Startup Lightrun Integrates with Developer and Observability Tools to Speed Real-Time Production Debugging

Lightrun, provider of a continuous observability and debugging platform, has announced integrations with six popular tools used by developers—Datadog, IntelliJ IDEA,, Prometheus, Slack, and StatsD—to help streamline the delivery of real-time observability for enterprise organizations. The new integrations expand Lightrun’s real-time capabilities to seamlessly provide the on-demand addition of logs, metrics, and traces inside the organization's existing observability infrastructure. 

A Tel Aviv-based startup that makes it easier for developers to debug their production code, Lightrun announced in June 2020 that it had raised a $4 million seed round led by Glilot Capital Partners, with participation from a number of engineering executives from several Fortune 500 firms.

Lightrun natively integrates with existing developer tooling such as IDEs, APMs, and observability solutions to deliver on-demand production debugging. According to the company, adding Lightrun code-level information to existing data in present solutions enables faster detection of anomalies and repeating patterns, increases the quality of the information being extracted and speeds up the overall debugging process.   

Developers can use Lightrun's IDE plugin to add log-lines, performance metrics and traces to code that is already running live in production, collecting the information needed to understand a problem and sending it to their IDE, APM or logging platform. Lightrun is platform-agnostic, working on-premise, in the cloud, inside containers and with serverless code.

The new integrations increase developer productivity and extend Lightrun’s ability to provide code-level visibility.

  • Cloud monitoring for application performance: Integrating with Datadog enables the aggregation and analysis of Lightrun’s on-demand logs inside Datadog’s log platform, as well as visualization of Lightrun’s on-demand metrics inside Datadog’s APM solution. 
  • Extract deeper application insights: Applying’s aggregation and analysis tools onto Lightrun logs and performance metrics to extract deeper insights about the running application.
  • Track performance metrics with visualization: Expose Lightrun on-demand performance metrics via a dedicated Prometheus metrics endpoint, allowing the scraping of those metrics by Prometheus (and visualization inside Grafana or similar tools).
  • Capture virtual snapshots of the running process: Actionable insight into the state of the running process during execution through local debugging using IntelliJ IDEA by showing the exact stack trace and variables inside the IDE.
  • Update teams on code-level events: Alerting developers based on specific code-level events and specific flows without adding new code by connecting a Slack webhook to your Lightrun actions.
  • Collect and transport metrics: Lightrun pipes on-demand performance metrics into the industry-leading metrics collection tool StatsD, enabling connectivity to various back-ends like Datadog, Graphite, Influx and more.
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