This is the first post of a 2 part series where we will set up production-grade Kubernetes logging for applications deployed in the cluster and the cluster itself. We will be using Elasticsearch as the logging backend for this. The Elasticsearch setup will be extremely scalable and fault-tolerant. ‍

OpenTelemetry (OTel) is steadily gaining broad industry adoption. As one of the major Cloud Native Computing Foundation (CNCF) projects, with as many commits as Kubernetes, it is gaining support from major ISVs and cloud providers delivering support for the framework. Many global companies from finance, insurance, tech, and other industries are starting to standardize on OpenTelemetry.

Loki is a open source log aggregation tool developed by Grafana labs. It is inspired by Prometheus and is designed to be cost-effective and easy to operate. But Loki also has some limitations, and you might want to explore some Loki alternatives for your log analytics. In this article, we will look at 11 log management tools you can use as a Loki alternative. Loki is designed to keep indexing low. It does this by making use of labels.

In Python development, logging is not only good practice; it is vital. Logging is critical for understanding the execution flow of an application and helps in debugging potential issues. The importance of logging for developing reliable and maintainable Python applications cannot be overstated. Python provides capabilities for running concurrent operations—either in a threaded (single process) or multiple process environment. But what implications do these different approaches have on logging?

We’re excited to announce query rules in Elasticsearch 8.10! Query rules allow you to change a query based on the query terms they are searching for, or based on context information provided as part of the search query.

Observability has been tied up with causality from its origins in the mathematical realm of control theory in the early 1960s. A system (of any kind, hardware or software, natural or engineered) was deemed to be ‘observable’ if it generated self-descriptive data from which it was possible to infer how states of the system were causally related to one another.

Modern retail businesses need to deliver exceptional customer experiences that are not only fast and secure but also completely frictionless. Amazon Web Services (AWS) and Splunk have come together to provide a value proposition that goes beyond what either platform can offer independently.

One of the primary goals of any IT team is to ensure seamless operation and consistent uptime. This is typically achieved via monitoring — whether on-premises, in an application or across a network, monitoring allows teams to respond quickly to a given issue or even understand potential problems before they arise. For today’s complex distributed systems, one of the more common monitoring methods comes in the form of cloud monitoring.

Docker’s power and versatility have cemented its place in developers’ and administrators’ toolkits. Along with this widespread adoption comes the critical need for effective logging in Docker containers. However, once you scale beyond a single container on a single machine, effectively capturing and working with logs from Docker presents a challenge. The native docker logs command quickly becomes inadequate, and you’ll need a more scalable solution.

Application performance management (APM) has moved beyond traditional monitoring to become an essential tool for developers, offering deep insights into applications at the code level. With APM, teams can not only detect issues but also understand their root causes, optimizing software performance and end-user experiences. The modern landscape presents a wide range of APM tools and companies offering different solutions. Additionally, OpenTelemetry is becoming the open ingestion standard for APM.