diff --git a/multi/multi__integrations.html b/multi/multi__integrations.html index f09d5ed90..33830237d 100644 --- a/multi/multi__integrations.html +++ b/multi/multi__integrations.html @@ -158,5 +158,6 @@ it’s enough for you to register beans of types:

spring.sleuth.messaging.rabbit.enabled to false.

15.9.3 Spring Kafka

We instrument the Spring Kafka’s ProducerFactory and ConsumerFactory so that tracing headers get injected into the created Spring Kafka’s Producer and Consumer.

To block this feature, set spring.sleuth.messaging.kafka.enabled to false.

[Note]Note

We do not support context propagation via @KafkaListener annotation. -Check this issue for more information.

15.10 Zuul

We instrument the Zuul Ribbon integration by enriching the Ribbon requests with tracing information. +Check this issue for more information.

15.9.4 Spring JMS

We instrument the JmsTemplate so that tracing headers get injected +into the message. We also support @JmsListener annotated methods on the consumer side.

To block this feature, set spring.sleuth.messaging.jms.enabled to false.

15.10 Zuul

We instrument the Zuul Ribbon integration by enriching the Ribbon requests with tracing information. To disable Zuul support, set the spring.sleuth.zuul.enabled property to false.

Prev   Next
14. Zipkin Stream Span Consumer Home 16. Running examples
\ No newline at end of file diff --git a/multi/multi_spring-cloud-sleuth.html b/multi/multi_spring-cloud-sleuth.html index f52ccbb79..4e22be643 100644 --- a/multi/multi_spring-cloud-sleuth.html +++ b/multi/multi_spring-cloud-sleuth.html @@ -1,3 +1,3 @@ - Spring Cloud Sleuth
Spring Cloud Sleuth
   Next

Spring Cloud Sleuth

Adrian Cole, Spencer Gibb, Marcin Grzejszczak, Dave Syer, Jay Bryant

Table of Contents

1. Introduction
1.1. Terminology
1.2. Purpose
1.2.1. Distributed Tracing with Zipkin
1.2.2. Visualizing errors
1.2.3. Distributed Tracing with Brave
1.2.4. Live examples
1.2.5. Log correlation
JSON Logback with Logstash
1.2.6. Propagating Span Context
Baggage versus Span Tags
1.3. Adding Sleuth to the Project
1.3.1. Only Sleuth (log correlation)
1.3.2. Sleuth with Zipkin via HTTP
1.3.3. Sleuth with Zipkin over RabbitMQ or Kafka
2. Additional Resources
3. Features
3.1. Introduction to Brave
3.1.1. Tracing
3.1.2. Local Tracing
3.1.3. Customizing Spans
3.1.4. Implicitly Looking up the Current Span
3.1.5. RPC tracing
One-Way tracing
4. Sampling
4.1. Declarative sampling
4.2. Custom sampling
4.3. Sampling in Spring Cloud Sleuth
5. Propagation
5.1. Propagating extra fields
5.1.1. Prefixed fields
5.1.2. Extracting a Propagated Context
5.1.3. Sharing span IDs between Client and Server
5.1.4. Implementing Propagation
6. Current Tracing Component
7. Current Span
7.1. Setting a span in scope manually
8. Instrumentation
9. Span lifecycle
9.1. Creating and finishing spans
9.2. Continuing Spans
9.3. Creating a Span with an explicit Parent
10. Naming spans
10.1. @SpanName Annotation
10.2. toString() method
11. Managing Spans with Annotations
11.1. Rationale
11.2. Creating New Spans
11.3. Continuing Spans
11.4. Advanced Tag Setting
11.4.1. Custom extractor
11.4.2. Resolving Expressions for a Value
11.4.3. Using the toString() method
12. Customizations
12.1. HTTP
12.2. TracingFilter
12.3. Custom service name
12.4. Customization of Reported Spans
12.5. Host Locator
13. Sending Spans to Zipkin
14. Zipkin Stream Span Consumer
15. Integrations
15.1. OpenTracing
15.2. Runnable and Callable
15.3. Hystrix
15.3.1. Custom Concurrency Strategy
15.3.2. Manual Command setting
15.4. RxJava
15.5. HTTP integration
15.5.1. HTTP Filter
15.5.2. HandlerInterceptor
15.5.3. Async Servlet support
15.5.4. WebFlux support
15.5.5. Dubbo RPC support
15.6. HTTP Client Integration
15.6.1. Synchronous Rest Template
15.6.2. Asynchronous Rest Template
Multiple Asynchronous Rest Templates
15.6.3. WebClient
15.6.4. Traverson
15.6.5. Apache HttpClientBuilder and HttpAsyncClientBuilder
15.6.6. Netty HttpClient
15.6.7. UserInfoRestTemplateCustomizer
15.7. Feign
15.8. Asynchronous Communication
15.8.1. @Async Annotated methods
15.8.2. @Scheduled Annotated Methods
15.8.3. Executor, ExecutorService, and ScheduledExecutorService
Customization of Executors
15.9. Messaging
15.9.1. Spring Integration and Spring Cloud Stream
15.9.2. Spring RabbitMq
15.9.3. Spring Kafka
15.10. Zuul
16. Running examples
   Next
   
\ No newline at end of file + Spring Cloud Sleuth
Spring Cloud Sleuth
   Next

Spring Cloud Sleuth

Adrian Cole, Spencer Gibb, Marcin Grzejszczak, Dave Syer, Jay Bryant

Table of Contents

1. Introduction
1.1. Terminology
1.2. Purpose
1.2.1. Distributed Tracing with Zipkin
1.2.2. Visualizing errors
1.2.3. Distributed Tracing with Brave
1.2.4. Live examples
1.2.5. Log correlation
JSON Logback with Logstash
1.2.6. Propagating Span Context
Baggage versus Span Tags
1.3. Adding Sleuth to the Project
1.3.1. Only Sleuth (log correlation)
1.3.2. Sleuth with Zipkin via HTTP
1.3.3. Sleuth with Zipkin over RabbitMQ or Kafka
2. Additional Resources
3. Features
3.1. Introduction to Brave
3.1.1. Tracing
3.1.2. Local Tracing
3.1.3. Customizing Spans
3.1.4. Implicitly Looking up the Current Span
3.1.5. RPC tracing
One-Way tracing
4. Sampling
4.1. Declarative sampling
4.2. Custom sampling
4.3. Sampling in Spring Cloud Sleuth
5. Propagation
5.1. Propagating extra fields
5.1.1. Prefixed fields
5.1.2. Extracting a Propagated Context
5.1.3. Sharing span IDs between Client and Server
5.1.4. Implementing Propagation
6. Current Tracing Component
7. Current Span
7.1. Setting a span in scope manually
8. Instrumentation
9. Span lifecycle
9.1. Creating and finishing spans
9.2. Continuing Spans
9.3. Creating a Span with an explicit Parent
10. Naming spans
10.1. @SpanName Annotation
10.2. toString() method
11. Managing Spans with Annotations
11.1. Rationale
11.2. Creating New Spans
11.3. Continuing Spans
11.4. Advanced Tag Setting
11.4.1. Custom extractor
11.4.2. Resolving Expressions for a Value
11.4.3. Using the toString() method
12. Customizations
12.1. HTTP
12.2. TracingFilter
12.3. Custom service name
12.4. Customization of Reported Spans
12.5. Host Locator
13. Sending Spans to Zipkin
14. Zipkin Stream Span Consumer
15. Integrations
15.1. OpenTracing
15.2. Runnable and Callable
15.3. Hystrix
15.3.1. Custom Concurrency Strategy
15.3.2. Manual Command setting
15.4. RxJava
15.5. HTTP integration
15.5.1. HTTP Filter
15.5.2. HandlerInterceptor
15.5.3. Async Servlet support
15.5.4. WebFlux support
15.5.5. Dubbo RPC support
15.6. HTTP Client Integration
15.6.1. Synchronous Rest Template
15.6.2. Asynchronous Rest Template
Multiple Asynchronous Rest Templates
15.6.3. WebClient
15.6.4. Traverson
15.6.5. Apache HttpClientBuilder and HttpAsyncClientBuilder
15.6.6. Netty HttpClient
15.6.7. UserInfoRestTemplateCustomizer
15.7. Feign
15.8. Asynchronous Communication
15.8.1. @Async Annotated methods
15.8.2. @Scheduled Annotated Methods
15.8.3. Executor, ExecutorService, and ScheduledExecutorService
Customization of Executors
15.9. Messaging
15.9.1. Spring Integration and Spring Cloud Stream
15.9.2. Spring RabbitMq
15.9.3. Spring Kafka
15.9.4. Spring JMS
15.10. Zuul
16. Running examples
   Next
   
\ No newline at end of file diff --git a/single/spring-cloud-sleuth.html b/single/spring-cloud-sleuth.html index b0339ecd8..eff9c10d7 100644 --- a/single/spring-cloud-sleuth.html +++ b/single/spring-cloud-sleuth.html @@ -1,6 +1,6 @@ - Spring Cloud Sleuth

Spring Cloud Sleuth

Adrian Cole, Spencer Gibb, Marcin Grzejszczak, Dave Syer, Jay Bryant

Table of Contents

1. Introduction
1.1. Terminology
1.2. Purpose
1.2.1. Distributed Tracing with Zipkin
1.2.2. Visualizing errors
1.2.3. Distributed Tracing with Brave
1.2.4. Live examples
1.2.5. Log correlation
JSON Logback with Logstash
1.2.6. Propagating Span Context
Baggage versus Span Tags
1.3. Adding Sleuth to the Project
1.3.1. Only Sleuth (log correlation)
1.3.2. Sleuth with Zipkin via HTTP
1.3.3. Sleuth with Zipkin over RabbitMQ or Kafka
2. Additional Resources
3. Features
3.1. Introduction to Brave
3.1.1. Tracing
3.1.2. Local Tracing
3.1.3. Customizing Spans
3.1.4. Implicitly Looking up the Current Span
3.1.5. RPC tracing
One-Way tracing
4. Sampling
4.1. Declarative sampling
4.2. Custom sampling
4.3. Sampling in Spring Cloud Sleuth
5. Propagation
5.1. Propagating extra fields
5.1.1. Prefixed fields
5.1.2. Extracting a Propagated Context
5.1.3. Sharing span IDs between Client and Server
5.1.4. Implementing Propagation
6. Current Tracing Component
7. Current Span
7.1. Setting a span in scope manually
8. Instrumentation
9. Span lifecycle
9.1. Creating and finishing spans
9.2. Continuing Spans
9.3. Creating a Span with an explicit Parent
10. Naming spans
10.1. @SpanName Annotation
10.2. toString() method
11. Managing Spans with Annotations
11.1. Rationale
11.2. Creating New Spans
11.3. Continuing Spans
11.4. Advanced Tag Setting
11.4.1. Custom extractor
11.4.2. Resolving Expressions for a Value
11.4.3. Using the toString() method
12. Customizations
12.1. HTTP
12.2. TracingFilter
12.3. Custom service name
12.4. Customization of Reported Spans
12.5. Host Locator
13. Sending Spans to Zipkin
14. Zipkin Stream Span Consumer
15. Integrations
15.1. OpenTracing
15.2. Runnable and Callable
15.3. Hystrix
15.3.1. Custom Concurrency Strategy
15.3.2. Manual Command setting
15.4. RxJava
15.5. HTTP integration
15.5.1. HTTP Filter
15.5.2. HandlerInterceptor
15.5.3. Async Servlet support
15.5.4. WebFlux support
15.5.5. Dubbo RPC support
15.6. HTTP Client Integration
15.6.1. Synchronous Rest Template
15.6.2. Asynchronous Rest Template
Multiple Asynchronous Rest Templates
15.6.3. WebClient
15.6.4. Traverson
15.6.5. Apache HttpClientBuilder and HttpAsyncClientBuilder
15.6.6. Netty HttpClient
15.6.7. UserInfoRestTemplateCustomizer
15.7. Feign
15.8. Asynchronous Communication
15.8.1. @Async Annotated methods
15.8.2. @Scheduled Annotated Methods
15.8.3. Executor, ExecutorService, and ScheduledExecutorService
Customization of Executors
15.9. Messaging
15.9.1. Spring Integration and Spring Cloud Stream
15.9.2. Spring RabbitMq
15.9.3. Spring Kafka
15.10. Zuul
16. Running examples

2.1.0.BUILD-SNAPSHOT

1. Introduction

Spring Cloud Sleuth implements a distributed tracing solution for Spring Cloud.

1.1 Terminology

Spring Cloud Sleuth borrows Dapper’s terminology.

Span: The basic unit of work. For example, sending an RPC is a new span, as is sending a response to an RPC. + Spring Cloud Sleuth

Spring Cloud Sleuth

Adrian Cole, Spencer Gibb, Marcin Grzejszczak, Dave Syer, Jay Bryant

Table of Contents

1. Introduction
1.1. Terminology
1.2. Purpose
1.2.1. Distributed Tracing with Zipkin
1.2.2. Visualizing errors
1.2.3. Distributed Tracing with Brave
1.2.4. Live examples
1.2.5. Log correlation
JSON Logback with Logstash
1.2.6. Propagating Span Context
Baggage versus Span Tags
1.3. Adding Sleuth to the Project
1.3.1. Only Sleuth (log correlation)
1.3.2. Sleuth with Zipkin via HTTP
1.3.3. Sleuth with Zipkin over RabbitMQ or Kafka
2. Additional Resources
3. Features
3.1. Introduction to Brave
3.1.1. Tracing
3.1.2. Local Tracing
3.1.3. Customizing Spans
3.1.4. Implicitly Looking up the Current Span
3.1.5. RPC tracing
One-Way tracing
4. Sampling
4.1. Declarative sampling
4.2. Custom sampling
4.3. Sampling in Spring Cloud Sleuth
5. Propagation
5.1. Propagating extra fields
5.1.1. Prefixed fields
5.1.2. Extracting a Propagated Context
5.1.3. Sharing span IDs between Client and Server
5.1.4. Implementing Propagation
6. Current Tracing Component
7. Current Span
7.1. Setting a span in scope manually
8. Instrumentation
9. Span lifecycle
9.1. Creating and finishing spans
9.2. Continuing Spans
9.3. Creating a Span with an explicit Parent
10. Naming spans
10.1. @SpanName Annotation
10.2. toString() method
11. Managing Spans with Annotations
11.1. Rationale
11.2. Creating New Spans
11.3. Continuing Spans
11.4. Advanced Tag Setting
11.4.1. Custom extractor
11.4.2. Resolving Expressions for a Value
11.4.3. Using the toString() method
12. Customizations
12.1. HTTP
12.2. TracingFilter
12.3. Custom service name
12.4. Customization of Reported Spans
12.5. Host Locator
13. Sending Spans to Zipkin
14. Zipkin Stream Span Consumer
15. Integrations
15.1. OpenTracing
15.2. Runnable and Callable
15.3. Hystrix
15.3.1. Custom Concurrency Strategy
15.3.2. Manual Command setting
15.4. RxJava
15.5. HTTP integration
15.5.1. HTTP Filter
15.5.2. HandlerInterceptor
15.5.3. Async Servlet support
15.5.4. WebFlux support
15.5.5. Dubbo RPC support
15.6. HTTP Client Integration
15.6.1. Synchronous Rest Template
15.6.2. Asynchronous Rest Template
Multiple Asynchronous Rest Templates
15.6.3. WebClient
15.6.4. Traverson
15.6.5. Apache HttpClientBuilder and HttpAsyncClientBuilder
15.6.6. Netty HttpClient
15.6.7. UserInfoRestTemplateCustomizer
15.7. Feign
15.8. Asynchronous Communication
15.8.1. @Async Annotated methods
15.8.2. @Scheduled Annotated Methods
15.8.3. Executor, ExecutorService, and ScheduledExecutorService
Customization of Executors
15.9. Messaging
15.9.1. Spring Integration and Spring Cloud Stream
15.9.2. Spring RabbitMq
15.9.3. Spring Kafka
15.9.4. Spring JMS
15.10. Zuul
16. Running examples

2.1.0.BUILD-SNAPSHOT

1. Introduction

Spring Cloud Sleuth implements a distributed tracing solution for Spring Cloud.

1.1 Terminology

Spring Cloud Sleuth borrows Dapper’s terminology.

Span: The basic unit of work. For example, sending an RPC is a new span, as is sending a response to an RPC. Spans are identified by a unique 64-bit ID for the span and another 64-bit ID for the trace the span is a part of. Spans also have other data, such as descriptions, timestamped events, key-value annotations (tags), the ID of the span that caused them, and process IDs (normally IP addresses).

Spans can be started and stopped, and they keep track of their timing information. Once you create a span, you must stop it at some point in the future.

[Tip]Tip

The initial span that starts a trace is called a root span. The value of the ID @@ -892,7 +892,8 @@ it’s enough for you to register beans of types:

spring.sleuth.messaging.rabbit.enabled to false.

15.9.3 Spring Kafka

We instrument the Spring Kafka’s ProducerFactory and ConsumerFactory so that tracing headers get injected into the created Spring Kafka’s Producer and Consumer.

To block this feature, set spring.sleuth.messaging.kafka.enabled to false.

[Note]Note

We do not support context propagation via @KafkaListener annotation. -Check this issue for more information.

15.10 Zuul

We instrument the Zuul Ribbon integration by enriching the Ribbon requests with tracing information. +Check this issue for more information.

15.9.4 Spring JMS

We instrument the JmsTemplate so that tracing headers get injected +into the message. We also support @JmsListener annotated methods on the consumer side.

To block this feature, set spring.sleuth.messaging.jms.enabled to false.

15.10 Zuul

We instrument the Zuul Ribbon integration by enriching the Ribbon requests with tracing information. To disable Zuul support, set the spring.sleuth.zuul.enabled property to false.

16. Running examples

You can see the running examples deployed in the Pivotal Web Services. Check them out at the following links:

  • Zipkin for apps presented in the samples to the top. First make a request to Service 1 and then check out the trace in Zipkin.
  • Zipkin for Brewery on PWS, its Github Code. diff --git a/spring-cloud-sleuth.xml b/spring-cloud-sleuth.xml index 6a52b8622..bc31a9b98 100644 --- a/spring-cloud-sleuth.xml +++ b/spring-cloud-sleuth.xml @@ -1959,6 +1959,12 @@ so that tracing headers get injected into the created Spring Kafka’s Check this issue for more information. +
    +Spring JMS +We instrument the JmsTemplate so that tracing headers get injected +into the message. We also support @JmsListener annotated methods on the consumer side. +To block this feature, set spring.sleuth.messaging.jms.enabled to false. +
    Zuul