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stroke-width="2" stroke-linecap="round"/> </svg> </a> <a href="https://logz.io/platform/" class="header_platform__top_description">Simplified observability, <br/> powered by open source.</a> </span> <span class="header_platform__top_col image"> <a href="https://logz.io/platform/" class="header_platform__top_image"> <img src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2022/12/IM_Kiosk_Front-1.png" alt="Open 360TM Platform" class="img"/> </a> </span> </span></li><li><ul class="header_platform__sub_items"><li class="header_platform__sub_item odd"> <a class="header_platform__sub_item_link" href="https://logz.io/platform/app-360/"> <span class="header_platform__sub_item_link_icon"> <svg xmlns="http://www.w3.org/2000/svg" width="33" height="33" viewbox="0 0 33 33" fill="none"> <path fill-rule="evenodd" clip-rule="evenodd" d="M28.9999 16.5C28.9999 23.4035 23.4034 29 16.5 29C9.59642 29 4 23.4035 4 16.5C4 9.59644 9.59642 4 16.5 4C23.4034 4 28.9999 9.59644 28.9999 16.5ZM8.14764 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href="https://logz.io/platform/kubernetes-360/"> <span class="header_platform__sub_item_link_icon"> <svg xmlns="http://www.w3.org/2000/svg" width="33" height="33" viewbox="0 0 33 33" fill="none"> <path fill-rule="evenodd" clip-rule="evenodd" d="M15.3637 4.1695C15.5731 4.07064 15.8004 4.01377 16.0324 4.00222C16.3243 3.98763 16.6152 4.04515 16.8785 4.1695L26.0044 8.47716C26.2405 8.58866 26.448 8.75089 26.6115 8.95181C26.7749 9.15272 26.8901 9.38717 26.9485 9.63772L29.2031 19.319C29.2614 19.5697 29.2614 19.83 29.203 20.0807C29.1446 20.3313 29.0294 20.5658 28.8658 20.7668L22.5524 28.5308C22.3888 28.7314 22.1812 28.8934 21.9451 29.0046C21.7089 29.1159 21.4502 29.1735 21.1882 29.1733L11.0599 29.1756C10.7978 29.1757 10.539 29.1179 10.3029 29.0064C10.0667 28.895 9.85916 28.7328 9.69568 28.5319L3.37993 20.7691C3.34462 20.7254 3.31152 20.6799 3.28074 20.633C3.15379 20.441 3.06742 20.226 3.02673 20.0007C2.98604 19.7754 2.99186 19.5444 3.04384 19.3213L5.29496 9.64002C5.35331 9.38947 5.46849 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19.6559C16.2583 19.6212 16.1816 19.6058 16.106 19.6086C15.9397 19.6146 15.7819 19.7063 15.6987 19.8601H15.6964L14.31 22.3381C15.2695 22.6613 16.329 22.7336 17.392 22.495C17.5817 22.4524 17.7671 22.4013 17.9475 22.3415L16.5576 19.8589H16.5471C16.5006 19.773 16.4268 19.7014 16.3312 19.6559Z" fill="#112031"/> </svg> </span> <span class="header_platform__sub_item_link_content"> <span class="header_platform__sub_item_link_title">Kubernetes 360</span> <span class="header_platform__sub_item_link_subtitle"/> </span> </a></li><li class="header_platform__sub_item odd"> <a class="header_platform__sub_item_link" href="https://logz.io/platform/log-management/"> <span class="header_platform__sub_item_link_icon"> <svg width="32" height="24" viewbox="0 0 32 24" fill="none" xmlns="http://www.w3.org/2000/svg"> <path fill-rule="evenodd" clip-rule="evenodd" d="M20.673 23.5369C26.9285 23.5369 31.9997 18.268 31.9997 11.7685C31.9997 5.26892 26.9285 0 20.673 0C17.5793 0 14.7753 1.28869 12.7311 3.37759C12.1653 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class="single_hero__tag"> <a href="https://logz.io/tag/tutorials/"> #Tutorials </a></div></div><header class="single_hero__header"><h1 class="single_hero__title"> Distributed Tracing with Jaeger and the ELK Stack</h1></header><div class="single_hero__authors"> <span class="single_hero__author_prefix">By:</span> <a href="https://logz.io/author/danielberman/">Daniel Berman</a></div></div></div><div class="single_hero__right_col"><div class="single_hero__image"> <img width="1200" height="628" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-type="image" data-lazy-src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/distributed_tracing_with_jaeger_and_the_elk_stack_main.jpg" class="lazy lazy-hidden attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" fetchpriority="high"/><noscript><img width="1200" height="628" src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/distributed_tracing_with_jaeger_and_the_elk_stack_main.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" fetchpriority="high"/></noscript></div></div></div></div></div><div class="content_section"><div class="container"><div class="row"><div class="content_section__navigation"/><div class="content_section__container"><div class="html_content"><p><span style="font-weight: 400;">Over the past few years, and coupled with the growing adoption of microservices, distributed tracing has emerged as one of the most commonly used monitoring and troubleshooting methodologies. <div class="internal_links"><div class="internal_links_title">More on the subject:</div><ul class="internal_links_inner"><div class="widget_recent_entries dcrp-row"><li class="internal_link"><a class="internal_body" target="_blank" href="https://logz.io/learn/logging-trial-overview-2/" title="Logz.io Trial Sign-up: Overview">Logz.io Trial Sign-up: Overview</a></li><li class="internal_link"><a class="internal_body" target="_blank" href="https://logz.io/blog/continuous-profiling-new-observability-signal-in-opentelemetry/" title="Continuous Profiling: A New Observability Signal">Continuous Profiling: A New Observability Signal</a></li><li class="internal_link"><a class="internal_body" target="_blank" href="https://logz.io/blog/database-decision-making-druid-clickhouse/" title="Database Decision-Making for Observability, from Simple to Complex">Database Decision-Making for Observability, from Simple to Complex</a></li></div></ul></div></span></p><p><span style="font-weight: 400;">New tracing tools and frameworks are increasingly being introduced, driving adoption even further. One of these tools is </span><a target="_blank" href="https://github.com/jaegertracing/jaeger"><span style="font-weight: 400;">Jaeger</span></a><span style="font-weight: 400;">, a popular open source tracing tool. This article explores the integration of Jaeger with the ELK Stack for analysis and visualization of traces.</span></p><h2><span style="font-weight: 400;">What is Jaeger?</span></h2><p><span style="font-weight: 400;">Jaeger was developed by Uber and open sourced in 2016. Inspired by other existing tracing tools, </span><a href="https://logz.io/blog/distributed-tracing-with-zipkin/" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">Zipkin</span></a><span style="font-weight: 400;"> and Dapper, Jaeger is quickly becoming one of the most popular open source distributed tracing tools, enabling users to perform root cause analysis, performance optimization and distributed transaction monitoring. </span></p><p><span style="font-weight: 400;">Jaeger features </span><a href="https://opentracing.io/" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">OpenTracing</span></a><span style="font-weight: 400;">-based instrumentation for Go, Java, Node, Python and C++ apps, uses </span><a href="https://www.jaegertracing.io/docs/1.12/sampling/" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">consistent upfront sampling</span></a><span style="font-weight: 400;"> with individual per service/endpoint probabilities, and supports multiple storage backends — Cassandra, Elasticsearch, Kafka and memory.</span></p><p><span style="font-weight: 400;">From an architectural perspective, Jaeger is comprised of multiple components, three of which provide the core backend functionality: Jaeger </span><b>Clients</b><span style="font-weight: 400;"> implement </span><a href="https://opentracing.io" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">OpenTracing API</span></a><span style="font-weight: 400;"> in applications, creating spans when receiving new requests. Jaeger </span><b>Agents</b><span style="font-weight: 400;"> are responsible for listening for spans and sending them to the </span><b>Collector</b><span style="font-weight: 400;">. The collector, in turn, receives the traces from the </span><a href="https://www.jaegertracing.io/docs/1.12/architecture#agent" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">agents</span></a><span style="font-weight: 400;"> and runs them through a processing pipeline which ends with storing them in backend storage. </span></p><h2><span style="font-weight: 400;">Jaeger and ELK</span></h2><p><span style="font-weight: 400;">The default storage is Cassandra but Jaeger can also store traces in Elasticsearch. This capability means users can analyze traces using the full power of the ELK Stack (Elasticsearch, Logstash and Kibana). Why does this matter?</span></p><p><span style="font-weight: 400;">Sure, Jaeger ships with a nifty GUI that allows users to dive into the traces and spans. Using the ELK Stack, though, offers users additional querying and visualization capabilities. Depending on your ELK architecture, you will also be able to store trace data for extended retention periods. If you’re already using the ELK Stack for centralized logging and monitoring, why not add traces into the mix?</span></p><p><span style="font-weight: 400;">Let’s take a closer look at how to set up the integration between Jaeger and the ELK Stack and some examples of what can be done with it. </span></p><h2><span style="font-weight: 400;">Step 1: Setting Up Elasticsearch and Kibana</span></h2><p><span style="font-weight: 400;">Our first step is to set up a local ELK (without Logstash) with Docker. Jaeger currently only supports versions 5.x and 6.x, so I will use the following commands to run Elasticsearch and Kibana.</span></p><p><span style="font-weight: 400;">Elasticsearch 6.8.0:</span></p><pre class="lang:default decode:true ">docker run --rm -it --name=elasticsearch -e "ES_JAVA_OPTS=-Xms2g -Xmx2g" -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -e "xpack.security.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:6.8.0 </pre><p><span style="font-weight: 400;">Kibana 6.8.0:</span></p><pre class="lang:default decode:true ">docker run --rm -it --link=elasticsearch --name=kibana -p 5601:5601 docker.elastic.co/kibana/kibana:6.8.0 </pre><h2><span style="font-weight: 400;">Step 2: Setting Up Jaeger</span></h2><p><span style="font-weight: 400;">Next, we will deploy Jaeger.</span></p><p><span style="font-weight: 400;">Designed for tracing distributed architectures, Jaeger itself can be deployed as a distributed system. However, the easiest way to get started is to deploy Jaeger as an all-in-one binary that runs all the backend components in one single process.</span></p><p><span style="font-weight: 400;">To do this, you can either run the jaeger all-in-one binary, available for download </span><a href="https://www.jaegertracing.io/download/" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">here</span></a><span style="font-weight: 400;">, or use Docker. We will opt for the latter option.</span></p><p><span style="font-weight: 400;">Before you copy the command below, a few words explaining some of the flags I’m using:</span></p><ul><li style="font-weight: 400;"><span style="font-weight: 400;">–link=elasticsearch</span><span style="font-weight: 400;"> – link to the Elasticsearch container.</span></li><li style="font-weight: 400;"><span style="font-weight: 400;">SPAN_STORAGE_TYPE=elasticsearch</span><span style="font-weight: 400;"> – defining the Elasticsearch storage type for storing the Jaeger traces.</span></li><li style="font-weight: 400;"><span style="font-weight: 400;">-e ES_TAGS_AS_FIELDS_ALL=true</span><span style="font-weight: 400;"> – enables correct mapping in Elasticsearch of tags in the Jaeger traces.</span></li></ul><pre class="lang:default decode:true ">docker run --rm -it --link=elasticsearch --name=jaeger -e SPAN_STORAGE_TYPE=elasticsearch -e ES_SERVER_URLS=http://elasticsearch:9200 -e ES_TAGS_AS_FIELDS_ALL=true -p 16686:16686 jaegertracing/all-in-one:1.12 </pre><p><span style="font-weight: 400;">You can now browse to the Jager UI with: </span><span style="font-weight: 400;">http://localhost:16686</span></p><p><a target="_blank" href="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/beaver.png" rel="lightbox[27147]" data-lightbox="image-1" title="Distributed Tracing with Jaeger and the ELK Stack"><img decoding="async" class="lazy lazy-hidden aligncenter size-full wp-image-25912" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-type="image" data-lazy-src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/beaver.png" alt="beaver" width="1367" height="844"/><noscript><img decoding="async" class="aligncenter size-full wp-image-25912" src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/beaver.png" alt="beaver" width="1367" height="844"/></noscript></a></p><h2><span style="font-weight: 400;">Step 3: Simulating trace data</span></h2><p><span style="font-weight: 400;">Great, we’ve got Jaeger running. It’s now time to create some traces to verify they are being stored on our Elasticsearch instance. </span></p><p><span style="font-weight: 400;">To do this, I’m going to deploy </span><a href="https://github.com/jaegertracing/jaeger/tree/master/examples/hotrod" target="_blank" rel="noopener noreferrer"><span style="font-weight: 400;">HOT R.O.D</span></a><span style="font-weight: 400;"> – an example application provided as part of the project that consists of a few microservices and is perfect for easily demonstrating Jaeger’s capabilities.</span></p><p><span style="font-weight: 400;">Again, using Docker:</span></p><pre class="lang:default decode:true">docker run --rm --link jaeger --env JAEGER_AGENT_HOST=jaeger --env JAEGER_AGENT_PORT=6831 -p8080-8083:8080-8083 jaegertracing/example-hotrod:latest all </pre><p><span style="font-weight: 400;">Open your browser at: </span><span style="font-weight: 400;">http://localhost:</span><span style="font-weight: 400;">8080</span><span style="font-weight: 400;">:</span></p><p><a target="_blank" href="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/hot-rod.png" rel="lightbox[27147]" data-lightbox="image-1" title="Distributed Tracing with Jaeger and the ELK Stack"><img decoding="async" class="lazy lazy-hidden aligncenter size-full wp-image-25913" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-type="image" data-lazy-src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/hot-rod.png" alt="hot rod" width="1336" height="301"/><noscript><img decoding="async" class="aligncenter size-full wp-image-25913" src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/hot-rod.png" alt="hot rod" width="1336" height="301"/></noscript></a></p><p><span style="font-weight: 400;">Start calling the different services by playing around with the buttons in the app. Each button represents a customer and by clicking the buttons, we’re ordering a car to the customer’s location. Once a request for a car is sent to the backend, it responds with details on the car’s license plate and the car’s ETA:</span></p><p><a target="_blank" href="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/rod-data.png" rel="lightbox[27147]" data-lightbox="image-1" title="Distributed Tracing with Jaeger and the ELK Stack"><img decoding="async" class="lazy lazy-hidden aligncenter size-full wp-image-25914" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-type="image" data-lazy-src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/rod-data.png" alt="rod data" width="1403" height="460"/><noscript><img decoding="async" class="aligncenter size-full wp-image-25914" src="https://dytvr9ot2sszz.cloudfront.net/wp-content/uploads/2019/07/rod-data.png" alt="rod data" width="1403" height="460"/></noscript></a></p><p><span style="font-weight: 400;">Within seconds, traces will be created and indexed in Elasticsearch. </span></p><p><span style="font-weight: 400;">To verify, cURL Elasticsearch:</span></p><pre class="lang:default decode:true ">curl -X GET "localhost:9200/_cat/indices?v"