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Kafka exporter github software#
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This query language can be used to select and aggregate time series data in real time and the result of the query can be shown as a graph, table or can be used by other clients to visualize the data. Prometheus also provides a powerful query language called PromQL. There are many libraries for various languages that simplifies the process of producing metrics in applications (For instance, Spring Actuator for Java).

Prometheus collect metrics provided by the components of a system by polling a HTTP endpoint on each of them. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud.

Prometheus is here to help us regarding this issue. These metrics will be stored as time-series data in an optimized storage provided by the monitoring tools. Since there are many moving parts in a microservice architecture that communicate with each other, we need monitoring tools to gather various metrics produced from each component to see how well our system performs and find problems easily so we can optimize our system. Observability plays an important role in the world of microservices. Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.

Kafka exporter github software#

Observability is an important aspect in software engineering.

Part 3: Monitoring our Strimzi Kafka Cluster with Prometheus and Grafana.

Part 2: Creating Producer and Consumer using Go and Scala and deploying on Kubernetes.

Part 1: Creating and Deploying a Strimzi Kafka Cluster on Kubernetes.

In case you haven’t read the previous posts, I recommend you to check it out: This is the Part 3 of the Apache Kafka on Kubernetes with Strimzi blog post. In the last part of the series, we are going to use Prometheus and Grafana to fulfill our destiny! Now, what are these two really? That’s why we need some monitoring tools to easily monitor our cluster. But we had no idea how they are really performing. In the second part, we have built a producer application with Go and a consumer application with Scala, created Dockerfiles and Kubernetes deployment files and published them to the Kubernetes.įrom the look of the logs it seems like our applications and Kafka are performing well. In the first part of the series, we have learnt about Strimzi and deployed a Kafka Cluster on Minikube and also tested our cluster with the console producer/consumer of the Kafka package.