zipkin VS Fluentd

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Zipkin is a distributed tracing system used for tracking and analyzing how requests move through complex systems, especially in setups with many interconnected services, known as microservices.

Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

For microservice tracing, you might want to look at Zipkin [0], or OpenTelemetry [1]

[0] https://zipkin.io/

I’ve not used a self-hosted solution before, but here’s one I found. https://zipkin.io/

> IMO the reason these vendors can and do charge so much is not because telemetry software is hard.

I always saw it as "they are charging for their polished UI/experience"

The UI of https://zipkin.io/ versus DataDog is kind of... not really in the same ballpark?

There are the zipkin https://zipkin.io/ and jaeger https://www.jaegertracing.io/ packages/components you can use both have quickstarts if you consider that to be a beginner's guide.

Zipkin, which was developed by Twitter, is an open source tool for distributed tracing that can also be used to troubleshoot latency issues in your application. While Zipkin is Java-based, py_zipkin is an implementation for Python.

Get Started with Fluentd

Currently, there is no cluster-wide logging. Fluentd can be used to have a unified logging layer for the cluster.

Fluentd hasn't been touched for 8 years? Looking at the repo it looks like it's alive and well. https://github.com/fluent/fluentd

Fluentd is an open-source log management and data collection tool. Just like Logstash, Fluentd uses a pipeline-based architecture. This allows it to collect data from various sources and network traffic and forward it to various destinations.

Fluentd is a powerful log management tool that offers organizations the flexibility and scalability required to handle large volumes of log data from a variety of sources and transport it to various destinations. Utilizing a flexible and modular architecture, Fluentd allows users to easily add new input and output plugins to integrate with a wide range of systems and applications. It supports a wide range of data sources and destinations, including databases, message queues, and data stores.

Substation is an affordable alternative to products like Cribl (~10x cost savings) and is easier to manage than similar open-source projects such as Logstash and fluentd. It's been used in production by the security team at Brex for 2+ years and is ready for any scale, even beyond 100,000 events per second!

There are probably too many to chose from. Logstash, Promtail, Vector, Filebeat, FluentD, Logagent and probably many more

To alleviate some of the pain, it’s a good idea to use industry standards and tooling like OpenTelemetry (https://opentelemetry.io). For data collection specific to logs, open-source tools like LogStash and Fluentd are also popular.

Created and maintained by the creators of fluentd, fluentbit is a lightweight, fast, and scalable logging and metrics processor and forwarder. Built specifically for the cloud and containerized environments, it allows users to collect data from any source, enrich it with filters and forward it to the tool of their choice.