Introduction to Application Performance Management (APM) and Apache SkyWalking: Concepts, Architecture, and Installation Guide

Application Performance Management (APM) collects and reports key performance metrics from applications using probes, enabling systematic performance and fault management. Popular APM tools include Cat, Zipkin, Pinpoint, and SkyWalking, the latter offering distributed tracing, metric analysis, and service dependency visualization.

Unlike traditional monitoring systems such as Zabbix or Prometheus that focus on hardware and service health, APM emphasizes internal execution metrics and inter‑service call chains, making it complementary for pinpointing slow requests.

Distributed tracing captures the complete request flow across microservices, databases, and caches, showing precise response times, invoked methods, and accessed resources, which is essential for debugging complex architectures.

Apache SkyWalking is a cloud‑native APM designed for microservice, container (Docker, Kubernetes, Mesos) environments. It supports multiple languages (Java, .NET Core, Node.js, Go) and follows the OpenTracing standards.

SkyWalking’s visualization includes six dimensions:

Global view – overall service and component overview.

Topology view – service dependency graph.

Application view – upstream/downstream relations, JVM and host info.

Service view – entry point performance and dependencies.

Trace view – detailed request execution path.

Alarm view – real‑time alerts based on thresholds.

The core modules are:

SkyWalking Agent – non‑intrusive tracing and metric collection, reporting via HTTP/gRPC.

SkyWalking Collector – aggregates data, processes it in the Analysis Core, and stores it.

Storage – supports Elasticsearch, MySQL, TiDB, H2, etc.

SkyWalking UI – web dashboard (RocketBot) for visualizing collected data.

Installation steps (Docker environment):

mkdir -p /data/ent/skywalking

cd /data/ent/skywalking

vim docker-compose.yml

# docker‑compose.yml content (excerpt)

version: '3.8'

services:

elasticsearch:

image: docker.elastic.co/elasticsearch/elasticsearch:7.5.0

ports:

- 9200:9200

skywalking-oap:

image: apache/skywalking-oap-server:8.1.0-es7

ports:

- 11800:11800

- 12800:12800

skywalking-ui:

image: apache/skywalking-ui:8.1.0

ports:

- 28080:8080

After configuring the services, start them with:

docker-compose up -d

Open the UI at http:// :28080 to verify that services are registered.

To instrument a Java application, download the SkyWalking package, copy the agent directory into the project, and launch the app with the Java agent:

java -javaagent:agent/skywalking-agent.jar=agent.service_name=my-service,collector.backend_service=192.168.35.226:11800 -jar my-app.war --spring.profiles.active=local_dev

The UI provides dashboards for global metrics, topology, service details, trace analysis, performance profiling, and alarm management, allowing users to monitor request latency, throughput, error rates, JVM resources, and more across microservice architectures.