Zipkin is a distributed tracing system. It helps gather timing data needed to troubleshoot latency problems in service architectures. Features include both the collection and lookup of this data. If you have a trace ID in a log file, you can jump directly to it. Otherwise, you can query based on attributes such as service, operation name, tags and duration. Some interesting data will be summarized for you, such as the percentage of time spent in a service, and whether or not operations failed.
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Applications need to be “instrumented” to report trace data to Zipkin. This usually means configuration of a tracer or instrumentation library. The most popular ways to report data to Zipkin are via HTTP or Kafka, though many other options exist, such as Apache ActiveMQ, gRPC and RabbitMQ. The data served to the UI are stored in-memory, or persistently with a supported backend such as Apache Cassandra or Elasticsearch.

Zipkin is a distributed tracing system. It helps gather timing data needed to troubleshoot latency problems in service architectures. Features include both the collection and lookup of this data. If you have a trace ID in a log file, you can jump directly to it. Otherwise, you can query based on attributes such as service, operation name, tags and duration. Some interesting data will be summarized for you, such as the percentage of time spent in a service, and whether or not operations failed.

Zipkin - Distributed tracing system

The MicroProfile is a baseline platform definition that optimizes Enterprise Java for a microservices architecture and delivers application portability across multiple MicroProfile runtimes. It has support for OpenAPI, OpenTracing, JWT, Rest Client, Fault Tolerance, Metrics, CDI, JSON-P, Jax-RS, Open Telemetry.



MicroProfile - Optimizing Enterprise Java for a Microservices Architecture

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