It has been 5 years since I have started working on open-source (OSS) distributed tracing projects. It is certainly a long time and in this article I would like to summarize what I have learned and show you the evolution in OSS distributed tracing space and peek into the future. We will talk about projects like Hawkular-APM, Zipkin, Jaeger, OpenTracing, OpenTelemetry, SigNoz and Hypertrace. Quite a lot right!? So let’s start!
First let’s have a look at the timeline when these projects were open-sourced:
In this article, we are going to have a look at using Jaeger clients with W3C Trace-Context propagation format. The standardized context propagation format assures interoperability between different tracing systems and instrumentation libraries. In this regard we are going to explore two use cases. First how to use OpenTelemetry SDKs in Jaeger instrumented environment. This scenario simulates interoperability between different tracing systems but also a migration path from Jaeger clients to OpenTelemetry SDK. In the second use case we are going to configure Jaeger native clients to use W3C Trace-Context.
In this article we are going to explain Jaeger integration with the OpenTelemetry collector, describe differences between these two and have a look at Kubernetes deployment via Jaeger Operator. See Jaeger and OpenTelemetry post by Yuri Shkuro on the long-term integration roadmap.
OpenTelemetry collector is a vendor-agnostic service for receiving, processing and exporting telemetry data. In the Jaeger project we have decided to deprecate the Jaeger collector and migrate its functionality to an implementation based on OpenTelemetry collector. This has several benefits:
In the previous blog post Data analytics with Jaeger aka traces tell us more! we have introduced our data science initiative and platform. The ultimate goal is to develop new functionality within the Jaeger project based on AI/ML that will provide new insights into our applications. This type of functionality is also referred to as AI operations (AIOps).
Jupyter notebooks provide a simple user interface for experimenting with data. There are two main use cases that we want to accomplish with the notebooks:
In this article we will look at the agent-like instrumentation tool T-Trace. The tool provides non-intrusive instrumentation capabilities for applications running on GraalVM. We will instrument a simple NodeJS application by using T-Trace and OpenTracing API with Jaeger NodeJS tracer.
So first things first, GraalVM is a polyglot virtual machine. It can run any JVM based language but also other mainstream languages like NodeJS, Python and Ruby… It also supports LLVM languages like C and C++. The sweet spot is that the languages can be combined together in a single application. Another very interesting feature is that GraalVM can produce…
I will get straight to the point, Jaeger at the moment only visualizes collected data from instrumented applications. It does not perform any post-processing (except service dependency diagram) or any calculations to derive other interesting metrics or features from traces it collects. This is a pity because traces contain the richest information from all telemetry signals combined!
In Jaeger we want to address this problem and provide a platform for data scientists and operators to verify a hypothesis and ultimately answer the question of what caused an outage or why the system behaves in a certain way. Apart from on-demand…
In this article, we are going to look at deploying Jaeger on Kubernetes and OpenShift with Elasticsearch storage using operators. We will go through the configuration (custom resources) and also see what benefits OpenShift offers over vanilla Kubernetes.
Kubernetes operator is a software component that simplifies deployment and operations of an application running in a Kubernetes cluster — in our case Jaeger and Elasticsearch. Think of it as software that knows how to deploy, monitor, upgrade and scale an application.
In this article you will learn how to configure and use the Elasticsearch rollover feature in Jaeger. Note that this feature has been introduced in Jaeger 1.10.0.
Jaeger uses index-per-day pattern to store its data to Elasticsearch. It creates a new index for each day based on span’s timestamp. These indices have to be periodically removed by
jaeger-es-index-cleaner cron job. Typically users keep data from one week up to one month which results in 7 or 30 indices only for spans.
Index-per-day pattern might not be effective if data or resources are not evenly distributed. For example indices which do…
In this article we will demonstrate some of the tracing features of the MicroProfile-OpenTracing project while evaluating performance of new Java runtime Quarkus. You will also learn how a Java application can be compiled to native code for supersonic performance!
First things first, MicroProfile is a project for building modern cloud-native Java applications. MicroProfile-OpenTracing provides tracing capabilities for MicroProfile technologies by integrating with the OpenTracing project.
The Quarkus project is a modern cloud-native Java runtime, which can be compiled as a native Linux executable. How cool is that? It promises a quick startup time, small memory footprint and generally better…
In the latest release of Jaeger 1.7 we have added a couple of major features to improve the user experience and integration with other tools. One of these features is a better support for querying Jaeger data in Kibana.
This feature allows storing tags as object fields, as opposed to an array of nested objects, to overcome the issue. The issue is that Kibana does not support query or aggregations on nested objects. This functionality can be simply enabled by
Let’s look at an example query in Kibana. …