Open Source Observability Tools - Page 4

This project is part of the landscape of Cloud Native Computing Foundation in the Observability and Analysis - Chaos Engineering section. Backed by the teams at platform engineering.it and devopstribe.it, which provides enterprise-grade features and certified resilience services for your Kubernetes infrastructure.

This tool allows exporting the often missed Kubernetes events to various outputs so that they can be used for observability or alerting purposes. You won't believe what you are missing. Configuration is done via a YAML file, when run in Kubernetes, ConfigMap. The tool watches all the events and the user has to option to filter out some events, according to their properties. Critical events can be routed to alerting tools such as Opsgenie, or all events can be dumped to an Elasticsearch instance. You can use namespaces, and labels on the related object to route some Pod-related events to owners via Slack. The final routing is a tree which allows flexibility.

Memphis enables building modern queue-based applications that require large volumes of streamed and enriched data, modern protocols, zero ops, up to x9 faster development, up to x46 fewer costs, and significantly lower dev time for data-oriented developers and data engineers. Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible. Provide great performance while maintaining efficient resource consumption. Increase observability, integrations with 3rd-party monitoring tools, real-time notifications, stream lineage, and therefore troubleshooting time reduction. Enable rapid development and ultra-short time-to-production.

A miscellaneous collection of in-development and unsupported performance analysis tools for Linux ftrace and perf_events (aka the "perf" command). Both ftrace and perf are core Linux tracing tools, included in the kernel source. Your system probably has ftrace already, and perf is often just a package add. These tools are designed to be easy to install (fewest dependencies), provide advanced performance observability, and be simple to use: do one thing and do it well. This collection was created by Brendan Gregg (author of the DTraceToolkit). Many of these tools employ workarounds so that functionality is possible on existing Linux kernels. Because of this, many tools have caveats (see man pages), and their implementation should be considered a placeholder until future kernel features, or new tracing subsystems, are added.

All the greatest observability formats and integrations you love, at once - LGTM Drop-in compatible. Let's get Polyglot. qryn independently implements popular observability standards, protocols and query languages. Make sure you have sufficient memory and disk resources allocated for your node service and clickhouse server when dealing with large amounts of data and fingerprints. We suggest 8GB RAM or higher for most setups with 100k-1M fingerprints. Observe your daily and weekly data consumption to forecast your disk usage requirements. Compression codecs and other optimizations can be performed at the ClickHouse level.

Swagger-stats is a middleware for monitoring REST APIs, offering real-time metrics, detailed performance analysis, and request tracking.