We’re excited to introduce Honeycomb Tracing! Now, you can both: Visualize individual traces to deeply understand request execution, and Break down, filter, and aggregate trace data to uncover patterns, find outliers, and understand historical trends.

In the previous three parts of our OpenTracing series, we provided an Overview of OpenTracing, explaining what OpenTracing is and does, how it works and what it aims to achieve, we looked at Zipkin – a popular open-source distributed tracer and then at Jaeger – a newer open-source distributed tracer developed under the CNCF umbrella. In this blog post – the last part of the OpenTracing series – we will compare Jaeger vs. Zipkin side by side!

While logs can tell us whether a specific request failed to execute or not and metrics can help us monitor how many times this request failed and how long the failed request took, traces help us debug the reason why the request failed, or took so long to execute by breaking up the execution flow and dissecting it into smaller events.

In the previous two parts of OpenTracing series, we provided a good OpenTracing overview, explaining what OpenTracing is and does, how it works and what it aims to achieve and looked at Zipkin – a popular open-source distributed tracer. In this blog post, we will look at Jaeger, a newer open-source distributed tracer developed under the CNCF umbrella.

Dear community, yesterday Diego and Ivan had a call to discuss a bit of what we think the future of Kamon should look like and we wanted to summarize the ideas and share with the community.

In part one of the OpenTracing blog series we provided a good OpenTracing overview, explaining what OpenTracing is and does, how it works and what it aims to achieve. One of the key aspects of OpenTracing is that it is vendor neutral, and also that OpenTracing is just a specification. In order to instrument an application via OpenTracing API, it’s necessary to have an OpenTracing-compatible tracer correctly deployed and listening for incoming span requests.