, the lookup
if the region was defined in an imported {data-store-name} native `cache.xml` configuration file. Therefore, the lookup
was performed first to avoid any errors. This was by design and subject to change.
This behavior has been altered and the default behavior is now to create the region first. If the region
already exists, then the creation logic fails-fast and an appropriate exception is thrown. However, much like the
`CREATE TABLE IF NOT EXISTS ...` DDL syntax, the Spring Data for Pivotal GemFire `<*-region>` namespace elements now include
`CREATE TABLE IF NOT EXISTS ...` DDL syntax, the Spring Data for {data-store-name} `<*-region>` namespace elements now include
a `ignore-if-exists` attribute, which reinstates the old behavior by first performing a lookup of an existing region
identified by name. If an existing region is found by name if and `ignore-if-exists` is set to `true`, then
the region bean definition defined in the Spring configuration is ignored.
@@ -519,7 +519,7 @@ by the application. Even worse, you might want to define the region as a distrib
IMPORTANT: Recommended Practice - Use only `replicated-region`, `partitioned-region`, `local-region`, and `client-region`
namespace elements to define new Regions.
Consider the following native Pivotal GemFire `cache.xml` configuration file:
Consider the following native {data-store-name} `cache.xml` configuration file:
[source,xml]
----
@@ -609,9 +609,9 @@ The Region beans defined in the Spring application context consist of the follow
shown in the earlier example (that is, `@Resource(name = "Customers/Accounts"))` is now broken, since no bean with name `Customers/Accounts`
is actually defined. For this reason, you should not configure regions as shown in the two preceding examples.
Pivotal GemFire is flexible in referencing both parent regions and subregions with or without the leading forward slash.
{data-store-name} is flexible in referencing both parent regions and subregions with or without the leading forward slash.
For example, the parent can be referenced as `/Customers` or `Customers` and the child as `/Customers/Accounts`
or `Customers/Accounts`. However, Spring Data Pivotal GemFire is very specific when it comes to naming beans after regions. It
or `Customers/Accounts`. However, Spring Data {data-store-name} is very specific when it comes to naming beans after regions. It
always uses the forward slash (/) to represent subregions (for example, `/Customers/Accounts`).
Therefore, you should use the nested `lookup-region` syntax shown earlier
@@ -625,18 +625,18 @@ or define direct references with a leading forward slash (/), as follows:
The earlier example, where the nested `replicated-region` elements were used to reference the subregions, shows
the problem stated earlier. Are the customers, accounts and orders regions and subregions persistent or not?
They are not persistent, because the regions were defined in the native Pivotal GemFire `cache.xml` configuration file as `REPLICATES` and exist
They are not persistent, because the regions were defined in the native {data-store-name} `cache.xml` configuration file as `REPLICATES` and exist
before the cache is initialized (once the `<gfe:cache>` bean is processed).
[[bootstrap:region:eviction]]
== Data Eviction (with Overflow)
Based on various constraints, each Region can have an eviction policy in place for evicting data from memory.
Currently, in Pivotal GemFire, eviction applies to the Least Recently Used entry (also known as
Currently, in {data-store-name}, eviction applies to the Least Recently Used entry (also known as
http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used[LRU]). Evicted entries are either destroyed
or paged to disk (referred to as "`overflow to disk`").
Spring Data for Pivotal GemFire supports all eviction policies (entry count, memory, and heap usage) for PARTITION regions,
Spring Data for {data-store-name} supports all eviction policies (entry count, memory, and heap usage) for PARTITION regions,
REPLICATE regions, and client, local regions by using the nested `eviction` element.
For example, to configure a PARTITION Region to overflow to disk if the memory size exceeds more than 512 MB,
@@ -650,22 +650,22 @@ you can specify the following configuration:
----
IMPORTANT: Replicas cannot use `local destroy` eviction since that would invalidate them.
See the Pivotal GemFire docs for more information.
See the {data-store-name} docs for more information.
When configuring regions for overflow, you should configure the storage through the `disk-store` element
for maximum efficiency.
For a detailed description of eviction policies, see the Pivotal GemFire documentation on
http://geode.apache.org/docs/guide/11/developing/eviction/chapter_overview.html[Eviction].
For a detailed description of eviction policies, see the {data-store-name} documentation on
{x-data-store-docs}/developing/eviction/chapter_overview.html[Eviction].
[[bootstrap:region:expiration]]
== Data Expiration
Pivotal GemFire lets you control how long entries exist in the cache. Expiration is driven by elapsed time,
{data-store-name} lets you control how long entries exist in the cache. Expiration is driven by elapsed time,
as opposed to eviction, which is driven by the entry count or heap or memory usage. Once an entry expires,
it may no longer be accessed from the cache.
Pivotal GemFire supports the following Expiration types:
{data-store-name} supports the following Expiration types:
* *Time-to-Live (TTL)*: The amount of time in seconds that an object may remain in the cache after the last creation
or update. For entries, the counter is set to zero for create and put operations. Region counters are reset when
@@ -675,7 +675,7 @@ The Idle Timeout counter for an object is reset any time its TTL counter is rese
Idle Timeout counter is reset any time the entry is accessed through a get operation or a `netSearch`.
The Idle Timeout counter for a Region is reset whenever the Idle Timeout is reset for one of its entries.
Each of these may be applied to the region itself or to entries in the region. Spring Data for Pivotal GemFire provides `<region-ttl>`,
Each of these may be applied to the region itself or to entries in the region. Spring Data for {data-store-name} provides `<region-ttl>`,
`<region-tti>`, `<entry-ttl>`, and `<entry-tti>` region child elements to specify timeout values and expiration actions.
The following example shows a partition region with expiration values set:
@@ -688,13 +688,13 @@ The following example shows a partition region with expiration values set:
</gfe:replicated-region>
----
For a detailed description of expiration policies, see the Pivotal GemFire documentation on
http://geode.apache.org/docs/guide/11/developing/expiration/chapter_overview.html[expiration].
For a detailed description of expiration policies, see the {data-store-name} documentation on
{x-data-store-docs}/developing/expiration/chapter_overview.html[expiration].
[[bootstrap:region:expiration:annotation]]
=== Annotation-based Data Expiration
With Spring Data for Pivotal GemFire, you can define expiration policies and settings on individual
With Spring Data for {data-store-name}, you can define expiration policies and settings on individual
region entry values (or, to put it differently, directly on application domain objects). For instance, you can define Expiration
settings on a Session-based application domain object as follows:
@@ -728,7 +728,7 @@ expiration types, such as TTL and TTI, are configured.
[NOTE]
====
All `@Expiration`-based annotations apply only to region entry values. Expiration for a region is not covered
by Spring Data for Pivotal GemFire's expiration annotation support. However, Pivotal GemFire and Spring Data for Pivotal GemFire do let you
by Spring Data for {data-store-name}'s expiration annotation support. However, {data-store-name} and Spring Data for {data-store-name} do let you
set region expiration by using the SDG XML namespace, as follows:
[source,xml]
@@ -740,16 +740,16 @@ set region expiration by using the SDG XML namespace, as follows:
----
====
Spring Data for Pivotal GemFire's `@Expiration` annotation support is implemented with Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/CustomExpiry.html[`CustomExpiry`] interface.
See Pivotal GemFire's documentation on http://geode.apache.org/docs/guide/11/developing/expiration/configuring_data_expiration.html[configuring data expiration]
Spring Data for {data-store-name}'s `@Expiration` annotation support is implemented with {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/CustomExpiry.html[`CustomExpiry`] interface.
See {data-store-name}'s documentation on {x-data-store-docs}/developing/expiration/configuring_data_expiration.html[configuring data expiration]
for more details
The Spring Data for Pivotal GemFire `AnnotationBasedExpiration` class (and `CustomExpiry` implementation) is responsible
The Spring Data for {data-store-name} `AnnotationBasedExpiration` class (and `CustomExpiry` implementation) is responsible
for processing the SDG `@Expiration` annotations and applying the expiration policy and settings appropriately
for region entry expiration on request.
To use Spring Data for Pivotal GemFire to configure specific Pivotal GemFire Regions to appropriately apply the Expiration policy
To use Spring Data for {data-store-name} to configure specific {data-store-name} Regions to appropriately apply the Expiration policy
and settings applied to your application domain objects annotated with `@Expiration`-based annotations, you must:
. Define a bean in the Spring `ApplicationContext` of type `AnnotationBasedExpiration` by using the appropriate
@@ -774,11 +774,11 @@ along with the `<gfe:custom-entry-tti ref="ttiExpiration"/>` element to set TTI.
====
. (optional) Annotate your application domain objects that are stored in the region with expiration policies
and custom settings by using one of Spring Data for Pivotal GemFire's `@Expiration` annotations: `@Expiration`,
and custom settings by using one of Spring Data for {data-store-name}'s `@Expiration` annotations: `@Expiration`,
`@IdleTimeoutExpiration`, or `@TimeToLiveExpiration`
. (optional) In cases where particular application domain objects have not been annotated with Spring Data for Pivotal GemFire's
`@Expiration` annotations at all, but the Pivotal GemFire Region is configured to use SDG's custom `AnnotationBasedExpiration`
. (optional) In cases where particular application domain objects have not been annotated with Spring Data for {data-store-name}'s
`@Expiration` annotations at all, but the {data-store-name} Region is configured to use SDG's custom `AnnotationBasedExpiration`
class to determine the Expiration policy and settings for objects stored in the Region, you can set
"`default`" expiration attributes on the `AnnotationBasedExpiration` bean by doing the following:
@@ -799,11 +799,11 @@ class to determine the Expiration policy and settings for objects stored in the
</gfe:partitioned-region>
----
You may have noticed that Spring Data for Pivotal GemFire's `@Expiration` annotations use a `String` as the attribute type rather
You may have noticed that Spring Data for {data-store-name}'s `@Expiration` annotations use a `String` as the attribute type rather
than, and perhaps more appropriately, being strongly typed -- for example, `int` for 'timeout' and SDG'S `ExpirationActionType`
for 'action'. Why is that?
Well, enter one of Spring Data for Pivotal GemFire's other features, leveraging Spring's core infrastructure
Well, enter one of Spring Data for {data-store-name}'s other features, leveraging Spring's core infrastructure
for configuration convenience: property placeholders and the Spring Expression Language (SpEL).
For instance, a developer can specify both the expiration 'timeout' and 'action' by using Property Placeholders
@@ -867,18 +867,18 @@ instance of `java.util.Properties`. In the preceding example, the `properties` e
the action value on the actual expiration action enumerated type, leading to more quickly identified failures
if the types ever change.
As an example, all of this has been demonstrated and tested in the Spring Data for Pivotal GemFire test suite. See the
As an example, all of this has been demonstrated and tested in the Spring Data for {data-store-name} test suite. See the
https://github.com/spring-projects/spring-data-geode[source] for further details.
[[bootstrap:region:persistence]]
== Data Persistence
Regions can be persistent. Pivotal GemFire ensures that all the data you put into a region that is configured for persistence
Regions can be persistent. {data-store-name} ensures that all the data you put into a region that is configured for persistence
is written to disk in a way that is recoverable the next time you recreate the region. Doing so lets data
be recovered after machine or process failure or even after an orderly shutdown and subsequent restart of
the Pivotal GemFire data node.
the {data-store-name} data node.
To enable persistence with Spring Data for Pivotal GemFire, set the `persistent` attribute to `true` on
To enable persistence with Spring Data for {data-store-name}, set the `persistent` attribute to `true` on
any of the `<*-region>` elements, as the following example shows:
[source,xml]
@@ -887,7 +887,7 @@ any of the `<*-region>` elements, as the following example shows:
----
Persistence may also be configured by setting the `data-policy` attribute. To do so, set the attribute's value to one of
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/DataPolicy.html[Pivotal GemFire's DataPolicy settings], as the folloiwng example shows:
{x-data-store-javadoc}/org/apache/geode/cache/DataPolicy.html[{data-store-name}'s DataPolicy settings], as the folloiwng example shows:
[source,xml]
----
@@ -913,8 +913,8 @@ This is discussed further in <<bootstrap:diskstore>>.
[[bootstrap:region:subscription]]
== Subscription Policy
Pivotal GemFire allows configuration of http://geode.apache.org/docs/guide/11/developing/events/configure_p2p_event_messaging.html[peer-to-peer (P2P) event messaging]
to control the entry events that the region receives. Spring Data for Pivotal GemFire provides the `<gfe:subscription/>`
{data-store-name} allows configuration of {x-data-store-docs}/developing/events/configure_p2p_event_messaging.html[peer-to-peer (P2P) event messaging]
to control the entry events that the region receives. Spring Data for {data-store-name} provides the `<gfe:subscription/>`
sub-element to set the subscription policy on `REPLICATE` and `PARTITION` regions to either `ALL` or `CACHE_CONTENT`. The following example shows a region with its subscription policy set to `CACHE_CONTENT`:
[source,xml]
@@ -927,11 +927,11 @@ sub-element to set the subscription policy on `REPLICATE` and `PARTITION` region
[[bootstrap:region:local]]
== Local Region
Spring Data for Pivotal GemFire offers a dedicated `local-region` element for creating local regions. Local regions, as the name
Spring Data for {data-store-name} offers a dedicated `local-region` element for creating local regions. Local regions, as the name
implies, are standalone, meaning that they do not share data with any other distributed system member. Other than that,
all common region configuration options apply.
The following example shows a minimal declaration (again, the example relies on the Spring Data for Pivotal GemFire namespace
The following example shows a minimal declaration (again, the example relies on the Spring Data for {data-store-name} namespace
naming conventions to wire the cache):
[source,xml]
@@ -940,7 +940,7 @@ naming conventions to wire the cache):
----
In the preceding example, a local region is created (if one does not already exist). The name of the region is the same as the bean ID
(`exampleLocalRegion`), and the bean assumes the existence of a Pivotal GemFire cache named `gemfireCache`.
(`exampleLocalRegion`), and the bean assumes the existence of a {data-store-name} cache named `gemfireCache`.
[[bootstrap:region:replicate]]
== Replicated Region
@@ -952,29 +952,29 @@ an initialization stage, in which it discovers other replicas and automatically
While one replica is initializing, you can still continue to use the other replicas.
All common configuration options are available for REPLICATE Regions.
Spring Data for Pivotal GemFire offers a `replicated-region` element. The following example shows a minimal declaration:
Spring Data for {data-store-name} offers a `replicated-region` element. The following example shows a minimal declaration:
[source,xml]
----
<gfe:replicated-region id="exampleReplica"/>
----
See Pivotal GemFire's documentation on
http://geode.apache.org/docs/guide/11/developing/distributed_regions/chapter_overview.html[Distributed and Replicated Regions]
See {data-store-name}'s documentation on
{x-data-store-docs}/developing/distributed_regions/chapter_overview.html[Distributed and Replicated Regions]
for more details.
[[bootstrap:region:partition]]
== Partitioned Region
The Spring Data for Pivotal GemFire namespace also supports `PARTITION` regions.
The Spring Data for {data-store-name} namespace also supports `PARTITION` regions.
To quote the Pivotal GemFire docs:
To quote the {data-store-name} docs:
"`A partitioned region is a region where data is divided between peer servers hosting the region so that
each peer stores a subset of the data. When using a partitioned region, applications are presented with
a logical view of the region that looks like a single map containing all of the data in the region.
Reads or writes to this map are transparently routed to the peer that hosts the entry that is the target of
the operation. Pivotal GemFire divides the domain of hashcodes into buckets. Each bucket is assigned to a specific peer,
the operation. {data-store-name} divides the domain of hashcodes into buckets. Each bucket is assigned to a specific peer,
but may be relocated at any time to another peer in order to improve the utilization of resources across the cluster.`"
A partition is created by using the `partitioned-region` element. Its configuration options are similar to that of
@@ -992,8 +992,8 @@ The following example shows how to set up a `PARTITION` region with two redundan
</gfe:partitioned-region>
----
See Pivotal GemFire's documentation on
http://geode.apache.org/docs/guide/11/developing/partitioned_regions/chapter_overview.html[Partitioned Regions]
See {data-store-name}'s documentation on
{x-data-store-docs}/developing/partitioned_regions/chapter_overview.html[Partitioned Regions]
for more details.
[[bootstrap:region:partition:attributes]]
@@ -1049,12 +1049,12 @@ immediately when a new member is added.
[[bootstrap:region:client]]
== Client Region
Pivotal GemFire supports various deployment topologies for managing and distributing data. The topic of Pivotal GemFire topologies is outside
the scope of this documentation. However, to quickly recap, Pivotal GemFire's supported topologies can be classified as:
{data-store-name} supports various deployment topologies for managing and distributing data. The topic of {data-store-name} topologies is outside
the scope of this documentation. However, to quickly recap, {data-store-name}'s supported topologies can be classified as:
peer-to-peer (p2p), client-server, and wide area network (WAN). In the last two configurations, it is common
to declare client regions that connect to a cache server.
Spring Data for Pivotal GemFire offers dedicated support for each configuration through its <<bootstrap:cache:client, client-cache>> elements:
Spring Data for {data-store-name} offers dedicated support for each configuration through its <<bootstrap:cache:client, client-cache>> elements:
`client-region` and `pool`. As the names imply, `client-region` defines a client region, while `pool` defines
a pool of connections to be used and shared by the various client regions.
@@ -1089,14 +1089,14 @@ cache servers and peer data members in the distributed system and is recommended
possible to configure the `Pool` to connect directly to one or more cache servers by using the `server` element.
For a full list of options to set on the client and especially on the `Pool`, see
the Spring Data for Pivotal GemFire schema ("`<<appendix-schema>>`") and Pivotal GemFire's documentation on
http://geode.apache.org/docs/guide/11/topologies_and_comm/cs_configuration/chapter_overview.html[Client-Server Configuration].
the Spring Data for {data-store-name} schema ("`<<appendix-schema>>`") and {data-store-name}'s documentation on
{x-data-store-docs}/topologies_and_comm/cs_configuration/chapter_overview.html[Client-Server Configuration].
[[bootstrap:region:client:interests]]
=== Client Interests
To minimize network traffic, each client can separately define its own 'interests' policies, indicating to Pivotal GemFire
the data it actually requires. In Spring Data for Pivotal GemFire, 'interests' can be defined for each client region separately.
To minimize network traffic, each client can separately define its own 'interests' policies, indicating to {data-store-name}
the data it actually requires. In Spring Data for {data-store-name}, 'interests' can be defined for each client region separately.
Both key-based and regular expression-based interest types are supported.
The following example shows both key-based and regular expression-based `interest` types:
@@ -1183,20 +1183,20 @@ as follows:
A full, in-depth discussion of how client interests work and capabilities is beyond the scope of this document.
See Pivotal GemFire's documentation on
http://gemfire.docs.pivotal.io/geode/developing/events/how_client_server_distribution_works.html[Client-to-Server Event Distribution]
See {data-store-name}'s documentation on
{x-data-store-docs}/developing/events/how_client_server_distribution_works.html[Client-to-Server Event Distribution]
for more details.
[[bootstrap:region:json]]
== JSON Support
Pivotal GemFire has support for caching JSON documents in regions, along with the ability to query stored JSON documents
using the Pivotal GemFire OQL (Object Query Language). JSON documents are stored internally as
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/pdx/PdxInstance.html[PdxInstance] types by
using the http://geode.apache.org/releases/latest/javadoc/org/apache/geode/pdx/JSONFormatter.html[JSONFormatter] class
{data-store-name} has support for caching JSON documents in regions, along with the ability to query stored JSON documents
using the {data-store-name} OQL (Object Query Language). JSON documents are stored internally as
{x-data-store-javadoc}/org/apache/geode/pdx/PdxInstance.html[PdxInstance] types by
using the {x-data-store-javadoc}/org/apache/geode/pdx/JSONFormatter.html[JSONFormatter] class
to perform conversion to and from JSON documents (as a `String`).
Spring Data for Pivotal GemFire provides the `<gfe-data:json-region-autoproxy/>` element to enable an
Spring Data for {data-store-name} provides the `<gfe-data:json-region-autoproxy/>` element to enable an
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#aop-introduction[AOP]
component to advise appropriate, proxied region operations, which effectively encapsulates the `JSONFormatter`,
thereby letting your applications work directly with JSON Strings.
@@ -1213,7 +1213,7 @@ configured regions. This is done by creating a parallel data structure in local
significant overhead for large collections, so set the `convert-returned-collections` to `false`
if you would like to disable automatic conversion for these region operations.
NOTE: Certain Region operations (specifically those that use Pivotal GemFire's proprietary `Region.Entry`, such as:
NOTE: Certain Region operations (specifically those that use {data-store-name}'s proprietary `Region.Entry`, such as:
`entries(boolean)`, `entrySet(boolean)` and `getEntry()` type) are not targeted for AOP advice. In addition,
the `entrySet()` method (which returns a `Set<java.util.Map.Entry<?, ?>>`) is also not affected.

66
src/main/asciidoc/reference/repositories.adoc
View File

@@ -1,17 +1,17 @@
[[gemfire-repositories]]
= Spring Data for Pivotal GemFire Repositories
= Spring Data for {data-store-name} Repositories
Spring Data for Pivotal GemFire provides support for using the Spring Data Repository abstraction to easily persist entities
into Pivotal GemFire along with execute queries. A general introduction to the repository programming model is provided
Spring Data for {data-store-name} provides support for using the Spring Data Repository abstraction to easily persist entities
into {data-store-name} along with execute queries. A general introduction to the repository programming model is provided
http://docs.spring.io/spring-data/data-commons/docs/current/reference/html/#repositories[here].
[[gemfire-repositories.spring-configuration-xml]]
== Spring XML Configuration
To bootstrap Spring Data Repositories, use the `<repositories/>` element from the Spring Data for Pivotal GemFire
To bootstrap Spring Data Repositories, use the `<repositories/>` element from the Spring Data for {data-store-name}
Data namespace, as the following example shows:
.Bootstrap Spring Data for Pivotal GemFire Repositories in XML
.Bootstrap Spring Data for {data-store-name} Repositories in XML
====
[source,xml]
----
@@ -37,12 +37,12 @@ IMPORTANT: The bootstrap process fails unless you have your application domain c
== Spring Java-based Configuration
Alternatively, many developers prefer to use Spring's
https://docs.spring.io/spring/docs/current/spring-framework-reference/core.html#beans-java[Java-based container configuration].
{spring-framework-docs}/core.html#beans-java[Java-based container configuration].
Using this approach, you can bootstrap Spring Data Repositories by using the SDG `@EnableGemfireRepositories`
annotation, as the following example shows:
.Bootstrap _Spring Data for Pivotal GemFire Repositories_ with `@EnableGemfireRepositories`
.Bootstrap _Spring Data for {data-store-name} Repositories_ with `@EnableGemfireRepositories`
====
[source, java]
----
@@ -61,12 +61,12 @@ or interface in each package that serves no purpose other than to identify the l
referenced by this attribute.
In addition to the `basePackage[sClasses]` attributes, such as Spring's
https://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/context/annotation/ComponentScan.html[`@ComponentScan`] annotation,
{spring-framework-javadoc}/org/springframework/context/annotation/ComponentScan.html[`@ComponentScan`] annotation,
the `@EnableGemfireRepositories` annotation provides include and exclude filters, based on Spring's
https://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/context/annotation/ComponentScan.Filter.html[`ComponentScan.Filter`] type.
{spring-framework-javadoc}/org/springframework/context/annotation/ComponentScan.Filter.html[`ComponentScan.Filter`] type.
You can use the `filterType` attribute to filter by different aspects, such as whether an application repository type
is annotated with a particular annotation or extends a particular class type and so on. See the
https://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/context/annotation/FilterType.html[`FilterType` Javadoc]
{spring-framework-javadoc}/org/springframework/context/annotation/FilterType.html[`FilterType` Javadoc]
for more details.
The `@EnableGemfireRepositories` annotation also lets you specify the location of named OQL queries,
@@ -75,16 +75,16 @@ the name of a repository query method and the property value is the OQL query yo
the repository query method is called.
The `repositoryImplementationPostfix` attribute can be set to an alternate value (defaults to `Impl`) if your
application requires one or more https://docs.spring.io/spring-data/commons/docs/current/reference/html/#repositories.custom-implementations[custom repository implementations].
application requires one or more {spring-data-commons-docs-html}/#repositories.custom-implementations[custom repository implementations].
This feature is commonly used to extend the Spring Data repository infrastructure to implement a feature
not provided by the data store (for example, SDG).
One example of where custom repository implementations are needed with Pivotal GemFire is when performing joins.
Joins are not supported by SDG repositories. With a Pivotal GemFire `PARTITION` Region, the join must be
performed on co-located `PARTITION` regions, since Pivotal GemFire does not support "`distributed`" joins.
In addition, the Equi-Join OQL Query must be performed inside a Pivotal GemFire function.
One example of where custom repository implementations are needed with {data-store-name} is when performing joins.
Joins are not supported by SDG repositories. With a {data-store-name} `PARTITION` Region, the join must be
performed on co-located `PARTITION` regions, since {data-store-name} does not support "`distributed`" joins.
In addition, the Equi-Join OQL Query must be performed inside a {data-store-name} function.
See http://gemfire91.docs.pivotal.io/geode/developing/partitioned_regions/join_query_partitioned_regions.html[here]
for more details on Pivotal GemFire _Equi-Join Queries_.
for more details on {data-store-name} _Equi-Join Queries_.
Many other aspects of the SDG's repository infrastructure extension may be customized as well. See the
https://docs.spring.io/spring-data/gemfire/docs/current/api/org/springframework/data/gemfire/repository/config/EnableGemfireRepositories.html[`@EnableGemfireRepositories` Javadoc]
@@ -93,7 +93,7 @@ for more details on all configuration settings.
[[gemfire-repositories.queries.executing]]
== Executing OQL Queries
Spring Data for Pivotal GemFire Repositories enable the definition of query methods to easily execute Pivotal GemFire OQL queries
Spring Data for {data-store-name} Repositories enable the definition of query methods to easily execute {data-store-name} OQL queries
against the region the managed entity maps to, as the following example shows:
.Sample Repository
@@ -197,7 +197,7 @@ The following table gives brief samples of the supported keywords that you can u
[[gemfire-repositories.queries.oql-extensions]]
== OQL Query Extensions Using Annotations
Many query languages, such as Pivotal GemFire's OQL (Object Query Language), have extensions that are not directly
Many query languages, such as {data-store-name}'s OQL (Object Query Language), have extensions that are not directly
supported by Spring Data Commons' repository infrastructure.
One of Spring Data Commons' repository infrastructure goals is to function as the lowest common denominator
@@ -206,48 +206,48 @@ for application development today. Technically, this means developers can access
supported by Spring Data Commons within their applications by reusing their existing application-specific
repository interfaces -- a convenient and powerful abstraction.
To support Pivotal GemFire's OQL Query language extensions and preserve portability across different data stores,
Spring Data for Pivotal GemFire adds support for OQL Query extensions by using Java annotations. These Annotations are ignored
To support {data-store-name}'s OQL Query language extensions and preserve portability across different data stores,
Spring Data for {data-store-name} adds support for OQL Query extensions by using Java annotations. These Annotations are ignored
by other Spring Data repository implementations (such as Spring Data JPA or Spring Data Redis) that do not have
similar query language extensions.
For instance, many data stores most likely do not implement Pivotal GemFire's OQL `IMPORT` keyword. Implementing `IMPORT`
For instance, many data stores most likely do not implement {data-store-name}'s OQL `IMPORT` keyword. Implementing `IMPORT`
as an annotation (that is, `@Import`) rather than as part of the query method signature (specifically, the method 'name')
does not interfere with the parsing infrastructure when evaluating the query method name to construct
another data store language appropriate query.
Currently, the set of Pivotal GemFire OQL Query language extensions that are supported by Spring Data for Pivotal GemFire include:
Currently, the set of {data-store-name} OQL Query language extensions that are supported by Spring Data for {data-store-name} include:
[cols="1,2,2,2", options="header"]
.Supported Pivotal GemFire OQL extensions for Repository query methods
.Supported {data-store-name} OQL extensions for Repository query methods
|===
| Keyword
| Annotation
| Description
| Arguments
| http://gemfire.docs.pivotal.io/docs-gemfire/latest/developing/query_index/query_index_hints.html#topic_cfb_mxn_jq[HINT]
| {x-data-store-docs}/developing/query_index/query_index_hints.html#topic_cfb_mxn_jq[HINT]
| `@Hint`
| OQL query index hints
| `String[]` (e.g. @Hint({ "IdIdx", "TxDateIdx" }))
| http://gemfire.docs.pivotal.io/docs-gemfire/latest/developing/query_select/the_import_statement.html#concept_2E9F15B2FE9041238B54736103396BF7[IMPORT]
| {x-data-store-docs}/developing/query_select/the_import_statement.html#concept_2E9F15B2FE9041238B54736103396BF7[IMPORT]
| `@Import`
| Qualify application-specific types.
| `String` (e.g. @Import("org.example.app.domain.Type"))
| http://gemfire.docs.pivotal.io/docs-gemfire/latest/developing/query_select/the_select_statement.html#concept_85AE7D6B1E2941ED8BD2A8310A81753E__section_25D7055B33EC47B19B1B70264B39212F[LIMIT]
| {x-data-store-docs}/developing/query_select/the_select_statement.html#concept_85AE7D6B1E2941ED8BD2A8310A81753E__section_25D7055B33EC47B19B1B70264B39212F[LIMIT]
| `@Limit`
| Limit the returned query result set.
| `Integer` (e.g. @Limit(10); default is Integer.MAX_VALUE)
| http://gemfire.docs.pivotal.io/docs-gemfire/latest/developing/query_additional/query_debugging.html#concept_2D557E24AAB24044A3DB36B3124F6748[TRACE]
| {x-data-store-docs}/developing/query_additional/query_debugging.html#concept_2D557E24AAB24044A3DB36B3124F6748[TRACE]
| `@Trace`
| Enable OQL query-specific debugging.
| NA
|===
As an example, suppose you have a `Customers` application domain class and corresponding Pivotal GemFire region along with a
As an example, suppose you have a `Customers` application domain class and corresponding {data-store-name} region along with a
`CustomerRepository` and a query method to lookup `Customers` by last name, as follows:
.Sample Customers Repository
@@ -294,7 +294,7 @@ The preceding example results in the following OQL Query:
`<TRACE> <HINT 'LastNameIdx'> IMPORT org.example.app.domain.Customer; SELECT * FROM /Customers x WHERE x.lastName = $1 LIMIT 10`
Spring Data for Pivotal GemFire's repository extension and support is careful not to create conflicting declarations when
Spring Data for {data-store-name}'s repository extension and support is careful not to create conflicting declarations when
the OQL Annotation extensions are used in combination with the `@Query` annotation.
As another example, suppose you have a raw `@Query` annotated query method defined in your `CustomerRepository`, as follows:
@@ -340,7 +340,7 @@ Thanks to using the Spring Data repository abstraction, the query method convent
(e.g. OQL) is easy and convenient. However, it is sometimes desirable to still want to inspect or even possibly
modify the query generated from the repository query method.
Since 2.0.x, Spring Data for Pivotal GemFire includes the `o.s.d.gemfire.repository.query.QueryPostProcessor`
Since 2.0.x, Spring Data for {data-store-name} includes the `o.s.d.gemfire.repository.query.QueryPostProcessor`
functional interface. The interface is loosely defined as follows:
.QueryPostProcessor
@@ -375,8 +375,8 @@ and used to create a pipeline of processing for a group of generated query metho
Finally, the `QueryPostProcessor` accepts type arguments corresponding to the type parameters, `T` and `QUERY`,
respectively. Type of `T` extends the Spring Data Commons marker interface,
https://docs.spring.io/spring-data/commons/docs/current/api/org/springframework/data/repository/Repository.html[`org.springframework.data.repository.Repository`].
We discuss this further later in this section. All `QUERY` type parameter arguments in Spring Data for Pivotal GemFire's case
{spring-data-commons-javadoc}/org/springframework/data/repository/Repository.html[`org.springframework.data.repository.Repository`].
We discuss this further later in this section. All `QUERY` type parameter arguments in Spring Data for {data-store-name}'s case
are of type `java.lang.String`.
NOTE: It is useful to define the query as type `QUERY`, since this `QueryPostProcessor` interface may be ported to
@@ -476,7 +476,7 @@ class OrderedLimitedCustomerByLastNameQueryPostProcessor implements QueryPostPro
====
While the preceding example works, you can achieve the same effect by using the Spring Data repository convention and extensions
provided by Spring Data for Pivotal GemFire. For instance, the same query could be defined as follows:
provided by Spring Data for {data-store-name}. For instance, the same query could be defined as follows:
.CustomerRepository using the convention
====

20
src/main/asciidoc/reference/samples.adoc
View File

@@ -2,12 +2,12 @@
= Sample Applications
NOTE: Sample applications are now maintained in the
https://github.com/spring-projects/spring-gemfire-examples[Spring Pivotal GemFire Examples] repository.
https://github.com/spring-projects/spring-gemfire-examples[Spring {data-store-name} Examples] repository.
The Spring Data for Pivotal GemFire project also includes one sample application. Named "`Hello World`", the sample application
demonstrates how to configure and use Pivotal GemFire inside a Spring application. At run time, the sample offers
The Spring Data for {data-store-name} project also includes one sample application. Named "`Hello World`", the sample application
demonstrates how to configure and use {data-store-name} inside a Spring application. At run time, the sample offers
a shell that lets you run various commands against the data grid. It provides an excellent
starting point for developers who are unfamiliar with the essential components or with Spring and Pivotal GemFire concepts.
starting point for developers who are unfamiliar with the essential components or with Spring and {data-store-name} concepts.
The sample is bundled with the distribution and is Maven-based. You can import it into any
Maven-aware IDE (such as the https://spring.io/tools/sts[Spring Tool Suite]) or run them from the command-line.
@@ -15,13 +15,13 @@ Maven-aware IDE (such as the https://spring.io/tools/sts[Spring Tool Suite]) or
[[samples:hello-world]]
== Hello World
The "`Hello World`" sample application demonstrates the core functionality of the Spring Data for Pivotal GemFire project.
It bootstraps Pivotal GemFire, configures it, executes arbitrary commands against the cache, and shuts it down
The "`Hello World`" sample application demonstrates the core functionality of the Spring Data for {data-store-name} project.
It bootstraps {data-store-name}, configures it, executes arbitrary commands against the cache, and shuts it down
when the application exits. Multiple instances of the application can be started at the same time
and work together, sharing data without any user intervention.
.Running under Linux
NOTE: If you experience networking problems when starting Pivotal GemFire or the samples, try adding the following
NOTE: If you experience networking problems when starting {data-store-name} or the samples, try adding the following
system property `java.net.preferIPv4Stack=true` to the command line (for example, `-Djava.net.preferIPv4Stack=true`).
For an alternative (global) fix (especially on Ubuntu), see https://jira.spring.io/browse/SGF-28[SGF-28].
@@ -43,7 +43,7 @@ The output should resemble the following:
[source]
----
INFO: Created Pivotal GemFire Cache [Spring Pivotal GemFire World] v. X.Y.Z
INFO: Created {data-store-name} Cache [Spring {data-store-name} World] v. X.Y.Z
INFO: Created new cache region [myWorld]
INFO: Member xxxxxx:50694/51611 connecting to region [myWorld]
Hello World!
@@ -80,7 +80,7 @@ and its information, as the following example shows:
[source]
----
INFO: Connected to Distributed System ['Spring Pivotal GemFire World'=xxxx:56218/49320@yyyyy]
INFO: Connected to Distributed System ['Spring {data-store-name} World'=xxxx:56218/49320@yyyyy]
Hello World!
...
@@ -106,7 +106,7 @@ http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/
for Spring
http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#beans-annotation-config[components].
The cache configuration defines the Pivotal GemFire cache, a region, and for illustrative purposes, a `CacheListener`
The cache configuration defines the {data-store-name} cache, a region, and for illustrative purposes, a `CacheListener`
that acts as a logger.
The main beans are `HelloWorld` and `CommandProcessor`, which rely on the `GemfireTemplate` to interact with

26
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View File

@@ -1,16 +1,16 @@
[[serialization]]
= Working with Pivotal GemFire Serialization
= Working with {data-store-name} Serialization
To improve overall performance of the Pivotal GemFire In-memory Data Grid, Pivotal GemFire supports a dedicated
To improve overall performance of the {data-store-name} In-memory Data Grid, {data-store-name} supports a dedicated
serialization protocol, called PDX, that is both faster and offers more compact results over
standard Java serialization in addition to working transparently across various language platforms (Java, C++, and .NET).
See
http://geode.apache.org/docs/guide/11/developing/data_serialization/PDX_Serialization_Features.html[PDX Serialization Features]
{x-data-store-docs}/developing/data_serialization/PDX_Serialization_Features.html[PDX Serialization Features]
and
https://cwiki.apache.org/confluence/display/GEODE/PDX+Serialization+Internals[PDX Serialization Internals]
{x-data-store-wiki}/PDX+Serialization+Internals[PDX Serialization Internals]
for more details.
This chapter discusses the various ways in which Spring Data for Pivotal GemFire simplifies and improves Pivotal GemFire's
This chapter discusses the various ways in which Spring Data for {data-store-name} simplifies and improves {data-store-name}'s
custom serialization in Java.
[[serialization:wiring]]
@@ -19,9 +19,9 @@ custom serialization in Java.
It is fairly common for serialized objects to have transient data. Transient data is often dependent on the system
or environment where it lives at a certain point in time. For instance, a `DataSource` is environment specific.
Serializing such information is useless and potentially even dangerous, since it is local to a certain VM or machine.
For such cases, Spring Data for Pivotal GemFire offers a special
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/Instantiator.html[`Instantiator`]
that performs wiring for each new instance created by Pivotal GemFire during deserialization.
For such cases, Spring Data for {data-store-name} offers a special
{x-data-store-javadoc}/org/apache/geode/Instantiator.html[`Instantiator`]
that performs wiring for each new instance created by {data-store-name} during deserialization.
Through such a mechanism, you can rely on the Spring container to inject and manage certain dependencies,
making it easy to split transient from persistent data and have rich domain objects in a transparent manner.
@@ -46,16 +46,16 @@ To use this SDG `Instantiator`, declare it as a bean, as the following example s
----
During the Spring container startup, once it is being initialized, the `Instantiator`, by default, registers
itself with the Pivotal GemFire serialization system and performs wiring on all instances of `SomeDataSerializableClass`
created by Pivotal GemFire during deserialization.
itself with the {data-store-name} serialization system and performs wiring on all instances of `SomeDataSerializableClass`
created by {data-store-name} during deserialization.
[[serialization:instance-generator]]
== Auto-generating Custom `Instantiators`
For data intensive applications, a large number of instances might be created on each machine as data flows in.
Pivotal GemFire uses reflection to create new types, but, for some scenarios, this might prove to be expensive.
{data-store-name} uses reflection to create new types, but, for some scenarios, this might prove to be expensive.
As always, it is good to perform profiling to quantify whether this is the case or not. For such cases,
Spring Data for Pivotal GemFire allows the automatic generation of `Instatiator` classes, which instantiate a new type
Spring Data for {data-store-name} allows the automatic generation of `Instatiator` classes, which instantiate a new type
(using the default constructor) without the use of reflection. The following example shows how to create an instantiator:
[source,xml]
@@ -71,5 +71,5 @@ Spring Data for Pivotal GemFire allows the automatic generation of `Instatiator`
----
The preceding definition automatically generates two `Instantiators` for two classes (`CustomTypeA`
and `CustomTypeB`) and registers them with Pivotal GemFire under user ID `1025` and `1026`. The two `Instantiators` avoid
and `CustomTypeB`) and registers them with {data-store-name} under user ID `1025` and `1026`. The two `Instantiators` avoid
the use of reflection and create the instances directly through Java code.

60
src/main/asciidoc/reference/snapshot.adoc
View File

@@ -1,21 +1,21 @@
[[bootstrap:snapshot]]
= Configuring the Snapshot Service
Spring Data for Pivotal GemFire supports `cache` and `region` snapshots by using
http://geode.apache.org/docs/guide/11/managing/cache_snapshots/chapter_overview.html[Pivotal GemFire's Snapshot Service].
The out-of-the-box Snapshot Service support offers several convenient features to simplify the use of Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/CacheSnapshotService.html[Cache]
and http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/RegionSnapshotService.html[Region]
Spring Data for {data-store-name} supports `cache` and `region` snapshots by using
{x-data-store-docs}/managing/cache_snapshots/chapter_overview.html[{data-store-name}'s Snapshot Service].
The out-of-the-box Snapshot Service support offers several convenient features to simplify the use of {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/snapshot/CacheSnapshotService.html[Cache]
and {x-data-store-javadoc}/org/apache/geode/cache/snapshot/RegionSnapshotService.html[Region]
Snapshot Service APIs.
As the http://geode.apache.org/docs/guide/11/managing/cache_snapshots/chapter_overview.html[Pivotal GemFire documentation]
As the {x-data-store-docs}/managing/cache_snapshots/chapter_overview.html[{data-store-name} documentation]
describes, snapshots let you save and subsequently reload the cached data later, which can be useful for
moving data between environments, such as from production to a staging or test environment in order to reproduce
data-related issues in a controlled context. You can combine Spring Data for Pivotal GemFire's Snapshot Service support
data-related issues in a controlled context. You can combine Spring Data for {data-store-name}'s Snapshot Service support
with http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#beans-definition-profiles[Spring's bean definition profiles]
to load snapshot data specific to the environment as necessary.
Spring Data for Pivotal GemFire's support for Pivotal GemFire's Snapshot Service begins with the `<gfe-data:snapshot-service>` element
Spring Data for {data-store-name}'s support for {data-store-name}'s Snapshot Service begins with the `<gfe-data:snapshot-service>` element
from the `<gfe-data>` namespace.
For example, you can define cache-wide snapshots to be loaded as well as saved by using a couple of snapshot imports
@@ -32,10 +32,10 @@ and a data export definition, as follows:
----
You can define as many imports and exports as you like. You can define only imports or only exports.
The file locations and directory paths can be absolute or relative to the Spring Data for Pivotal GemFire application, which is the
The file locations and directory paths can be absolute or relative to the Spring Data for {data-store-name} application, which is the
JVM process's working directory.
The preceding example is pretty simple, and the Snapshot Service defined in this case refers to the Pivotal GemFire `cache` with
The preceding example is pretty simple, and the Snapshot Service defined in this case refers to the {data-store-name} `cache` with
the default name of `gemfireCache` (as described in <<bootstrap:cache>>). If you name your cache bean definition
something other than the default, you can use the `cache-ref` attribute to refer to the cache bean by name, as follows:
@@ -48,7 +48,7 @@ something other than the default, you can use the `cache-ref` attribute to refer
</gfe-data:snapshot-service>
----
You can also define a Snapshot Service for a particular Pivotal GemFire Region by specifying
You can also define a Snapshot Service for a particular {data-store-name} Region by specifying
the `region-ref` attribute, as follows:
[source,xml]
@@ -61,17 +61,17 @@ the `region-ref` attribute, as follows:
</gfe-data:snapshot-service>
----
When the `region-ref` attribute is specified, Spring Data for Pivotal GemFire's `SnapshotServiceFactoryBean` resolves
When the `region-ref` attribute is specified, Spring Data for {data-store-name}'s `SnapshotServiceFactoryBean` resolves
the `region-ref` attribute value to a region bean defined in the Spring context and creates a
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/RegionSnapshotService.html[`RegionSnapshotService`].
{x-data-store-javadoc}/org/apache/geode/cache/snapshot/RegionSnapshotService.html[`RegionSnapshotService`].
The snapshot import and export definitions function the same way. However, the `location` must refer to a file
on export.
NOTE: Pivotal GemFire is strict about imported snapshot files actually existing before they are referenced. For exports,
Pivotal GemFire creates the snapshot file. If the snapshot file for export already exists,
NOTE: {data-store-name} is strict about imported snapshot files actually existing before they are referenced. For exports,
{data-store-name} creates the snapshot file. If the snapshot file for export already exists,
the data is overwritten.
TIP: Spring Data for Pivotal GemFire includes a `suppress-import-on-init` attribute on the `<gfe-data:snapshot-service>` element
TIP: Spring Data for {data-store-name} includes a `suppress-import-on-init` attribute on the `<gfe-data:snapshot-service>` element
to suppress the configured Snapshot Service from trying to import data into the cache or region on initialization.
Doing so is useful, for example, when data exported from one region is used to feed the import of another region.
@@ -79,20 +79,20 @@ Doing so is useful, for example, when data exported from one region is used to f
== Snapshot Location
For a `Cache`-based Snapshot Service
(that is, a http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/CacheSnapshotService.html[`CacheSnapshotService`])
(that is, a {x-data-store-javadoc}/org/apache/geode/cache/snapshot/CacheSnapshotService.html[`CacheSnapshotService`])
you would typically pass it a directory containing all the snapshot files to load rather than
individual snapshot files, as the overloaded
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/CacheSnapshotService.html#load-java.io.File-org.apache.geode.cache.snapshot.SnapshotOptions.SnapshotFormat[`load`]
{x-data-store-javadoc}/org/apache/geode/cache/snapshot/CacheSnapshotService.html#load-java.io.File-org.apache.geode.cache.snapshot.SnapshotOptions.SnapshotFormat[`load`]
method in the `CacheSnapshotService` API indicates.
NOTE: Of course, you can use the other, overloaded `load(:File[], :SnapshotFormat, :SnapshotOptions)` method
variant to get specific about which snapshot files to load into the Pivotal GemFire `cache`.
variant to get specific about which snapshot files to load into the {data-store-name} `cache`.
However, Spring Data for Pivotal GemFire recognizes that a typical developer workflow might be to extract and export data
However, Spring Data for {data-store-name} recognizes that a typical developer workflow might be to extract and export data
from one environment into several snapshot files, zip all of them up, and then conveniently move the zip file
to another environment for import.
Therefore, Spring Data for Pivotal GemFire lets you specify a jar or zip file on import for a `cache`-based
Therefore, Spring Data for {data-store-name} lets you specify a jar or zip file on import for a `cache`-based
Snapshot Service, as follows:
[source,xml]
@@ -102,18 +102,18 @@ Snapshot Service, as follows:
</gfe-data:snapshot-service>
----
Spring Data for Pivotal GemFire conveniently extracts the provided zip file and treats it as a directory import (load).
Spring Data for {data-store-name} conveniently extracts the provided zip file and treats it as a directory import (load).
[[bootstrap:snapshot:filters]]
== Snapshot Filters
The real power of defining multiple snapshot imports and exports is realized through the use of snapshot filters.
Snapshot filters implement Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/SnapshotFilter.html[`SnapshotFilter`]
Snapshot filters implement {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/snapshot/SnapshotFilter.html[`SnapshotFilter`]
interface and are used to filter region entries for inclusion into the region on import
and for inclusion into the snapshot on export.
Spring Data for Pivotal GemFire lets you use snapshot filters on import and export by using the `filter-ref`
Spring Data for {data-store-name} lets you use snapshot filters on import and export by using the `filter-ref`
attribute or an anonymous, nested bean definition, as the following example shows:
[source,xml]
@@ -141,8 +141,8 @@ attribute or an anonymous, nested bean definition, as the following example show
----
In addition, you can express more complex snapshot filters by using the `ComposableSnapshotFilter` class.
This class implements Pivotal GemFire's
http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/snapshot/SnapshotFilter.html[SnapshotFilter]
This class implements {data-store-name}'s
{x-data-store-javadoc}/org/apache/geode/cache/snapshot/SnapshotFilter.html[SnapshotFilter]
interface as well as the https://en.wikipedia.org/wiki/Composite_pattern[Composite] software design pattern.
In a nutshell, the https://en.wikipedia.org/wiki/Composite_pattern[Composite] software design pattern lets you
@@ -187,15 +187,15 @@ You could then go on to combine the `activesUsersSinceFilter` with another filte
[[bootstrap::snapshot::events]]
== Snapshot Events
By default, Spring Data for Pivotal GemFire uses Pivotal GemFire's Snapshot Services on startup to import data and on shutdown
By default, Spring Data for {data-store-name} uses {data-store-name}'s Snapshot Services on startup to import data and on shutdown
to export data. However, you may want to trigger periodic, event-based snapshots, for either import or export,
from within your Spring application.
For this purpose, Spring Data for Pivotal GemFire defines two additional Spring application events, extending Spring's
For this purpose, Spring Data for {data-store-name} defines two additional Spring application events, extending Spring's
http://docs.spring.io/spring/docs/current/javadoc-api/org/springframework/context/ApplicationEvent.html[`ApplicationEvent`]
class for imports and exports, respectively: `ImportSnapshotApplicationEvent` and `ExportSnapshotApplicationEvent`.
The two application events can be targeted at the entire Pivotal GemFire cache or individual Pivotal GemFire regions. The constructors
The two application events can be targeted at the entire {data-store-name} cache or individual {data-store-name} regions. The constructors
in these classes accept an optional region pathname (such as `/Example`) as well as xero or more `SnapshotMetadata` instances.
The array of `SnapshotMetadata` overrides the snapshot metadata defined by `<gfe-data:snapshot-import>`