In my case, its called grafanacloud-aquan-logs. In the top right corner, you can see the drop-down menu where you can set the time interval for the data. Kubernetes. Please watch out (follow & subscribe) for more blogs on big data and other latest technologies. But since our aim in the Strimzi project is to offer a Kubernetes-native experience when running Apache Kafka, we want to have the metrics exposed as a Prometheus endpoint. Very exciting! We will also look at some of the challenges of running a self-hosted Prometheus and Grafana instance versus the Hosted Grafana offered by MetricFire. Now you can see a beautiful dashboard: By default, Grafana does not refresh the page automatically but you can enable this by clicking on the Last 30 minutes button on the top right corner of the page and then click on refreshing every drop-down list and finally select 10s. Therefore, we shall copy the jmx_prometheus_javaagent jar file to /usr/local/kafka-server/libs/. Similar as you have done for Tempo connection information, we can collect Loki connection information from the Loki section in my account page: Finally, for Grafana Cloud Metrics, we can get all the connection information from the Prometheus section in my account page: With all the connection information collected, we are ready to configure our Grafana Agent so that the agent can send our logs, metrics, and traces to Grafana Cloud. As of writing today, traces are the most mature component of OpenTelemetry, and logs are still experimental. In the case above, anything over 80% CPU usage will be visualized as red. We will do a quick introduction of Grafana Cloud and Grafana Agent next, before looking at the instrument details. We selected the filesystem_free and filesystem_size metrics respectively for this graph. I appreciate you and the time you took out of your day to read this! As alex.dzyoba.com said: JMX is a common technology in Java world for exporting statistics of running application and also to control it (you can trigger GC with JMX, for example). So, our vampires, I mean lawyers want you to know that I may get answers wrong. It gives our users consumer lag monitoring out of the box. Visualizing metrics is really useful but nobody can sit at a computer watching a dashboard all day!!! As a result, we'll see the system, Kafka Broker, Kafka Consumer, and Kafka Producer metrics on our dashboard on Grafana side. Now we are on the last and the best part. We will create a Grafana dashboard for a VMs most important metrics, learn to create advanced dashboards with filters for multiple instance metrics, import and export dashboards, learn to refresh intervals in dashboards and learn about plugins. Service Monitors are one of the Custom Resources offered by the Prometheus Operator and basically provides a way to tell the operator to target new Kubernetes Services for scraping by Prometheus. Please change the directories as per your local installation. You can generate a new API Key by clicking on the Generate now link or navigate to the Security API Keys section. We also select where these alerts should be sent to, here we use the MetricFire slack that we just added. Lets add a variable for VM names so that we can select metrics for different VMs. But hopefully, you saw how easy it is to instrument a Spring Boot application for logs, metrics, and traces, and how Grafanas opinionated observability stack based on Prometheus, Grafana Loki, and Grafana Tempo can help you connect and correlate your telemetry data cohesively in Grafana Cloud. For Kafka, you can download this config file: Before we run Kafka we must pass an environment variable named KAFKA_OPTS to Kafka that enable JMX exporter to export JMX metrics from JVM. Prometheus is a time series database to capture real time metrics from any application over http. Information about message consumption rates. Once above basic installation steps are done, below is how to integrate them all together. If you are running everything locally, you will also be able to see the bonus Node Graph, which is still in beta, as of writing. But since our aim in the Strimzi project is to offer a Kubernetes-native experience when running Apache Kafka, we want to have the metrics exposed as a Prometheus endpoint. We will start with OpenTelemetry traces. I assume you have already installed Apache Kafka and Zookeeper and they are accessible using localhost:9092 and localhost:2181 respectively. It mainly contains one Java class HelloObservabilityBootApp, which contains two methods that can serve HTTP requests. The power of a unified observability platform! You can set up your own Grafana dashboards right on our platform, and apply what you learn from this article. They can be added to the Prometheus resource using spec.additionalScrapeConfigs property. The Requests per Minute visualization shows Prometheus metrics with exemplars, which are the green dots. This will be especially important to keep track of your producers, consumers and other metrics such as topics and partitions. LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads) on and off LinkedIn. One of those exporters is Java Management Extensions (JMX) Exporter which focuses on Java applications. Make sure you select the right account from the account dropdown. Running the exporter as a Java Agent is thus strongly encouraged. If you like the features which Kafka Exporter provides, do not forget to go and give it a star on GitHub. Apache Kafka is developed using Java and therefore we are going to need a Java exporter to scrape (extract) metrics so that Prometheus can be able to consume, store and expose. Grafana will connect on Prometheus to show some beautiful dashboards. You should see some log lines. Kafka Exporter is an important improvement to the monitoring capabilities of Strimzi. In the next section, we will demonstrate exactly that; we will start with sample dashboards and make few . We learned how to create a dashboard for Kafka metrics using Grafana, Prometheus and its JMX exporter. It will handle duplicates, silencing, inhibition and aggregation of alerts and send out notification to your system of choice. These dashboards can be exported from a JSON file or Grafanas dashboard repository. The exposed data can be used by tools such as Grafana as a data source to create beautiful and insightful graphs and charts for better visibility of your applications and servers. To explain this briefly, the configuration is a collection of regexps that names and filters the metrics to for Prometheus. In our previous blog post we focused on tracing which was one of the new features in our 0.14.0 release. It provides a way to integrate with your own notification/alerting systems via Webhooks as well. Examples of receivers could be chat clients like Microsoft Teams, Slack or on-call notification systems like OpsGenie or PagerDuty. Following Prometheus Operator guidelines, to create and configure AlertManager, we need. If successfully up, you should be able to see jmx metrics on below links. Go to Grafana Cloud, and the Hello Observability dashboard. I'm Grot. In this article, I want to show you what is JMX, how to use Prometheus to store Kafka JMX metrics and how to visualize them using Grafana to monitor your Kafka broker. Go to Grafana Cloud, then Explore, and select the Loki data source. Prometheus can also be used for alerting. This question is asked several times in StackOverflow even recently (see this and this). For more complex dashboard we need to install Grafana and import Prometheus data into that. Now Prometheus is able to pull monitoring data from localhost:7071/metrics. Learn more in our Cookie Policy. This will enable jmx ports for jconsole and Prometheus. Note: By signing up, you agree to be emailed related product-level information. Some useful examples of alerts that can be created based on these metrics are: Add the following snippet after spec.zookeeper to tls-kafka.yaml. Go to the directory where Kafka is installed on your system. Because ubuntu installation adds systemd service, we can set KAFKA_OPTS in the service file (located in /lib/systemd/system/confluent-kafka.service) and add the environment variable. You can update your choices at any time in your settings. Now run Kafka: Before we continue to the next section, let's create a simple topic: Kafka brokers expose their metric through JMX. Duplicate its contents in a file inside config directory within Kafkas home directory. Alertmanager will be then responsible for managing these alerts. https://groups.google.com/forum/#!searchin/prometheus-users/jvm_%7Csort:date/prometheus-users/OtYM7qGMbvA/dZ4vIfWLAgAJ, Self-healing code is the future of software development, How to keep your new tool from gathering dust, We are graduating the updated button styling for vote arrows, Statement from SO: June 5, 2023 Moderator Action. Strimzi provides a way to run an Apache Kafka cluster on Kubernetes in various deployment configurations. Memory: Lets add a visualization for the total memory and the current memory usage. All the agent configuration information is inside the agent.yaml file. On your server, you can use wget or curl to download it as follows: After you have the JMX Exporter downloaded, we will proceed to copy it to Kafkas lib directory where it stores its jar files. If you want, you can also download the latest version of the agent, and copy it into the hello-observability directory. Prometheus offers four core metric types Counter, Gauge, Histogram and Summary. Prometheus Exporters are used to extract and export data metrics to your Prometheus instance. The General tab can be used to modify the panel title, description, links, etc. The only thing remaining is to link the JMX exporter to our Kafka Broker. For the purpose of this article, I installed a fresh copy of Ubuntu 18 and assume that you installed the Confluent community platform using the Debian package. {"labels":{"span_id":"405e4b409d62cd38","trace_id":"7893cdf3ce9d2401ffcee7d7f0db569d"},"value":"1.090101375","timestamp":1647142355.737}. You can also book a demo and talk to us directly about what Grafana can do for you. For calc, choose the last value because we want to see the latest status. Lets set up a Slack alert on the dashboard we just built to alert us if CPU usage is high. Some data sources such as Prometheus are supported officially. In this blog, we will walk through detailed steps on how you can observe a Spring Boot application, by instrumenting it with Prometheus and OpenTelementry and by collecting and correlating logs, metrics, and traces from the application in Grafana Cloud. For the exemplar pop-up dialog to show a link to Tempo, you do have to configure the Label name to be trace_id in the Prometheus data source configuration. Variables can be different types, such as: Go to dashboard settings in the top right and click on variables add a new variable. Although you can view Kafka and Zookeeper metrics in jConsole, in a real-world scenario you probably want to automatically collect these metrics and show them in an informative dashboard. Prometheus is a time series database to capture real time metrics from any application over http. The JSON we made above can be pasted below to be imported, or we can paste the ID of the dashboard from Grafana dashboard repository, and it will be imported. My local setup is on Fedora (version 37). We can think of JMX as an API in JVM that allows us to pull metrics of the application that is running on JVM. Also, I choose port 7071 for the HTTP server. You cannot edit the pre-configured Prometheus data source in your cloud account. A dashboard called Hello Observability is also pre-loaded. More importantly, its about the ability to correlate this telemetry data and derive actionable insights from it. Strimzi, Strimzi Authors 2023 | Documentation distributed under CC-BY-4.0. (Grafana Labs product manager Jen Villa has a nice blog post about exemplars.). This will open the Data Sources Menu where you can add more. export JMX_PORT=9999export JMX_PROMETHEUS_PORT=7071. Email update@grafana.com for help. Prometheus offers four core metric types Counter, Gauge, Histogram and Summary. Once attached, it automatically captures telemetry data at the edges of applications and services, such as inbound requests, outbound HTTP calls, database calls, and others. c. Filesystem: We will add the graph for free disk space vs total disk space. Below I am going to demonstrate how we can use out of the box solutions using Prometheus and Grafana tools for kafka health monitoring. Observability is not only just about collecting all the telemetry data in the form of logs, metrics, and traces. If successfully added, you should see it as illustrated below. Once the metrics are stored in the database, you can query them using the PromQL query language. To duplicate a dashboard go to settings and save it as a different name and you will get a duplicate dashboard. We have the following Java class definition in our source code directory, in a file called RequestLoggingFilterConfig.java. Lets export the dashboard we just created. . A ServiceMonitor should be deployed in the same namespace where Prometheus is deployed, although it could target services deployed in other namespaces also using namespaceSelector property. The exemplars are set as metadata labels, as span_id and trace_id. To visualize the data we will use irate() of node_CPU (CPU average usage), for a 5m interval, with the instance variable as instance=~$instance. Otherwise, you will not see the link. (No worry about me exposing my API Key. Take a look at the HelloObservabilityBootApp Java class. Now lets add Prometheus as the data source in Grafana. OpenTelemetry is the result of merging OpenTracing and OpenCensus. We will talk about variables in the next section. Grafana Cloud Traces is the fully-managed, highly-scalable, and cost-effective distributed tracing system based on Grafana Tempo. We are also enabling and collecting Tomcat access logs from the application, so that we can create some interesting visualizations off it. See OpenTelemetry Java Agent Configuration for details about the system properties and environment variables that you can use to configure the agent. But some important metrics are missing. To install a plugin use the following steps. You now have your Kafka metrics well displayed on Grafana and hence have a deeper visibility of your topics and others. Plugins. All of the scripts are in the bin directory within Kafkas home folder. Here we are setting up the y-axis unit as megabytes, but it can be changed in the visualizations tab to gigabytes or terabytes (thats a lot of traffic). Its already deleted.). To start, we will need a metrics source from which we will add metrics to Grafana for visualization. Install with default value mostly, rbac are enabled. It does not index the contents of the logs, but rather a set of labels for each log stream. If you look at the hello-observability.log log file, you should see log lines like these. Make sure you know where your Kafkas home directory is for there you will find the libs directory. Start your zookeeper and kafa servers. Variables are a way to create dynamic Grafana dashboards. tls.server-name. For example, Prometheus exposes PromQL for time series data, and MySQL data source exposes SQL query language. We are basically writing the access logs to /tmp/access_log.log file. Thats also where the Grafana Agent picks it up. The above gist only shows the important piece of the puzzle the Prometheus Custom Resource. Sorry, an error occurred. The server option means that any request to a data source will be sent to the Grafana backend server, and the backend will send the request to the data source. Add a new panel and click on Add query on the panel. 4. And this is why having proper monitoring and alerting mechanisms in place is of quintessential importance. If you like Strimzi, star us on GitHub and follow us on Twitter to make sure you dont miss any of our future blog posts! Here is what the application.properties file looks like: We are telling Spring Boot to write logs into the /tmp/hello-observability.log log file, with information about the date/time, the log message, and the trace ID. Strimzi has supported Prometheus for Kafka metrics almost since the beginning. You can further add the Scrape Interval, Query Timeout and HTTP method. Also checkout : https://medium.com/@rramiz.rraza. The values are false per default. Import the Hello Observability dashboard from the exported cloud/dashboards/hello-observability.json file and select the corresponding Loki and Prometheus data sources. Kafka Exporter and JMX Exporter will collect some broker metrics from Kafka cluster. Now lets see how we can import a dashboard. Because dashboard creation can be done using configuration, some developers have created their own dashboard and added it to Grafana as a plugin so that other developers can use it. Installing Prometheus and Node-Exporter on Debian-based systems. So that explains Strimzis support for metrics, but how does the Kafka Exporter we mentioned fit in? Find centralized, trusted content and collaborate around the technologies you use most. How To Install Grafana on CentOS 8 / RHEL 8. Of particular interest is the trace_id. Grafana is a rich visualization tool on top of data stored in Prometheus. Sign up for free now! We suggest logging into MetricFires Hosted Graphite free trial for your monitoring, but we will cover Prometheus in this post. Also, since exemplars are still new, you do have to request it to be enabled for your Grafana Cloud account. Go to Visualization and select Gauge. It uses the variables $_interval, $_from, and $_to. All you need to do is to add it into a Kafka custom resource: Strimzi will create a new deployment with the Kafka Exporter and configure it to connect to the Kafka cluster. The server option is recommended for secure access so as to not expose credentials to all the users. 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