This guide is for anyone who is getting started monitoring their application with OpenTelemetry, and is generating unstructured logs. As is well understood at this point, structured logs are ideal for post-hoc incident analysis and broad-range querying of your data. However, it’s not always feasible to implement highly structured logging at the code level.

The needs of observability workloads can sometimes be orthogonal to the needs of compliance workloads. Honeycomb is designed for software developers to quickly fix problems in production, where reducing 100% data completeness to 99.99% is acceptable to receive immediate answers. Compliance and audit workloads require 100% data completeness over much longer (or "infinite") time spans, and are content to give up query performance in return.

Join Nočnica Mellifera and Pranay as they discuss architecting and collecting data with the OpenTelemetry Collector. We discuss using Apache Kafka queues to handle OTLP data, and why you probably shouldn't push OTel data straight to Postgres. Below is the recording and an edited transcript of the conversation. Find the conversation transcript below.👇 Nica: Hi everybody! If you're seeing this we're starting up we'll get started in just a moment here.

The team at SigNoz would like to share recent developments released this month that greatly enhance the ability to dynamically query your trace and log data. With these tools anyone can explore complex OpenTelemetry data and gain insight into their stack.

Jaeger, a renowned distributed tracing system, has been a trusted companion for developers and operations teams seeking to unravel the complexities of microservices architectures. However, as the landscape continues to evolve, the time has come to explore Jaeger alternatives that offer distinct features and advantages.

In a recent conversation on our SigNoz community Slack, a user shared their query that asks a deceptively simple question: what is the average time between two spans in a trace? The usefulness of this answer is evident if you think about how often the total trace time does not highlight the time you care about most. This could mean any number of things: that the total trace time of handling a web request might include lots of spans after a satisfying response was sent to the user.

At Grafana Labs, we are constantly improving our feature set, and tracing is no different. Traces are often overshadowed by logs and metrics, but they’re a pillar of observability for a reason. Used correctly, organizations that can quickly and successfully follow a chain of events through a system gain a more holistic view of their systems and are better equipped to find and fix issues faster.

In the context of application performance monitoring (APM) and observability, traces and spans are fundamental concepts that help users to track and understand the flow of requests and operations within a system. They are essential in assisting users to identify bottlenecks, troubleshoot issues, and optimize application performance.

In today’s rapidly evolving software landscape, ensuring observability is crucial for building robust and reliable applications. One of the critical components of observability is metrics, which provide valuable insights into the performance and behavior of our systems. OpenTelemetry, an open-source observability framework, offers a standardized approach to capturing, exporting, and analyzing metrics. This blog post explores seven OpenTelemetry metrics for tracking better visibility.

Get excited about Grafana Tempo 2.2! Not only is this release on time, but it is also chock full of TraceQL features and performance improvements. I was honestly a little shocked by how much we have accomplished in the last three months when summarizing the changelog.