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.
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
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
“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
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
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
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
Async Store Failed Wire Send POST /things POST /things KQueueArrayWrapper.kev UnboundedFuturePool-2 SelectorUtil.select LockSupport.parkNan ReferenceQueue.remove
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
present data reported by tracers. - aggregate spans into trace trees - provide query and visualization for latency analysis - have retention policy (usually days)
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
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
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
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
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