Elastic APM consists of four components: APM agents, APM Server, Elasticsearch, and Kibana.

APM Agentsedit

APM agents are open source libraries written in the same language as your service. You may only need one, or you might use all of them. You install them into your service as you would install any other library. They instrument your code and collect performance data and errors at runtime. This data is buffered for a short period and sent on to APM Server.

Each agent has its own documentation:

APM Serveredit

APM Server is a free and open application that receives performance data from your APM agents. It’s a separate component by design, which helps keep the agents light, prevents certain security risks, and improves compatibility across the Elastic Stack.

After the APM Server has validated and processed events from the APM agents, the server transforms the data into Elasticsearch documents and stores them in corresponding Elasticsearch indices. In a matter of seconds, you can start viewing your application performance data in the Kibana APM app.

The APM Server reference provides everything you need when it comes to working with the server. Here you can learn more about installation, configuration, security, monitoring, and more.

Elasticsearchedit

Elasticsearch is a highly scalable free and open full-text search and analytics engine. It allows you to store, search, and analyze large volumes of data quickly and in near real time. Elasticsearch is used to store APM performance metrics and make use of its aggregations.

Kibana APM appedit

Kibana is a free and open analytics and visualization platform designed to work with Elasticsearch. You use Kibana to search, view, and interact with data stored in Elasticsearch.

Since application performance monitoring is all about visualizing data and detecting bottlenecks, it’s crucial you understand how to use the APM app in Kibana. The following sections will help you get started:

APM also has built-in integrations with Machine learning. To learn more about this feature, or the anomaly detection feature that’s built on top of it, refer to Machine learning integration.