Logz.io Extends Open Source Observability Platform

Logz.io has enhanced its observability platform to include both unified dashboards and support for distributed tracing to monitor service performance enabled by the open source Jaeger agent software being advanced under the auspices of the Cloud Native Computing Foundation (CNCF).

At the same time, Logz.io is making available a preview of an anomaly detection capability that leverages machine learning algorithms to build an artificial intelligence (AI) model to monitor data.

The company has also added support for OpenSearch, a fork of the open source ElasticSearch software project that is no longer available via an Apache license. OpenSearch was created by Amazon Web Services (AWS) and Logz.io as an alternative search engine.

Finally, Logz.io has added a security event management capability that makes it easier to classify, prioritize and collaborate on mitigation of cyberattacks as they are discovered.

The Logz.io observability platform is entirely based on open source software. In addition, the company has made contributions to enhance both Jaeger and the OpenTelemetry project, including contributing an OTEL Span Metrics Processor that makes it easier to aggregate metrics.

Logz.io CEO Tomer Levy said that, in general, it’s still early days as far as observability is concerned. As a concept, observability traces its lineage to linear dynamic systems. Observability at its most basic level measures how well internal states of a system can be inferred from knowledge of its external outputs. The overall goal is to make it easier to query machine data in a way that enables DevOps teams to proactively discover the root cause of issues before they cause further disruption. That’s a major advance over monitoring platforms that typically only provide predefined metrics to identify when a specific platform or application is performing within expectations. The issue, however, is most IT teams really don’t know what questions to ask to take full advantage of an observability platform, said Levy.

However, as more organizations are exposed to observability platforms, it will become easier to advance both DevOps and DevSecOps as more platforms continue to leverage AI to provide remediation capabilities and automate processes based on the massive amounts of data collected via these platforms, said Levy. There is simply no way the average IT team will be able to manage all the microservices being strewn across a distributed computing environment without the aid of an AI model trained using observability data, he added.

As DevOps continues to evolve, it’s more than likely that the best DevOps engineers are not going to want to work for organizations that have not invested in some form of observability. The odds of being consistently successful as a DevOps engineer decline sharply as IT environments become more complex.

On the plus side, it should soon become a lot easier as the overall level of application instrumentation increases to manage those complex environments and to do so at a level of scale that doesn’t require a small army of DevOps engineers.