© 2016 Pivotal !1 Understanding microservice latency An introduction to Distributed Tracing and Zipkin Adrian Cole, Pivotal @adrianfcole

Introduction introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

@adrianfcole • spring cloud at pivotal • focused on distributed tracing • helped open zipkin

Latency Analysis introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

Latency Analysis Microservice and data pipeline architectures are a often a graph of components, distributed across a network. A call graph or data flow can become delayed or fail due to the nature of the operation, components, or edges between them. We want to understand our current architecture and troubleshoot latency problems, in production.

Why is POST /things slow? POST /things

Troubleshooting latency problems When was the event and how long did it take? Where did this happen? Which event was it? Is it abnormal?

When was the event and how long did it take? First log statement was at 15:31:29.103 GMT… last… 15:31:30.530 Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds

wombats:10.2.3.47:8080 Server log says Client IP was 1.2.3.4 This is a shard in the wombats cluster, listening on 10.2.3.47:8080 Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds Where did this happen? peer.ipv4 1.2.3.4

wombats:10.2.3.47:8080 Which event was it? The http response header had “request-id: abcd- ffe”? Is that what you mean? Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds peer.ipv4 1.2.3.4 http.request-id abcd-ffe

wombats:10.2.3.47:8080 Is it abnormal? I’ll check other logs for this request id and see what I can find out. Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds Well, average response time for POST /things in the last 2 days is 100ms peer.ipv4 1.2.3.4 http.request-id abcd-ffe

wombats:10.2.3.47:8080 Achieving understanding I searched the logs for others in that group.. took about the same time. Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds Ok, looks like this client is in the experimental group for HD uploads peer.ipv4 1.2.3.4 http.request-id abcd-ffe http.request.size 15 MiB http.url …&features=HD-uploads

POST /things There’s often two sides to the story Client Sent:15:31:28:500 Client Received:15:31:31:000 Duration: 2500 milliseconds Server Received:15:31:29:103 POST /things Server Sent:15:31:30:530 Duration: 1427 milliseconds

and not all operations are on the critical path Wire Send Store Async Store Wire Send POST /things POST /things

and not all operations are relevant Wire Send Store Async Async Store Failed Wire Send POST /things POST /things KQueueArrayWrapper.kev UnboundedFuturePool-2 SelectorUtil.select LockSupport.parkNan ReferenceQueue.remove

It’s getting harder to draw our architecture Polyglot microservice and data flow architectures are increasingly easy to write and deploy.

Can we make troubleshooting wizard-free? We no longer need wizards to deploy complex architectures. We shouldn’t need wizards to troubleshoot them, either!

Distributed Tracing commoditizes knowledge Distributed tracing systems collect end-to-end latency graphs (traces) in near real-time. You can compare traces to understand why certain requests take longer than others.

Distributed Tracing introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

Distributed Tracing Vocabulary A Span is an individual operation that took place. A span contains timestamped events and tags. A Trace is an end-to-end latency graph, composed of spans.

wombats:10.2.3.47:8080 A Span is an individual operation Server Received POST /things Server Sent Events Tags Operation peer.ipv4 1.2.3.4 http.request-id abcd-ffe http.request.size 15 MiB http.url …&features=HD-uploads

Tracing is logging important events Wire Send Store Async Store Wire Send POST /things POST /things

Tracers record time, duration and host Wire Send Store Async Store Wire Send POST /things POST /things

Tracers cement shape with IDs

Tracers cement shape with IDs No parent means this is the root of the trace Each span in a trace has its own ID The parent ID tells you where you are in the call graph

Tracers send trace data out of process Tracers propagate IDs in-band, to tell the receiver there’s a trace in progress Completed spans are reported out-of-band, to reduce overhead and allow for batching

Tracers usually live in your application Tracers execute in your production apps! They are written to not log too much, and to not cause applications to crash. - propagate structural data in-band, and the rest out-of-band - have instrumentation or sampling policy to manage volume - often include opinionated instrumentation of layers such as HTTP

Tracing Systems are Observability Tools Tracing systems collect, process and present data reported by tracers. - aggregate spans into trace trees - provide query and visualization for latency analysis - have retention policy (usually days)

Zipkin introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

Zipkin is a distributed tracing system

Zipkin has pluggable architecture Tracers report spans HTTP or Kafka. Servers collect spans, storing them in MySQL, Cassandra, or Elasticsearch. Users query for traces via Zipkin’s Web UI or Api. services: storage: image: openzipkin/zipkin-cassandra:1.1.5 container_name: cassandra ports: - 9042:9042 server: image: openzipkin/zipkin:1.1.5 environment: - STORAGE_TYPE=cassandra - CASSANDRA_CONTACT_POINTS=cassandra ports: - 9411:9411 depends_on: - storage

Zipkin has starter architecture Tracing is new for a lot of folks. For many, the MySQL option is a good start, as it is familiar. services: storage: image: openzipkin/zipkin-mysql:1.1.5 container_name: mysql ports: - 3306:3306 server: image: openzipkin/zipkin:1.1.5 environment: - STORAGE_TYPE=mysql - MYSQL_HOST=mysql ports: - 9411:9411 depends_on: - storage

Zipkin can be as simple as a single file $ curl -SL 'https://search.maven.org/remote_content?g=io.zipkin.java&a=zipkin-server&v=LATEST&c=exec' > zipkin.jar $ java -jar zipkin.jar . ____ _ __ _ _ /\\ / ___'_ __ _ _(_)_ __ __ _ \ \ \ \ ( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \ \\/ ___)| |_)| | | | | || (_| | ) ) ) ) ' |____| .__|_| |_|_| |_\__, | / / / / =========|_|==============|___/=/_/_/_/ :: Spring Boot :: (v1.3.5.RELEASE) 2016-06-23 20:17:09.123 INFO 37481 --- [ main] zipkin.server.ZipkinServer : Starting ZipkinServer on acole with PID 37481 —snip— $ curl -s localhost:9411/api/v1/services|jq . [ "zipkin-server" ]

Zipkin lives in GitHub Zipkin was created by Twitter in 2012. In 2015, OpenZipkin became the primary fork. OpenZipkin is an org on GitHub. It contains tracers, OpenApi spec, service components and docker images. https://github.com/openzipkin

Demo introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

Two Spring Boot (Java) services collaborate over http. Zipkin will show how long the whole operation took, as well how much time was spent in each service. https://github.com/adriancole/sleuth-webmvc-example Distributed Tracing across Spring Boot apps

Web requests in the demo are served by Spring MVC controllers. Tracing of these are automatically performed by Spring Cloud Sleuth. Spring Cloud Sleuth reports to Zipkin via HTTP by depending on spring-cloud-sleuth-zipkin. https://cloud.spring.io/spring-cloud-sleuth/ Spring Cloud Sleuth Java

Wrapping Up introduction latency analysis distributed tracing zipkin demo wrapping up @adrianfcole #zipkin

Wrapping up Start by sending traces directly to a zipkin server. Grow into fanciness as you need it: sampling, streaming, etc Remember you are not alone! @adrianfcole #zipkin gitter.im/spring-cloud/spring-cloud-sleuth gitter.im/openzipkin/zipkin