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How to filter metrics by labels using OpenTelemetry Collector.

As a big proponent of open source and all things open, I jumped at the opportunity to expand on Cribl Stream’s OpenTelemetry implementation. I’m happy to report that as of Cribl Stream 4.1, both our OpenTelemetry source and destination now support OTLP over HTTP!

It’s 5:00 pm on a Friday. You’re wrapping up work, ready to head into the weekend, when one of your high-value customers Slacks you that something’s not right. Requests to their service are randomly timing out and nobody can figure out what’s causing it, so they’re looking to your team for help. You sigh as you know it’s one of those all-hands-on-deck situations, so you dig out your phone and type the "going to miss dinner" text.

This article was originally published in The New Stack and is reposted here with permission. They require different approaches for storage and querying, making it a challenge to use a single solution. But InfluxDB is working to consolidate them into one. Time series data has unique characteristics that distinguish it from other types of data. But even within the scope of time series data, there are different types of data that require different workloads.

Here at InfluxData, we recently announced InfluxDB 3.0, which expands the number of use cases that are feasible with InfluxDB. One of the primary benefits of the new storage engine that powers InfluxDB 3.0 is its ability to store traces, metrics, events, and logs in a single database. Each of these types of time series data has unique workloads, which leaves some unanswered questions. For example: Luckily this is where our work within OpenTelemetry comes into play.

To get visibility into highly distributed applications, organizations often use various tracing tools that are best suited to each individual service owner’s specifications. However, when a request travels between services that have been instrumented with different tools, the trace data may be formatted differently, resulting in broken traces.

Parsing logs with regex is a valuable technique for extracting essential information from large volumes of log data. By employing this method, one can effectively identify patterns, errors, and other key insights, ultimately streamlining log analysis and enhancing system performance.

Grafana Tempo 2.1 is out and comes with a host of TraceQL improvements. Tempo 2.1 comes with some nice incremental improvements to TraceQL and likely some breaking changes. There’s a section down below about those, too.