When we have large-scale, distributed systems, Logging becomes essential for observability, monitoring, and security. No matter what architecture (Monolith/Microservices) our systems have, they are complex due to the number of moving parts they have and the challenges they face around management, deployment, and scaling.
Jaeger is an open-source end-to-end distributed tracing tool for microservices architecture. On the other hand, Elastic APM is an application performance monitoring system that is built on top of the ELK Stack (Elasticsearch, Logstash, Kibana, Beats). In this article, let's explore their key features, differences, and alternatives.
Morgan is a popular HTTP logging library for Node.js. It is designed to be a simple and flexible tool for logging HTTP requests and responses in Node.js applications.
OpenMetrics and OpenTelemetry are popular standards for instrumenting cloud-native applications. Both projects are part of the Cloud Native Computing Foundation (CNCF) and aim to simplify how we generate, collect and monitor services in a modern cloud-native distributed application environment.
Prometheus and the Elasticsearch stack are both used for monitoring applications. But while Prometheus is primarily meant to monitor metrics, the Elasticsearch stack or the ELK stack is mainly used to collect, store, analyze, and visualize application logs. In this article, we will see what Prometheus and ELK stack is and compare their differences.
In this article, learn how to setup application monitoring for Python apps using an open-source solution, SigNoz.
If you're looking for an open-source alternative to AppDynamics, then you're at the right place. SigNoz is a perfect open-source alternative to AppDynamics. SigNoz provides a unified UI for metrics, traces and logs with advanced tagging and filtering capabilities.
This guide is for anyone who is getting started monitoring their application with OpenTelemetry, and is generating unstructured logs. As is well understood at this point, structured logs are ideal for post-hoc incident analysis and broad-range querying of your data. However, it’s not always feasible to implement highly structured logging at the code level.
With SigNoz, you get some parsing automatically to identify details like timestamp, container ID, container name, and an optional body. But it’s possible to go much deeper with a relatively simple configuration. It’s also a good idea to check for attributes that may contain Personal Identifying Information (PII) and remove them with filters. Since the SigNoz collector is a fork of the OpenTelemetry collector, this tutorial will also work for configuring a baseline OpenTelemetry collector.
Join Nočnica Mellifera and Pranay as they discuss architecting and collecting data with the OpenTelemetry Collector. We discuss using Apache Kafka queues to handle OTLP data, and why you probably shouldn't push OTel data straight to Postgres.
Below is the recording and an edited transcript of the conversation.