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97 lines
5.3 KiB
Markdown
97 lines
5.3 KiB
Markdown
# Monitoring DataHub
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Monitoring DataHub's system components is critical for operating and improving DataHub. This doc explains how to add
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tracing and metrics measurements in the DataHub containers.
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## Tracing
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Traces let us track the life of a request across multiple components. Each trace is consisted of multiple spans, which
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are units of work, containing various context about the work being done as well as time taken to finish the work. By
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looking at the trace, we can more easily identify performance bottlenecks.
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We enable tracing by using
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the [OpenTelemetry java instrumentation library](https://github.com/open-telemetry/opentelemetry-java-instrumentation).
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This project provides a Java agent JAR that is attached to java applications. The agent injects bytecode to capture
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telemetry from popular libraries.
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Using the agent we are able to
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1) Plug and play different tracing tools based on the user's setup: Jaeger, Zipkin, or other tools
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2) Get traces for Kafka, JDBC, and Elasticsearch without any additional code
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3) Track traces of any function with a simple `@WithSpan` annotation
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You can enable the agent by setting env variable `ENABLE_OTEL` to `true` for GMS and MAE/MCE consumers. In our
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example [docker-compose](../../docker/monitoring/docker-compose.monitoring.yml), we export metrics to a local Jaeger
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instance by setting env variable `OTEL_TRACES_EXPORTER` to `jaeger`
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and `OTEL_EXPORTER_JAEGER_ENDPOINT` to `http://jaeger-all-in-one:14250`, but you can easily change this behavior by
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setting the correct env variables. Refer to
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this [doc](https://github.com/open-telemetry/opentelemetry-java/blob/main/sdk-extensions/autoconfigure/README.md) for
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all configs.
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Once the above is set up, you should be able to see a detailed trace as a request is sent to GMS. We added
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the `@WithSpan` annotation in various places to make the trace more readable. You should start to see traces in the
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tracing collector of choice. Our example [docker-compose](../../docker/monitoring/docker-compose.monitoring.yml) deploys
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an instance of Jaeger with port 16686. The traces should be available at http://localhost:16686.
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## Metrics
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With tracing, we can observe how a request flows through our system into the persistence layer. However, for a more
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holistic picture, we need to be able to export metrics and measure them across time. Unfortunately, OpenTelemetry's java
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metrics library is still in active development.
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As such, we decided to use [Dropwizard Metrics](https://metrics.dropwizard.io/4.2.0/) to export custom metrics to JMX,
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and then use [Prometheus-JMX exporter](https://github.com/prometheus/jmx_exporter) to export all JMX metrics to
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Prometheus. This allows our code base to be independent of the metrics collection tool, making it easy for people to use
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their tool of choice. You can enable the agent by setting env variable `ENABLE_PROMETHEUS` to `true` for GMS and MAE/MCE
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consumers. Refer to this example [docker-compose](../../docker/monitoring/docker-compose.monitoring.yml) for setting the
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variables.
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In our example [docker-compose](../../docker/monitoring/docker-compose.monitoring.yml), we have configured prometheus to
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scrape from 4318 ports of each container used by the JMX exporter to export metrics. We also configured grafana to
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listen to prometheus and create useful dashboards. By default, we provide two
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dashboards: [JVM dashboard](https://grafana.com/grafana/dashboards/14845) and DataHub dashboard.
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In the JVM dashboard, you can find detailed charts based on JVM metrics like CPU/memory/disk usage. In the DataHub
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dashboard, you can find charts to monitor each endpoint and the kafka topics. Using the example implementation, go
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to http://localhost:3001 to find the grafana dashboards! (Username: admin, PW: admin)
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To make it easy to track various metrics within the code base, we created MetricUtils class. This util class creates a
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central metric registry, sets up the JMX reporter, and provides convenient functions for setting up counters and timers.
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You can run the following to create a counter and increment.
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```java
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MetricUtils.counter(this.getClass(),"metricName").increment();
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```
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You can run the following to time a block of code.
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```java
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try(Timer.Context ignored=MetricUtils.timer(this.getClass(),"timerName").timer()){
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...block of code
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}
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```
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## Enable monitoring through docker-compose
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We provide some example configuration for enabling monitoring in
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this [directory](https://github.com/datahub-project/datahub/tree/master/docker/monitoring). Take a look at the docker-compose
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files, which adds necessary env variables to existing containers, and spawns new containers (Jaeger, Prometheus,
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Grafana).
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You can add in the above docker-compose using the `-f <<path-to-compose-file>>` when running docker-compose commands.
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For instance,
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```shell
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docker-compose \
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-f quickstart/docker-compose.quickstart.yml \
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-f monitoring/docker-compose.monitoring.yml \
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pull && \
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docker-compose -p datahub \
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-f quickstart/docker-compose.quickstart.yml \
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-f monitoring/docker-compose.monitoring.yml \
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up
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```
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We set up quickstart.sh, dev.sh, and dev-without-neo4j.sh to add the above docker-compose when MONITORING=true. For
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instance `MONITORING=true ./docker/quickstart.sh` will add the correct env variables to start collecting traces and
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metrics, and also deploy Jaeger, Prometheus, and Grafana. We will soon support this as a flag during quickstart. |