Adding OpenTelemetry data

Telemetry is an automated process used to collect measurements and other types of data remotely. The data is sent between devices and monitored for analysis to improve device performance. In other cases, telemetry refers to the data collected by tech companies on user activity, tracking things like usage, uptime, crashes, software installed, and more.

OpenTelemetry (also known as OTel) is an open-source Observability (= the ability to understand an application’s behavior and performance based only on its telemetry) framework for instrumenting, generating, collecting and exporting telemetry data such as traces, metrics and logs.

Through metrics, logs, and traces, observability data gives us the information we need to inspect how our applications run and while using OpenTelemetry, observability becomes accessible to everyone.

• With OpenTelemetry you can collect and export telemetry data to help you analyze your software’s performance and behavior in XpoLog Platform and evaluate your production environment state without changing your code.

• OpenTelemetry supports vendor-neutral APIs or vendor-agnostic, software development kits (SDKs) and other tools for collecting telemetry data (code instrumentation is supported for various languages like Java, Go, and Python).

OpenTelemetry supports two primary methods of exporting data from your process to XpoLog Platform, either directly from a process or by proxying it through the OpenTelemetry Collector.

Open

• Metrics: Numeric measurements. Metrics can include:

  • A numeric status at a moment in time (like CPU % used).

  • Aggregated measurements (like a count of events over a one-minute time, or a rate of events-per-minute).

• Logs: Lines of text a system produces when certain code blocks get executed. Log data is usually unstructured and therefore hard to parse in a systematic way. However, XpoLog Platform is equipped with automated log patterns for known systems data and has a very-friendly parsing language to work with. At the end, structured log data makes it easier and faster to search the data and derive events or metrics from the data.

• Traces: Records of activity. Trace data describes what happens when a request is made by user or an application.