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Editing pass for reference docs

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Issue #4083
This commit is contained in:
Jay Bryant
2022-03-25 09:15:46 -05:00
committed by Mahmoud Ben Hassine
parent d00cd947ca
commit 9b8782490f
20 changed files with 1324 additions and 1375 deletions
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108
spring-batch-docs/src/main/asciidoc/appendix.adoc
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@@ -3,133 +3,129 @@
:toclevels: 4
[[listOfReadersAndWriters]]
[appendix]
== List of ItemReaders and ItemWriters
[[itemReadersAppendix]]
=== Item Readers
.Available Item Readers
[options="header"]
|===============
|Item Reader|Description
|AbstractItemCountingItemStreamItemReader|Abstract base class that provides basic
|`AbstractItemCountingItemStreamItemReader`|Abstract base class that provides basic
restart capabilities by counting the number of items returned from
an `ItemReader`.
|AggregateItemReader|An `ItemReader` that delivers a list as its
|`AggregateItemReader`|An `ItemReader` that delivers a list as its
item, storing up objects from the injected `ItemReader` until they
are ready to be packed out as a collection. This class must be used
as a wrapper for a custom `ItemReader` that can identify the record
boundaries. The custom reader should mark the beginning and end of
records by returning an `AggregateItem` which responds `true` to its
query methods `isHeader()` and `isFooter()`. Note that this reader
are ready to be packed out as a collection. This class must be used
as a wrapper for a custom `ItemReader` that can identify the record
boundaries. The custom reader should mark the beginning and end of
records by returning an `AggregateItem` which responds `true` to its
query methods (`isHeader()` and `isFooter()`). Note that this reader
is not part of the library of readers provided by Spring Batch
but given as a sample in `spring-batch-samples`.
|AmqpItemReader|Given a Spring `AmqpTemplate`, it provides
|`AmqpItemReader`|Given a Spring `AmqpTemplate`, it provides
synchronous receive methods. The `receiveAndConvert()` method
lets you receive POJO objects.
|KafkaItemReader|An `ItemReader` that reads messages from an Apache Kafka topic.
|`KafkaItemReader`|An `ItemReader` that reads messages from an Apache Kafka topic.
It can be configured to read messages from multiple partitions of the same topic.
This reader stores message offsets in the execution context to support restart capabilities.
|FlatFileItemReader|Reads from a flat file. Includes `ItemStream`
and `Skippable` functionality. See link:readersAndWriters.html#flatFileItemReader[`FlatFileItemReader`].
|HibernateCursorItemReader|Reads from a cursor based on an HQL query. See
|`FlatFileItemReader`|Reads from a flat file. Includes `ItemStream`
and `Skippable` functionality. See link:readersAndWriters.html#flatFileItemReader["`FlatFileItemReader`"].
|`HibernateCursorItemReader`|Reads from a cursor based on an HQL query. See
link:readersAndWriters.html#cursorBasedItemReaders[`Cursor-based ItemReaders`].
|HibernatePagingItemReader|Reads from a paginated HQL query
|ItemReaderAdapter|Adapts any class to the
|`HibernatePagingItemReader`|Reads from a paginated HQL query.
|`ItemReaderAdapter`|Adapts any class to the
`ItemReader` interface.
|JdbcCursorItemReader|Reads from a database cursor via JDBC. See
link:readersAndWriters.html#cursorBasedItemReaders[`Cursor-based ItemReaders`].
|JdbcPagingItemReader|Given an SQL statement, pages through the rows,
|`JdbcCursorItemReader`|Reads from a database cursor over JDBC. See
link:readersAndWriters.html#cursorBasedItemReaders["`Cursor-based ItemReaders`"].
|`JdbcPagingItemReader`|Given an SQL statement, pages through the rows,
such that large datasets can be read without running out of
memory.
|JmsItemReader|Given a Spring `JmsOperations` object and a JMS
Destination or destination name to which to send errors, provides items
|`JmsItemReader`|Given a Spring `JmsOperations` object and a JMS
destination or destination name to which to send errors, provides items
received through the injected `JmsOperations#receive()`
method.
|JpaPagingItemReader|Given a JPQL statement, pages through the
|`JpaPagingItemReader`|Given a JPQL statement, pages through the
rows, such that large datasets can be read without running out of
memory.
|ListItemReader|Provides the items from a list, one at a
|`ListItemReader`|Provides the items from a list, one at a
time.
|MongoItemReader|Given a `MongoOperations` object and a JSON-based MongoDB
|`MongoItemReader`|Given a `MongoOperations` object and a JSON-based MongoDB
query, provides items received from the `MongoOperations#find()` method.
|Neo4jItemReader|Given a `Neo4jOperations` object and the components of a
|`Neo4jItemReader`|Given a `Neo4jOperations` object and the components of a
Cyhper query, items are returned as the result of the Neo4jOperations.query
method.
|RepositoryItemReader|Given a Spring Data `PagingAndSortingRepository` object,
|`RepositoryItemReader`|Given a Spring Data `PagingAndSortingRepository` object,
a `Sort`, and the name of method to execute, returns items provided by the
Spring Data repository implementation.
|StoredProcedureItemReader|Reads from a database cursor resulting from the
|`StoredProcedureItemReader`|Reads from a database cursor resulting from the
execution of a database stored procedure. See link:readersAndWriters.html#StoredProcedureItemReader[`StoredProcedureItemReader`]
|StaxEventItemReader|Reads via StAX. see link:readersAndWriters.html#StaxEventItemReader[`StaxEventItemReader`].
|JsonItemReader|Reads items from a Json document. see link:readersAndWriters.html#JsonItemReader[`JsonItemReader`].
|`StaxEventItemReader`|Reads over StAX. see link:readersAndWriters.html#StaxEventItemReader[`StaxEventItemReader`].
|`JsonItemReader`|Reads items from a Json document. see link:readersAndWriters.html#JsonItemReader[`JsonItemReader`].
|===============
[[itemWritersAppendix]]
=== Item Writers
.Available Item Writers
[options="header"]
|===============
|Item Writer|Description
|AbstractItemStreamItemWriter|Abstract base class that combines the
|`AbstractItemStreamItemWriter`|Abstract base class that combines the
`ItemStream` and
`ItemWriter` interfaces.
|AmqpItemWriter|Given a Spring `AmqpTemplate`, it provides
|`AmqpItemWriter`|Given a Spring `AmqpTemplate`, provides
for a synchronous `send` method. The `convertAndSend(Object)`
method lets you send POJO objects.
|CompositeItemWriter|Passes an item to the `write` method of each
|`CompositeItemWriter`|Passes an item to the `write` method of each item
in an injected `List` of `ItemWriter` objects.
|FlatFileItemWriter|Writes to a flat file. Includes `ItemStream` and
Skippable functionality. See link:readersAndWriters.html#flatFileItemWriter[`FlatFileItemWriter`].
|GemfireItemWriter|Using a `GemfireOperations` object, items are either written
|`FlatFileItemWriter`|Writes to a flat file. Includes `ItemStream` and
Skippable functionality. See link:readersAndWriters.html#flatFileItemWriter["`FlatFileItemWriter`"].
|`GemfireItemWriter`|Using a `GemfireOperations` object, items are either written
or removed from the Gemfire instance based on the configuration of the delete
flag.
|HibernateItemWriter|This item writer is Hibernate-session aware
and handles some transaction-related work that a non-"hibernate-aware"
|`HibernateItemWriter`|This item writer is Hibernate-session aware
and handles some transaction-related work that a non-"`hibernate-aware`"
item writer would not need to know about and then delegates
to another item writer to do the actual writing.
|ItemWriterAdapter|Adapts any class to the
|`ItemWriterAdapter`|Adapts any class to the
`ItemWriter` interface.
|JdbcBatchItemWriter|Uses batching features from a
|`JdbcBatchItemWriter`|Uses batching features from a
`PreparedStatement`, if available, and can
take rudimentary steps to locate a failure during a
`flush`.
|JmsItemWriter|Using a `JmsOperations` object, items are written
|`JmsItemWriter`|Using a `JmsOperations` object, items are written
to the default queue through the `JmsOperations#convertAndSend()` method.
|JpaItemWriter|This item writer is JPA EntityManager-aware
and handles some transaction-related work that a non-"JPA-aware"
|`JpaItemWriter`|This item writer is JPA `EntityManager`-aware
and handles some transaction-related work that a non-"`JPA-aware`"
`ItemWriter` would not need to know about and
then delegates to another writer to do the actual writing.
|KafkaItemWriter|Using a `KafkaTemplate` object, items are written to the default topic through the
`KafkaTemplate#sendDefault(Object, Object)` method using a `Converter` to map the key from the item.
|`KafkaItemWriter`|Using a `KafkaTemplate` object, items are written to the default topic through the
`KafkaTemplate#sendDefault(Object, Object)` method by using a `Converter` to map the key from the item.
A delete flag can also be configured to send delete events to the topic.
|MimeMessageItemWriter|Using Spring's `JavaMailSender`, items of type `MimeMessage`
|`MimeMessageItemWriter`|Using Spring's `JavaMailSender`, items of type `MimeMessage`
are sent as mail messages.
|MongoItemWriter|Given a `MongoOperations` object, items are written
|`MongoItemWriter`|Given a `MongoOperations` object, items are written
through the `MongoOperations.save(Object)` method. The actual write is delayed
until the last possible moment before the transaction commits.
|Neo4jItemWriter|Given a `Neo4jOperations` object, items are persisted through the
`save(Object)` method or deleted through the `delete(Object)` per the
|`Neo4jItemWriter`|Given a `Neo4jOperations` object, items are persisted through the
`save(Object)` method or deleted through the `delete(Object)`, as dictated by the
`ItemWriter's` configuration
|PropertyExtractingDelegatingItemWriter|Extends `AbstractMethodInvokingDelegator`
|`PropertyExtractingDelegatingItemWriter`|Extends `AbstractMethodInvokingDelegator`
creating arguments on the fly. Arguments are created by retrieving
the values from the fields in the item to be processed (through a
`SpringBeanWrapper`), based on an injected array of field
names.
|RepositoryItemWriter|Given a Spring Data `CrudRepository` implementation,
|`RepositoryItemWriter`|Given a Spring Data `CrudRepository` implementation,
items are saved through the method specified in the configuration.
|StaxEventItemWriter|Uses a `Marshaller` implementation to
convert each item to XML and then writes it to an XML file using
|`StaxEventItemWriter`|Uses a `Marshaller` implementation to
convert each item to XML and then writes it to an XML file by using
StAX.
|JsonFileItemWriter|Uses a `JsonObjectMarshaller` implementation to
convert each item to Json and then writes it to an Json file.
|`JsonFileItemWriter`|Uses a `JsonObjectMarshaller` implementation to
convert each item to Json and then writes it to a Json file.
|===============

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spring-batch-docs/src/main/asciidoc/common-patterns.adoc
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@@ -3,7 +3,6 @@
:toclevels: 4
[[commonPatterns]]
== Common Batch Patterns
ifndef::onlyonetoggle[]

225
spring-batch-docs/src/main/asciidoc/domain.adoc
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@@ -3,7 +3,6 @@
:toclevels: 4
[[domainLanguageOfBatch]]
== The Domain Language of Batch
ifndef::onlyonetoggle[]
@@ -11,7 +10,7 @@ include::toggle.adoc[]
endif::onlyonetoggle[]
To any experienced batch architect, the overall concepts of batch processing used in
Spring Batch should be familiar and comfortable. There are "Jobs" and "Steps" and
Spring Batch should be familiar and comfortable. There are "`Jobs`" and "`Steps`" and
developer-supplied processing units called `ItemReader` and `ItemWriter`. However,
because of the Spring patterns, operations, templates, callbacks, and idioms, there are
opportunities for the following:
@@ -19,14 +18,14 @@ opportunities for the following:
* Significant improvement in adherence to a clear separation of concerns.
* Clearly delineated architectural layers and services provided as interfaces.
* Simple and default implementations that allow for quick adoption and ease of use
out-of-the-box.
out of the box.
* Significantly enhanced extensibility.
The following diagram is a simplified version of the batch reference architecture that
has been used for decades. It provides an overview of the components that make up the
domain language of batch processing. This architecture framework is a blueprint that has
been proven through decades of implementations on the last several generations of
platforms (COBOL/Mainframe, C++/Unix, and now Java/anywhere). JCL and COBOL developers
platforms (COBOL on mainframes, C++ on Unix, and now Java anywhere). JCL and COBOL developers
are likely to be as comfortable with the concepts as C++, C#, and Java developers. Spring
Batch provides a physical implementation of the layers, components, and technical
services commonly found in the robust, maintainable systems that are used to address the
@@ -37,7 +36,7 @@ to address very complex processing needs.
image::{batch-asciidoc}images/spring-batch-reference-model.png[Figure 2.1: Batch Stereotypes, scaledwidth="60%"]
The preceding diagram highlights the key concepts that make up the domain language of
Spring Batch. A Job has one to many steps, each of which has exactly one `ItemReader`,
Spring Batch. A `Job` has one to many steps, each of which has exactly one `ItemReader`,
one `ItemProcessor`, and one `ItemWriter`. A job needs to be launched (with
`JobLauncher`), and metadata about the currently running process needs to be stored (in
`JobRepository`).
@@ -47,28 +46,29 @@ one `ItemProcessor`, and one `ItemWriter`. A job needs to be launched (with
This section describes stereotypes relating to the concept of a batch job. A `Job` is an
entity that encapsulates an entire batch process. As is common with other Spring
projects, a `Job` is wired together with either an XML configuration file or Java-based
configuration. This configuration may be referred to as the "job configuration". However,
`Job` is just the top of an overall hierarchy, as shown in the following diagram:
configuration. This configuration may be referred to as the "`job configuration`". However,
`Job` is only the top of an overall hierarchy, as shown in the following diagram:
.Job Hierarchy
image::{batch-asciidoc}images/job-heirarchy.png[Job Hierarchy, scaledwidth="60%"]
In Spring Batch, a `Job` is simply a container for `Step` instances. It combines multiple
steps that belong logically together in a flow and allows for configuration of properties
steps that logically belong together in a flow and allows for configuration of properties
global to all steps, such as restartability. The job configuration contains:
* The simple name of the job.
* The name of the job.
* Definition and ordering of `Step` instances.
* Whether or not the job is restartable.
ifdef::backend-html5[]
[role="javaContent"]
For those who use Java configuration, Spring Batch provides a default implementation of
the Job interface in the form of the `SimpleJob` class, which creates some standard
functionality on top of `Job`. When using java based configuration, a collection of
builders is made available for the instantiation of a `Job`, as shown in the following
example:
the `Job` interface in the form of the `SimpleJob` class, which creates some standard
functionality on top of `Job`. When using Java-based configuration, a collection of
builders is made available for the instantiation of a `Job`, as the following
example shows:
====
[source, java, role="javaContent"]
----
@Bean
@@ -80,14 +80,16 @@ public Job footballJob() {
.build();
}
----
====
[role="xmlContent"]
For those who use XML configuration, Spring Batch provides a default implementation of the
`Job` interface in the form of the `SimpleJob` class, which creates some standard
functionality on top of `Job`. However, the batch namespace abstracts away the need to
instantiate it directly. Instead, the `<job>` element can be used, as shown in the
following example:
instantiate it directly. Instead, you can use the `<job>` element, as the
following example shows:
====
[source, xml, role="xmlContent"]
----
<job id="footballJob">
@@ -96,14 +98,16 @@ following example:
<step id="playerSummarization"/>
</job>
----
====
endif::backend-html5[]
ifdef::backend-pdf[]
Spring Batch provides a default implementation of the Job interface in the form of the
Spring Batch provides a default implementation of the `Job` interface in the form of the
`SimpleJob` class, which creates some standard functionality on top of `Job`. When using
Java-based configuration, a collection of builders are made available for the
instantiation of a `Job`, as shown in the following example:
instantiation of a `Job`, as the following example shows:
====
[source, java]
----
@Bean
@@ -115,11 +119,13 @@ public Job footballJob() {
.build();
}
----
====
However, when using XML configuration, the batch namespace abstracts away the need to
instantiate it directly. Instead, the `<job>` tag can be used as shown in the following
example:
instantiate it directly. Instead, you can use the `<job>` element, as the following
example shows:
====
[source, xml]
----
<job id="footballJob">
@@ -128,46 +134,47 @@ example:
<step id="playerSummarization"/>
</job>
----
====
endif::backend-pdf[]
==== JobInstance
A `JobInstance` refers to the concept of a logical job run. Consider a batch job that
should be run once at the end of the day, such as the 'EndOfDay' `Job` from the preceding
diagram. There is one 'EndOfDay' job, but each individual run of the `Job` must be
should be run once at the end of the day, such as the `EndOfDay` `Job` from the preceding
diagram. There is one `EndOfDay` job, but each individual run of the `Job` must be
tracked separately. In the case of this job, there is one logical `JobInstance` per day.
For example, there is a January 1st run, a January 2nd run, and so on. If the January 1st
run fails the first time and is run again the next day, it is still the January 1st run.
(Usually, this corresponds with the data it is processing as well, meaning the January
1st run processes data for January 1st). Therefore, each `JobInstance` can have multiple
executions (`JobExecution` is discussed in more detail later in this chapter), and only
one `JobInstance` corresponding to a particular `Job` and identifying `JobParameters` can
one `JobInstance` (which corresponds to a particular `Job` and identifying `JobParameters`) can
run at a given time.
The definition of a `JobInstance` has absolutely no bearing on the data to be loaded.
It is entirely up to the `ItemReader` implementation to determine how data is loaded. For
example, in the EndOfDay scenario, there may be a column on the data that indicates the
'effective date' or 'schedule date' to which the data belongs. So, the January 1st run
example, in the `EndOfDay` scenario, there may be a column on the data that indicates the
`effective date` or `schedule date` to which the data belongs. So, the January 1st run
would load only data from the 1st, and the January 2nd run would use only data from the
2nd. Because this determination is likely to be a business decision, it is left up to the
`ItemReader` to decide. However, using the same `JobInstance` determines whether or not
the 'state' (that is, the `ExecutionContext`, which is discussed later in this chapter)
from previous executions is used. Using a new `JobInstance` means 'start from the
beginning', and using an existing instance generally means 'start from where you left
off'.
the "`state`" (that is, the `ExecutionContext`, which is discussed later in this chapter)
from previous executions is used. Using a new `JobInstance` means "`start from the
beginning,`" and using an existing instance generally means "`start from where you left
off`".
==== JobParameters
Having discussed `JobInstance` and how it differs from Job, the natural question to ask
is: "How is one `JobInstance` distinguished from another?" The answer is:
Having discussed `JobInstance` and how it differs from `Job`, the natural question to ask
is: "`How is one `JobInstance` distinguished from another?`" The answer is:
`JobParameters`. A `JobParameters` object holds a set of parameters used to start a batch
job. They can be used for identification or even as reference data during the run, as
shown in the following image:
job. They can be used for identification or even as reference data during the run, as the
following image shows:
.Job Parameters
image::{batch-asciidoc}images/job-stereotypes-parameters.png[Job Parameters, scaledwidth="60%"]
In the preceding example, where there are two instances, one for January 1st, and another
In the preceding example, where there are two instances, one for January 1st and another
for January 2nd, there is really only one `Job`, but it has two `JobParameter` objects:
one that was started with a job parameter of 01-01-2017 and another that was started with
a parameter of 01-02-2017. Thus, the contract can be defined as: `JobInstance` = `Job`
@@ -183,7 +190,7 @@ of a `Job` with parameters that do not contribute to the identity of a `JobInsta
A `JobExecution` refers to the technical concept of a single attempt to run a Job. An
execution may end in failure or success, but the `JobInstance` corresponding to a given
execution is not considered to be complete unless the execution completes successfully.
Using the EndOfDay `Job` described previously as an example, consider a `JobInstance` for
Using the `EndOfDay` `Job` described previously as an example, consider a `JobInstance` for
01-01-2017 that failed the first time it was run. If it is run again with the same
identifying job parameters as the first run (01-01-2017), a new `JobExecution` is
created. However, there is still only one `JobInstance`.
@@ -192,52 +199,52 @@ A `Job` defines what a job is and how it is to be executed, and a `JobInstance`
purely organizational object to group executions together, primarily to enable correct
restart semantics. A `JobExecution`, however, is the primary storage mechanism for what
actually happened during a run and contains many more properties that must be controlled
and persisted, as shown in the following table:
and persisted, as the following table shows:
.JobExecution Properties
|===
|Property |Definition
|Status
|`Status`
|A `BatchStatus` object that indicates the status of the execution. While running, it is
`BatchStatus#STARTED`. If it fails, it is `BatchStatus#FAILED`. If it finishes
successfully, it is `BatchStatus#COMPLETED`
|startTime
|`startTime`
|A `java.util.Date` representing the current system time when the execution was started.
This field is empty if the job has yet to start.
|endTime
|`endTime`
|A `java.util.Date` representing the current system time when the execution finished,
regardless of whether or not it was successful. The field is empty if the job has yet to
finish.
|exitStatus
|`exitStatus`
|The `ExitStatus`, indicating the result of the run. It is most important, because it
contains an exit code that is returned to the caller. See chapter 5 for more details. The
field is empty if the job has yet to finish.
|createTime
|`createTime`
|A `java.util.Date` representing the current system time when the `JobExecution` was
first persisted. The job may not have been started yet (and thus has no start time), but
it always has a createTime, which is required by the framework for managing job level
it always has a `createTime`, which is required by the framework for managing job-level
`ExecutionContexts`.
|lastUpdated
|`lastUpdated`
|A `java.util.Date` representing the last time a `JobExecution` was persisted. This field
is empty if the job has yet to start.
|executionContext
|The "property bag" containing any user data that needs to be persisted between
|`executionContext`
|The "`property bag`" containing any user data that needs to be persisted between
executions.
|failureExceptions
|`failureExceptions`
|The list of exceptions encountered during the execution of a `Job`. These can be useful
if more than one exception is encountered during the failure of a `Job`.
|===
These properties are important because they are persisted and can be used to completely
determine the status of an execution. For example, if the EndOfDay job for 01-01 is
determine the status of an execution. For example, if the `EndOfDay` job for 01-01 is
executed at 9:00 PM and fails at 9:30, the following entries are made in the batch
metadata tables:
@@ -273,7 +280,7 @@ NOTE: Column names may have been abbreviated or removed for the sake of clarity
formatting.
Now that the job has failed, assume that it took the entire night for the problem to be
determined, so that the 'batch window' is now closed. Further assuming that the window
determined, so that the "`batch window`" is now closed. Further assuming that the window
starts at 9:00 PM, the job is kicked off again for 01-01, starting where it left off and
completing successfully at 9:30. Because it is now the next day, the 01-02 job must be
run as well, and it is kicked off just afterwards at 9:31 and completes in its normal one
@@ -347,7 +354,7 @@ formatting.
=== Step
A `Step` is a domain object that encapsulates an independent, sequential phase of a batch
job. Therefore, every Job is composed entirely of one or more steps. A `Step` contains
job. Therefore, every `Job` is composed entirely of one or more steps. A `Step` contains
all of the information necessary to define and control the actual batch processing. This
is a necessarily vague description because the contents of any given `Step` are at the
discretion of the developer writing a `Job`. A `Step` can be as simple or complex as the
@@ -355,7 +362,7 @@ developer desires. A simple `Step` might load data from a file into the database
requiring little or no code (depending upon the implementations used). A more complex
`Step` may have complicated business rules that are applied as part of the processing. As
with a `Job`, a `Step` has an individual `StepExecution` that correlates with a unique
`JobExecution`, as shown in the following image:
`JobExecution`, as the following image shows:
.Job Hierarchy With Steps
image::{batch-asciidoc}images/jobHeirarchyWithSteps.png[Figure 2.1: Job Hierarchy With Steps, scaledwidth="60%"]
@@ -368,7 +375,7 @@ to execute because the step before it fails, no execution is persisted for it. A
`StepExecution` is created only when its `Step` is actually started.
`Step` executions are represented by objects of the `StepExecution` class. Each execution
contains a reference to its corresponding step and `JobExecution` and transaction related
contains a reference to its corresponding step and `JobExecution` and transaction-related
data, such as commit and rollback counts and start and end times. Additionally, each step
execution contains an `ExecutionContext`, which contains any data a developer needs to
have persisted across batch runs, such as statistics or state information needed to
@@ -377,76 +384,78 @@ restart. The following table lists the properties for `StepExecution`:
.StepExecution Properties
|===
|Property|Definition
|Status
|`Status`
|A `BatchStatus` object that indicates the status of the execution. While running, the
status is `BatchStatus.STARTED`. If it fails, the status is `BatchStatus.FAILED`. If it
finishes successfully, the status is `BatchStatus.COMPLETED`.
|startTime
|`startTime`
|A `java.util.Date` representing the current system time when the execution was started.
This field is empty if the step has yet to start.
|endTime
|`endTime`
|A `java.util.Date` representing the current system time when the execution finished,
regardless of whether or not it was successful. This field is empty if the step has yet to
exit.
|exitStatus
|`exitStatus`
|The `ExitStatus` indicating the result of the execution. It is most important, because
it contains an exit code that is returned to the caller. See chapter 5 for more details.
This field is empty if the job has yet to exit.
|executionContext
|The "property bag" containing any user data that needs to be persisted between
|`executionContext`
|The "`property bag`" containing any user data that needs to be persisted between
executions.
|readCount
|`readCount`
|The number of items that have been successfully read.
|writeCount
|`writeCount`
|The number of items that have been successfully written.
|commitCount
|`commitCount`
|The number of transactions that have been committed for this execution.
|rollbackCount
|`rollbackCount`
|The number of times the business transaction controlled by the `Step` has been rolled
back.
|readSkipCount
|`readSkipCount`
|The number of times `read` has failed, resulting in a skipped item.
|processSkipCount
|`processSkipCount`
|The number of times `process` has failed, resulting in a skipped item.
|filterCount
|The number of items that have been 'filtered' by the `ItemProcessor`.
|`filterCount`
|The number of items that have been "`filtered`" by the `ItemProcessor`.
|writeSkipCount
|`writeSkipCount`
|The number of times `write` has failed, resulting in a skipped item.
|===
=== ExecutionContext
An `ExecutionContext` represents a collection of key/value pairs that are persisted and
controlled by the framework in order to allow developers a place to store persistent
state that is scoped to a `StepExecution` object or a `JobExecution` object. For those
familiar with Quartz, it is very similar to JobDataMap. The best usage example is to
controlled by the framework to give developers a place to store persistent
state that is scoped to a `StepExecution` object or a `JobExecution` object. (For those
familiar with Quartz, it is very similar to `JobDataMap`.) The best usage example is to
facilitate restart. Using flat file input as an example, while processing individual
lines, the framework periodically persists the `ExecutionContext` at commit points. Doing
so allows the `ItemReader` to store its state in case a fatal error occurs during the run
so lets the `ItemReader` store its state in case a fatal error occurs during the run
or even if the power goes out. All that is needed is to put the current number of lines
read into the context, as shown in the following example, and the framework will do the
read into the context, as the following example shows, and the framework does the
rest:
====
[source, java]
----
executionContext.putLong(getKey(LINES_READ_COUNT), reader.getPosition());
----
====
Using the EndOfDay example from the `Job` Stereotypes section as an example, assume there
is one step, 'loadData', that loads a file into the database. After the first failed run,
Using the `EndOfDay` example from the `Job` stereotypes section as an example, assume there
is one step, `loadData`, that loads a file into the database. After the first failed run,
the metadata tables would look like the following example:
.BATCH_JOB_INSTANCE
@@ -493,7 +502,7 @@ the metadata tables would look like the following example:
|{piece.count=40321}
|===
In the preceding case, the `Step` ran for 30 minutes and processed 40,321 'pieces', which
In the preceding case, the `Step` ran for 30 minutes and processed 40,321 "`pieces`", which
would represent lines in a file in this scenario. This value is updated just before each
commit by the framework and can contain multiple rows corresponding to entries within the
`ExecutionContext`. Being notified before a commit requires one of the various
@@ -502,8 +511,9 @@ later in this guide. As with the previous example, it is assumed that the `Job`
restarted the next day. When it is restarted, the values from the `ExecutionContext` of
the last run are reconstituted from the database. When the `ItemReader` is opened, it can
check to see if it has any stored state in the context and initialize itself from there,
as shown in the following example:
as the following example shows:
====
[source, java]
----
if (executionContext.containsKey(getKey(LINES_READ_COUNT))) {
@@ -519,16 +529,18 @@ if (executionContext.containsKey(getKey(LINES_READ_COUNT))) {
}
}
----
In this case, after the above code runs, the current line is 40,322, allowing the `Step`
to start again from where it left off. The `ExecutionContext` can also be used for
====
In this case, after the preceding code runs, the current line is 40,322, letting the `Step`
start again from where it left off. You can also use the `ExecutionContext` for
statistics that need to be persisted about the run itself. For example, if a flat file
contains orders for processing that exist across multiple lines, it may be necessary to
store how many orders have been processed (which is much different from the number of
lines read), so that an email can be sent at the end of the `Step` with the total number
of orders processed in the body. The framework handles storing this for the developer, in
order to correctly scope it with an individual `JobInstance`. It can be very difficult to
of orders processed in the body. The framework handles storing this for the developer,
to correctly scope it with an individual `JobInstance`. It can be very difficult to
know whether an existing `ExecutionContext` should be used or not. For example, using the
'EndOfDay' example from above, when the 01-01 run starts again for the second time, the
`EndOfDay` example from above, when the 01-01 run starts again for the second time, the
framework recognizes that it is the same `JobInstance` and on an individual `Step` basis,
pulls the `ExecutionContext` out of the database, and hands it (as part of the
`StepExecution`) to the `Step` itself. Conversely, for the 01-02 run, the framework
@@ -541,16 +553,18 @@ keyspace. As a result, care should be taken when putting values in to ensure no
overwritten. However, the `Step` stores absolutely no data in the context, so there is no
way to adversely affect the framework.
It is also important to note that there is at least one `ExecutionContext` per
Note that there is at least one `ExecutionContext` per
`JobExecution` and one for every `StepExecution`. For example, consider the following
code snippet:
====
[source, java]
----
ExecutionContext ecStep = stepExecution.getExecutionContext();
ExecutionContext ecJob = jobExecution.getExecutionContext();
//ecStep does not equal ecJob
----
====
As noted in the comment, `ecStep` does not equal `ecJob`. They are two different
`ExecutionContexts`. The one scoped to the `Step` is saved at every commit point in the
@@ -558,30 +572,33 @@ As noted in the comment, `ecStep` does not equal `ecJob`. They are two different
=== JobRepository
`JobRepository` is the persistence mechanism for all of the Stereotypes mentioned above.
`JobRepository` is the persistence mechanism for all of the stereotypes mentioned earlier.
It provides CRUD operations for `JobLauncher`, `Job`, and `Step` implementations. When a
`Job` is first launched, a `JobExecution` is obtained from the repository, and, during
`Job` is first launched, a `JobExecution` is obtained from the repository. Also, during
the course of execution, `StepExecution` and `JobExecution` implementations are persisted
by passing them to the repository.
[role="xmlContent"]
The Spring Batch XML namespace provides support for configuring a `JobRepository` instance
with the `<job-repository>` tag, as shown in the following example:
with the `<job-repository>` tag, as the following example shows:
====
[source, xml, role="xmlContent"]
----
<job-repository id="jobRepository"/>
----
====
[role="javaContent"]
When using Java configuration, the `@EnableBatchProcessing` annotation provides a
`JobRepository` as one of the components automatically configured out of the box.
`JobRepository` as one of the components that is automatically configured.
=== JobLauncher
`JobLauncher` represents a simple interface for launching a `Job` with a given set of
`JobParameters`, as shown in the following example:
`JobParameters`, as the following example shows:
====
[source, java]
----
public interface JobLauncher {
@@ -591,43 +608,46 @@ public JobExecution run(Job job, JobParameters jobParameters)
JobInstanceAlreadyCompleteException, JobParametersInvalidException;
}
----
====
It is expected that implementations obtain a valid `JobExecution` from the
`JobRepository` and execute the `Job`.
=== Item Reader
=== ItemReader
`ItemReader` is an abstraction that represents the retrieval of input for a `Step`, one
item at a time. When the `ItemReader` has exhausted the items it can provide, it
indicates this by returning `null`. More details about the `ItemReader` interface and its
various implementations can be found in
indicates this by returning `null`. You can find more details about the `ItemReader` interface and its
various implementations in
<<readersAndWriters.adoc#readersAndWriters,Readers And Writers>>.
=== Item Writer
=== ItemWriter
`ItemWriter` is an abstraction that represents the output of a `Step`, one batch or chunk
of items at a time. Generally, an `ItemWriter` has no knowledge of the input it should
receive next and knows only the item that was passed in its current invocation. More
details about the `ItemWriter` interface and its various implementations can be found in
of items at a time. Generally, an `ItemWriter` has no knowledge of the input it should
receive next and knows only the item that was passed in its current invocation. You can find more
details about the `ItemWriter` interface and its various implementations in
<<readersAndWriters.adoc#readersAndWriters,Readers And Writers>>.
=== Item Processor
=== ItemProcessor
`ItemProcessor` is an abstraction that represents the business processing of an item.
While the `ItemReader` reads one item, and the `ItemWriter` writes them, the
While the `ItemReader` reads one item, and the `ItemWriter` writes one item, the
`ItemProcessor` provides an access point to transform or apply other business processing.
If, while processing the item, it is determined that the item is not valid, returning
`null` indicates that the item should not be written out. More details about the
`ItemProcessor` interface can be found in
`null` indicates that the item should not be written out. You can find more details about the
`ItemProcessor` interface in
<<readersAndWriters.adoc#readersAndWriters,Readers And Writers>>.
[role="xmlContent"]
=== Batch Namespace
Many of the domain concepts listed previously need to be configured in a Spring
`ApplicationContext`. While there are implementations of the interfaces above that can be
used in a standard bean definition, a namespace has been provided for ease of
configuration, as shown in the following example:
`ApplicationContext`. While there are implementations of the interfaces above that you can
use in a standard bean definition, a namespace has been provided for ease of
configuration, as the following example shows:
====
[source, xml, role="xmlContent"]
----
<beans:beans xmlns="http://www.springframework.org/schema/batch"
@@ -649,9 +669,10 @@ xsi:schemaLocation="
</beans:beans>
----
====
[role="xmlContent"]
As long as the batch namespace has been declared, any of its elements can be used. More
information on configuring a Job can be found in <<job.adoc#configureJob,Configuring and
Running a Job>>. More information on configuring a `Step` can be found in
As long as the batch namespace has been declared, any of its elements can be used. You can find more
information on configuring a Job in <<job.adoc#configureJob,Configuring and
Running a Job>>. You can find more information on configuring a `Step` in
<<step.adoc#configureStep,Configuring a Step>>.

21
spring-batch-docs/src/main/asciidoc/glossary.adoc
View File

@@ -1,6 +1,7 @@
[[glossary]]
[appendix]
== Glossary
[glossary]
=== Spring Batch Glossary
@@ -27,7 +28,7 @@ Batch Window::
Step::
The main batch task or unit of work. It initializes the business logic and controls the
transaction environment, based on commit interval setting and other factors.
transaction environment, based on the commit interval setting and other factors.
Tasklet::
A component created by an application developer to process the business logic for a
@@ -41,7 +42,7 @@ Batch Job Type::
Driving Query::
A driving query identifies the set of work for a job to do. The job then breaks that
work into individual units of work. For instance, a driving query might be to identify
all financial transactions that have a status of "pending transmission" and send them
all financial transactions that have a status of "`pending transmission`" and send them
to a partner system. The driving query returns a set of record IDs to process. Each
record ID then becomes a unit of work. A driving query may involve a join (if the
criteria for selection falls across two or more tables) or it may work with a single
@@ -52,7 +53,7 @@ Item::
terms, this might be a line in a file, a row in a database table, or a particular
element in an XML file.
Logical Unit of Work (LUW)::
Logicial Unit of Work (LUW)::
A batch job iterates through a driving query (or other input source, such as a file) to
perform the set of work that the job must accomplish. Each iteration of work performed
is a unit of work.
@@ -70,27 +71,27 @@ Staging Table::
Restartable::
A job that can be executed again and assumes the same identity as when run initially.
In other words, it is has the same job instance ID.
In other words, it has the same job instance ID.
Rerunnable::
A job that is restartable and manages its own state in terms of the previous run's
record processing. An example of a rerunnable step is one based on a driving query. If
record processing. An example of a re-runnable step is one based on a driving query. If
the driving query can be formed so that it limits the processed rows when the job is
restarted, then it is re-runnable. This is managed by the application logic. Often, a
condition is added to the `where` statement to limit the rows returned by the driving
query with logic resembling "and processedFlag!= true".
query with logic resembling `and processedFlag!= true`.
Repeat::
One of the most basic units of batch processing, it defines by repeatability calling a
One of the most basic units of batch processing, it defines by repeatedly calling a
portion of code until it is finished and while there is no error. Typically, a batch
process would be repeatable as long as there is input.
Retry::
Simplifies the execution of operations with retry semantics most frequently associated
with handling transactional output exceptions. Retry is slightly different from repeat,
rather than continually calling a block of code, retry is stateful and continually
with handling transactional output exceptions. Retry is slightly different from repeat.
Rather than continually calling a block of code, retry is stateful and continually
calls the same block of code with the same input, until it either succeeds or some type
of retry limit has been exceeded. It is only generally useful when a subsequent
of retry limit has been exceeded. It is generally useful only when a subsequent
invocation of the operation might succeed because something in the environment has
improved.

2
spring-batch-docs/src/main/asciidoc/index-single.adoc
View File

@@ -10,6 +10,8 @@ include::toggle.adoc[]
include::spring-batch-intro.adoc[]
include::spring-batch-architecture.adoc[]
include::whatsnew.adoc[]
include::domain.adoc[]

16
spring-batch-docs/src/main/asciidoc/index.adoc
View File

@@ -10,19 +10,19 @@ The reference documentation is divided into several sections:
[horizontal]
<<spring-batch-intro.adoc#spring-batch-intro,Spring Batch Introduction>> :: Background, usage
scenarios and general guidelines.
scenarios, and general guidelines.
<<whatsnew.adoc#whatsNew,What's new in Spring Batch 5.0>> :: New features introduced in version 5.0.
<<domain.adoc#domainLanguageOfBatch,The Domain Language of Batch>> :: Core concepts and abstractions
of the Batch domain language.
<<job.adoc#configureJob,Configuring and Running a Job>> :: Job configuration, execution and
<<job.adoc#configureJob,Configuring and Running a Job>> :: Job configuration, execution, and
administration.
<<step.adoc#configureStep,Configuring a Step>> :: Step configuration, different types of steps,
<<step.adoc#configureStep,Configuring a Step>> :: Step configuration, different types of steps, and
controlling step flow.
<<readersAndWriters.adoc#readersAndWriters,Item reading and writing>> :: `ItemReader`
and `ItemWriter` interfaces and how to use them.
<<processor.adoc#itemProcessor,Item processing>> :: `ItemProcessor` interface and how to use it.
<<scalability.adoc#scalability,Scaling and Parallel Processing>> :: Multi-threaded steps,
parallel steps, remote chunking and partitioning.
parallel steps, remote chunking, and partitioning.
<<repeat.adoc#repeat,Repeat>> :: Completion policies and exception handling of repetitive actions.
<<retry.adoc#retry,Retry>> :: Retry and backoff policies of retryable operations.
<<testing.adoc#testing,Unit Testing>> :: Job and Step testing facilities and APIs.
@@ -31,18 +31,18 @@ and guidelines.
<<spring-batch-integration.adoc#springBatchIntegration,Spring Batch Integration>> :: Integration
between Spring Batch and Spring Integration projects.
<<monitoring-and-metrics.adoc#monitoring-and-metrics,Monitoring and metrics>> :: Batch jobs
monitoring and metrics
monitoring and metrics.
The following appendices are available:
[horizontal]
<<appendix.adoc#listOfReadersAndWriters,List of ItemReaders and ItemWriters>> :: List of
all item readers and writers provided out-of-the box.
all provided item readers and writers.
<<schema-appendix.adoc#metaDataSchema,Meta-Data Schema>> :: Core tables used by the Batch
domain model.
<<transaction-appendix.adoc#transactions,Batch Processing and Transactions>> :: Transaction
boundaries, propagation and isolation levels used in Spring Batch.
<<glossary.adoc#glossary,Glossary>> :: Glossary of common terms, concepts and vocabulary of
boundaries, propagation, and isolation levels used in Spring Batch.
<<glossary.adoc#glossary,Glossary>> :: Glossary of common terms, concepts, and vocabulary of
the Batch domain.
include::footer/index-footer.adoc[]

118
spring-batch-docs/src/main/asciidoc/job.adoc
View File

@@ -19,7 +19,7 @@ image::{batch-asciidoc}images/spring-batch-reference-model.png[Figure 2.1: Batch
While the `Job` object may seem like a simple
container for steps, you must be aware of many configuration options.
Furthermore, you must consider many options about
how a `Job` can be run and how its meta-data can be
how a `Job` can be run and how its metadata can be
stored during that run. This chapter explains the various configuration
options and runtime concerns of a `Job`.
@@ -52,13 +52,14 @@ configuration of the `JobRepository` is handled through the <<job.adoc#javaConfi
[role="javaContent"]
The preceding example illustrates a `Job` that consists of three `Step` instances. The job related
builders can also contain other elements that help with parallelisation (`Split`),
builders can also contain other elements that help with parallelization (`Split`),
declarative flow control (`Decision`), and externalization of flow definitions (`Flow`).
[role="xmlContent"]
Whether you use Java or XML, there are multiple implementations of the <<job.adoc#configureJob,`Job`>>
There are multiple implementations of the <<job.adoc#configureJob,`Job`>>
interface. However, the namespace abstracts away the differences in configuration. It has
only three required dependencies: a name, `JobRepository` , and a list of `Step` instances.
The following example creates a `footballJob`:
====
[source, xml, role="xmlContent"]
@@ -282,7 +283,7 @@ public Job footballJob() {
----
====
Noted that the `afterJob` method is called regardless of the success or
Note that the `afterJob` method is called regardless of the success or
failure of the `Job`. If you need to determine success or failure, you can get that information
from the `JobExecution`:
@@ -309,11 +310,16 @@ The annotations corresponding to this interface are:
[role="xmlContent"]
==== Inheriting from a Parent Job
ifdef::backend-pdf[]
This section applies only to XML based configuration, as Java configuration provides better
reuse capabilities.
endif::backend-pdf[]
[role="xmlContent"]
If a group of Jobs share similar but not
identical configurations, it may help to define a "`parent`"
`Job` from which the concrete
`Job` instances may inherit properties. Similar to class
`Job` instances can inherit properties. Similar to class
inheritance in Java, a "`child`" `Job` combines
its elements and attributes with the parent's.
@@ -324,7 +330,7 @@ listeners. The `Job` (`job1`) is a concrete
definition that inherits the list of listeners from `baseJob` and merges
it with its own list of listeners to produce a
`Job` with two listeners and one
`Step`, `step1`.
`Step` (`step1`).
====
[source, xml, role="xmlContent"]
@@ -348,12 +354,6 @@ it with its own list of listeners to produce a
See the section on <<step.adoc#inheritingFromParentStep,Inheriting from a Parent Step>>
for more detailed information.
ifdef::backend-pdf[]
This section applies only to XML based configuration, as Java configuration provides better
reuse capabilities.
endif::backend-pdf[]
==== JobParametersValidator
A job declared in the XML namespace or using any subclass of
@@ -367,7 +367,7 @@ constraints, you can implement the interface yourself.
ifdef::backend-html5[]
[role="xmlContent"]
The configuration of a validator is supported through the XML namespace through a child
element of the job, as shown in the following example:
element of the job, as the following example shows:
====
[source, xml, role="xmlContent"]
@@ -437,7 +437,7 @@ endif::backend-pdf[]
[[javaConfig]]
=== Java Configuration
Spring 3 brought the ability to configure applications with java instead of XML. As of
Spring 3 brought the ability to configure applications with Java instead of XML. As of
Spring Batch 2.2.0, you can configure batch jobs by using the same Java configuration.
There are three components for the Java-based configuration: the `@EnableBatchProcessing`
annotation and two builders.
@@ -445,7 +445,7 @@ annotation and two builders.
The `@EnableBatchProcessing` annotation works similarly to the other `@Enable*` annotations in the
Spring family. In this case, `@EnableBatchProcessing` provides a base configuration for
building batch jobs. Within this base configuration, an instance of `StepScope` is
created in addition to a number of beans being made available to be autowired:
created, in addition to a number of beans being made available to be autowired:
* `JobRepository`: a bean named `jobRepository`
* `JobLauncher`: a bean named `jobLauncher`
@@ -547,7 +547,7 @@ framework features, such as the `JobLauncher`,
[role="xmlContent"]
The batch namespace abstracts away many of the implementation details of the
`JobRepository` implementations and their collaborators. However, there are still a few
configuration options available, as shown in the following example:
configuration options available, as the following example shows:
.XML Configuration
====
@@ -570,7 +570,7 @@ The `max-varchar-length` defaults to `2500`, which is the length of the long
scripts>>.
[role="javaContent"]
When you use Java configuration, a `JobRepository` is provided for you. A JDBC-based one is
When you use Java configuration, a `JobRepository` is provided for you. A JDBC-based one is
provided if a `DataSource` is provided, and the `Map`-based one is provided if no `DataSource` is provided. However,
you can customize the configuration of the `JobRepository` through an implementation of the
`BatchConfigurer` interface, as the following example shows:
@@ -613,7 +613,7 @@ The behavior of the framework is not well defined if the repository methods are
transactional. The isolation level in the `create*` method attributes is specified
separately to ensure that, when jobs are launched, if two processes try to launch
the same job at the same time, only one succeeds. The default isolation level for that
method is `SERIALIZABLE`, which is quite aggressive. `READ_COMMITTED` usually works just as
method is `SERIALIZABLE`, which is quite aggressive. `READ_COMMITTED` usually works equally
well. `READ_UNCOMMITTED` is fine if two processes are not likely to collide in this
way. However, since a call to the `create*` method is quite short, it is unlikely that
`SERIALIZED` causes problems, as long as the database platform supports it. However, you
@@ -650,7 +650,7 @@ protected JobRepository createJobRepository() throws Exception {
====
If the namespace or factory beans are not used, you must also configure the
transactional behavior of the repository using AOP.
transactional behavior of the repository by using AOP.
[role="xmlContent"]
The following example shows how to configure the transactional behavior of the repository
@@ -750,7 +750,7 @@ Only the table prefix is configurable. The table and column names are not.
[[nonStandardDatabaseTypesInRepository]]
==== Non-standard Database Types in a Repository
If you are using a database platform that is not in the list of supported platforms, you
If you use a database platform that is not in the list of supported platforms, you
may be able to use one of the supported types, if the SQL variant is close enough. To do
this, you can use the raw `JobRepositoryFactoryBean` instead of the namespace shortcut and
use it to set the database type to the closest match.
@@ -794,7 +794,7 @@ If the database type is not specified, the `JobRepositoryFactoryBean` tries to
auto-detect the database type from the `DataSource`.
The major differences between platforms are
mainly accounted for by the strategy for incrementing primary keys, so
it is often be necessary to override the
it is often necessary to override the
`incrementerFactory` as well (by using one of the standard
implementations from the Spring Framework).
@@ -848,8 +848,8 @@ protected JobLauncher createJobLauncher() throws Exception {
====
Once a <<domain.adoc#domainLanguageOfBatch,JobExecution>> is obtained, it is passed to the
execute method of `Job`, ultimately returning the `JobExecution` to the caller, as shown
in the following image:
execute method of `Job`, ultimately returning the `JobExecution` to the caller, as
the following image shows:
.Job Launcher Sequence
image::{batch-asciidoc}images/job-launcher-sequence-sync.png[Job Launcher Sequence, scaledwidth="60%"]
@@ -868,7 +868,7 @@ You can configure the `SimpleJobLauncher` to allow for this scenario by configur
`TaskExecutor`.
[role="xmlContent"]
The following XML example shows a `SimpleJobLauncher` configured to return immediately:
The following XML example configures a `SimpleJobLauncher` to return immediately:
.XML Configuration
====
@@ -885,7 +885,7 @@ The following XML example shows a `SimpleJobLauncher` configured to return immed
====
[role="javaContent"]
The following Java example shows a `SimpleJobLauncher` configured to return immediately:
The following Java example configures a `SimpleJobLauncher` to return immediately:
.Java Configuration
====
@@ -902,8 +902,8 @@ public JobLauncher jobLauncher() {
----
====
Any implementation of the spring `TaskExecutor`
interface can be used to control how jobs are asynchronously
You can use any implementation of the spring `TaskExecutor`
interface to control how jobs are asynchronously
executed.
[[runningAJob]]
@@ -915,7 +915,7 @@ At a minimum, launching a batch job requires two things: the
context or different contexts. For example, if you launch jobs from the
command line, a new JVM is instantiated for each `Job`. Thus, every
job has its own `JobLauncher`. However, if
you run from within a web container within the scope of an
you run from within a web container that is within the scope of an
`HttpRequest`, there is usually one
`JobLauncher` (configured for asynchronous job
launching) that multiple requests invoke to launch their jobs.
@@ -923,13 +923,13 @@ launching) that multiple requests invoke to launch their jobs.
[[runningJobsFromCommandLine]]
==== Running Jobs from the Command Line
For users that want to run their jobs from an enterprise
If you want to run your jobs from an enterprise
scheduler, the command line is the primary interface. This is because
most schedulers (with the exception of Quartz, unless using
`NativeJob`) work directly with operating system
processes, primarily kicked off with shell scripts. There are many ways
to launch a Java process besides a shell script, such as Perl, Ruby, or
even build tools, such as ant or maven. However, because most people
even build tools, such as Ant or Maven. However, because most people
are familiar with shell scripts, this example focuses on them.
[[commandLineJobRunner]]
@@ -945,12 +945,12 @@ many ways to launch a Java process, and this class should in no way be
viewed as definitive. The `CommandLineJobRunner`
performs four tasks:
* Load the appropriate `ApplicationContext`
* Parse command line arguments into `JobParameters`
* Locate the appropriate job based on arguments
* Load the appropriate `ApplicationContext`.
* Parse command line arguments into `JobParameters`.
* Locate the appropriate job based on arguments.
* Use the `JobLauncher` provided in the application context to launch the job.
All of these tasks are accomplished using only the arguments passed in.
All of these tasks are accomplished with only the arguments passed in.
The following table describes the required arguments:
.CommandLineJobRunner arguments
@@ -962,7 +962,7 @@ should contain everything needed to run the complete
|`jobName`|The name of the job to be run.
|===============
These arguments must be passed in with the path first and the name second. All arguments
These arguments must be passed in, with the path first and the name second. All arguments
after these are considered to be job parameters, are turned into a `JobParameters` object,
and must be in the format of `name=value`.
@@ -1016,7 +1016,7 @@ You can override this behavior by using a custom `JobParametersConverter`.
ifdef::backend-html5[]
[role="xmlContent"]
In most cases, you would want to use a manifest to declare your `main` class in a jar. However,
for simplicity, the class was used directly. This example uses the same `EndOfDay`
for simplicity, the class was used directly. This example uses the `EndOfDay`
example from the <<domain.adoc#domainLanguageOfBatch,The Domain Language of Batch>>. The first
argument is `endOfDayJob.xml`, which is the Spring ApplicationContext that contains the
`Job`. The second argument, `endOfDay,` represents the job name. The final argument,
@@ -1040,7 +1040,7 @@ The following example shows a sample configuration for `endOfDay` in XML:
[role="javaContent"]
In most cases, you would want to use a manifest to declare your `main` class in a jar. However,
for simplicity, the class was used directly. This example uses the same `EndOfDay`
for simplicity, the class was used directly. This example uses the `EndOfDay`
example from the <<domain.adoc#domainLanguageOfBatch,The Domain Language of Batch>>. The first
argument is `io.spring.EndOfDayJobConfiguration`, which is the fully qualified class name
to the configuration class that contains the Job. The second argument, `endOfDay`, represents
@@ -1083,7 +1083,7 @@ endif::backend-html5[]
ifdef::backend-pdf[]
In most cases, you would want to use a manifest to declare your `main` class in a jar. However,
for simplicity, the class was used directly. This example uses the same `EndOfDay`
for simplicity, the class was used directly. This example uses the `EndOfDay`
example from the <<domain.adoc#domainLanguageOfBatch,The Domain Language of Batch>>. The first
argument is where your job is configured (either an XML file or a fully qualified class
name). The second argument, `endOfDay`, represents the job name. The final argument,
@@ -1197,15 +1197,15 @@ implementation used by the job runner is the `SimpleJvmExitCodeMapper`
that returns 0 for completion, 1 for generic errors, and 2 for any job
runner errors such as not being able to find a
`Job` in the provided context. If anything more
complex than the 3 values above is needed, then a custom
complex than the three values above is needed, a custom
implementation of the `ExitCodeMapper` interface
must be supplied. Because the
`CommandLineJobRunner` is the class that creates
an `ApplicationContext`, and thus cannot be
an `ApplicationContext` and, thus, cannot be
'wired together', any values that need to be overwritten must be
autowired. This means that if an implementation of
`ExitCodeMapper` is found within the `BeanFactory`,
it will be injected into the runner after the context is created. All
it is injected into the runner after the context is created. All
that needs to be done to provide your own
`ExitCodeMapper` is to declare the implementation
as a root level bean and ensure that it is part of the
@@ -1220,7 +1220,7 @@ launched from the command-line, as described earlier. However, there are
many cases where launching from an `HttpRequest` is
a better option. Many such use cases include reporting, ad-hoc job
running, and web application support. Because a batch job (by definition)
is long running, the most important concern is ensuring to launch the
is long running, the most important concern is to launch the
job asynchronously:
.Asynchronous Job Launcher Sequence From Web Container
@@ -1465,7 +1465,7 @@ example has been given an `id` so that it can be included in child
contexts (for example, as a parent bean definition) and cause all jobs created
there to also be registered automatically.
===== `AutomaticJobRegistrar`
===== AutomaticJobRegistrar
This is a lifecycle component that creates child contexts and registers jobs from those
contexts as they are created. One advantage of doing this is that, while the job names in
@@ -1552,10 +1552,10 @@ locations.
As previously discussed, the `JobRepository`
provides CRUD operations on the meta-data, and the
`JobExplorer` provides read-only operations on the
meta-data. However, those operations are most useful when used together
metadata. However, those operations are most useful when used together
to perform common monitoring tasks such as stopping, restarting, or
summarizing a Job, as is commonly done by batch operators. Spring Batch
provides these types of operations via the
provides these types of operations in the
`JobOperator` interface:
====
@@ -1597,7 +1597,7 @@ public interface JobOperator {
----
====
The above operations represent methods from many different interfaces, such as
The preceding operations represent methods from many different interfaces, such as
`JobLauncher`, `JobRepository`, `JobExplorer`, and `JobRegistry`. For this reason, the
provided implementation of `JobOperator` (`SimpleJobOperator`) has many dependencies.
@@ -1647,14 +1647,14 @@ The following example shows a typical bean definition for `SimpleJobOperator` in
----
====
NOTE: If you set the table prefix on the job repository, don't forget to set it on the job explorer as well.
NOTE: If you set the table prefix on the job repository, do not forget to set it on the job explorer as well.
[[JobParametersIncrementer]]
==== JobParametersIncrementer
Most of the methods on `JobOperator` are
self-explanatory, and you can find more detailed explanations in the
https://docs.spring.io/spring-batch/docs/current/api/org/springframework/batch/core/launch/JobOperator.html[javadoc of the interface]. However, the
https://docs.spring.io/spring-batch/docs/current/api/org/springframework/batch/core/launch/JobOperator.html[Javadoc of the interface]. However, the
`startNextInstance` method is worth noting. This
method always starts a new instance of a `Job`.
This can be extremely useful if there are serious issues in a
@@ -1682,15 +1682,15 @@ public interface JobParametersIncrementer {
The contract of `JobParametersIncrementer` is
that, given a <<job.adoc#jobParameters,JobParameters>>
object, it returns the 'next' JobParameters
object, it returns the "`next`" `JobParameters`
object by incrementing any necessary values it may contain. This
strategy is useful because the framework has no way of knowing what
changes to the `JobParameters` make it the "`next`"
instance. For example, if the only value in
`JobParameters` is a date and the next instance
should be created, should that value be incremented by one day? Or one
should be created, should that value be incremented by one day or one
week (if the job is weekly, for instance)? The same can be said for any
numerical values that help to identify the Job,
numerical values that help to identify the `Job`,
as the following example shows:
====
@@ -1709,11 +1709,11 @@ public class SampleIncrementer implements JobParametersIncrementer {
----
====
In this example, the value with a key of 'run.id' is used to
In this example, the value with a key of `run.id` is used to
discriminate between `JobInstances`. If the
`JobParameters` passed in is null, it can be
assumed that the `Job` has never been run before
and thus its initial state can be returned. However, if not, the old
and, thus, its initial state can be returned. However, if not, the old
value is obtained, incremented by one, and returned.
ifdef::backend-html5[]
@@ -1749,8 +1749,8 @@ public Job footballJob() {
endif::backend-html5[]
ifdef::backend-pdf[]
You can associate an incrementer can
be associated with a `Job` by using the `incrementer`
You can associate an incrementer
with a `Job` by using the `incrementer`
attribute in the namespace:
====
@@ -1799,7 +1799,7 @@ developer code that the framework has no control over, such as a
business service. However, as soon as control is returned back to the
framework, it sets the status of the current
`StepExecution` to
`BatchStatus.STOPPED`, saves it, and then does the same
`BatchStatus.STOPPED`, saves it, and does the same
for the `JobExecution` before finishing.
==== Aborting a Job
@@ -1814,12 +1814,12 @@ job is running and encounters a step that has been marked
moves on to the next step (as determined by the job flow definition
and the step execution exit status).
If the process died (`"kill -9"` or server
failure) the job is, of course, not running, but the `JobRepository` has
If the process died (`kill -9` or server
failure), the job is, of course, not running, but the `JobRepository` has
no way of knowing because no one told it before the process died. You
have to tell it manually that you know that the execution either failed
or should be considered aborted (change its status to
`FAILED` or `ABANDONED`). This is
a business decision, and there is no way to automate it. Change the
status to `FAILED` only if it is not restartable or if
you know the restart data is valid.
you know the restart data is not valid.

18
spring-batch-docs/src/main/asciidoc/monitoring-and-metrics.adoc
View File

@@ -3,7 +3,6 @@
:toclevels: 4
[[monitoring-and-metrics]]
== Monitoring and metrics
Since version 4.2, Spring Batch provides support for batch monitoring and metrics
@@ -11,7 +10,6 @@ based on link:$$https://micrometer.io/$$[Micrometer]. This section describes
which metrics are provided out-of-the-box and how to contribute custom metrics.
[[built-in-metrics]]
=== Built-in metrics
Metrics collection does not require any specific configuration. All metrics provided
@@ -32,7 +30,6 @@ under the `spring.batch` prefix. The following table explains all the metrics in
NOTE: The `status` tag can be either `SUCCESS` or `FAILURE`.
[[custom-metrics]]
=== Custom metrics
If you want to use your own metrics in your custom components, we recommend using
@@ -71,20 +68,19 @@ public class MyTimedTasklet implements Tasklet {
----
[[disabling-metrics]]
=== Disabling metrics
=== Disabling Metrics
Metrics collection is a concern similar to logging. Disabling logs is typically
done by configuring the logging library and this is no different for metrics.
There is no feature in Spring Batch to disable micrometer's metrics, this should
be done on micrometer's side. Since Spring Batch stores metrics in the global
registry of micrometer with the `spring.batch` prefix, it is possible to configure
micrometer to ignore/deny batch metrics with the following snippet:
done by configuring the logging library, and this is no different for metrics.
There is no feature in Spring Batch to disable Micrometer's metrics. This should
be done on Micrometer's side. Since Spring Batch stores metrics in the global
registry of Micrometer with the `spring.batch` prefix, you can configure
micrometer to ignore or deny batch metrics with the following snippet:
[source, java]
----
Metrics.globalRegistry.config().meterFilter(MeterFilter.denyNameStartsWith("spring.batch"))
----
Please refer to micrometer's link:$$http://micrometer.io/docs/concepts#_meter_filters$$[reference documentation]
See Micrometer's link:$$http://micrometer.io/docs/concepts#_meter_filters$$[reference documentation]
for more details.

54
spring-batch-docs/src/main/asciidoc/processor.adoc
View File

@@ -38,12 +38,12 @@ public class CompositeItemWriter<T> implements ItemWriter<T> {
The preceding class contains another `ItemWriter` to which it delegates after having
provided some business logic. This pattern could easily be used for an `ItemReader` as
well, perhaps to obtain more reference data based upon the input that was provided by the
well, perhaps to obtain more reference data based on the input that was provided by the
main `ItemReader`. It is also useful if you need to control the call to `write` yourself.
However, if you only want to 'transform' the item passed in for writing before it is
However, if you only want to "`transform`" the item passed in for writing before it is
actually written, you need not `write` yourself. You can just modify the item. For this
scenario, Spring Batch provides the `ItemProcessor` interface, as shown in the following
interface definition:
scenario, Spring Batch provides the `ItemProcessor` interface, as the following
interface definition shows:
[source, java]
----
@@ -83,12 +83,12 @@ public class BarWriter implements ItemWriter<Bar> {
}
----
In the preceding example, there is a class `Foo`, a class `Bar`, and a class
`FooProcessor` that adheres to the `ItemProcessor` interface. The transformation is
In the preceding example, there is a class named `Foo`, a class named `Bar`, and a class
named `FooProcessor` that adheres to the `ItemProcessor` interface. The transformation is
simple, but any type of transformation could be done here. The `BarWriter` writes `Bar`
objects, throwing an exception if any other type is provided. Similarly, the
`FooProcessor` throws an exception if anything but a `Foo` is provided. The
`FooProcessor` can then be injected into a `Step`, as shown in the following example:
`FooProcessor` can then be injected into a `Step`, as the following example shows:
.XML Configuration
[source, xml, role="xmlContent"]
@@ -131,10 +131,10 @@ is optional for a `Step`.
=== Chaining ItemProcessors
Performing a single transformation is useful in many scenarios, but what if you want to
'chain' together multiple `ItemProcessor` implementations? This can be accomplished using
"`chain`" together multiple `ItemProcessor` implementations? You can do so by using
the composite pattern mentioned previously. To update the previous, single
transformation, example, `Foo` is transformed to `Bar`, which is transformed to `Foobar`
and written out, as shown in the following example:
and written out, as the following example shows:
[source, java]
----
@@ -182,7 +182,7 @@ itemProcessors.add(new BarProcessor());
compositeProcessor.setDelegates(itemProcessors);
----
Just as with the previous example, the composite processor can be configured into the
Just as with the previous example, you can configure the composite processor into the
`Step`:
.XML Configuration
@@ -247,36 +247,36 @@ public CompositeItemProcessor compositeProcessor() {
One typical use for an item processor is to filter out records before they are passed to
the `ItemWriter`. Filtering is an action distinct from skipping. Skipping indicates that
a record is invalid, while filtering simply indicates that a record should not be
a record is invalid, while filtering indicates that a record should not be
written.
For example, consider a batch job that reads a file containing three different types of
records: records to insert, records to update, and records to delete. If record deletion
is not supported by the system, then we would not want to send any "delete" records to
the `ItemWriter`. But, since these records are not actually bad records, we would want to
is not supported by the system, we would not want to send any deletable records to
the `ItemWriter`. However, since these records are not actually bad records, we would want to
filter them out rather than skip them. As a result, the `ItemWriter` would receive only
"insert" and "update" records.
insertable and updatable records.
To filter a record, you can return `null` from the `ItemProcessor`. The framework detects
that the result is `null` and avoids adding that item to the list of records delivered to
the `ItemWriter`. As usual, an exception thrown from the `ItemProcessor` results in a
the `ItemWriter`. An exception thrown from the `ItemProcessor` results in a
skip.
[[validatingInput]]
=== Validating Input
In the <<readersAndWriters.adoc#readersAndWriters,ItemReaders and ItemWriters>> chapter, multiple approaches to parsing input have been
discussed. Each major implementation throws an exception if it is not 'well-formed'. The
The <<readersAndWriters.adoc#readersAndWriters,ItemReaders and ItemWriters>> chapter discusses multiple approaches to parsing input.
Each major implementation throws an exception if it is not "`well formed.`" The
`FixedLengthTokenizer` throws an exception if a range of data is missing. Similarly,
attempting to access an index in a `RowMapper` or `FieldSetMapper` that does not exist or
is in a different format than the one expected causes an exception to be thrown. All of
these types of exceptions are thrown before `read` returns. However, they do not address
the issue of whether or not the returned item is valid. For example, if one of the fields
is an age, it obviously cannot be negative. It may parse correctly, because it exists and
is an age, it cannot be negative. It may parse correctly, because it exists and
is a number, but it does not cause an exception. Since there are already a plethora of
validation frameworks, Spring Batch does not attempt to provide yet another. Rather, it
provides a simple interface, called `Validator`, that can be implemented by any number of
frameworks, as shown in the following interface definition:
provides a simple interface, called `Validator`, that you can implement by any number of
frameworks, as the following interface definition shows:
[source, java]
----
@@ -288,8 +288,8 @@ public interface Validator<T> {
----
The contract is that the `validate` method throws an exception if the object is invalid
and returns normally if it is valid. Spring Batch provides an out of the box
`ValidatingItemProcessor`, as shown in the following bean definition:
and returns normally if it is valid. Spring Batch provides an
`ValidatingItemProcessor`, as the following bean definition shows:
.XML Configuration
[source, xml, role="xmlContent"]
@@ -328,7 +328,7 @@ public SpringValidator validator() {
----
You can also use the `BeanValidatingItemProcessor` to validate items annotated with
the Bean Validation API (JSR-303) annotations. For example, given the following type `Person`:
the Bean Validation API (JSR-303) annotations. For example, consider the following type `Person`:
[source, java]
----
@@ -352,7 +352,7 @@ class Person {
}
----
you can validate items by declaring a `BeanValidatingItemProcessor` bean in your
You can validate items by declaring a `BeanValidatingItemProcessor` bean in your
application context and register it as a processor in your chunk-oriented step:
[source, java]
@@ -370,8 +370,8 @@ public BeanValidatingItemProcessor<Person> beanValidatingItemProcessor() throws
=== Fault Tolerance
When a chunk is rolled back, items that have been cached during reading may be
reprocessed. If a step is configured to be fault tolerant (typically by using skip or
reprocessed. If a step is configured to be fault-tolerant (typically by using skip or
retry processing), any `ItemProcessor` used should be implemented in a way that is
idempotent. Typically that would consist of performing no changes on the input item for
the `ItemProcessor` and only updating the
idempotent. Typically that would consist of performing no changes on the input item for
the `ItemProcessor` and updating only the
instance that is the result.

64
spring-batch-docs/src/main/asciidoc/repeat.adoc
View File

@@ -41,17 +41,15 @@ public interface RepeatCallback {
----
The callback is executed repeatedly until the implementation determines that the
iteration should end. The return value in these interfaces is an enumeration that can
either be `RepeatStatus.CONTINUABLE` or `RepeatStatus.FINISHED`. A `RepeatStatus`
iteration should end. The return value in these interfaces is an enumeration value that can
be either `RepeatStatus.CONTINUABLE` or `RepeatStatus.FINISHED`. A `RepeatStatus`
enumeration conveys information to the caller of the repeat operations about whether
there is any more work to do. Generally speaking, implementations of `RepeatOperations`
should inspect the `RepeatStatus` and use it as part of the decision to end the
iteration. Any callback that wishes to signal to the caller that there is no more work to
do can return `RepeatStatus.FINISHED`.
The simplest general purpose implementation of `RepeatOperations` is `RepeatTemplate`, as
shown in the following example:
any work remains. Generally speaking, implementations of `RepeatOperations`
should inspect `RepeatStatus` and use it as part of the decision to end the
iteration. Any callback that wishes to signal to the caller that there is no work remains
can return `RepeatStatus.FINISHED`.
The simplest general purpose implementation of `RepeatOperations` is `RepeatTemplate`:
[source, java]
----
@@ -71,9 +69,9 @@ template.iterate(new RepeatCallback() {
In the preceding example, we return `RepeatStatus.CONTINUABLE`, to show that there is
more work to do. The callback can also return `RepeatStatus.FINISHED`, to signal to the
caller that there is no more work to do. Some iterations can be terminated by
caller that there is no work remains. Some iterations can be terminated by
considerations intrinsic to the work being done in the callback. Others are effectively
infinite loops as far as the callback is concerned and the completion decision is
infinite loops (as far as the callback is concerned), and the completion decision is
delegated to an external policy, as in the case shown in the preceding example.
[[repeatContext]]
@@ -81,7 +79,7 @@ delegated to an external policy, as in the case shown in the preceding example.
==== RepeatContext
The method parameter for the `RepeatCallback` is a `RepeatContext`. Many callbacks ignore
the context. However, if necessary, it can be used as an attribute bag to store transient
the context. However, if necessary, you can use it as an attribute bag to store transient
data for the duration of the iteration. After the `iterate` method returns, the context
no longer exists.
@@ -91,28 +89,26 @@ calls to `iterate`. This is the case, for instance, if you want to count the num
occurrences of an event in the iteration and remember it across subsequent calls.
[[repeatStatus]]
==== RepeatStatus
`RepeatStatus` is an enumeration used by Spring Batch to indicate whether processing has
finished. It has two possible `RepeatStatus` values, described in the following table:
finished. It has two possible `RepeatStatus` values:
.RepeatStatus Properties
|===============
|__Value__|__Description__
|CONTINUABLE|There is more work to do.
|FINISHED|No more repetitions should take place.
|`CONTINUABLE`|There is more work to do.
|`FINISHED`|No more repetitions should take place.
|===============
`RepeatStatus` values can also be combined with a logical AND operation by using the
You can combine `RepeatStatus` values with a logical AND operation by using the
`and()` method in `RepeatStatus`. The effect of this is to do a logical AND on the
continuable flag. In other words, if either status is `FINISHED`, then the result is
continuable flag. In other words, if either status is `FINISHED`, the result is
`FINISHED`.
[[completionPolicies]]
=== Completion Policies
Inside a `RepeatTemplate`, the termination of the loop in the `iterate` method is
@@ -132,7 +128,6 @@ decisions. For example, a batch processing window that prevents batch jobs from
once the online systems are in use would require a custom policy.
[[repeatExceptionHandling]]
=== Exception Handling
If there is an exception thrown inside a `RepeatCallback`, the `RepeatTemplate` consults
@@ -165,7 +160,6 @@ current `RepeatContext`. When set to `true`, the limit is kept across sibling co
a nested iteration (such as a set of chunks inside a step).
[[repeatListeners]]
=== Listeners
Often, it is useful to be able to receive additional callbacks for cross-cutting concerns
@@ -195,7 +189,6 @@ order. In this case, `open` and `before` are called in the same order while `aft
`onError`, and `close` are called in reverse order.
[[repeatParallelProcessing]]
=== Parallel Processing
Implementations of `RepeatOperations` are not restricted to executing the callback
@@ -207,21 +200,21 @@ of executing the whole iteration in the same thread (the same as a normal
`RepeatTemplate`).
[[declarativeIteration]]
=== Declarative Iteration
Sometimes there is some business processing that you know you want to repeat every time
it happens. The classic example of this is the optimization of a message pipeline. It is
more efficient to process a batch of messages, if they are arriving frequently, than to
Sometimes, there is some business processing that you know you want to repeat every time
it happens. The classic example of this is the optimization of a message pipeline.
If a batch of messages arrives frequently, it is more efficient to process them than to
bear the cost of a separate transaction for every message. Spring Batch provides an AOP
interceptor that wraps a method call in a `RepeatOperations` object for just this
interceptor that wraps a method call in a `RepeatOperations` object for this
purpose. The `RepeatOperationsInterceptor` executes the intercepted method and repeats
according to the `CompletionPolicy` in the provided `RepeatTemplate`.
[role="xmlContent"]
The following example shows declarative iteration using the Spring AOP namespace to
The following example shows declarative iteration that uses the Spring AOP namespace to
repeat a service call to a method called `processMessage` (for more detail on how to
configure AOP interceptors, see the Spring User Guide):
configure AOP interceptors, see the
<<https://docs.spring.io/spring-framework/docs/current/reference/html/core.html#aop,Spring User Guide>>):
[source, xml, role="xmlContent"]
----
@@ -236,9 +229,10 @@ configure AOP interceptors, see the Spring User Guide):
----
[role="javaContent"]
The following example demonstrates using Java configuration to
The following example uses Java configuration to
repeat a service call to a method called `processMessage` (for more detail on how to
configure AOP interceptors, see the Spring User Guide):
configure AOP interceptors, see the
<<https://docs.spring.io/spring-framework/docs/current/reference/html/core.html#aop,Spring User Guide>>):
[source, java, role="javaContent"]
----
@@ -264,11 +258,11 @@ The preceding example uses a default `RepeatTemplate` inside the interceptor. To
the policies, listeners, and other details, you can inject an instance of
`RepeatTemplate` into the interceptor.
If the intercepted method returns `void`, then the interceptor always returns
If the intercepted method returns `void`, the interceptor always returns
`RepeatStatus.CONTINUABLE` (so there is a danger of an infinite loop if the
`CompletionPolicy` does not have a finite end point). Otherwise, it returns
`RepeatStatus.CONTINUABLE` until the return value from the intercepted method is `null`,
at which point it returns `RepeatStatus.FINISHED`. Consequently, the business logic
`RepeatStatus.CONTINUABLE` until the return value from the intercepted method is `null`.
At that point, it returns `RepeatStatus.FINISHED`. Consequently, the business logic
inside the target method can signal that there is no more work to do by returning `null`
or by throwing an exception that is re-thrown by the `ExceptionHandler` in the provided
or by throwing an exception that is rethrown by the `ExceptionHandler` in the provided
`RepeatTemplate`.

4
spring-batch-docs/src/main/asciidoc/retry.adoc
View File

@@ -14,9 +14,9 @@ Examples include remote calls to a web service that fails because of a network g
[NOTE]
====
The retry functionality was pulled out of Spring Batch as of 2.2.0.
As of version 2.2.0, the retry functionality was pulled out of Spring Batch.
It is now part of a new library, https://github.com/spring-projects/spring-retry[Spring Retry].
Spring Batch still relies on Spring Retry to automate retry operations within the framework.
Please refer to the reference documentation of Spring Retry for details about
See the reference documentation of Spring Retry for details about
key APIs and how to use them.
====

117
spring-batch-docs/src/main/asciidoc/scalability.adoc
View File

@@ -10,7 +10,7 @@ ifndef::onlyonetoggle[]
include::toggle.adoc[]
endif::onlyonetoggle[]
Many batch processing problems can be solved with single threaded, single process jobs,
Many batch processing problems can be solved with single-threaded, single-process jobs,
so it is always a good idea to properly check if that meets your needs before thinking
about more complex implementations. Measure the performance of a realistic job and see if
the simplest implementation meets your needs first. You can read and write a file of
@@ -21,27 +21,26 @@ Batch offers a range of options, which are described in this chapter, although s
features are covered elsewhere. At a high level, there are two modes of parallel
processing:
* Single process, multi-threaded
* Single-process, multi-threaded
* Multi-process
These break down into categories as well, as follows:
* Multi-threaded Step (single process)
* Parallel Steps (single process)
* Remote Chunking of Step (multi process)
* Partitioning a Step (single or multi process)
* Multi-threaded Step (single-process)
* Parallel Steps (single-process)
* Remote Chunking of Step (multi-process)
* Partitioning a Step (single or multi-process)
First, we review the single-process options. Then we review the multi-process options.
[[multithreadedStep]]
=== Multi-threaded Step
The simplest way to start parallel processing is to add a `TaskExecutor` to your Step
configuration.
[role="xmlContent"]
For example, you might add an attribute of the `tasklet`, as follows:
For example, you might add an attribute TO the `tasklet`, as follows:
[source, xml, role="xmlContent"]
----
@@ -51,8 +50,8 @@ For example, you might add an attribute of the `tasklet`, as follows:
----
[role="javaContent"]
When using java configuration, a `TaskExecutor` can be added to the step,
as shown in the following example:
When using Java configuration, you can add a `TaskExecutor` to the step,
as the following example shows:
.Java Configuration
[source, java, role="javaContent"]
@@ -80,16 +79,16 @@ is a standard Spring interface, so consult the Spring User Guide for details of
implementations. The simplest multi-threaded `TaskExecutor` is a
`SimpleAsyncTaskExecutor`.
The result of the above configuration is that the `Step` executes by reading, processing,
The result of the preceding configuration is that the `Step` executes by reading, processing,
and writing each chunk of items (each commit interval) in a separate thread of execution.
Note that this means there is no fixed order for the items to be processed, and a chunk
might contain items that are non-consecutive compared to the single-threaded case. In
addition to any limits placed by the task executor (such as whether it is backed by a
thread pool), there is a throttle limit in the tasklet configuration which defaults to 4.
You may need to increase this to ensure that a thread pool is fully utilized.
thread pool), the tasklet configuration has a throttle limit (default: 4).
You may need to increase this limit to ensure that a thread pool is fully used.
[role="xmlContent"]
For example you might increase the throttle-limit, as shown in the following example:
For example, you might increase the throttle-limit, as follows:
[source, xml, role="xmlContent"]
----
@@ -100,8 +99,8 @@ For example you might increase the throttle-limit, as shown in the following exa
----
[role="javaContent"]
When using Java configuration, the builders provide access to the throttle limit, as shown
in the following example:
When using Java configuration, the builders provide access to the throttle limit, as
follows:
.Java Configuration
[source, java, role="javaContent"]
@@ -119,13 +118,13 @@ public Step sampleStep(TaskExecutor taskExecutor) {
----
Note also that there may be limits placed on concurrency by any pooled resources used in
your step, such as a `DataSource`. Be sure to make the pool in those resources at least
your step, such as a `DataSource`. Be sure to make the pool in those resources at least
as large as the desired number of concurrent threads in the step.
There are some practical limitations of using multi-threaded `Step` implementations for
some common batch use cases. Many participants in a `Step` (such as readers and writers)
are stateful. If the state is not segregated by thread, then those components are not
usable in a multi-threaded `Step`. In particular, most of the off-the-shelf readers and
are stateful. If the state is not segregated by thread, those components are not
usable in a multi-threaded `Step`. In particular, most of the readers and
writers from Spring Batch are not designed for multi-threaded use. It is, however,
possible to work with stateless or thread safe readers and writers, and there is a sample
(called `parallelJob`) in the
@@ -136,25 +135,23 @@ of items that have been processed in a database input table.
Spring Batch provides some implementations of `ItemWriter` and `ItemReader`. Usually,
they say in the Javadoc if they are thread safe or not or what you have to do to avoid
problems in a concurrent environment. If there is no information in the Javadoc, you can
check the implementation to see if there is any state. If a reader is not thread safe,
problems in a concurrent environment. If there is no information in the Javadoc, you can
check the implementation to see if there is any state. If a reader is not thread safe,
you can decorate it with the provided `SynchronizedItemStreamReader` or use it in your own
synchronizing delegator. You can synchronize the call to `read()` and as long as the
synchronizing delegator. You can synchronize the call to `read()`, and, as long as the
processing and writing is the most expensive part of the chunk, your step may still
complete much faster than it would in a single threaded configuration.
complete much more quickly than it would in a single-threaded configuration.
[[scalabilityParallelSteps]]
=== Parallel Steps
As long as the application logic that needs to be parallelized can be split into distinct
responsibilities and assigned to individual steps, then it can be parallelized in a
responsibilities and assigned to individual steps, it can be parallelized in a
single process. Parallel Step execution is easy to configure and use.
[role="xmlContent"]
For example, executing steps `(step1,step2)` in parallel with `step3` is straightforward,
as shown in the following example:
as follows:
[source, xml, role="xmlContent"]
----
@@ -176,7 +173,7 @@ as shown in the following example:
[role="javaContent"]
When using Java configuration, executing steps `(step1,step2)` in parallel with `step3`
is straightforward, as shown in the following example:
is straightforward, as follows:
.Java Configuration
[source, java, role="javaContent"]
@@ -220,7 +217,7 @@ public TaskExecutor taskExecutor() {
----
The configurable task executor is used to specify which `TaskExecutor`
implementation should be used to execute the individual flows. The default is
implementation should execute the individual flows. The default is
`SyncTaskExecutor`, but an asynchronous `TaskExecutor` is required to run the steps in
parallel. Note that the job ensures that every flow in the split completes before
aggregating the exit statuses and transitioning.
@@ -228,7 +225,6 @@ aggregating the exit statuses and transitioning.
See the section on <<step.adoc#split-flows,Split Flows>> for more detail.
[[remoteChunking]]
=== Remote Chunking
In remote chunking, the `Step` processing is split across multiple processes,
@@ -245,13 +241,13 @@ expensive than the reading of items (as is often the case in practice).
The manager is an implementation of a Spring Batch `Step` with the `ItemWriter` replaced
by a generic version that knows how to send chunks of items to the middleware as
messages. The workers are standard listeners for whatever middleware is being used (for
example, with JMS, they would be `MessageListener` implementations), and their role is
to process the chunks of items using a standard `ItemWriter` or `ItemProcessor` plus
example, with JMS, they would be `MesssageListener` implementations), and their role is
to process the chunks of items by using a standard `ItemWriter` or `ItemProcessor` plus an
`ItemWriter`, through the `ChunkProcessor` interface. One of the advantages of using this
pattern is that the reader, processor, and writer components are off-the-shelf (the same
as would be used for a local execution of the step). The items are divided up dynamically
as would be used for a local execution of the step). The items are divided up dynamically,
and work is shared through the middleware, so that, if the listeners are all eager
consumers, then load balancing is automatic.
consumers, load balancing is automatic.
The middleware has to be durable, with guaranteed delivery and a single consumer for each
message. JMS is the obvious candidate, but other options (such as JavaSpaces) exist in
@@ -262,7 +258,6 @@ See the section on
for more detail.
[[partitioning]]
=== Partitioning
Spring Batch also provides an SPI for partitioning a `Step` execution and executing it
@@ -285,7 +280,7 @@ each `Job` execution.
The SPI in Spring Batch consists of a special implementation of `Step` (called the
`PartitionStep`) and two strategy interfaces that need to be implemented for the specific
environment. The strategy interfaces are `PartitionHandler` and `StepExecutionSplitter`,
and their role is shown in the following sequence diagram:
and the following sequence diagram shows their role:
.Partitioning SPI
image::{batch-asciidoc}images/partitioning-spi.png[Partitioning SPI, scaledwidth="60%"]
@@ -325,36 +320,41 @@ public Step step1Manager() {
}
----
[role="xmlContent"]
Similar to the multi-threaded step's `throttle-limit` attribute, the `grid-size`
attribute prevents the task executor from being saturated with requests from a single
step.
There is a simple example that can be copied and extended in the unit test suite for
https://github.com/spring-projects/spring-batch/tree/main/spring-batch-samples/src/main/resources/jobs[Spring
Batch Samples] (see `partition*Job.xml` configuration).
[role="javaContent"]
Similar to the multi-threaded step's `throttleLimit` method, the `gridSize`
method prevents the task executor from being saturated with requests from a single
step.
Spring Batch creates step executions for the partitions called "step1:partition0", and so
on. Many people prefer to call the manager step "step1:manager" for consistency. You can
The unit test suite for
https://github.com/spring-projects/spring-batch/tree/main/spring-batch-samples/src/main/resources/jobs[Spring
Batch Samples] (see `partition*Job.xml` configuration) has a simple example that you can copy and extend.
Spring Batch creates step executions for the partition called `step1:partition0` and so
on. Many people prefer to call the manager step `step1:manager` for consistency. You can
use an alias for the step (by specifying the `name` attribute instead of the `id`
attribute).
[[partitionHandler]]
==== PartitionHandler
The `PartitionHandler` is the component that knows about the fabric of the remoting or
`PartitionHandler` is the component that knows about the fabric of the remoting or
grid environment. It is able to send `StepExecution` requests to the remote `Step`
instances, wrapped in some fabric-specific format, like a DTO. It does not have to know
how to split the input data or how to aggregate the result of multiple `Step` executions.
Generally speaking, it probably also does not need to know about resilience or failover,
since those are features of the fabric in many cases. In any case, Spring Batch always
provides restartability independent of the fabric. A failed `Job` can always be restarted
and only the failed `Steps` are re-executed.
provides restartability independent of the fabric. A failed `Job` can always be restarted,
and, in that case, only the failed `Steps` are re-executed.
The `PartitionHandler` interface can have specialized implementations for a variety of
fabric types, including simple RMI remoting, EJB remoting, custom web service, JMS, Java
Spaces, shared memory grids (like Terracotta or Coherence), and grid execution fabrics
(like GridGain). Spring Batch does not contain implementations for any proprietary grid
Spaces, shared memory grids (such as Terracotta or Coherence), and grid execution fabrics
(such as GridGain). Spring Batch does not contain implementations for any proprietary grid
or remoting fabrics.
Spring Batch does, however, provide a useful implementation of `PartitionHandler` that
@@ -364,8 +364,7 @@ executes `Step` instances locally in separate threads of execution, using the
[role="xmlContent"]
The `TaskExecutorPartitionHandler` is the default for a step configured with the XML
namespace shown previously. It can also be configured explicitly, as shown in the
following example:
namespace shown previously. You can also configure it explicitly, as follows:
[source, xml, role="xmlContent"]
----
@@ -381,8 +380,8 @@ following example:
----
[role="javaContent"]
The `TaskExecutorPartitionHandler` can be configured explicitly within java configuration,
as shown in the following example:
You can explicitly configure the `TaskExecutorPartitionHandler` with Java configuration,
as follows:
.Java Configuration
[source, java, role="javaContent"]
@@ -416,12 +415,11 @@ systems. It can also be used for remote execution by providing a `Step` implemen
that is a proxy for a remote invocation (such as using Spring Remoting).
[[partitioner]]
==== Partitioner
The `Partitioner` has a simpler responsibility: to generate execution contexts as input
parameters for new step executions only (no need to worry about restarts). It has a
single method, as shown in the following interface definition:
single method, as the following interface definition shows:
[source, java]
----
@@ -435,24 +433,23 @@ The return value from this method associates a unique name for each step executi
later in the Batch metadata as the step name in the partitioned `StepExecutions`. The
`ExecutionContext` is just a bag of name-value pairs, so it might contain a range of
primary keys, line numbers, or the location of an input file. The remote `Step` then
normally binds to the context input using `#{...}` placeholders (late binding in step
scope), as illustrated in the next section.
normally binds to the context input by using `#{...}` placeholders (late binding in step
scope), as shown in the next section.
The names of the step executions (the keys in the `Map` returned by `Partitioner`) need
to be unique amongst the step executions of a `Job` but do not have any other specific
requirements. The easiest way to do this (and to make the names meaningful for users) is
to use a prefix+suffix naming convention, where the prefix is the name of the step that
is being executed (which itself is unique in the `Job`), and the suffix is just a
is being executed (which itself is unique in the `Job`) and the suffix is just a
counter. There is a `SimplePartitioner` in the framework that uses this convention.
An optional interface called `PartitionNameProvider` can be used to provide the partition
You can use an optional interface called `PartitionNameProvider` to provide the partition
names separately from the partitions themselves. If a `Partitioner` implements this
interface, then, on a restart, only the names are queried. If partitioning is expensive,
interface, only the names are queried on a restart. If partitioning is expensive,
this can be a useful optimization. The names provided by the `PartitionNameProvider` must
match those provided by the `Partitioner`.
[[bindingInputDataToSteps]]
==== Binding Input Data to Steps
It is very efficient for the steps that are executed by the `PartitionHandler` to have
@@ -471,7 +468,7 @@ the `Partitioner` output might resemble the content of the following table:
|filecopy:partition2|fileName=/home/data/three
|===============
Then the file name can be bound to a step using late binding to the execution context.
Then the file name can be bound to a step by using late binding to the execution context.
[role="xmlContent"]
The following example shows how to define late binding in XML:

100
spring-batch-docs/src/main/asciidoc/schema-appendix.adoc
View File

@@ -9,18 +9,18 @@
[[metaDataSchemaOverview]]
=== Overview
The Spring Batch Metadata tables closely match the Domain objects that represent them in
The Spring Batch Metadata tables closely match the domain objects that represent them in
Java. For example, `JobInstance`, `JobExecution`, `JobParameters`, and `StepExecution`
map to `BATCH_JOB_INSTANCE`, `BATCH_JOB_EXECUTION`, `BATCH_JOB_EXECUTION_PARAMS`, and
`BATCH_STEP_EXECUTION`, respectively. `ExecutionContext` maps to both
`BATCH_JOB_EXECUTION_CONTEXT` and `BATCH_STEP_EXECUTION_CONTEXT`. The `JobRepository` is
responsible for saving and storing each Java object into its correct table. This appendix
describes the metadata tables in detail, along with many of the design decisions that
were made when creating them. When viewing the various table creation statements below,
it is important to realize that the data types used are as generic as possible. Spring
Batch provides many schemas as examples, all of which have varying data types, due to
were made when creating them. When viewing the various table creation statements described
later in this appendix, note that the data types used are as generic as possible. Spring
Batch provides many schemas as examples. All of them have varying data types, due to
variations in how individual database vendors handle data types. The following image
shows an ERD model of all 6 tables and their relationships to one another:
shows an ERD model of all six tables and their relationships to one another:
.Spring Batch Meta-Data ERD
image::{batch-asciidoc}images/meta-data-erd.png[Spring Batch Meta-Data ERD, scaledwidth="60%"]
@@ -31,8 +31,8 @@ image::{batch-asciidoc}images/meta-data-erd.png[Spring Batch Meta-Data ERD, scal
The Spring Batch Core JAR file contains example scripts to create the relational tables
for a number of database platforms (which are, in turn, auto-detected by the job
repository factory bean or namespace equivalent). These scripts can be used as is or
modified with additional indexes and constraints as desired. The file names are in the
form `schema-\*.sql`, where "*" is the short name of the target database platform.
modified with additional indexes and constraints, as desired. The file names are in the
form `schema-\*.sql`, where `*` is the short name of the target database platform.
The scripts are in the package `org.springframework.batch.core`.
[[migrationDDLScripts]]
@@ -42,18 +42,18 @@ Spring Batch provides migration DDL scripts that you need to execute when you up
These scripts can be found in the Core Jar file under `org/springframework/batch/core/migration`.
Migration scripts are organized into folders corresponding to version numbers in which they were introduced:
* `2.2`: contains scripts needed if you are migrating from a version before `2.2` to version `2.2`
* `4.1`: contains scripts needed if you are migrating from a version before `4.1` to version `4.1`
* `2.2`: Contains scripts you need to migrate from a version before `2.2` to version `2.2`
* `4.1`: Contains scripts you need to migrate from a version before `4.1` to version `4.1`
[[metaDataVersion]]
==== Version
Many of the database tables discussed in this appendix contain a version column. This
column is important because Spring Batch employs an optimistic locking strategy when
dealing with updates to the database. This means that each time a record is 'touched'
(updated) the value in the version column is incremented by one. When the repository goes
back to save the value, if the version number has changed it throws an
`OptimisticLockingFailureException`, indicating there has been an error with concurrent
column is important, because Spring Batch employs an optimistic locking strategy when
dealing with updates to the database. This means that each time a record is "`touched`"
(updated), the value in the version column is incremented by one. When the repository goes
back to save the value, if the version number has changed, it throws an
`OptimisticLockingFailureException`, indicating that there has been an error with concurrent
access. This check is necessary, since, even though different batch jobs may be running
in different machines, they all use the same database tables.
@@ -91,13 +91,13 @@ INSERT INTO BATCH_JOB_SEQ values(0);
----
In the preceding case, a table is used in place of each sequence. The Spring core class,
`MySQLMaxValueIncrementer`, then increments the one column in this sequence in order to
`MySQLMaxValueIncrementer`, then increments the one column in this sequence to
give similar functionality.
[[metaDataBatchJobInstance]]
=== `BATCH_JOB_INSTANCE`
=== The `BATCH_JOB_INSTANCE` Table
The `BATCH_JOB_INSTANCE` table holds all information relevant to a `JobInstance`, and
The `BATCH_JOB_INSTANCE` table holds all information relevant to a `JobInstance` and
serves as the top of the overall hierarchy. The following generic DDL statement is used
to create it:
@@ -124,15 +124,15 @@ instances of the same job from one another. (`JobInstances` with the same job na
have different `JobParameters` and, thus, different `JOB_KEY` values).
[[metaDataBatchJobParams]]
=== `BATCH_JOB_EXECUTION_PARAMS`
=== The `BATCH_JOB_EXECUTION_PARAMS` Table
The `BATCH_JOB_EXECUTION_PARAMS` table holds all information relevant to the
`JobParameters` object. It contains 0 or more key/value pairs passed to a `Job` and
serves as a record of the parameters with which a job was run. For each parameter that
contributes to the generation of a job's identity, the `IDENTIFYING` flag is set to true.
Note that the table has been denormalized. Rather than creating a separate table for each
type, there is one table with a column indicating the type, as shown in the following
listing:
type, there is one table with a column indicating the type, as the following
listing shows:
[source, sql]
----
@@ -158,22 +158,22 @@ key/value pairs) may exist for each execution.
* TYPE_CD: String representation of the type of value stored, which can be a string, a
date, a long, or a double. Because the type must be known, it cannot be null.
* KEY_NAME: The parameter key.
* STRING_VAL: Parameter value, if the type is string.
* DATE_VAL: Parameter value, if the type is date.
* LONG_VAL: Parameter value, if the type is long.
* DOUBLE_VAL: Parameter value, if the type is double.
* STRING_VAL: Parameter value if the type is string.
* DATE_VAL: Parameter value if the type is date.
* LONG_VAL: Parameter value if the type is long.
* DOUBLE_VAL: Parameter value if the type is double.
* IDENTIFYING: Flag indicating whether the parameter contributed to the identity of the
related `JobInstance`.
Note that there is no primary key for this table. This is because the framework has no
use for one and, thus, does not require it. If need be, you can add a primary key may be
added with a database generated key without causing any issues to the framework itself.
use for one and, thus, does not require it. If need be, you can add a primary key
with a database generated key without causing any issues to the framework itself.
[[metaDataBatchJobExecution]]
=== `BATCH_JOB_EXECUTION`
=== The `BATCH_JOB_EXECUTION` Table
The `BATCH_JOB_EXECUTION` table holds all information relevant to the `JobExecution`
object. Every time a `Job` is run, there is always a new `JobExecution`, and a new row in
object. Every time a `Job` is run, there is always a new called `JobExecution` and a new row in
this table. The following listing shows the definition of the `BATCH_JOB_EXECUTION`
table:
@@ -220,9 +220,9 @@ possible.
* `LAST_UPDATED`: Timestamp representing the last time this execution was persisted.
[[metaDataBatchStepExecution]]
=== `BATCH_STEP_EXECUTION`
=== The `BATCH_STEP_EXECUTION` Table
The BATCH_STEP_EXECUTION table holds all information relevant to the `StepExecution`
The `BATCH_STEP_EXECUTION` table holds all information relevant to the `StepExecution`
object. This table is similar in many ways to the `BATCH_JOB_EXECUTION` table, and there
is always at least one entry per `Step` for each `JobExecution` created. The following
listing shows the definition of the `BATCH_STEP_EXECUTION` table:
@@ -253,7 +253,7 @@ CREATE TABLE BATCH_STEP_EXECUTION (
) ;
----
The following list describes for each column:
The following list describes each column:
* `STEP_EXECUTION_ID`: Primary key that uniquely identifies this execution. The value of
this column should be obtainable by calling the `getId` method of the `StepExecution`
@@ -291,13 +291,13 @@ possible.
* `LAST_UPDATED`: Timestamp representing the last time this execution was persisted.
[[metaDataBatchJobExecutionContext]]
=== `BATCH_JOB_EXECUTION_CONTEXT`
=== The `BATCH_JOB_EXECUTION_CONTEXT` Table
The `BATCH_JOB_EXECUTION_CONTEXT` table holds all information relevant to the
`ExecutionContext` of a `Job`. There is exactly one `Job` `ExecutionContext` per
`ExecutionContext` of a `Job`. There is exactly one `Job` `ExecutionContext` for each
`JobExecution`, and it contains all of the job-level data that is needed for a particular
job execution. This data typically represents the state that must be retrieved after a
failure, so that a `JobInstance` can "start from where it left off". The following
failure, so that a `JobInstance` can "`start where it left off`". The following
listing shows the definition of the `BATCH_JOB_EXECUTION_CONTEXT` table:
[source, sql]
@@ -319,14 +319,14 @@ belongs. There may be more than one row associated with a given execution.
* `SERIALIZED_CONTEXT`: The entire context, serialized.
[[metaDataBatchStepExecutionContext]]
=== `BATCH_STEP_EXECUTION_CONTEXT`
=== The `BATCH_STEP_EXECUTION_CONTEXT` Table
The `BATCH_STEP_EXECUTION_CONTEXT` table holds all information relevant to the
`ExecutionContext` of a `Step`. There is exactly one `ExecutionContext` per
`StepExecution`, and it contains all of the data that
needs to be persisted for a particular step execution. This data typically represents the
state that must be retrieved after a failure, so that a `JobInstance` can 'start from
where it left off'. The following listing shows the definition of the
state that must be retrieved after a failure so that a `JobInstance` can "`start
where it left off`". The following listing shows the definition of the
`BATCH_STEP_EXECUTION_CONTEXT` table:
[source, sql]
@@ -343,7 +343,7 @@ CREATE TABLE BATCH_STEP_EXECUTION_CONTEXT (
The following list describes each column:
* `STEP_EXECUTION_ID`: Foreign key representing the `StepExecution` to which the context
belongs. There may be more than one row associated to a given execution.
belongs. There may be more than one row associated with a given execution.
* `SHORT_CONTEXT`: A string version of the `SERIALIZED_CONTEXT`.
* `SERIALIZED_CONTEXT`: The entire context, serialized.
@@ -356,47 +356,47 @@ to show a record of what happened in the past and generally do not affect the ru
job, with a few notable exceptions pertaining to restart:
* The framework uses the metadata tables to determine whether a particular `JobInstance`
has been run before. If it has been run and if the job is not restartable, then an
has been run before. If it has been run and if the job is not restartable, an
exception is thrown.
* If an entry for a `JobInstance` is removed without having completed successfully, the
framework thinks that the job is new rather than a restart.
* If a job is restarted, the framework uses any data that has been persisted to the
`ExecutionContext` to restore the `Job's` state. Therefore, removing any entries from
this table for jobs that have not completed successfully prevents them from starting at
the correct point if run again.
the correct point if they are run again.
[[multiByteCharacters]]
=== International and Multi-byte Characters
If you are using multi-byte character sets (such as Chinese or Cyrillic) in your business
processing, then those characters might need to be persisted in the Spring Batch schema.
If you use multi-byte character sets (such as Chinese or Cyrillic) in your business
processing, those characters might need to be persisted in the Spring Batch schema.
Many users find that simply changing the schema to double the length of the `VARCHAR`
columns is enough. Others prefer to configure the
columns is enough. Others prefer to configure the
<<job.adoc#configuringJobRepository,JobRepository>> with `max-varchar-length` half the
value of the `VARCHAR` column length. Some users have also reported that they use
`NVARCHAR` in place of `VARCHAR` in their schema definitions. The best result depends on
the database platform and the way the database server has been configured locally.
[[recommendationsForIndexingMetaDataTables]]
=== Recommendations for Indexing Meta Data Tables
=== Recommendations for Indexing Metadata Tables
Spring Batch provides DDL samples for the metadata tables in the core jar file for
several common database platforms. Index declarations are not included in that DDL,
because there are too many variations in how users may want to index, depending on their
precise platform, local conventions, and the business requirements of how the jobs are
operated. The following below provides some indication as to which columns are going to
operated. The following table provides some indication as to which columns are going to
be used in a `WHERE` clause by the DAO implementations provided by Spring Batch and how
frequently they might be used, so that individual projects can make up their own minds
frequently they might be used so that individual projects can make up their own minds
about indexing:
.Where clauses in SQL statements (excluding primary keys) and their approximate frequency of use.
|===============
|Default Table Name|Where Clause|Frequency
|BATCH_JOB_INSTANCE|JOB_NAME = ? and JOB_KEY = ?|Every time a job is launched
|BATCH_JOB_EXECUTION|JOB_INSTANCE_ID = ?|Every time a job is restarted
|BATCH_STEP_EXECUTION|VERSION = ?|On commit interval, a.k.a. chunk (and at start and end of
|`BATCH_JOB_INSTANCE`|`JOB_NAME = ? and JOB_KEY = ?`|Every time a job is launched
|`BATCH_JOB_EXECUTION`|`JOB_INSTANCE_ID = ?`|Every time a job is restarted
|`BATCH_STEP_EXECUTION`|`VERSION = ?`|On commit interval, a.k.a. chunk (and at start and end of
step)
|BATCH_STEP_EXECUTION|STEP_NAME = ? and JOB_EXECUTION_ID = ?|Before each step execution
|`BATCH_STEP_EXECUTION`|`STEP_NAME = ? and JOB_EXECUTION_ID = ?`|Before each step execution
|===============

427
spring-batch-docs/src/main/asciidoc/spring-batch-architecture.adoc Normal file
View File

@@ -0,0 +1,427 @@
[[springBatchArchitecture]]
=== Spring Batch Architecture
// TODO Make a separate document
Spring Batch is designed with extensibility and a diverse group of end users in mind. The
following image shows the layered architecture that supports the extensibility and ease of
use for end-user developers.
.Spring Batch Layered Architecture
image::{batch-asciidoc}images/spring-batch-layers.png[Figure 1.1: Spring Batch Layered Architecture, scaledwidth="60%"]
This layered architecture highlights three major high-level components: Application,
Core, and Infrastructure. The application contains all batch jobs and custom code written
by developers using Spring Batch. The Batch Core contains the core runtime classes
necessary to launch and control a batch job. It includes implementations for
`JobLauncher`, `Job`, and `Step`. Both Application and Core are built on top of a common
infrastructure. This infrastructure contains common readers and writers and services
(such as the `RetryTemplate`), which are used both by application developers(readers and
writers, such as `ItemReader` and `ItemWriter`), and the core framework itself (retry,
which is its own library).
[[batchArchitectureConsiderations]]
==== General Batch Principles and Guidelines
The following key principles, guidelines, and general considerations should be considered
when building a batch solution.
* Remember that a batch architecture typically affects on-line architecture and vice
versa. Design with both architectures and environments in mind by using common building
blocks when possible.
* Simplify as much as possible and avoid building complex logical structures in single
batch applications.
* Keep the processing and storage of data physically close together (in other words, keep
your data where your processing occurs).
* Minimize system resource use, especially I/O. Perform as many operations as possible in
internal memory.
* Review application I/O (analyze SQL statements) to ensure that unnecessary physical I/O
is avoided. In particular, the following four common flaws need to be looked for:
** Reading data for every transaction when the data could be read once and cached or kept
in the working storage.
** Rereading data for a transaction where the data was read earlier in the same
transaction.
** Causing unnecessary table or index scans.
** Not specifying key values in the `WHERE` clause of an SQL statement.
* Do not do things twice in a batch run. For instance, if you need data summarization for
reporting purposes, you should (if possible) increment stored totals when data is being
initially processed, so your reporting application does not have to reprocess the same
data.
* Allocate enough memory at the beginning of a batch application to avoid time-consuming
reallocation during the process.
* Always assume the worst with regard to data integrity. Insert adequate checks and
record validation to maintain data integrity.
* Implement checksums for internal validation where possible. For example, flat files
should have a trailer record telling the total of records in the file and an aggregate of
the key fields.
* Plan and execute stress tests as early as possible in a production-like environment
with realistic data volumes.
* In large batch systems, backups can be challenging, especially if the system is running
concurrent with online applications on a 24-7 basis. Database backups are typically well taken care
of in online design, but file backups should be considered to be just as important.
If the system depends on flat files, file backup procedures should not only be in place
and documented but be regularly tested as well.
[[batchProcessingStrategy]]
==== Batch Processing Strategies
To help design and implement batch systems, basic batch application building blocks and
patterns should be provided to the designers and programmers in the form of sample
structure charts and code shells. When starting to design a batch job, the business logic
should be decomposed into a series of steps that can be implemented by using the following
standard building blocks:
* __Conversion Applications:__ For each type of file supplied by or generated for an
external system, a conversion application must be created to convert the transaction
records supplied into a standard format required for processing. This type of batch
application can partly or entirely consist of translation utility modules (see Basic
Batch Services).
// TODO Add a link to "Basic Batch Services", once you discover where that content is.
* __Validation Applications:__ A validation application ensures that all input and output
records are correct and consistent. Validation is typically based on file headers and
trailers, checksums and validation algorithms, and record-level cross-checks.
* __Extract Applications:__ An extract application reads a set of records from a database or
input file, selects records based on predefined rules, and writes the records to an
output file.
* __Extract/Update Applications:__ An extract/update applications reads records from a database or
an input file and makes changes to a database or an output file, driven by the data found
in each input record.
* __Processing and Updating Applications:__ A processing and updating application performs processing on
input transactions from an extract or a validation application. The processing usually
involves reading a database to obtain data required for processing, potentially updating
the database and creating records for output processing.
* __Output/Format Applications:__ An output/format applications reads an input file, restructures data
from this record according to a standard format, and produces an output file for printing
or transmission to another program or system.
Additionally, a basic application shell should be provided for business logic that cannot
be built by using the previously mentioned building blocks.
// TODO What is an example of such a system?
In addition to the main building blocks, each application may use one or more standard
utility steps, such as:
* Sort: A program that reads an input file and produces an output file where records
have been re-sequenced according to a sort key field in the records. Sorts are usually
performed by standard system utilities.
* Split: A program that reads a single input file and writes each record to one of
several output files based on a field value. Splits can be tailored or performed by
parameter-driven standard system utilities.
* Merge: A program that reads records from multiple input files and produces one output
file with combined data from the input files. Merges can be tailored or performed by
parameter-driven standard system utilities.
Batch applications can additionally be categorized by their input source:
* Database-driven applications are driven by rows or values retrieved from the database.
* File-driven applications are driven by records or values retrieved from a file.
* Message-driven applications are driven by messages retrieved from a message queue.
The foundation of any batch system is the processing strategy. Factors affecting the
selection of the strategy include: estimated batch system volume, concurrency with
online systems or with other batch systems, available batch windows. (Note that, with
more enterprises wanting to be up and running 24x7, clear batch windows are
disappearing).
Typical processing options for batch are (in increasing order of implementation
complexity):
* Normal processing during a batch window in offline mode.
* Concurrent batch or online processing.
* Parallel processing of many different batch runs or jobs at the same time.
* Partitioning (processing of many instances of the same job at the same time).
* A combination of the preceding options.
Some or all of these options may be supported by a commercial scheduler.
The remainder of this section discusses these processing options in more detail.
Note that, as a rule of thumb, the commit and locking strategy adopted by batch
processes depends on the type of processing performed and that the online locking
strategy should also use the same principles. Therefore, the batch architecture cannot be
simply an afterthought when designing an overall architecture.
The locking strategy can be to use only normal database locks or to implement an
additional custom locking service in the architecture. The locking service would track
database locking (for example, by storing the necessary information in a dedicated
database table) and give or deny permissions to the application programs requesting a database
operation. Retry logic could also be implemented by this architecture to avoid aborting a
batch job in case of a lock situation.
*1. Normal processing in a batch window* For simple batch processes running in a separate
batch window where the data being updated is not required by online users or other batch
processes, concurrency is not an issue and a single commit can be done at the end of the
batch run.
In most cases, a more robust approach is more appropriate. Keep in mind that batch
systems have a tendency to grow as time goes by, both in terms of complexity and the data
volumes they handle. If no locking strategy is in place and the system still relies on a
single commit point, modifying the batch programs can be painful. Therefore, even with
the simplest batch systems, consider the need for commit logic for restart-recovery
options as well as the information concerning the more complex cases described later in
this section.
*2. Concurrent batch or on-line processing* Batch applications processing data that can
be simultaneously updated by online users should not lock any data (either in the
database or in files) that could be required by on-line users for more than a few
seconds. Also, updates should be committed to the database at the end of every few
transactions. Doing so minimizes the portion of data that is unavailable to other processes
and the elapsed time the data is unavailable.
Another option to minimize physical locking is to have logical row-level locking
implemented with either an optimistic locking pattern or a pessimistic locking pattern.
* Optimistic locking assumes a low likelihood of record contention. It typically means
inserting a timestamp column in each database table that is used concurrently by both batch and
online processing. When an application fetches a row for processing, it also fetches the
timestamp. As the application then tries to update the processed row, the update uses the
original timestamp in the `WHERE` clause. If the timestamp matches, the data and the
timestamp are updated. If the timestamp does not match, this indicates that another
application has updated the same row between the fetch and the update attempt. Therefore,
the update cannot be performed.
* Pessimistic locking is any locking strategy that assumes there is a high likelihood of
record contention and, therefore, either a physical or a logical lock needs to be obtained at
retrieval time. One type of pessimistic logical locking uses a dedicated lock-column in
the database table. When an application retrieves the row for update, it sets a flag in
the lock column. With the flag in place, other applications attempting to retrieve the
same row logically fail. When the application that sets the flag updates the row, it also
clears the flag, enabling the row to be retrieved by other applications. Note that
the integrity of data must be maintained also between the initial fetch and the setting
of the flag -- for example, by using database locks (such as `SELECT FOR UPDATE`). Note also that
this method suffers from the same downside as physical locking except that it is somewhat
easier to manage building a time-out mechanism that gets the lock released if the user
goes to lunch while the record is locked.
These patterns are not necessarily suitable for batch processing, but they might be used
for concurrent batch and online processing (such as in cases where the database does not
support row-level locking). As a general rule, optimistic locking is more suitable for
online applications, while pessimistic locking is more suitable for batch applications.
Whenever logical locking is used, the same scheme must be used for all applications
that access the data entities protected by logical locks.
Note that both of these solutions only address locking a single record. Often, we may
need to lock a logically related group of records. With physical locks, you have to
manage these very carefully to avoid potential deadlocks. With logical locks, it
is usually best to build a logical lock manager that understands the logical record
groups you want to protect and that can ensure that locks are coherent and
non-deadlocking. This logical lock manager usually uses its own tables for lock
management, contention reporting, time-out mechanism, and other concerns.
*3. Parallel Processing* Parallel processing lets multiple batch runs or jobs run in
parallel to minimize the total elapsed batch processing time. This is not a problem as
long as the jobs are not sharing the same files, database tables, or index spaces. If they do,
this service should be implemented by using partitioned data. Another option is to build an
architecture module for maintaining interdependencies by using a control table. A control
table should contain a row for each shared resource and whether it is in use by an
application or not. The batch architecture or the application in a parallel job would
then retrieve information from that table to determine whether it can get access to the
resource it needs.
If the data access is not a problem, parallel processing can be implemented through the
use of additional threads to process in parallel. In a mainframe environment, parallel
job classes have traditionally been used, to ensure adequate CPU time for all
the processes. Regardless, the solution has to be robust enough to ensure time slices for
all the running processes.
Other key issues in parallel processing include load balancing and the availability of
general system resources, such as files, database buffer pools, and so on. Also, note that
the control table itself can easily become a critical resource.
*4. Partitioning* Using partitioning lets multiple versions of large batch applications
run concurrently. The purpose of this is to reduce the elapsed time required to
process long batch jobs. Processes that can be successfully partitioned are those where
the input file can be split or the main database tables partitioned to let the
application run against different sets of data.
In addition, processes that are partitioned must be designed to process only their
assigned data set. A partitioning architecture has to be closely tied to the database
design and the database partitioning strategy. Note that database partitioning does not
necessarily mean physical partitioning of the database (although, in most cases, this is
advisable). The following image illustrates the partitioning approach:
.Partitioned Process
image::{batch-asciidoc}images/partitioned.png[Figure 1.2: Partitioned Process, scaledwidth="60%"]
The architecture should be flexible enough to allow dynamic configuration of the number
of partitions. You shoul consider both automatic and user controlled configuration.
Automatic configuration may be based on such parameters as the input file size and the
number of input records.
*4.1 Partitioning Approaches* Selecting a partitioning approach has to be done on a
case-by-case basis. The following list describes some of the possible partitioning
approaches:
_1. Fixed and Even Break-Up of Record Set_
This involves breaking the input record set into an even number of portions (for example,
10, where each portion has exactly 1/10th of the entire record set). Each portion is then
processed by one instance of the batch/extract application.
To use this approach, preprocessing is required to split the record set up. The
result of this split is a lower and upper bound placement number that you can use
as input to the batch/extract application to restrict its processing to only its
portion.
Preprocessing could be a large overhead, as it has to calculate and determine the bounds
of each portion of the record set.
_2. Break up by a Key Column_
This involves breaking up the input record set by a key column, such as a location code,
and assigning data from each key to a batch instance. To achieve this, column
values can be either:
* Assigned to a batch instance by a partitioning table (described later in this
section).
* Assigned to a batch instance by a portion of the value (such as 0000-0999, 1000 - 1999,
and so on).
Under option 1, adding new values means a manual reconfiguration of the batch or extract to
ensure that the new value is added to a particular instance.
Under option 2, this ensures that all values are covered by an instance of the batch
job. However, the number of values processed by one instance is dependent on the
distribution of column values (there may be a large number of locations in the 0000-0999
range and few in the 1000-1999 range). Under this option, the data range should be
designed with partitioning in mind.
Under both options, the optimal even distribution of records to batch instances cannot be
realized. There is no dynamic configuration of the number of batch instances used.
_3. Breakup by Views_
This approach is basically breakup by a key column but on the database level. It involves
breaking up the record set into views. These views are used by each instance of the batch
application during its processing. The breakup is done by grouping the data.
With this option, each instance of a batch application has to be configured to hit a
particular view (instead of the main table). Also, with the addition of new data
values, this new group of data has to be included into a view. There is no dynamic
configuration capability, as a change in the number of instances results in a change to
the views.
_4. Addition of a Processing Indicator_
This involves the addition of a new column to the input table, which acts as an
indicator. As a preprocessing step, all indicators are marked as being non-processed.
During the record fetch stage of the batch application, records are read on the condition
that an individual record is marked as being non-processed, and, once it is read (with lock),
it is marked as being in processing. When that record is completed, the indicator is
updated to either complete or error. You can start many instances of a batch application
without a change, as the additional column ensures that a record is only processed once.
// TODO On completion, what is the record marked as? Same for on error. (I expected a
// sentence or two on the order of "On completion, indicators are marked as having
// a particular status.")
With this option, I/O on the table increases dynamically. In the case of an updating
batch application, this impact is reduced, as a write must occur anyway.
_5. Extract Table to a Flat File_
This approach involves the extraction of the table into a flat file. This file can then be split into
multiple segments and used as input to the batch instances.
With this option, the additional overhead of extracting the table into a file and
splitting it may cancel out the effect of multi-partitioning. Dynamic configuration can
be achieved by changing the file splitting script.
_6. Use of a Hashing Column_
This scheme involves the addition of a hash column (key or index) to the database tables
used to retrieve the driver record. This hash column has an indicator to determine which
instance of the batch application processes this particular row. For example, if there
are three batch instances to be started, an indicator of 'A' marks a row for
processing by instance 1, an indicator of 'B' marks a row for processing by instance 2,
and an indicator of 'C' marks a row for processing by instance 3.
The procedure used to retrieve the records would then have an additional `WHERE` clause
to select all rows marked by a particular indicator. The inserts in this table would
involve the addition of the marker field, which would be defaulted to one of the
instances (such as 'A').
A simple batch application would be used to update the indicators, such as to
redistribute the load between the different instances. When a sufficiently large number
of new rows have been added, this batch can be run (anytime, except in the batch window)
to redistribute the new rows to other instances.
Additional instances of the batch application require only the running of the batch
application (as described in the preceding paragraphs) to redistribute the indicators to
work with a new number of instances.
*4.2 Database and Application Design Principles*
An architecture that supports multi-partitioned applications that run against
partitioned database tables and use the key column approach should include a central
partition repository for storing partition parameters. This provides flexibility and
ensures maintainability. The repository generally consists of a single table, known as
the partition table.
Information stored in the partition table is static and, in general, should be maintained
by the DBA. The table should consist of one row of information for each partition of a
multi-partitioned application. The table should have columns for Program ID Code,
Partition Number (the logical ID of the partition), Low Value of the database key column for this
partition, and High Value of the database key column for this partition.
On program start-up, the program `id` and partition number should be passed to the
application from the architecture (specifically, from the control processing tasklet). If
a key column approach is used, these variables are used to read the partition table
to determine what range of data the application is to process. In addition, the
partition number must be used throughout the processing to:
* Add to the output files or database updates, for the merge process to work
properly.
* Report normal processing to the batch log and any errors to the architecture error
handler.
*4.3 Minimizing Deadlocks*
When applications run in parallel or are partitioned, contention for database resources
and deadlocks may occur. It is critical that the database design team eliminate
potential contention situations as much as possible, as part of the database design.
Also, the developers must ensure that the database index tables are designed with
deadlock prevention and performance in mind.
Deadlocks or hot spots often occur in administration or architecture tables, such as log
tables, control tables, and lock tables. The implications of these should be taken into
account as well. Realistic stress tests are crucial for identifying the possible
bottlenecks in the architecture.
To minimize the impact of conflicts on data, the architecture should provide services
(such as wait-and-retry intervals) when attaching to a database or when encountering a
deadlock. This means a built-in mechanism to react to certain database return codes and,
instead of issuing an immediate error, waiting a predetermined amount of time and
retrying the database operation.
*4.4 Parameter Passing and Validation*
The partition architecture should be relatively transparent to application developers.
The architecture should perform all tasks associated with running the application in a
partitioned mode, including:
* Retrieving partition parameters before application start-up.
* Validating partition parameters before application start-up.
* Passing parameters to the application at start-up.
The validation should include checks to ensure that:
* The application has sufficient partitions to cover the whole data range.
* There are no gaps between partitions.
If the database is partitioned, some additional validation may be necessary to ensure
that a single partition does not span database partitions.
Also, the architecture should take into consideration the consolidation of partitions.
Key questions include:
* Must all the partitions be finished before going into the next job step?
* What happens if one of the partitions aborts?

260
spring-batch-docs/src/main/asciidoc/spring-batch-integration.adoc
View File

@@ -3,16 +3,12 @@
:toclevels: 4
[[springBatchIntegration]]
== Spring Batch Integration
ifndef::onlyonetoggle[]
include::toggle.adoc[]
endif::onlyonetoggle[]
[[spring-batch-integration-introduction]]
=== Spring Batch Integration Introduction
Many users of Spring Batch may encounter requirements that are
outside the scope of Spring Batch but that may be efficiently and
concisely implemented by using Spring Integration. Conversely, Spring
@@ -23,41 +19,36 @@ addresses those requirements.
The line between Spring Batch and Spring Integration is not always
clear, but two pieces of advice can
help: Think about granularity, and apply common patterns. Some
of those common patterns are described in this reference manual
section.
help: Thinking about granularity and applying common patterns. Some
of those common patterns are described in this section.
Adding messaging to a batch process enables automation of
operations and also separation and strategizing of key concerns.
For example, a message might trigger a job to execute, and then the
sending of the message can be exposed in a variety of ways. Alternatively, when
For example, a message might trigger a job to execute, and then
sending the message can be exposed in a variety of ways. Alternatively, when
a job completes or fails, that event might trigger a message to be sent,
and the consumers of those messages might have operational concerns
that have nothing to do with the application itself. Messaging can
also be embedded in a job (for example reading or writing items for
processing via channels). Remote partitioning and remote chunking
also be embedded in a job (for example, reading or writing items for
processing through channels). Remote partitioning and remote chunking
provide methods to distribute workloads over a number of workers.
This section covers the following key concepts:
[role="xmlContent"]
* <<spring-batch-integration.adoc#namespace-support,Namespace Support>>
[[continue-section-list]]
* <<spring-batch-integration.adoc#launching-batch-jobs-through-messages,Launching Batch Jobs through Messages>>
* <<spring-batch-integration.adoc#providing-feedback-with-informational-messages,Providing Feedback with Informational Messages>>
* <<spring-batch-integration.adoc#asynchronous-processors,Asynchronous Processors>>
* <<spring-batch-integration.adoc#externalizing-batch-process-execution,Externalizing
Batch Process Execution>>
* <<namespace-support>>
* <<launching-batch-jobs-through-messages>>
* <<providing-feedback-with-informational-messages>>
* <<asynchronous-processors>>
* <<externalizing-batch-process-execution>>
[[namespace-support]]
[role="xmlContent"]
==== Namespace Support
Since Spring Batch Integration 1.3, dedicated XML Namespace
support was added, with the aim to provide an easier configuration
experience. In order to activate the namespace, add the following
Dedicated XML namespace support was added to Spring Batch Integration in version 1.3,
with the aim to provide an easier configuration
experience. To use the namespace, add the following
namespace declarations to your Spring XML Application Context
file:
@@ -75,8 +66,8 @@ file:
</beans>
----
A fully configured Spring XML Application Context file for Spring
Batch Integration may look like the following:
The following example shows a fully configured Spring XML application context file for Spring
Batch Integration:
[source, xml]
----
@@ -101,7 +92,7 @@ Batch Integration may look like the following:
----
Appending version numbers to the referenced XSD file is also
allowed, but, as a version-less declaration always uses the
allowed. However, because a version-less declaration always uses the
latest schema, we generally do not recommend appending the version
number to the XSD name. Adding a version number
could possibly create issues when updating the Spring Batch
@@ -110,59 +101,49 @@ of the XML schema.
[[launching-batch-jobs-through-messages]]
==== Launching Batch Jobs through Messages
When starting batch jobs by using the core Spring Batch API, you
basically have 2 options:
basically have two options:
* From the command line, with the `CommandLineJobRunner`
* Programmatically, with either `JobOperator.start()` or `JobLauncher.run()`
For example, you may want to use the
`CommandLineJobRunner` when invoking Batch Jobs by
using a shell script. Alternatively, you may use the
`CommandLineJobRunner` when invoking batch jobs by
using a shell script. Alternatively, you can use the
`JobOperator` directly (for example, when using
Spring Batch as part of a web application). However, what about
more complex use cases? Maybe you need to poll a remote (S)FTP
server to retrieve the data for the Batch Job or your application
has to support multiple different data sources simultaneously. For
example, you may receive data files not only from the web, but also from
example, you may receive data files not only from the web but also from
FTP and other sources. Maybe additional transformation of the input files is
needed before invoking Spring Batch.
Therefore, it would be much more powerful to execute the batch job
using Spring Integration and its numerous adapters. For example,
you can use a __File Inbound Channel Adapter__ to
monitor a directory in the file-system and start the Batch Job as
by using Spring Integration and its numerous adapters. For example,
you can use a _File Inbound Channel Adapter_ to
monitor a directory in the file-system and start the batch job as
soon as the input file arrives. Additionally, you can create Spring
Integration flows that use multiple different adapters to easily
ingest data for your batch jobs from multiple sources
simultaneously using only configuration. Implementing all these
simultaneously by using only configuration. Implementing all these
scenarios with Spring Integration is easy, as it allows for
decoupled, event-driven execution of the
`JobLauncher`.
Spring Batch Integration provides the
`JobLaunchingMessageHandler` class that you can
use to launch batch jobs. The input for the
`JobLaunchingMessageHandler` is provided by a
Spring Integration message, which has a payload of type
`JobLaunchRequest`. This class is a wrapper around the `Job`
that needs to be launched and around the `JobParameters`
to be launched and around the `JobParameters` that are
necessary to launch the Batch job.
The following image illustrates the typical Spring Integration
message flow in order to start a Batch job. The
The following image shows the typical Spring Integration
message flow that is needed to start a Batch job. The
link:$$https://www.enterpriseintegrationpatterns.com/toc.html$$[EIP (Enterprise Integration Patterns) website]
provides a full overview of messaging icons and their descriptions.
@@ -171,9 +152,9 @@ image::{batch-asciidoc}images/launch-batch-job.png[Launch Batch Job, scaledwidth
[[transforming-a-file-into-a-joblaunchrequest]]
===== Transforming a File into a JobLaunchRequest
===== Transforming a file into a JobLaunchRequest
The following example transforms a file into a `JobLaunchRequest`:
[source, java]
----
@@ -213,18 +194,15 @@ public class FileMessageToJobRequest {
----
[[the-jobexecution-response]]
===== The `JobExecution` Response
===== The JobExecution Response
When a batch job is being executed, a
`JobExecution` instance is returned. This
instance can be used to determine the status of an execution. If
`JobExecution` instance is returned. You can use this
instance to determine the status of an execution. If
a `JobExecution` is able to be created
successfully, it is always returned, regardless of whether
or not the actual execution is successful.
The exact behavior on how the `JobExecution`
instance is returned depends on the provided
`TaskExecutor`. If a
@@ -235,29 +213,24 @@ instance is returned depends on the provided
`asynchronous`
`TaskExecutor`, the
`JobExecution` instance is returned
immediately. Users can then take the `id` of
immediately. You can then take the `id` of
`JobExecution` instance
(with `JobExecution.getJobId()`) and query the
`JobRepository` for the job's updated status
using the `JobExplorer`. For more
information, please refer to the Spring
Batch reference documentation on
information, see
<<job.adoc#queryingRepository,Querying the Repository>>.
[[spring-batch-integration-configuration]]
===== Spring Batch Integration Configuration
Consider a case where someone needs to create a file `inbound-channel-adapter` to listen
for CSV files in the provided directory, hand them off to a transformer
(`FileMessageToJobRequest`), launch the job through the _Job Launching Gateway_, and then
(`FileMessageToJobRequest`), launch the job through the job launching gateway, and
log the output of the `JobExecution` with the `logging-channel-adapter`.
[role="xmlContent"]
The following example shows how that common case can be configured in XML:
.XML Configuration
[source, xml, role="xmlContent"]
----
@@ -328,7 +301,7 @@ public IntegrationFlow integrationFlow(JobLaunchingGateway jobLaunchingGateway)
Now that we are polling for files and launching jobs, we need to configure our Spring
Batch `ItemReader` (for example) to use the files found at the location defined by the job
parameter called "input.file.name", as shown in the following bean configuration:
parameter called "input.file.name", as the following bean configuration shows:
[role="xmlContent"]
The following XML example shows the necessary bean configuration:
@@ -363,7 +336,7 @@ public ItemReader sampleReader(@Value("#{jobParameters[input.file.name]}") Strin
The main points of interest in the preceding example are injecting the value of
`#{jobParameters['input.file.name']}`
as the Resource property value and setting the `ItemReader` bean
to have __Step scope__. Setting the bean to have Step scope takes advantage of
to have step scope. Setting the bean to have step scope takes advantage of
the late binding support, which allows access to the
`jobParameters` variable.
@@ -377,14 +350,14 @@ The job-launching gateway has the following attributes that you can set to contr
** `EventDrivenConsumer`
** `PollingConsumer`
(The exact implementation depends on whether the component's input channel is a
`SubscribableChannel` or `PollableChannel`.)
`SubscribableChannel` or a `PollableChannel`.)
* `auto-startup`: Boolean flag to indicate that the endpoint should start automatically on
startup. The default is __true__.
startup. The default is `true`.
* `request-channel`: The input `MessageChannel` of this endpoint.
* `reply-channel`: `MessageChannel` to which the resulting `JobExecution` payload is sent.
* `reply-timeout`: Lets you specify how long (in milliseconds) this gateway waits for the reply message
to be sent successfully to the reply channel before throwing
an exception. This attribute only applies when the channel
an exception. This attribute applies only when the channel
might block (for example, when using a bounded queue channel
that is currently full). Also, keep in mind that, when sending to a
`DirectChannel`, the invocation occurs
@@ -393,20 +366,21 @@ operation may be caused by other components further downstream.
The `reply-timeout` attribute maps to the
`sendTimeout` property of the underlying
`MessagingTemplate` instance. If not specified, the attribute
defaults to<emphasis>-1</emphasis>,
defaults to -1,
meaning that, by default, the `Gateway` waits indefinitely.
* `job-launcher`: Optional. Accepts a
custom
`JobLauncher`
bean reference.
If not specified the adapter
If not specified, the adapter
re-uses the instance that is registered under the `id` of
`jobLauncher`. If no default instance
exists, an exception is thrown.
* `order`: Specifies the order of invocation when this endpoint is connected as a subscriber
to a `SubscribableChannel`.
=== Sub-Elements
=== Sub-elements
When this `Gateway` is receiving messages from a
`PollableChannel`, you must either provide
a global default `Poller` or provide a `Poller` sub-element to the
@@ -443,7 +417,7 @@ public JobLaunchingGateway sampleJobLaunchingGateway() {
==== Providing Feedback with Informational Messages
As Spring Batch jobs can run for long times, providing progress
information is often critical. For example, stake-holders may want
information is often critical. For example, stakeholders may want
to be notified if some or all parts of a batch job have failed.
Spring Batch provides support for this information being gathered
through:
@@ -451,11 +425,11 @@ through:
* Active polling
* Event-driven listeners
When starting a Spring Batch job asynchronously (for example, by using the `Job Launching
Gateway`), a `JobExecution` instance is returned. Thus, `JobExecution.getJobId()` can be
used to continuously poll for status updates by retrieving updated instances of the
When starting a Spring Batch job asynchronously (for example, by using the Job Launching
Gateway), a `JobExecution` instance is returned. Thus, you can use `JobExecution.getJobId()`
to continuously poll for status updates by retrieving updated instances of the
`JobExecution` from the `JobRepository` by using the `JobExplorer`. However, this is
considered sub-optimal, and an event-driven approach should be preferred.
considered sub-optimal, and an event-driven approach is preferred.
Therefore, Spring Batch provides listeners, including the three most commonly used
listeners:
@@ -466,15 +440,14 @@ listeners:
In the example shown in the following image, a Spring Batch job has been configured with a
`StepExecutionListener`. Thus, Spring Integration receives and processes any step before
or after events. For example, the received `StepExecution` can be inspected by using a
or after events. For example, you can inspect the received `StepExecution` by using a
`Router`. Based on the results of that inspection, various things can occur (such as
routing a message to a Mail Outbound Channel Adapter), so that an Email notification can
routing a message to a mail outbound channel adapter), so that an email notification can
be sent out based on some condition.
.Handling Informational Messages
image::{batch-asciidoc}images/handling-informational-messages.png[Handling Informational Messages, scaledwidth="60%"]
The following two-part example shows how a listener is configured to send a
message to a `Gateway` for a `StepExecution` events and log its output to a
`logging-channel-adapter`.
@@ -553,21 +526,20 @@ public Job importPaymentsJob() {
.chunk(200)
.listener(notificationExecutionsListener())
...
)
}
----
[[asynchronous-processors]]
==== Asynchronous Processors
Asynchronous Processors help you to scale the processing of items. In the asynchronous
Asynchronous Processors help you scale the processing of items. In the asynchronous
processor use case, an `AsyncItemProcessor` serves as a dispatcher, executing the logic of
the `ItemProcessor` for an item on a new thread. Once the item completes, the `Future` is
passed to the `AsynchItemWriter` to be written.
Therefore, you can increase performance by using asynchronous item processing, basically
letting you implement _fork-join_ scenarios. The `AsyncItemWriter` gathers the results and
letting you implement fork-join scenarios. The `AsyncItemWriter` gathers the results and
writes back the chunk as soon as all the results become available.
[role="xmlContent"]
@@ -638,35 +610,29 @@ actually a reference to your `ItemWriter` bean.
[[externalizing-batch-process-execution]]
==== Externalizing Batch Process Execution
The integration approaches discussed so far suggest use cases
where Spring Integration wraps Spring Batch like an outer-shell.
where Spring Integration wraps Spring Batch like an outer shell.
However, Spring Batch can also use Spring Integration internally.
Using this approach, Spring Batch users can delegate the
By using this approach, Spring Batch users can delegate the
processing of items or even chunks to outside processes. This
allows you to offload complex processing. Spring Batch Integration
lets you offload complex processing. Spring Batch Integration
provides dedicated support for:
* Remote Chunking
* Remote Partitioning
[[remote-chunking]]
===== Remote Chunking
The following image shows one way that remote chunking works when you use Spring Batch
together with Spring Integration:
.Remote Chunking
image::{batch-asciidoc}images/remote-chunking-sbi.png[Remote Chunking, scaledwidth="60%"]
Taking things one step further, one can also externalize the
Taking things one step further, you can also externalize the
chunk processing by using the
`ChunkMessageChannelItemWriter`
(provided by Spring Batch Integration), which sends items out
@@ -675,13 +641,12 @@ process of reading and grouping items, without waiting for the results.
Rather, it is the responsibility of the `ChunkMessageChannelItemWriter`
to gather the results and integrate them back into the Spring Batch process.
With Spring Integration, you have full
control over the concurrency of your processes (for instance, by
using a `QueueChannel` instead of a
`DirectChannel`). Furthermore, by relying on
Spring Integration's rich collection of Channel Adapters (such as
JMS and AMQP), you can distribute chunks of a Batch job to
Spring Integration's rich collection of channel adapters (such as
JMS and AMQP), you can distribute chunks of a batch job to
external systems for processing.
[role="xmlContent"]
@@ -721,7 +686,7 @@ public Job chunkJob() {
The `ItemReader` reference points to the bean you want to use for reading data on the
manager. The `ItemWriter` reference points to a special `ItemWriter` (called
`ChunkMessageChannelItemWriter`), as described above. The processor (if any) is left off
`ChunkMessageChannelItemWriter`), as described earlier. The processor (if any) is left off
the manager configuration, as it is configured on the worker. You should check any
additional component properties, such as throttle limits and so on, when implementing
your use case.
@@ -822,14 +787,14 @@ public ItemWriter<Integer> itemWriter() {
----
The preceding configuration provides us with a number of beans. We
configure our messaging middleware using ActiveMQ and the
inbound/outbound JMS adapters provided by Spring Integration. As
configure our messaging middleware by using ActiveMQ and the
inbound and outbound JMS adapters provided by Spring Integration. As
shown, our `itemWriter` bean, which is
referenced by our job step, uses the
`ChunkMessageChannelItemWriter` for writing chunks over the
`ChunkMessageChannelItemWriter` to write chunks over the
configured middleware.
Now we can move on to the worker configuration, as shown in the following example:
Now we can move on to the worker configuration, as the following example shows:
[role="xmlContent"]
The following example shows the worker configuration in XML:
@@ -945,17 +910,17 @@ configured `SimpleChunkProcessor`, which is where you would provide a reference
`ItemProcessor`) that will run on the worker
when it receives chunks from the manager.
For more information, see the section of the "Scalability" chapter on
For more information, see the section of the "`Scalability`" chapter on
link:$$https://docs.spring.io/spring-batch/docs/current/reference/html/scalability.html#remoteChunking$$[Remote Chunking].
Starting from version 4.1, Spring Batch Integration introduces the `@EnableBatchIntegration`
annotation that can be used to simplify a remote chunking setup. This annotation provides
two beans that can be autowired in the application context:
two beans that you can autowire in your application context:
* `RemoteChunkingManagerStepBuilderFactory`: used to configure the manager step
* `RemoteChunkingWorkerBuilder`: used to configure the remote worker integration flow
* `RemoteChunkingManagerStepBuilderFactory`: Configures the manager step
* `RemoteChunkingWorkerBuilder`: Configures the remote worker integration flow
These APIs take care of configuring a number of components as described in the following diagram:
These APIs take care of configuring a number of components, as the following diagram shows:
.Remote Chunking Configuration
image::{batch-asciidoc}images/remote-chunking-config.png[Remote Chunking Configuration, scaledwidth="80%"]
@@ -963,22 +928,23 @@ image::{batch-asciidoc}images/remote-chunking-config.png[Remote Chunking Configu
On the manager side, the `RemoteChunkingManagerStepBuilderFactory` lets you
configure a manager step by declaring:
* the item reader to read items and send them to workers
* the output channel ("Outgoing requests") to send requests to workers
* the input channel ("Incoming replies") to receive replies from workers
* The item reader to read items and send them to workers
* The output channel ("Outgoing requests") to send requests to workers
* The input channel ("Incoming replies") to receive replies from workers
A `ChunkMessageChannelItemWriter` and the `MessagingTemplate` are not needed to be explicitly configured
(Those can still be explicitly configured if required).
You need not explicitly configure `ChunkMessageChannelItemWriter` and the `MessagingTemplate`.
(You can still explicitly configure them if find a reason to do so).
On the worker side, the `RemoteChunkingWorkerBuilder` allows you to configure a worker to:
On the worker side, the `RemoteChunkingWorkerBuilder` lets you configure a worker to:
* listen to requests sent by the manager on the input channel ("Incoming requests")
* call the `handleChunk` method of `ChunkProcessorChunkHandler` for each request
* Listen to requests sent by the manager on the input channel ("`Incoming requests`")
* Call the `handleChunk` method of `ChunkProcessorChunkHandler` for each request
with the configured `ItemProcessor` and `ItemWriter`
* send replies on the output channel ("Outgoing replies") to the manager
* Send replies on the output channel ("`Outgoing replies`") to the manager
There is no need to explicitly configure the `SimpleChunkProcessor`
and the `ChunkProcessorChunkHandler` (Those can be explicitly configured if required).
You need not explicitly configure the `SimpleChunkProcessor`
and the `ChunkProcessorChunkHandler`. (You can still explicitly configure them if you find
a reason to do so).
The following example shows how to use these APIs:
@@ -1035,32 +1001,28 @@ You can find a complete example of a remote chunking job
link:$$https://github.com/spring-projects/spring-batch/tree/main/spring-batch-samples#remote-chunking-sample$$[here].
[[remote-partitioning]]
===== Remote Partitioning
The following image shows a typical remote partitioning situation:
.Remote Partitioning
image::{batch-asciidoc}images/remote-partitioning.png[Remote Partitioning, scaledwidth="60%"]
Remote Partitioning, on the other hand, is useful when it
is not the processing of items but rather the associated I/O that
causes the bottleneck. Using Remote Partitioning, work can
be farmed out to workers that execute complete Spring Batch
causes the bottleneck. With remote partitioning, you can send work
to workers that execute complete Spring Batch
steps. Thus, each worker has its own `ItemReader`, `ItemProcessor`, and
`ItemWriter`. For this purpose, Spring Batch
Integration provides the `MessageChannelPartitionHandler`.
This implementation of the `PartitionHandler`
interface uses `MessageChannel` instances to
send instructions to remote workers and receive their responses.
This provides a nice abstraction from the transports (such as JMS
and AMQP) being used to communicate with the remote workers.
The section of the "Scalability" chapter that addresses
The section of the "`Scalability`" chapter that addresses
<<scalability.adoc#partitioning,remote partitioning>> provides an overview of the concepts and
components needed to configure remote partitioning and shows an
example of using the default
@@ -1072,9 +1034,7 @@ to multiple JVMs, two additional components are required:
* A `PartitionHandler` implementation that supports the desired
remoting fabric or grid environment
Similar to remote chunking, JMS can be used as the "`remoting fabric`". In that case, use
Similar to remote chunking, you can use JMS as the "`remoting fabric`". In that case, use
a `MessageChannelPartitionHandler` instance as the `PartitionHandler` implementation,
as described earlier.
@@ -1141,7 +1101,6 @@ The following example assumes an existing partitioned job and focuses on the
.Java Configuration
[source, java, role="javaContent"]
----
/*
* Configuration of the manager side
*/
@@ -1284,13 +1243,13 @@ Java:
You can find a complete example of a remote partitioning job
link:$$https://github.com/spring-projects/spring-batch/tree/main/spring-batch-samples#remote-partitioning-sample$$[here].
The `@EnableBatchIntegration` annotation that can be used to simplify a remote
partitioning setup. This annotation provides two beans useful for remote partitioning:
You can use the `@EnableBatchIntegration` annotation to simplify a remote
partitioning setup. This annotation provides two beans that are useful for remote partitioning:
* `RemotePartitioningManagerStepBuilderFactory`: used to configure the manager step
* `RemotePartitioningWorkerStepBuilderFactory`: used to configure the worker step
* `RemotePartitioningManagerStepBuilderFactory`: Configures the manager step
* `RemotePartitioningWorkerStepBuilderFactory`: Configures the worker step
These APIs take care of configuring a number of components as described in the following diagram:
These APIs take care of configuring a number of components, as the following diagrams show:
.Remote Partitioning Configuration (with job repository polling)
image::{batch-asciidoc}images/remote-partitioning-polling-config.png[Remote Partitioning Configuration (with job repository polling), scaledwidth="80%"]
@@ -1298,24 +1257,25 @@ image::{batch-asciidoc}images/remote-partitioning-polling-config.png[Remote Part
.Remote Partitioning Configuration (with replies aggregation)
image::{batch-asciidoc}images/remote-partitioning-aggregation-config.png[Remote Partitioning Configuration (with replies aggregation), scaledwidth="80%"]
On the manager side, the `RemotePartitioningManagerStepBuilderFactory` allows you to
On the manager side, the `RemotePartitioningManagerStepBuilderFactory` lets you
configure a manager step by declaring:
* the `Partitioner` used to partition data
* the output channel ("Outgoing requests") to send requests to workers
* the input channel ("Incoming replies") to receive replies from workers (when configuring replies aggregation)
* the poll interval and timeout parameters (when configuring job repository polling)
* The `Partitioner` used to partition data
* The output channel ("`Outgoing requests`") on which to send requests to workers
* The input channel ("`Incoming replies`") on which to receive replies from workers (when configuring replies aggregation)
* The poll interval and timeout parameters (when configuring job repository polling)
The `MessageChannelPartitionHandler` and the `MessagingTemplate` are not needed to be explicitly configured
(Those can still be explicitly configured if required).
You need not explicitly configure The `MessageChannelPartitionHandler` and the `MessagingTemplate`.
(You can still explicitly configured them if you find a reason to do so).
On the worker side, the `RemotePartitioningWorkerStepBuilderFactory` allows you to configure a worker to:
On the worker side, the `RemotePartitioningWorkerStepBuilderFactory` lets you configure a worker to:
* listen to requests sent by the manager on the input channel ("Incoming requests")
* call the `handle` method of `StepExecutionRequestHandler` for each request
* send replies on the output channel ("Outgoing replies") to the manager
* Listen to requests sent by the manager on the input channel ("`Incoming requests`")
* Call the `handle` method of `StepExecutionRequestHandler` for each request
* Send replies on the output channel ("`Outgoing replies`") to the manager
There is no need to explicitly configure the `StepExecutionRequestHandler` (which can be explicitly configured if required).
You need not explicitly configure the `StepExecutionRequestHandler`.
(You can explicitly configure it if you find a reason to do so).
The following example shows how to use these APIs:

503
spring-batch-docs/src/main/asciidoc/spring-batch-intro.adoc
View File

@@ -24,7 +24,7 @@ endif::[]
== Spring Batch Introduction
Many applications within the enterprise domain require bulk processing to perform
business operations in mission critical environments. These business operations include:
business operations in mission-critical environments. These business operations include:
* Automated, complex processing of large volumes of information that is most efficiently
processed without user interaction. These operations typically include time-based events
@@ -37,27 +37,26 @@ the system of record. Batch processing is used to process billions of transactio
day for enterprises.
Spring Batch is a lightweight, comprehensive batch framework designed to enable the
development of robust batch applications vital for the daily operations of enterprise
development of robust batch applications that are vital for the daily operations of enterprise
systems. Spring Batch builds upon the characteristics of the Spring Framework that people
have come to expect (productivity, POJO-based development approach, and general ease of
use), while making it easy for developers to access and leverage more advance enterprise
use), while making it easy for developers to access and use more advanced enterprise
services when necessary. Spring Batch is not a scheduling framework. There are many good
enterprise schedulers (such as Quartz, Tivoli, Control-M, etc.) available in both the
commercial and open source spaces. It is intended to work in conjunction with a
scheduler, not replace a scheduler.
enterprise schedulers (such as Quartz, Tivoli, Control-M, and others) available in both the
commercial and open source spaces. Spring Batch is intended to work in conjunction with a
scheduler rather than replace a scheduler.
Spring Batch provides reusable functions that are essential in processing large volumes
of records, including logging/tracing, transaction management, job processing statistics,
of records, including logging and tracing, transaction management, job processing statistics,
job restart, skip, and resource management. It also provides more advanced technical
services and features that enable extremely high-volume and high performance batch jobs
through optimization and partitioning techniques. Spring Batch can be used in both simple
use cases (such as reading a file into a database or running a stored procedure) as well
as complex, high volume use cases (such as moving high volumes of data between databases,
transforming it, and so on). High-volume batch jobs can leverage the framework in a
through optimization and partitioning techniques. You can use Spring Batch in both simple
use cases (such as reading a file into a database or running a stored procedure) and
complex, high volume use cases (such as moving high volumes of data between databases,
transforming it, and so on). High-volume batch jobs can use the framework in a
highly scalable manner to process significant volumes of information.
[[springBatchBackground]]
=== Background
While open source software projects and associated communities have focused greater
@@ -68,13 +67,13 @@ environments. The lack of a standard, reusable batch architecture has resulted i
proliferation of many one-off, in-house solutions developed within client enterprise IT
functions.
SpringSource (now Pivotal) and Accenture collaborated to change this. Accenture's
SpringSource (now VMware) and Accenture collaborated to change this. Accenture's
hands-on industry and technical experience in implementing batch architectures,
SpringSource's depth of technical experience, and Spring's proven programming model
together made a natural and powerful partnership to create high-quality, market-relevant
software aimed at filling an important gap in enterprise Java. Both companies worked with
a number of clients who were solving similar problems by developing Spring-based batch
architecture solutions. This provided some useful additional detail and real-life
architecture solutions. This input provided some useful additional detail and real-life
constraints that helped to ensure the solution can be applied to the real-world problems
posed by clients.
@@ -82,16 +81,15 @@ Accenture contributed previously proprietary batch processing architecture frame
the Spring Batch project, along with committer resources to drive support, enhancements,
and the existing feature set. Accenture's contribution was based upon decades of
experience in building batch architectures with the last several generations of
platforms: COBOL/Mainframe, C++/Unix, and now Java/anywhere.
platforms: COBOL on mainframes, C++ on Unix, and, now, Java anywhere.
The collaborative effort between Accenture and SpringSource aimed to promote the
standardization of software processing approaches, frameworks, and tools that can be
consistently leveraged by enterprise users when creating batch applications. Companies
standardization of software processing approaches, frameworks, and tools
enterprise users can consistently use when creating batch applications. Companies
and government agencies desiring to deliver standard, proven solutions to their
enterprise IT environments can benefit from Spring Batch.
[[springBatchUsageScenarios]]
=== Usage Scenarios
A typical batch program generally:
@@ -105,463 +103,34 @@ similar transactions as a set, typically in an offline environment without any u
interaction. Batch jobs are part of most IT projects, and Spring Batch is the only open
source framework that provides a robust, enterprise-scale solution.
Business Scenarios
==== Business Scenarios
* Commit batch process periodically
* Concurrent batch processing: parallel processing of a job
* Staged, enterprise message-driven processing
* Massively parallel batch processing
* Manual or scheduled restart after failure
* Sequential processing of dependent steps (with extensions to workflow-driven batches)
* Partial processing: skip records (for example, on rollback)
Spring Batch supports the following business scenarios:
* Commit batch process periodically.
* Concurrent batch processing: parallel processing of a job.
* Staged, enterprise message-driven processing.
* Massively parallel batch processing.
* Manual or scheduled restart after failure.
* Sequential processing of dependent steps (with extensions to workflow-driven batches).
* Partial processing: skip records (for example, on rollback).
* Whole-batch transaction, for cases with a small batch size or existing stored
procedures/scripts
procedures or scripts.
Technical Objectives
==== Technical Objectives
* Batch developers use the Spring programming model: Concentrate on business logic and
let the framework take care of infrastructure.
* Clear separation of concerns between the infrastructure, the batch execution
Spring Batch has the following technical objectives:
* Let batch developers use the Spring programming model: Concentrate on business logic and
let the framework take care of the infrastructure.
* Provide clear separation of concerns between the infrastructure, the batch execution
environment, and the batch application.
* Provide common, core execution services as interfaces that all projects can implement.
* Provide simple and default implementations of the core execution interfaces that can be
used 'out of the box'.
* Easy to configure, customize, and extend services, by leveraging the spring framework
used "`out of the box`".
* Make it easy to configure, customize, and extend services, by using the Spring framework
in all layers.
* All existing core services should be easy to replace or extend, without any impact to
the infrastructure layer.
* Provide a simple deployment model, with the architecture JARs completely separate from
the application, built using Maven.
[[springBatchArchitecture]]
=== Spring Batch Architecture
// TODO Make a separate document
Spring Batch is designed with extensibility and a diverse group of end users in mind. The
figure below shows the layered architecture that supports the extensibility and ease of
use for end-user developers.
.Spring Batch Layered Architecture
image::{batch-asciidoc}images/spring-batch-layers.png[Figure 1.1: Spring Batch Layered Architecture, scaledwidth="60%"]
This layered architecture highlights three major high-level components: Application,
Core, and Infrastructure. The application contains all batch jobs and custom code written
by developers using Spring Batch. The Batch Core contains the core runtime classes
necessary to launch and control a batch job. It includes implementations for
`JobLauncher`, `Job`, and `Step`. Both Application and Core are built on top of a common
infrastructure. This infrastructure contains common readers and writers and services
(such as the `RetryTemplate`), which are used both by application developers(readers and
writers, such as `ItemReader` and `ItemWriter`) and the core framework itself (retry,
which is its own library).
[[batchArchitectureConsiderations]]
=== General Batch Principles and Guidelines
The following key principles, guidelines, and general considerations should be considered
when building a batch solution.
* Remember that a batch architecture typically affects on-line architecture and vice
versa. Design with both architectures and environments in mind using common building
blocks when possible.
* Simplify as much as possible and avoid building complex logical structures in single
batch applications.
* Keep the processing and storage of data physically close together (in other words, keep
your data where your processing occurs).
* Minimize system resource use, especially I/O. Perform as many operations as possible in
internal memory.
* Review application I/O (analyze SQL statements) to ensure that unnecessary physical I/O
is avoided. In particular, the following four common flaws need to be looked for:
** Reading data for every transaction when the data could be read once and cached or kept
in the working storage.
** Rereading data for a transaction where the data was read earlier in the same
transaction.
** Causing unnecessary table or index scans.
** Not specifying key values in the WHERE clause of an SQL statement.
* Do not do things twice in a batch run. For instance, if you need data summarization for
reporting purposes, you should (if possible) increment stored totals when data is being
initially processed, so your reporting application does not have to reprocess the same
data.
* Allocate enough memory at the beginning of a batch application to avoid time-consuming
reallocation during the process.
* Always assume the worst with regard to data integrity. Insert adequate checks and
record validation to maintain data integrity.
* Implement checksums for internal validation where possible. For example, flat files
should have a trailer record telling the total of records in the file and an aggregate of
the key fields.
* Plan and execute stress tests as early as possible in a production-like environment
with realistic data volumes.
* In large batch systems, backups can be challenging, especially if the system is running
concurrent with on-line on a 24-7 basis. Database backups are typically well taken care
of in the on-line design, but file backups should be considered to be just as important.
If the system depends on flat files, file backup procedures should not only be in place
and documented but be regularly tested as well.
[[batchProcessingStrategy]]
=== Batch Processing Strategies
To help design and implement batch systems, basic batch application building blocks and
patterns should be provided to the designers and programmers in the form of sample
structure charts and code shells. When starting to design a batch job, the business logic
should be decomposed into a series of steps that can be implemented using the following
standard building blocks:
* __Conversion Applications:__ For each type of file supplied by or generated to an
external system, a conversion application must be created to convert the transaction
records supplied into a standard format required for processing. This type of batch
application can partly or entirely consist of translation utility modules (see Basic
Batch Services).
// TODO Add a link to "Basic Batch Services", once you discover where that content is.
* __Validation Applications:__ Validation applications ensure that all input/output
records are correct and consistent. Validation is typically based on file headers and
trailers, checksums and validation algorithms, and record level cross-checks.
* __Extract Applications:__ An application that reads a set of records from a database or
input file, selects records based on predefined rules, and writes the records to an
output file.
* __Extract/Update Applications:__ An application that reads records from a database or
an input file and makes changes to a database or an output file driven by the data found
in each input record.
* __Processing and Updating Applications:__ An application that performs processing on
input transactions from an extract or a validation application. The processing usually
involves reading a database to obtain data required for processing, potentially updating
the database and creating records for output processing.
* __Output/Format Applications:__ Applications that read an input file, restructure data
from this record according to a standard format, and produce an output file for printing
or transmission to another program or system.
Additionally, a basic application shell should be provided for business logic that cannot
be built using the previously mentioned building blocks.
// TODO What is an example of such a system?
In addition to the main building blocks, each application may use one or more of standard
utility steps, such as:
* Sort: A program that reads an input file and produces an output file where records
have been re-sequenced according to a sort key field in the records. Sorts are usually
performed by standard system utilities.
* Split: A program that reads a single input file and writes each record to one of
several output files based on a field value. Splits can be tailored or performed by
parameter-driven standard system utilities.
* Merge: A program that reads records from multiple input files and produces one output
file with combined data from the input files. Merges can be tailored or performed by
parameter-driven standard system utilities.
Batch applications can additionally be categorized by their input source:
* Database-driven applications are driven by rows or values retrieved from the database.
* File-driven applications are driven by records or values retrieved from a file.
* Message-driven applications are driven by messages retrieved from a message queue.
The foundation of any batch system is the processing strategy. Factors affecting the
selection of the strategy include: estimated batch system volume, concurrency with
on-line systems or with other batch systems, available batch windows. (Note that, with
more enterprises wanting to be up and running 24x7, clear batch windows are
disappearing).
Typical processing options for batch are (in increasing order of implementation
complexity):
* Normal processing during a batch window in off-line mode.
* Concurrent batch or on-line processing.
* Parallel processing of many different batch runs or jobs at the same time.
* Partitioning (processing of many instances of the same job at the same time).
* A combination of the preceding options.
Some or all of these options may be supported by a commercial scheduler.
The following section discusses these processing options in more detail. It is important
to notice that, as a rule of thumb, the commit and locking strategy adopted by batch
processes depends on the type of processing performed and that the on-line locking
strategy should also use the same principles. Therefore, the batch architecture cannot be
simply an afterthought when designing an overall architecture.
The locking strategy can be to use only normal database locks or to implement an
additional custom locking service in the architecture. The locking service would track
database locking (for example, by storing the necessary information in a dedicated
db-table) and give or deny permissions to the application programs requesting a db
operation. Retry logic could also be implemented by this architecture to avoid aborting a
batch job in case of a lock situation.
*1. Normal processing in a batch window* For simple batch processes running in a separate
batch window where the data being updated is not required by on-line users or other batch
processes, concurrency is not an issue and a single commit can be done at the end of the
batch run.
In most cases, a more robust approach is more appropriate. Keep in mind that batch
systems have a tendency to grow as time goes by, both in terms of complexity and the data
volumes they handle. If no locking strategy is in place and the system still relies on a
single commit point, modifying the batch programs can be painful. Therefore, even with
the simplest batch systems, consider the need for commit logic for restart-recovery
options as well as the information concerning the more complex cases described later in
this section.
*2. Concurrent batch or on-line processing* Batch applications processing data that can
be simultaneously updated by on-line users should not lock any data (either in the
database or in files) which could be required by on-line users for more than a few
seconds. Also, updates should be committed to the database at the end of every few
transactions. This minimizes the portion of data that is unavailable to other processes
and the elapsed time the data is unavailable.
Another option to minimize physical locking is to have logical row-level locking
implemented with either an Optimistic Locking Pattern or a Pessimistic Locking Pattern.
* Optimistic locking assumes a low likelihood of record contention. It typically means
inserting a timestamp column in each database table used concurrently by both batch and
on-line processing. When an application fetches a row for processing, it also fetches the
timestamp. As the application then tries to update the processed row, the update uses the
original timestamp in the WHERE clause. If the timestamp matches, the data and the
timestamp are updated. If the timestamp does not match, this indicates that another
application has updated the same row between the fetch and the update attempt. Therefore,
the update cannot be performed.
* Pessimistic locking is any locking strategy that assumes there is a high likelihood of
record contention and therefore either a physical or logical lock needs to be obtained at
retrieval time. One type of pessimistic logical locking uses a dedicated lock-column in
the database table. When an application retrieves the row for update, it sets a flag in
the lock column. With the flag in place, other applications attempting to retrieve the
same row logically fail. When the application that sets the flag updates the row, it also
clears the flag, enabling the row to be retrieved by other applications. Please note that
the integrity of data must be maintained also between the initial fetch and the setting
of the flag, for example by using db locks (such as `SELECT FOR UPDATE`). Note also that
this method suffers from the same downside as physical locking except that it is somewhat
easier to manage building a time-out mechanism that gets the lock released if the user
goes to lunch while the record is locked.
These patterns are not necessarily suitable for batch processing, but they might be used
for concurrent batch and on-line processing (such as in cases where the database does not
support row-level locking). As a general rule, optimistic locking is more suitable for
on-line applications, while pessimistic locking is more suitable for batch applications.
Whenever logical locking is used, the same scheme must be used for all applications
accessing data entities protected by logical locks.
Note that both of these solutions only address locking a single record. Often, we may
need to lock a logically related group of records. With physical locks, you have to
manage these very carefully in order to avoid potential deadlocks. With logical locks, it
is usually best to build a logical lock manager that understands the logical record
groups you want to protect and that can ensure that locks are coherent and
non-deadlocking. This logical lock manager usually uses its own tables for lock
management, contention reporting, time-out mechanism, and other concerns.
*3. Parallel Processing* Parallel processing allows multiple batch runs or jobs to run in
parallel to minimize the total elapsed batch processing time. This is not a problem as
long as the jobs are not sharing the same files, db-tables, or index spaces. If they do,
this service should be implemented using partitioned data. Another option is to build an
architecture module for maintaining interdependencies by using a control table. A control
table should contain a row for each shared resource and whether it is in use by an
application or not. The batch architecture or the application in a parallel job would
then retrieve information from that table to determine if it can get access to the
resource it needs or not.
If the data access is not a problem, parallel processing can be implemented through the
use of additional threads to process in parallel. In the mainframe environment, parallel
job classes have traditionally been used, in order to ensure adequate CPU time for all
the processes. Regardless, the solution has to be robust enough to ensure time slices for
all the running processes.
Other key issues in parallel processing include load balancing and the availability of
general system resources such as files, database buffer pools, and so on. Also note that
the control table itself can easily become a critical resource.
*4. Partitioning* Using partitioning allows multiple versions of large batch applications
to run concurrently. The purpose of this is to reduce the elapsed time required to
process long batch jobs. Processes that can be successfully partitioned are those where
the input file can be split and/or the main database tables partitioned to allow the
application to run against different sets of data.
In addition, processes which are partitioned must be designed to only process their
assigned data set. A partitioning architecture has to be closely tied to the database
design and the database partitioning strategy. Note that database partitioning does not
necessarily mean physical partitioning of the database, although in most cases this is
advisable. The following picture illustrates the partitioning approach:
.Partitioned Process
image::{batch-asciidoc}images/partitioned.png[Figure 1.2: Partitioned Process, scaledwidth="60%"]
The architecture should be flexible enough to allow dynamic configuration of the number
of partitions. Both automatic and user controlled configuration should be considered.
Automatic configuration may be based on parameters such as the input file size and the
number of input records.
*4.1 Partitioning Approaches* Selecting a partitioning approach has to be done on a
case-by-case basis. The following list describes some of the possible partitioning
approaches:
_1. Fixed and Even Break-Up of Record Set_
This involves breaking the input record set into an even number of portions (for example,
10, where each portion has exactly 1/10th of the entire record set). Each portion is then
processed by one instance of the batch/extract application.
In order to use this approach, preprocessing is required to split the record set up. The
result of this split will be a lower and upper bound placement number which can be used
as input to the batch/extract application in order to restrict its processing to only its
portion.
Preprocessing could be a large overhead, as it has to calculate and determine the bounds
of each portion of the record set.
_2. Break up by a Key Column_
This involves breaking up the input record set by a key column, such as a location code,
and assigning data from each key to a batch instance. In order to achieve this, column
values can be either:
* Assigned to a batch instance by a partitioning table (described later in this
section).
* Assigned to a batch instance by a portion of the value (such as 0000-0999, 1000 - 1999,
and so on).
Under option 1, adding new values means a manual reconfiguration of the batch/extract to
ensure that the new value is added to a particular instance.
Under option 2, this ensures that all values are covered via an instance of the batch
job. However, the number of values processed by one instance is dependent on the
distribution of column values (there may be a large number of locations in the 0000-0999
range, and few in the 1000-1999 range). Under this option, the data range should be
designed with partitioning in mind.
Under both options, the optimal even distribution of records to batch instances cannot be
realized. There is no dynamic configuration of the number of batch instances used.
_3. Breakup by Views_
This approach is basically breakup by a key column but on the database level. It involves
breaking up the record set into views. These views are used by each instance of the batch
application during its processing. The breakup is done by grouping the data.
With this option, each instance of a batch application has to be configured to hit a
particular view (instead of the main table). Also, with the addition of new data
values, this new group of data has to be included into a view. There is no dynamic
configuration capability, as a change in the number of instances results in a change to
the views.
_4. Addition of a Processing Indicator_
This involves the addition of a new column to the input table, which acts as an
indicator. As a preprocessing step, all indicators are marked as being non-processed.
During the record fetch stage of the batch application, records are read on the condition
that that record is marked as being non-processed, and once they are read (with lock),
they are marked as being in processing. When that record is completed, the indicator is
updated to either complete or error. Many instances of a batch application can be started
without a change, as the additional column ensures that a record is only processed once.
// TODO On completion, what is the record marked as? Same for on error. (I expected a
// sentence or two on the order of "On completion, indicators are marked as being
// complete.")
With this option, I/O on the table increases dynamically. In the case of an updating
batch application, this impact is reduced, as a write must occur anyway.
_5. Extract Table to a Flat File_
This involves the extraction of the table into a file. This file can then be split into
multiple segments and used as input to the batch instances.
With this option, the additional overhead of extracting the table into a file and
splitting it may cancel out the effect of multi-partitioning. Dynamic configuration can
be achieved by changing the file splitting script.
_6. Use of a Hashing Column_
This scheme involves the addition of a hash column (key/index) to the database tables
used to retrieve the driver record. This hash column has an indicator to determine which
instance of the batch application processes this particular row. For example, if there
are three batch instances to be started, then an indicator of 'A' marks a row for
processing by instance 1, an indicator of 'B' marks a row for processing by instance 2,
and an indicator of 'C' marks a row for processing by instance 3.
The procedure used to retrieve the records would then have an additional `WHERE` clause
to select all rows marked by a particular indicator. The inserts in this table would
involve the addition of the marker field, which would be defaulted to one of the
instances (such as 'A').
A simple batch application would be used to update the indicators, such as to
redistribute the load between the different instances. When a sufficiently large number
of new rows have been added, this batch can be run (anytime, except in the batch window)
to redistribute the new rows to other instances.
// TODO Why not in the batch window?
Additional instances of the batch application only require the running of the batch
application as described in the preceding paragraphs to redistribute the indicators to
work with a new number of instances.
*4.2 Database and Application Design Principles*
An architecture that supports multi-partitioned applications which run against
partitioned database tables using the key column approach should include a central
partition repository for storing partition parameters. This provides flexibility and
ensures maintainability. The repository generally consists of a single table, known as
the partition table.
Information stored in the partition table is static and, in general, should be maintained
by the DBA. The table should consist of one row of information for each partition of a
multi-partitioned application. The table should have columns for Program ID Code,
Partition Number (logical ID of the partition), Low Value of the db key column for this
partition, and High Value of the db key column for this partition.
On program start-up, the program `id` and partition number should be passed to the
application from the architecture (specifically, from the Control Processing Tasklet). If
a key column approach is used, these variables are used to read the partition table in
order to determine what range of data the application is to process. In addition the
partition number must be used throughout the processing to:
* Add to the output files/database updates in order for the merge process to work
properly.
* Report normal processing to the batch log and any errors to the architecture error
handler.
*4.3 Minimizing Deadlocks*
When applications run in parallel or are partitioned, contention in database resources
and deadlocks may occur. It is critical that the database design team eliminates
potential contention situations as much as possible as part of the database design.
Also, the developers must ensure that the database index tables are designed with
deadlock prevention and performance in mind.
Deadlocks or hot spots often occur in administration or architecture tables, such as log
tables, control tables, and lock tables. The implications of these should be taken into
account as well. A realistic stress test is crucial for identifying the possible
bottlenecks in the architecture.
To minimize the impact of conflicts on data, the architecture should provide services
such as wait-and-retry intervals when attaching to a database or when encountering a
deadlock. This means a built-in mechanism to react to certain database return codes and,
instead of issuing an immediate error, waiting a predetermined amount of time and
retrying the database operation.
*4.4 Parameter Passing and Validation*
The partition architecture should be relatively transparent to application developers.
The architecture should perform all tasks associated with running the application in a
partitioned mode, including:
* Retrieving partition parameters before application start-up.
* Validating partition parameters before application start-up.
* Passing parameters to the application at start-up.
The validation should include checks to ensure that:
* The application has sufficient partitions to cover the whole data range.
* There are no gaps between partitions.
If the database is partitioned, some additional validation may be necessary to ensure
that a single partition does not span database partitions.
Also, the architecture should take into consideration the consolidation of partitions.
Key questions include:
* Must all the partitions be finished before going into the next job step?
* What happens if one of the partitions aborts?
the application, built by using Maven.

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@@ -3,7 +3,6 @@
:toclevels: 4
[[testing]]
== Unit Testing
ifndef::onlyonetoggle[]
@@ -13,36 +12,31 @@ endif::onlyonetoggle[]
As with other application styles, it is extremely important to unit test any code written
as part of a batch job. The Spring core documentation covers how to unit and integration
test with Spring in great detail, so it is not be repeated here. It is important, however,
to think about how to 'end to end' test a batch job, which is what this chapter covers.
The spring-batch-test project includes classes that facilitate this end-to-end test
to think about how to "`end to end`" test a batch job, which is what this chapter covers.
The `spring-batch-test` project includes classes that facilitate this end-to-end test
approach.
[[creatingUnitTestClass]]
=== Creating a Unit Test Class
In order for the unit test to run a batch job, the framework must load the job's
ApplicationContext. Two annotations are used to trigger this behavior:
For the unit test to run a batch job, the framework must load the job's
`ApplicationContext`. Two annotations are used to trigger this behavior:
* `@RunWith(SpringJUnit4ClassRunner.class)`: Indicates that the class should use Spring's
* `@RunWith(SpringJUnit4ClassRunner.class)` indicates that the class should use Spring's
JUnit facilities
* `@ContextConfiguration(...)`: Indicates which resources to configure the
* `@ContextConfiguration(...)` indicates which resources to configure the
`ApplicationContext` with.
Starting from v4.1, it is also possible to inject Spring Batch test utilities
like the `JobLauncherTestUtils` and `JobRepositoryTestUtils` in the test context
using the `@SpringBatchTest` annotation.
(such as the `JobLauncherTestUtils` and `JobRepositoryTestUtils`) in the test context
by using the `@SpringBatchTest` annotation.
[NOTE]
====
It should be noted that `JobLauncherTestUtils` requires a `Job` bean and that
NOTE: Note that `JobLauncherTestUtils` requires a `Job` bean and that
`JobRepositoryTestUtils` requires a `DataSource` bean. Since `@SpringBatchTest`
registers a `JobLauncherTestUtils` and a `JobRepositoryTestUtils` in the test
context, it is expected that the test context contains a single autowire candidate
for a `Job` and a `DataSource` (either a single bean definition or one that is
annotated with `org.springframework.context.annotation.Primary`).
====
[role="javaContent"]
The following Java example shows the annotations in use:
@@ -70,23 +64,21 @@ public class SkipSampleFunctionalTests { ... }
----
[[endToEndTesting]]
=== End-To-End Testing of Batch Jobs
'End To End' testing can be defined as testing the complete run of a batch job from
"`End To end`" testing can be defined as testing the complete run of a batch job from
beginning to end. This allows for a test that sets up a test condition, executes the job,
and verifies the end result.
Consider an example of a batch job that reads from the database and writes to a flat file.
The test method begins by setting up the database with test data. It clears the CUSTOMER
The test method begins by setting up the database with test data. It clears the `CUSTOMER`
table and then inserts 10 new records. The test then launches the `Job` by using the
`launchJob()` method. The `launchJob()` method is provided by the `JobLauncherTestUtils`
class. The `JobLauncherTestUtils` class also provides the `launchJob(JobParameters)`
method, which allows the test to give particular parameters. The `launchJob()` method
method, which lets the test give particular parameters. The `launchJob()` method
returns the `JobExecution` object, which is useful for asserting particular information
about the `Job` run. In the following case, the test verifies that the `Job` ended with
status "COMPLETED".
a status of `COMPLETED`.
[role="xmlContent"]
The following listing shows the example in XML:
@@ -164,8 +156,6 @@ public class SkipSampleFunctionalTests {
----
[[testingIndividualSteps]]
=== Testing Individual Steps
For complex batch jobs, test cases in the end-to-end testing approach may become
@@ -192,7 +182,7 @@ execution. That is the goal of two components in Spring Batch:
`StepScopeTestExecutionListener` and `StepScopeTestUtils`.
The listener is declared at the class level, and its job is to create a step execution
context for each test method, as shown in the following example:
context for each test method, as the following example shows:
[source, java]
----
@@ -228,7 +218,7 @@ The other is the Spring Batch `StepScopeTestExecutionListener`. It works by look
factory method in the test case for a `StepExecution`, using that as the context for the
test method, as if that execution were active in a `Step` at runtime. The factory method
is detected by its signature (it must return a `StepExecution`). If a factory method is
not provided, then a default `StepExecution` is created.
not provided, a default `StepExecution` is created.
Starting from v4.1, the `StepScopeTestExecutionListener` and
`JobScopeTestExecutionListener` are imported as test execution listeners
@@ -284,7 +274,6 @@ int count = StepScopeTestUtils.doInStepScope(stepExecution,
----
[[validatingOutputFiles]]
=== Validating Output Files
When a batch job writes to the database, it is easy to query the database to verify that
@@ -293,7 +282,7 @@ important that the output be verified. Spring Batch provides a class called `Ass
to facilitate the verification of output files. The method called `assertFileEquals` takes
two `File` objects (or two `Resource` objects) and asserts, line by line, that the two
files have the same content. Therefore, it is possible to create a file with the expected
output and to compare it to the actual result, as shown in the following example:
output and to compare it to the actual result, as the following example shows:
[source, java]
----
@@ -305,13 +294,11 @@ AssertFile.assertFileEquals(new FileSystemResource(EXPECTED_FILE),
----
[[mockingDomainObjects]]
=== Mocking Domain Objects
Another common issue encountered while writing unit and integration tests for Spring Batch
components is how to mock domain objects. A good example is a `StepExecutionListener`, as
illustrated in the following code snippet:
the following code snippet shows:
[source, java]
----
@@ -326,10 +313,10 @@ public class NoWorkFoundStepExecutionListener extends StepExecutionListenerSuppo
}
----
The preceding listener example is provided by the framework and checks a `StepExecution`
The framework provides the preceding listener example and checks a `StepExecution`
for an empty read count, thus signifying that no work was done. While this example is
fairly simple, it serves to illustrate the types of problems that may be encountered when
attempting to unit test classes that implement interfaces requiring Spring Batch domain
fairly simple, it serves to illustrate the types of problems that you may encounter when
you try to unit test classes that implement interfaces requiring Spring Batch domain
objects. Consider the following unit test for the listener's in the preceding example:
[source, java]
@@ -356,7 +343,7 @@ Because the Spring Batch domain model follows good object-oriented principles, t
model, it does make creating stub objects for unit testing verbose. To address this issue,
the Spring Batch test module includes a factory for creating domain objects:
`MetaDataInstanceFactory`. Given this factory, the unit test can be updated to be more
concise, as shown in the following example:
concise, as the following example shows:
[source, java]
----
@@ -374,6 +361,6 @@ public void testAfterStep() {
}
----
The preceding method for creating a simple `StepExecution` is just one convenience method
available within the factory. A full method listing can be found in its
The preceding method for creating a simple `StepExecution` is only one convenience method
available within the factory. You can find a full method listing in its
link:$$http://docs.spring.io/spring-batch/apidocs/org/springframework/batch/test/MetaDataInstanceFactory.html$$[Javadoc].

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@@ -3,7 +3,6 @@
:toclevels: 4
[[transactions]]
[appendix]
== Batch Processing and Transactions
@@ -12,7 +11,7 @@
Consider the following simple example of a nested batch with no retries. It shows a
common scenario for batch processing: An input source is processed until exhausted, and
we commit periodically at the end of a "chunk" of processing.
it commits periodically at the end of a "`chunk`" of processing.
----
@@ -29,7 +28,7 @@ we commit periodically at the end of a "chunk" of processing.
----
The input operation (3.1) could be a message-based receive (such as from JMS), or a
The input operation (3.1) could be a message-based receive (such as from JMS) or a
file-based read, but to recover and continue processing with a chance of completing the
whole job, it must be transactional. The same applies to the operation at 3.2. It must
be either transactional or idempotent.
@@ -41,7 +40,7 @@ must roll back the whole chunk.
=== Simple Stateless Retry
It is also useful to use a retry for an operation which is not transactional, such as a
call to a web-service or other remote resource, as shown in the following example:
call to a web-service or other remote resource, as the following example shows:
----
@@ -58,14 +57,14 @@ call to a web-service or other remote resource, as shown in the following exampl
This is actually one of the most useful applications of a retry, since a remote call is
much more likely to fail and be retryable than a database update. As long as the remote
access (2.1) eventually succeeds, the transaction, `TX` (0), commits. If the remote
access (2.1) eventually fails, then the transaction, `TX` (0), is guaranteed to roll
access (2.1) eventually fails, the transaction, `TX` (0), is guaranteed to roll
back.
[[repeatRetry]]
=== Typical Repeat-Retry Pattern
The most typical batch processing pattern is to add a retry to the inner block of the
chunk, as shown in the following example:
chunk, as the following example shows:
----
@@ -89,46 +88,46 @@ chunk, as shown in the following example:
----
The inner `RETRY` (4) block is marked as "stateful". See <<transactionsNoRetry,the
typical use case>> for a description of a stateful retry. This means that if the
The inner `RETRY` (4) block is marked as "`stateful`". See <<transactionsNoRetry,the
typical use case>> for a description of a stateful retry. This means that, if the
retry `PROCESS` (5) block fails, the behavior of the `RETRY` (4) is as follows:
. Throw an exception, rolling back the transaction, `TX` (2), at the chunk level, and
allowing the item to be re-presented to the input queue.
. When the item re-appears, it might be retried depending on the retry policy in place,
executing `PROCESS` (5) again. The second and subsequent attempts might fail again and
. When the item re-appears, it might be retried, depending on the retry policy in place, and
executing `PROCESS` (5) again. The second and subsequent attempts might fail again and
re-throw the exception.
. Eventually, the item reappears for the final time. The retry policy disallows another
attempt, so `PROCESS` (5) is never executed. In this case, we follow the `RECOVER` (6)
path, effectively "skipping" the item that was received and is being processed.
path, effectively "`skipping`" the item that was received and is being processed.
Note that the notation used for the `RETRY` (4) in the plan above explicitly shows that
Note that the notation used for the `RETRY` (4) in the plan explicitly shows that
the input step (4.1) is part of the retry. It also makes clear that there are two
alternate paths for processing: the normal case, as denoted by `PROCESS` (5), and the
recovery path, as denoted in a separate block by `RECOVER` (6). The two alternate paths
are completely distinct. Only one is ever taken in normal circumstances.
In special cases (such as a special `TransactionValidException` type), the retry policy
In special cases (such as a special `TranscationValidException` type), the retry policy
might be able to determine that the `RECOVER` (6) path can be taken on the last attempt
after `PROCESS` (5) has just failed, instead of waiting for the item to be re-presented.
This is not the default behavior, because it requires detailed knowledge of what has
happened inside the `PROCESS` (5) block, which is not usually available. For example, if
the output included write access before the failure, then the exception should be
the output included write access before the failure, the exception should be
re-thrown to ensure transactional integrity.
The completion policy in the outer `REPEAT` (1) is crucial to the success of the above
The completion policy in the outer `REPEAT` (1) is crucial to the success of the
plan. If the output (5.1) fails, it may throw an exception (it usually does, as
described), in which case the transaction, `TX` (2), fails, and the exception could
propagate up through the outer batch `REPEAT` (1). We do not want the whole batch to
propagate up through the outer batch `REPEAT` (1). We do not want the whole batch to
stop, because the `RETRY` (4) might still be successful if we try again, so we add
`exception=not critical` to the outer `REPEAT` (1).
Note, however, that if the `TX` (2) fails and we __do__ try again, by virtue of the outer
Note, however, that if the `TX` (2) fails and we _do_ try again, by virtue of the outer
completion policy, the item that is next processed in the inner `REPEAT` (3) is not
guaranteed to be the one that just failed. It might be, but it depends on the
implementation of the input (4.1). Thus, the output (5.1) might fail again on either a
new item or the old one. The client of the batch should not assume that each `RETRY` (4)
attempt is going to process the same items as the last one that failed. For example, if
guaranteed to be the one that just failed. It might be, but it depends on the
implementation of the input (4.1). Thus, the output (5.1) might fail again on either a
new item or the old one. The client of the batch should not assume that each `RETRY` (4)
attempt is going to process the same items as the last one that failed. For example, if
the termination policy for `REPEAT` (1) is to fail after 10 attempts, it fails after 10
consecutive attempts but not necessarily at the same item. This is consistent with the
overall retry strategy. The inner `RETRY` (4) is aware of the history of each item and
@@ -170,7 +169,7 @@ asynchronous chunk processing:
The individual items in chunks in the <<repeatRetry,typical example>> can also, in
principle, be processed concurrently. In this case, the transaction boundary has to move
to the level of the individual item, so that each transaction is on a single thread, as
shown in the following example:
the following example shows:
----
@@ -195,7 +194,7 @@ shown in the following example:
----
This plan sacrifices the optimization benefit, which the simple plan had, of having all
the transactional resources chunked together. It is only useful if the cost of the
the transactional resources chunked together. It is useful only if the cost of the
processing (5) is much higher than the cost of transaction management (3).
[[transactionPropagation]]
@@ -227,7 +226,7 @@ Again, and for the same reason, the inner transaction, `TX` (3), can cause the o
transaction, `TX` (1), to fail, even if the `RETRY` (2) is eventually successful.
Unfortunately, the same effect percolates from the retry block up to the surrounding
repeat batch if there is one, as shown in the following example:
repeat batch if there is one, as the following example shows:
----
@@ -253,25 +252,25 @@ back at the end.
What about non-default propagation?
* In the preceding example, `PROPAGATION_REQUIRES_NEW` at `TX` (3) prevents the outer
`TX` (1) from being polluted if both transactions are eventually successful. But if `TX`
(3) commits and `TX` (1) rolls back, then `TX` (3) stays committed, so we violate the
transaction contract for `TX` (1). If `TX` (3) rolls back, `TX` (1) does not necessarily
`TX` (1) from being polluted if both transactions are eventually successful. But if `TX`
(3) commits and `TX` (1) rolls back, `TX` (3) stays committed, so we violate the
transaction contract for `TX` (1). If `TX` (3) rolls back, `TX` (1) does not necessarily roll back
(but it probably does in practice, because the retry throws a roll back exception).
* `PROPAGATION_NESTED` at `TX` (3) works as we require in the retry case (and for a
batch with skips): `TX` (3) can commit but subsequently be rolled back by the outer
transaction, `TX` (1). If `TX` (3) rolls back, `TX` (1) rolls back in practice. This
transaction, `TX` (1). If `TX` (3) rolls back, `TX` (1) rolls back in practice. This
option is only available on some platforms, not including Hibernate or
JTA, but it is the only one that consistently works.
Consequently, the `NESTED` pattern is best if the retry block contains any database
access.
[[specialTransactionOrthogonal]]
[[specialTransactionOrthonogonal]]
=== Special Case: Transactions with Orthogonal Resources
Default propagation is always OK for simple cases where there are no nested database
transactions. Consider the following example, where the `SESSION` and `TX` are not
transactions. Consider the following example, where the `SESSION` and `TX` are not
global `XA` resources, so their resources are orthogonal:
----
@@ -287,19 +286,19 @@ global `XA` resources, so their resources are orthogonal:
----
Here there is a transactional message `SESSION` (0), but it does not participate in other
Here there is a transactional message, `SESSION` (0), but it does not participate in other
transactions with `PlatformTransactionManager`, so it does not propagate when `TX` (3)
starts. There is no database access outside the `RETRY` (2) block. If `TX` (3) fails and
starts. There is no database access outside the `RETRY` (2) block. If `TX` (3) fails and
then eventually succeeds on a retry, `SESSION` (0) can commit (independently of a `TX`
block). This is similar to the vanilla "best-efforts-one-phase-commit" scenario. The
block). This is similar to the vanilla "`best-efforts-one-phase-commit`" scenario. The
worst that can happen is a duplicate message when the `RETRY` (2) succeeds and the
`SESSION` (0) cannot commit (for example, because the message system is unavailable).
[[statelessRetryCannotRecover]]
=== Stateless Retry Cannot Recover
The distinction between a stateless and a stateful retry in the typical example above is
important. It is actually ultimately a transactional constraint that forces the
The distinction between a stateless and a stateful retry in the typical example shown earlier is
important. It is actually ultimately a transactional constraint that forces the
distinction, and this constraint also makes it obvious why the distinction exists.
We start with the observation that there is no way to skip an item that failed and
@@ -332,14 +331,14 @@ follows:
The preceding example shows a stateless `RETRY` (3) with a `RECOVER` (5) path that kicks
in after the final attempt fails. The `stateless` label means that the block is repeated
without re-throwing any exception up to some limit. This only works if the transaction,
`TX` (4), has propagation NESTED.
without re-throwing any exception up to some limit. This works only if the transaction,
`TX` (4), has propagation nested.
If the inner `TX` (4) has default propagation properties and rolls back, it pollutes the
outer `TX` (1). The inner transaction is assumed by the transaction manager to have
corrupted the transactional resource, so it cannot be used again.
Support for NESTED propagation is sufficiently rare that we choose not to support
recovery with stateless retries in the current versions of Spring Batch. The same effect
Support for nested propagation is sufficiently rare that we choose not to support
recovery with stateless retries in the current versions of Spring Batch. The same effect
can always be achieved (at the expense of repeating more processing) by using the
typical pattern above.
typical pattern shown earlier.

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@@ -3,10 +3,9 @@
:toclevels: 4
[[whatsNew]]
== What's New in Spring Batch 5.0
Spring Batch 5.0 release has the following major themes:
Spring Batch 5.0 has the following major themes:
* Java 17 Requirement
* Dependencies Re-baseline
@@ -15,17 +14,18 @@ Spring Batch 5.0 release has the following major themes:
* Pruning
For more details about the changes,
please refer to the link:$$https://github.com/spring-projects/spring-batch/wiki/Spring-Batch-5.0-Migration-Guide$$[migration guide].
see the link:$$https://github.com/spring-projects/spring-batch/wiki/Spring-Batch-5.0-Migration-Guide$$[migration guide].
=== Java 17 Requirement
Spring Batch follows Spring Framework's baselines for both Java version and third party dependencies.
With Spring Batch 5, the Spring Framework version is being upgraded to Spring Framework 6 which requires Java 17.
With Spring Batch 5, the Spring Framework version is being upgraded to Spring Framework 6, which requires Java 17.
As a result, the Java version requirement for Spring Batch is also increasing to Java 17.
[[dependencies-re-baseline]]
=== Dependencies Re-baseline
In order to continue the integration with supported versions of the third party libraries that Spring Batch uses,
To continue the integration with supported versions of the third party libraries that Spring Batch uses,
Spring Batch 5 is updating the dependencies across the board to the following versions:
* Spring Framework 6
@@ -37,24 +37,32 @@ Spring Batch 5 is updating the dependencies across the board to the following ve
This release also marks the migration to Jakarta EE 9 APIs.
=== Batch infrastructure configuration updates
[[batch-infrastructure-configuration-updates]]
=== Batch Infrastructure Configuration Updates
==== DataSource requirement updates
Spring Batch 5 includes the following infrastructure configuration updates:
Historically, Spring Batch provided a Map-based job repository and job explorer implementations to work with
* <<datasource-requirement-updates>>
* <<transaction-manager-bean-exposure>>
[[datasource-requirement-updates]]
==== DataSource Requirement Updates
Historically, Spring Batch provided a map-based job repository and job explorer implementations to work with
an in-memory job repository. These implementations were deprecated in version 4 and completely removed in version 5.
The recommended replacement is to use the Jdbc-based implementations with an embedded database like H2, HSQL, etc.
The recommended replacement is to use the JDBC-based implementations with an embedded database, such as H2, HSQL, and others.
In this release, the `@EnableBatchProcessing` annotation will configure a Jdbc-based `JobRepository` which requires a
In this release, the `@EnableBatchProcessing` annotation configures a JDBC-based `JobRepository`, which requires a
`DataSource` bean in the application context. The `DataSource` bean could refer to an embedded database to work with
an in-memory job repository.
==== Transaction manager bean exposure
[[transaction-manager-bean-exposure]]
==== Transaction Manager Bean Exposure
Up until version 4.3, the `@EnableBatchProcessing` annotation exposed a tranasaction manager bean in the application
context. While this was convenient in many cases, the unconditional exposure of a tranasaction manager could
interfere with a user-defined transaction manager. In this release, `@EnableBatchProcessing` does not expose a
transaction manager bean in the application context anymore.
Until version 4.3, the `@EnableBatchProcessing` annotation exposed a transaction manager bean in the application
context. While this was convenient in many cases, the unconditional exposure of a transaction manager could
interfere with a user-defined transaction manager. In this release, `@EnableBatchProcessing` no longer exposes a
transaction manager bean in the application context.
=== New features
@@ -70,7 +78,14 @@ This release introduces the support of SAP HANA as an additional supported datab
=== Pruning
==== Deprecated APIs removal
Spring Batch 5 removes a number of items that are no longer needed, including:
* <<deprecated-apis-removal>>
* <<sqlfire-support-removal>>
* <<jsr-352-implementation-removal>>
[[deprecated-apis-removal]]
==== Deprecated APIs Removal
The following APIs were deprecated in previous versions and have been removed in this release:
@@ -109,11 +124,13 @@ The following APIs were deprecated in previous versions and have been removed in
* Method `org.springframework.batch.integration.config.annotation.BatchIntegrationConfiguration#remotePartitioningMasterStepBuilderFactory()`
* Method `org.springframework.batch.item.util.FileUtils#setUpOutputFile(File file, boolean restarted, boolean overwriteOutputFile)`
==== SQLFire support removal
[[sqlfire-support-removal]]
==== SQLFire Support Removal
SqlFire has been announced to be EOL as of November 1st, 2014. The support of SQLFire as a job repository
was deprecated in version 4.3 and removed in version 5.0.
==== JSR-352 implementation removal
[[jsr-352-implementation-removal]]
==== JSR-352 Implementation Removal
Due to a lack of adoption, the implementation of the JSR-352 has been discontinued in this release.
Due to a lack of adoption, the implementation of JSR-352 has been removed from this release.