SkyWalking is an APM (application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. SkyWalking is an open source APM system, including monitoring, tracing, diagnosing capabilities for distributed system in Cloud Native architecture. It supports to collect telemetry (metrics, traces, and logs) data from multiple sources and multiple formats.
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Features:
<ul>
<li>Service, service instance, endpoint metrics analysis</li>
<li>Root cause analysis. Profile the code on the runtime</li>
<li>Service topology map analysis</li>
<li>Service, service instance and endpoint dependency analysis</li>
<li>Slow services and endpoints detected</li>
<li>Performance optimization</li>
<li>Distributed tracing and context propagation</li>
<li>Database access metrics. Detect slow database access statements(including SQL statements)</li>
<li>Alarm</li>
<li>Browser performance monitoring</li>
<li>Infrastructure(VM, network, disk etc.) monitoring</li>
<li>Collaboration across metrics, traces, and logs</li>
</ul>

SkyWalking is an APM (application performance monitor) system, especially designed for microservices, cloud native and container-based (Docker, Kubernetes, Mesos) architectures. SkyWalking is an open source APM system, including monitoring, tracing, diagnosing capabilities for distributed system in Cloud Native architecture. It supports to collect telemetry (metrics, traces, and logs) data from multiple sources and multiple formats.

Skywalking - APM, Application Performance Monitoring System

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

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Skywalking - APM, Application Performance Monitoring System

Zipkin - Distributed tracing system

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