Preemptive

Observability Analysis

Unlike traditional observability tools focused on alerting to problems in retrospect, Digma leverages a Preemptive Observability Analysis Engine (POAE) that identifies issues in pre-prod environments at the code level and enables the team to prevent them altogether

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POA Engine Preemptive Observability Analysis - Continuous over Manual

Continuous over Manual

The POA engine identifies issues automatically, as the developer works, and does not rely on manual processes

POA Engine Preemptive Observability Analysis - Issues over raw data

Insights over raw data

Raw observability takes too much effort to translate into something practical. An individual trace is not useful but analysis can reveal critical actionable insights

POA Engine Preemptive Observability Analysis - Focus on code not services

Focus on code

Traditional APMs focus on the infrastructure and APIs, whereas the POA engine focuses on code-level issues and patterns

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The Preemptive Observability Analysis engine

The Digma Preemptive Observability Analysis (POA) engine introduces an advanced approach to observability by proactively identifying potential issues before they materialize in production.

POA Engine Preemptive Observability Analysis - about poa

It achieves this by analyzing observability tracing data, even when data volumes are low. Leveraging pattern matching and anomaly detection techniques, Digma’s algorithm extrapolates expected application performance metrics, enabling it to detect deviations or potential problems that have not yet impacted the application. In analyzing the tracing data, Digma pinpoints the issue to the specific responsible code and commits.

Learn how to analyze your code with preemptive observability

We’ve curated a list of blog posts about concepts and practices. We also published an Udemy course which you can access below.

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Frequently Asked Questions

We don’t compete with any tool existing today because there isn’t any other tool that generates this type of feedback and delivers it in context. We do work very well together with other tools looking at the same data, like Jaeger and Prometheus, as well as traditional observability tools like Datadog or Splunk.

There are plenty of tools that offer troubleshooting or debugging capabilities after an issue is already identified, or give some raw realtime data. However, Digma is fundamentally different because we turn raw data into usable insights, and make those insights easily accessible to developers so they can take a proactive (not reactive) approach to optimization.

Yes! Here’s an example app with more coming soon.

Absolutely not! We rely on the OpenTelemetry vanilla instrumentation with a few added attributes of our own. We leave your code untouched.

No. It uses your production data to generate real insights – not predictions.

No code changes are necessary. Digma can be set up within minutes as an IDE plugin.

Digma runs all observability analytics locally on your machine via Docker containers. It is not a SaaS service and does not upload observability data.

Digma is free when deployed locally on a laptop. However, it is not free when connected to a central environment.

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Digma is the first Continuous Feedback platform backed by a highly engaged developer community. Curious how others use Digma? Have questions or ideas? See you there!

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