--- title: "Apache Hadoop integration | Grafana Cloud documentation" description: "Learn about Apache Hadoop Grafana Cloud integration." --- > For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt). # Apache Hadoop integration for Grafana Cloud Apache Hadoop is an open-source framework designed to process and store large amounts of data across distributed clusters of computers. It provides a scalable and fault-tolerant solution for handling big data by utilizing a distributed file system (Hadoop Distributed File System) and a parallel processing model (MapReduce). This integration for Grafana Cloud allows users to monitor a Hadoop cluster with 4 separate dashboards for visualizing metrics and logs for a Hadoop NameNode, DataNode, NodeManager, and ResourceManager component. This integration supports Apache Hadoop version 3.3.1+ Apache Hadoop 3.3 and upper supports Java 8 and Java 11 (runtime only) This integration supports the JMX exporter 0.17.0+ This integration includes 8 useful alerts and 4 pre-built dashboards to help monitor and visualize Apache Hadoop metrics and logs. ## Before you begin In order for the integration to properly work, you must set up the [JMX Exporter for Prometheus](https://github.com/prometheus/jmx_exporter) on each instance in your cluster. #### Set up JMX Exporter Each instance requires a JMX to Prometheus exporter. This is a collector that creates an HTTP server that serves mBean metrics of the local JVM target. Depending on the Apache Hadoop component being monitored - i.e., the NameNode, DataNode, NodeManager and/or ResourceManager - a slightly different configuration will be used. Typically, a DataNode and a NodeManager are grouped together, while the ResourceManager and NameNode are separate. However, each Hadoop deployment architecture may vary. ### Expose JMX Remotely To expose the JMX remotely, it needs to be enabled for the components being monitored. A configuration option enables JMX on the Hadoop daemons, which exists in a `hadoop-env.sh` file located in the `/etc/hadoop/conf`. Within the bash file, there is a `HDFS__OPTS` value that will take an argument to enable JMX, specify the <JMX\_PORT>, and manage security options. It should look similar to: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh export HDFS__OPTS="-Dcom.sun.management.jmxremote=true -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.port= -Dcom.sun.management.jmxremote.ssl=false" ``` Then save the configuration file and restart the Hadoop daemons. For more details on setting up an Apache Hadoop cluster environment, refer to the [official documentation](https://hadoop.apache.org/docs/r3.3.1/hadoop-project-dist/hadoop-common/ClusterSetup.html). ### Connect JMX to the Prometheus Exporter In order to connect JMX to the Prometheus Exporter, a collector is configured in a config file for each component being monitored. This .yaml file, such as `namenode.yaml`, can be placed anywhere and named anything but must specify the exposed `JMX_PORT` for the component being monitored. It should look similar to: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh hostPort: localhost: lowercaseOutputName: true lowercaseOutputLabelNames: true rules: - pattern: ".*" ``` ### Run the Prometheus Exporter To run the JMX to Prometheus exporter, the [Java Agent](https://github.com/prometheus/jmx_exporter#running-the-java-agent) or a [Standalone HTTP Server](https://github.com/prometheus/jmx_exporter#running-the-standalone-http-server) is used. These require two arguments: a .yaml file that was created above and the <JMX\_EXPORTER\_PORT> that exposes the metrics. To run the JMX to Prometheus exporter as a Java Agent: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh java -javaagent:./jmx_prometheus_javaagent-0.17.0.jar=:.yaml -jar yourJar.jar ``` To run the JMX to Prometheus exporter as a Standalone HTTP Server: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh java -jar jmx_prometheus_httpserver-0.17.0.jar /path-to-jmx-configs/.yaml ``` To validate that the JMX Exporter is setup correctly, the Prometheus metrics should be available locally via curl: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh curl http://localhost:/metrics ``` ## Install Apache Hadoop integration for Grafana Cloud 1. In your Grafana Cloud stack, click **Connections** in the left-hand menu. 2. Find **Apache Hadoop** and click its tile to open the integration. 3. Review the prerequisites in the **Configuration Details** tab and set up Grafana Alloy to send Apache Hadoop metrics and logs to your Grafana Cloud instance. 4. Click **Install** to add this integration’s pre-built dashboards and alerts to your Grafana Cloud instance, and you can start monitoring your Apache Hadoop setup. ## Configuration snippets for Grafana Alloy ### Advanced mode The following snippets provide examples to guide you through the configuration process. To instruct Grafana Alloy to scrape your Apache Hadoop instances, **manually** copy and append the snippets to your alloy configuration file, then follow subsequent instructions. ### Advanced metrics snippets Alloy ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```alloy prometheus.scrape "metrics_integrations_integrations_apache_hadoop" { targets = [{ __address__ = ":", hadoop_cluster = "", instance = constants.hostname, }] forward_to = [prometheus.remote_write.metrics_service.receiver] job_name = "integrations/apache-hadoop" } ``` To monitor your Apache Hadoop instance, you must use a [discovery.relabel](/docs/alloy/latest/reference/components/discovery.relabel/) component to discover your Apache Hadoop Prometheus endpoint and apply appropriate labels, followed by a [prometheus.scrape](/docs/alloy/latest/reference/components/prometheus.scrape) component to scrape it. Configure the following properties within each `discovery.relabel` component: - `__address__`: The address to your Apache Hadoop Prometheus metrics endpoint. - `instance` label: `constants.hostname` sets the `instance` label to your Grafana Alloy server hostname. If that is not suitable, change it to a value uniquely identifies this Apache Hadoop instance. Make sure this label value is the same for all telemetry data collected for this instance. - `hadoop_cluster` must be the value that identifies the Apache Hadoop cluster this node belongs to. If you have multiple Apache Hadoop servers to scrape, configure one `discovery.relabel` for each and scrape them by including each under `targets` within the `prometheus.scrape` component. ### Advanced logs snippets #### linux Alloy ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```alloy local.file_match "logs_integrations_integrations_apache_hadoop" { path_targets = [{ __address__ = "localhost", __path__ = "", hadoop_cluster = "", instance = constants.hostname, job = "integrations/apache-hadoop", }] } loki.source.file "logs_integrations_integrations_apache_hadoop" { targets = local.file_match.logs_integrations_integrations_apache_hadoop.targets forward_to = [loki.write.grafana_cloud_loki.receiver] } ``` To monitor your Apache Hadoop instance logs, you will use a combination of the following components: - [local.file\_match](/docs/alloy/latest/reference/components/local.file_match) defines where to find the log file to be scraped. Change the following properties according to your environment: - `__address__`: The Apache Hadoop instance address - `__path__`: this is the component log location, which by default is indicated in the `HADOOP_LOG_DIR` entry within `/etc/hadoop/conf` config gile. An example log path may look like `/hadoop/logs/*.log` on Linux. - `instance` label: `constants.hostname` sets the `instance` label to your Grafana Alloy server hostname. If that is not suitable, change it to a value uniquely identifies this Apache Hadoop instance. Make sure this label value is the same for all telemetry data collected for this instance. - `hadoop_cluster` must be the value that identifies the Apache Hadoop cluster this node belongs to. - [loki.source.file](/docs/alloy/latest/reference/components/loki.source.file) sends logs to Loki. On Linux, you will also need to add the `alloy` user to the `hadoop` group to get logs. Run the following command to configure the user as required: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh sudo usermod -a -G hadoop alloy ``` ## Kubernetes instructions Instructions for Kubernetes ### Before you begin with Kubernetes **Please note**: These instructions assume the use of the [Kubernetes Monitoring Helm chart](https://github.com/grafana/k8s-monitoring-helm) In order for the integration to properly work, you must set up the [JMX Exporter for Prometheus](https://github.com/prometheus/jmx_exporter) on each instance in your cluster. #### Set up JMX Exporter Each instance requires a JMX to Prometheus exporter. This is a collector that creates an HTTP server that serves mBean metrics of the local JVM target. Depending on the Apache Hadoop component being monitored - i.e., the NameNode, DataNode, NodeManager and/or ResourceManager - a slightly different configuration will be used. Typically, a DataNode and a NodeManager are grouped together, while the ResourceManager and NameNode are separate. However, each Hadoop deployment architecture may vary. ### Expose JMX Remotely To expose the JMX remotely, it needs to be enabled for the components being monitored. A configuration option enables JMX on the Hadoop daemons, which exists in a `hadoop-env.sh` file located in the `/etc/hadoop/conf`. Within the bash file, there is a `HDFS__OPTS` value that will take an argument to enable JMX, specify the <JMX\_PORT>, and manage security options. It should look similar to: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh export HDFS__OPTS="-Dcom.sun.management.jmxremote=true -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.port= -Dcom.sun.management.jmxremote.ssl=false" ``` Then save the configuration file and restart the Hadoop daemons. For more details on setting up an Apache Hadoop cluster environment, refer to the [official documentation](https://hadoop.apache.org/docs/r3.3.1/hadoop-project-dist/hadoop-common/ClusterSetup.html). ### Connect JMX to the Prometheus Exporter In order to connect JMX to the Prometheus Exporter, a collector is configured in a config file for each component being monitored. This .yaml file, such as `namenode.yaml`, can be placed anywhere and named anything but must specify the exposed `JMX_PORT` for the component being monitored. It should look similar to: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh hostPort: localhost: lowercaseOutputName: true lowercaseOutputLabelNames: true rules: - pattern: ".*" ``` ### Run the Prometheus Exporter To run the JMX to Prometheus exporter, the [Java Agent](https://github.com/prometheus/jmx_exporter#running-the-java-agent) or a [Standalone HTTP Server](https://github.com/prometheus/jmx_exporter#running-the-standalone-http-server) is used. These require two arguments: a .yaml file that was created above and the <JMX\_EXPORTER\_PORT> that exposes the metrics. To run the JMX to Prometheus exporter as a Java Agent: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh java -javaagent:./jmx_prometheus_javaagent-0.17.0.jar=:.yaml -jar yourJar.jar ``` To run the JMX to Prometheus exporter as a Standalone HTTP Server: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh java -jar jmx_prometheus_httpserver-0.17.0.jar /path-to-jmx-configs/.yaml ``` To validate that the JMX Exporter is setup correctly, the Prometheus metrics should be available locally via curl: sh ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```sh curl http://localhost:/metrics ``` ### Configuration snippets for Kubernetes Helm chart The following snippets provide examples to guide you through the configuration process. To scrape your Apache Hadoop instances, **manually** modify your Kubernetes Monitoring Helm chart with these configuration snippets. Replace any values between the angle brackets `<>` in the provided snippets with your desired configuration values. #### Metrics snippets Alloy ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```alloy alloy-metrics: extraConfig: |- discovery.kubernetes "hadoop_datanode_service_jmx" { role = "service" selectors { role = "service" field = "metadata.name=" } namespaces = [""] } discovery.relabel "hadoop_datanode_service_jmx" { targets = discovery.kubernetes.services.targets rule { source_labels = ["__meta_kubernetes_service_port_name"] regex = "" action = "keep" } } discovery.kubernetes "hadoop_namenode_service_jmx" { role = "service" selectors { role = "service" field = "metadata.name=" } namespaces = [""] } discovery.relabel "hadoop_namenode_service_jmx" { targets = discovery.kubernetes.hadoop_namenode_service_jmx.targets rule { source_labels = ["__meta_kubernetes_service_port_name"] regex = "" action = "keep" } } discovery.kubernetes "hadoop_nodemanager_service_jmx" { role = "service" selectors { role = "service" field = "metadata.name=" } namespaces = [""] } discovery.relabel "hadoop_nodemanager_service_jmx" { targets = discovery.kubernetes.hadoop_nodemanager_service_jmx.targets rule { source_labels = ["__meta_kubernetes_service_port_name"] regex = "" action = "keep" } } discovery.kubernetes "hadoop_resourcemanager_service_jmx" { role = "service" selectors { role = "service" field = "metadata.name=" } namespaces = [""] } discovery.relabel "hadoop_resourcemanager_service_jmx" { targets = discovery.kubernetes.hadoop_resourcemanager_service_jmx.targets rule { source_labels = ["__meta_kubernetes_service_port_name"] regex = "" action = "keep" } } discovery.relabel "hadoop_all_services_jmx" { targets = array.concat( discovery.relabel.hadoop_datanode_service_jmx.output, discovery.relabel.hadoop_namenode_service_jmx.output, discovery.relabel.hadoop_nodemanager_service_jmx.output, discovery.relabel.hadoop_resourcemanager_service_jmx.output, ) rule { target_label = "hadoop_cluster" replacement = "" } } prometheus.scrape "integrations_hadoop_prometheus_exporter" { targets = discovery.relabel.hadoop_all_services_jmx.output job_name = "integrations/apache-hadoop" forward_to = [prometheus.remote_write.grafana_cloud_metrics.receiver] } ``` #### Logs snippets Alloy ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```alloy podLogs: extraDiscoveryRules: |- rule { source_labels = ["__meta_kubernetes_namespace", "__meta_kubernetes_pod_name", "__meta_kubernetes_pod_container_name"] separator = ":" regex = "(::)" target_label = "integration" replacement = "hadoop" } rule { source_labels = ["__meta_kubernetes_namespace", "__meta_kubernetes_pod_name", "__meta_kubernetes_pod_container_name"] separator = ":" regex = "(::)" target_label = "integration" replacement = "hadoop" } rule { source_labels = ["__meta_kubernetes_namespace", "__meta_kubernetes_pod_name", "__meta_kubernetes_pod_container_name"] separator = ":" regex = "(::)" target_label = "integration" replacement = "hadoop" } rule { source_labels = ["__meta_kubernetes_namespace", "__meta_kubernetes_pod_name", "__meta_kubernetes_pod_container_name"] separator = ":" regex = "(::)" target_label = "integration" replacement = "hadoop" } rule { source_labels = ["integration","__address__"] regex = "hadoop-(datanode|namenode|nodemanager|resourcemanager):(.+)" replacement = "$2" target_label = "instance" } rule { source_labels = ["integration"] regex = "hadoop-(datanode|namenode|nodemanager|resourcemanager)" target_label = "log_type" } extraLogProcessingStages: |- stage.match { selector = "{integration=\\"hadoop\\"}" stage.static_labels { values = { hadoop_cluster = "", job = "integrations/apache-hadoop", } } stage.labels { values = { instance = null, log_type = null, } } } ``` ## Dashboards The Apache Hadoop integration installs the following dashboards in your Grafana Cloud instance to help monitor your system. - Apache Hadoop DataNode overview - Apache Hadoop NameNode overview - Apache Hadoop NodeManager overview - Apache Hadoop ResourceManager overview **Apache Hadoop NameNode overview (1/2)** **Apache Hadoop DataNode overview (1/1)** **Apache Hadoop NodeManger overview (1/2)** ## Alerts The Apache Hadoop integration includes the following useful alerts: Expand table | Alert | Description | |----------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------| | ApacheHadoopLowHDFSCapacity | Warning: Remaining HDFS cluster capacity is low which may result in DataNode failures or prevent DataNodes from writing data. | | ApacheHadoopHDFSMissingBlocks | Critical: There are missing blocks in the HDFS cluster which may indicate potential data loss. | | ApacheHadoopHDFSHighVolumeFailures | Critical: A volume failure in HDFS cluster may indicate hardware failures. | | ApacheHadoopHighDeadDataNodes | Critical: Number of dead DataNodes has increased, which could result in data loss and increased network activity. | | ApacheHadoopHighNodeManagerCPUUsage | Critical: A NodeManager has a CPU usage higher than the configured threshold. | | ApacheHadoopHighNodeManagerMemoryUsage | Critical: A NodeManager has a higher memory utilization than the configured threshold. | | ApacheHadoopHighResourceManagerVirtualCoreCPUUsage | Critical: A ResourceManager has a virtual core CPU usage higher than the configured threshold. | | ApacheHadoopHighResourceManagerMemoryUsage | Critical: A ResourceManager has a higher memory utilization than the configured threshold. | ## Metrics The most important metrics provided by the Apache Hadoop integration, which are used on the pre-built dashboards and Prometheus alerts, are as follows: - hadoop\_datanode\_blocksremoved - hadoop\_datanode\_ramdiskblocksevictedwithoutread - hadoop\_datanode\_volumefailures - hadoop\_namenode\_blockstotal - hadoop\_namenode\_capacityremaining - hadoop\_namenode\_capacitytotal - hadoop\_namenode\_capacityused - hadoop\_namenode\_filestotal - hadoop\_namenode\_missingblocks - hadoop\_namenode\_numdeaddatanodes - hadoop\_namenode\_numdecommissioningdatanodes - hadoop\_namenode\_numlivedatanodes - hadoop\_namenode\_numstaledatanodes - hadoop\_namenode\_totalload - hadoop\_namenode\_transactionssincelastcheckpoint - hadoop\_namenode\_underreplicatedblocks - hadoop\_namenode\_volumefailurestotal - hadoop\_nodemanager\_allocatedcontainers - hadoop\_nodemanager\_allocatedgb - hadoop\_nodemanager\_allocatedvcores - hadoop\_nodemanager\_applicationsrunning - hadoop\_nodemanager\_availablegb - hadoop\_nodemanager\_availablevcores - hadoop\_nodemanager\_containerlaunchdurationavgtime - hadoop\_nodemanager\_containerscompleted - hadoop\_nodemanager\_containersfailed - hadoop\_nodemanager\_containersiniting - hadoop\_nodemanager\_containerskilled - hadoop\_nodemanager\_containerslaunched - hadoop\_nodemanager\_containerspaused - hadoop\_nodemanager\_containersreiniting - hadoop\_nodemanager\_containerusedmemgb - hadoop\_nodemanager\_containerusedvmemgb - hadoop\_nodemanager\_gccount - hadoop\_nodemanager\_gctimemillis - hadoop\_nodemanager\_localizationdurationmillisavgtime - hadoop\_nodemanager\_memheapcommittedm - hadoop\_nodemanager\_memheapusedm - hadoop\_nodemanager\_memnonheapcommittedm - hadoop\_nodemanager\_memnonheapusedm - hadoop\_nodemanager\_nodecpuutilization - hadoop\_nodemanager\_nodegpuutilization - hadoop\_resourcemanager\_activeapplications - hadoop\_resourcemanager\_allocatedmb - hadoop\_resourcemanager\_allocatedvcores - hadoop\_resourcemanager\_appscompleted - hadoop\_resourcemanager\_appsfailed - hadoop\_resourcemanager\_appskilled - hadoop\_resourcemanager\_appspending - hadoop\_resourcemanager\_appsrunning - hadoop\_resourcemanager\_appssubmitted - hadoop\_resourcemanager\_availablemb - hadoop\_resourcemanager\_availablevcores - hadoop\_resourcemanager\_gccount - hadoop\_resourcemanager\_gctimemillis - hadoop\_resourcemanager\_memheapcommittedm - hadoop\_resourcemanager\_memheapusedm - hadoop\_resourcemanager\_memnonheapcommittedm - hadoop\_resourcemanager\_memnonheapusedm - hadoop\_resourcemanager\_numactivenms - hadoop\_resourcemanager\_numdecommissionednms - hadoop\_resourcemanager\_numlostnms - hadoop\_resourcemanager\_numrebootednms - hadoop\_resourcemanager\_numshutdownnms - hadoop\_resourcemanager\_numunhealthynms - up ## Changelog md ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```md # 1.0.0 - August 2024 * Add Kubernetes as a supported platform using Grafana Alloy # 0.0.3 - September 2023 * New Filter Metrics option for configuring the Grafana Agent, which saves on metrics cost by dropping any metric not used by this integration. Beware that anything custom built using metrics that are not on the snippet will stop working. * New hostname relabel option, which applies the instance name you write on the text box to the Grafana Agent configuration snippets, making it easier and less error prone to configure this mandatory label. # 0.0.2 - August 2023 * Add regex filter for logs datasource # 0.0.1 - July 2023 * Initial release ``` ## Cost By connecting your Apache Hadoop instance to Grafana Cloud, you might incur charges. To view information on the number of active series that your Grafana Cloud account uses for metrics included in each Cloud tier, see [Active series and dpm usage](/docs/grafana-cloud/fundamentals/active-series-and-dpm/) and [Cloud tier pricing](/products/cloud/pricing/).