kafkacat VS zipkin

To communicate with Kafka, you can use Kafkacat, a command-line tool that allows to produce and consume Kafka messages using a very simple syntax. It also allows you to view the topics' metadata.

Feels like you could get pretty far with kafkacat and a SQLite database.

This is the second release version of Kafcat. Kafcat is a Rust fully async rewrite of kafkacat.

Consume from the Kafka topic my.topic with kafkacat:

From the code above, you can see that to process the headers, simply use the ConsumerRecord.headers() method to return the headers. In our example above, we’re printing the headers out to the console for demonstration purposes. Once you have access to the headers, you can process them as needed. For reading headers from the command line, KIP-431 adds support for optionally printing headers from the ConsoleConsumer, which will be available in the Apache Kafka 2.7.0 release.You can also use kafkacat to view headers from the command line. Here’s an example command:

Note: In the example above, we have used kafkacat to consume the topics. The option -o-1 is used to only consume the latest message

Distributed tracing as a discipline is older than most engineers writing about it think. Google's 2010 Dapper paper, by Sigelman and colleagues, is the canonical reference; Twitter open-sourced Zipkin in 2012 as a Dapper-inspired implementation, and Uber open-sourced Jaeger in 2017 on similar lineage. For most of the 2010s, however, the operational reality was vendor-specific: each APM (Datadog, New Relic, AppDynamics, Dynatrace) shipped its own SDK, and instrumenting an application meant choosing a vendor and accepting that the instrumentation work was, structurally, lock-in.

Distributed tracing systems like zipkin aim to address these challenges by allowing visualisation of flows on environments with a full setup. Code can be traced by using mono-repos with the event names being the same across services. These are techniques to deal with the inability to trace code/flows across systems and while neither of them are as effective as tracing usages of your code, they help drive a balance between decoupling and ease of use.

The standard observability stack includes Prometheus for metrics collection, Grafana for visualization, and AlertManager for notifications. For logging, consider Fluent Bit or Fluentd with Elasticsearch or cloud logging services. Jaeger or Zipkin provide distributed tracing for microservices debugging.

Distributed tracing: This technology follows requests as they bounce between services, showing you exactly where things slow down or break. Tools like Jaeger and Zipkin support OpenTracing standards, and leveraging an OpenTelemetry plugin can make it possible to track requests across different service boundaries without losing the thread.

Getting Started: Use tools like Jaeger, Zipkin, or OpenTelemetry. Focus on critical paths, set smart sampling rules, and align trace data with system metrics.

Logging and Tracing: Use centralized logging and distributed tracing to gain visibility into the flow of requests across microservices. This helps you diagnose issues more effectively and understand the impact of changes. Tools like Jaeger or Zipkin can be integrated with your API gateway to provide detailed tracing information.

Zipkin is an open-source distributed tracing system that helps gather timing data needed to troubleshoot latency problems in microservice architectures. It manages the collection, storage, and querying of tracing data, providing a user-friendly interface to analyze traces.

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.