Writing a SparkApplication

Writing a SparkApplication

As with all other Kubernetes API objects, a SparkApplication needs the apiVersion, kind, and metadata fields. For general information about working with manifests, see object management using kubectl.

A SparkApplication also needs a .spec section. This section contains fields for specifying various aspects of an application including its type (Scala, Java, Python, or R), deployment mode (cluster or client), main application resource URI (e.g., the URI of the application jar), main class, arguments, etc. Node selectors are also supported via the optional field .spec.nodeSelector.

It also has fields for specifying the unified container image (to use for both the driver and executors) and the image pull policy, namely, .spec.image and .spec.imagePullPolicy respectively. If a custom init-container (in both the driver and executor pods) image needs to be used, the optional field .spec.initContainerImage can be used to specify it. If set, .spec.initContainerImage overrides .spec.image for the init-container image. Otherwise, the image specified by .spec.image will be used for the init-container. It is invalid if both .spec.image and .spec.initContainerImage are not set.

Below is an example showing part of a SparkApplication specification:

apiVersion: sparkoperator.k8s.io/v1beta2
kind: SparkApplication
metadata:
  name: spark-pi
  namespace: default
spec:
  type: Scala
  mode: cluster
  image: spark:3.5.1
  mainClass: org.apache.spark.examples.SparkPi
  mainApplicationFile: local:///opt/spark/examples/jars/spark-examples_2.12-3.5.1.jar

Specifying Deployment Mode

A SparkApplication should set .spec.deployMode to cluster, as client is not currently implemented. The driver pod will then run spark-submit in client mode internally to run the driver program. Additional details of how SparkApplications are run can be found in the design documentation.

Specifying Application Dependencies

Often Spark applications need additional files additionally to the main application resource to run. Such application dependencies can include for example jars and data files the application needs at runtime. When using the spark-submit script to submit a Spark application, such dependencies are specified using the --jars and --files options. To support specification of application dependencies, a SparkApplication uses an optional field .spec.deps that in turn supports specifying jars and files, respectively. More specifically, the optional fields .spec.deps.jars and.spec.deps.files correspond to the --jars and --files options of the spark-submit script, respectively.

Additionally, .spec.deps also has fields for specifying the locations in the driver and executor containers where jars and files should be downloaded to, namely, .spec.deps.jarsDownloadDir and .spec.deps.filesDownloadDir. The optional fields .spec.deps.downloadTimeout and .spec.deps.maxSimultaneousDownloads are used to control the timeout and maximum parallelism of downloading dependencies that are hosted remotely, e.g., on an HTTP server, or in external storage such as HDFS, Google Cloud Storage, or AWS S3.

The following is an example specification with both container-local (i.e., within the container) and remote dependencies:

spec:
  deps:
    jars:
      - local:///opt/spark-jars/gcs-connector.jar
    files:
      - gs://spark-data/data-file-1.txt
      - gs://spark-data/data-file-2.txt

It’s also possible to specify additional jars to obtain from a remote repository by adding maven coordinates to .spec.deps.packages. Conflicting transitive dependencies can be addressed by adding to the exclusion list with .spec.deps.excludePackages. Additional repositories can be added to the .spec.deps.repositories list. These directly translate to the spark-submit parameters --packages, --exclude-packages, and --repositories.

NOTE:

  • Each package in the packages list must be of the form “groupId:artifactId:version”
  • Each package in the excludePackages list must be of the form “groupId:artifactId”

The following example shows how to use these parameters.

spec:
  deps:
    repositories:
      - https://repository.example.com/prod
    packages:
      - com.example:some-package:1.0.0
    excludePackages:
      - com.example:other-package

Specifying Spark Configuration

There are two ways to add Spark configuration: setting individual Spark configuration properties using the optional field .spec.sparkConf or mounting a special Kubernetes ConfigMap storing Spark configuration files (e.g. spark-defaults.conf, spark-env.sh, log4j.properties) using the optional field .spec.sparkConfigMap. If .spec.sparkConfigMap is used, additionally to mounting the ConfigMap into the driver and executors, the operator additionally sets the environment variable SPARK_CONF_DIR to point to the mount path of the ConfigMap.

spec:
  sparkConf:
    spark.ui.port: "4045"
    spark.eventLog.enabled: "true"
    spark.eventLog.dir: "hdfs://hdfs-namenode-1:8020/spark/spark-events"

Specifying Hadoop Configuration

There are two ways to add Hadoop configuration: setting individual Hadoop configuration properties using the optional field .spec.hadoopConf or mounting a special Kubernetes ConfigMap storing Hadoop configuration files (e.g. core-site.xml) using the optional field .spec.hadoopConfigMap. The operator automatically adds the prefix spark.hadoop. to the names of individual Hadoop configuration properties in .spec.hadoopConf. If .spec.hadoopConfigMap is used, additionally to mounting the ConfigMap into the driver and executors, the operator additionally sets the environment variable HADOOP_CONF_DIR to point to the mount path of the ConfigMap.

The following is an example showing the use of individual Hadoop configuration properties:

spec:
  hadoopConf:
    "fs.gs.project.id": spark
    "fs.gs.system.bucket": spark
    "google.cloud.auth.service.account.enable": true
    "google.cloud.auth.service.account.json.keyfile": /mnt/secrets/key.json

Writing Driver Specification

The .spec section of a SparkApplication has a .spec.driver field for configuring the driver. It allows users to set the memory and CPU resources to request for the driver pod, and the container image the driver should use. It also has fields for optionally specifying labels, annotations, and environment variables for the driver pod. By default, the driver pod name of an application is automatically generated by the Spark submission client. If instead you want to use a particular name for the driver pod, the optional field .spec.driver.podName can be used. The driver pod by default uses the default service account in the namespace it is running in to talk to the Kubernetes API server. The default service account, however, may or may not have sufficient permissions to create executor pods and the headless service used by the executors to connect to the driver. If it does not and a custom service account that has the right permissions should be used instead, the optional field .spec.driver.serviceAccount can be used to specify the name of the custom service account. When a custom container image is needed for the driver, the field .spec.driver.image can be used to specify it. This overrides the image specified in .spec.image if it is also set. It is invalid if both .spec.image and .spec.driver.image are not set.

For applications that need to mount Kubernetes Secrets or ConfigMaps into the driver pod, fields .spec.driver.secrets and .spec.driver.configMaps can be used. For more details, please refer to Mounting Secrets and Mounting ConfigMaps.

The following is an example driver specification:

spec:
  driver:
    cores: 1
    coreLimit: 200m
    memory: 512m
    labels:
      version: 3.1.1
    serviceAccount: spark

Writing Executor Specification

The .spec section of a SparkApplication has a .spec.executor field for configuring the executors. It allows users to set the memory and CPU resources to request for the executor pods, and the container image the executors should use. It also has fields for optionally specifying labels, annotations, and environment variables for the executor pods. By default, a single executor is requested for an application. If more than one executor are needed, the optional field .spec.executor.instances can be used to specify the number of executors to request. When a custom container image is needed for the executors, the field .spec.executor.image can be used to specify it. This overrides the image specified in .spec.image if it is also set. It is invalid if both .spec.image and .spec.executor.image are not set.

For applications that need to mount Kubernetes Secrets or ConfigMaps into the executor pods, fields .spec.executor.secrets and .spec.executor.configMaps can be used. For more details, please refer to Mounting Secrets and Mounting ConfigMaps.

An example executor specification is shown below:

spec:
  executor:
    cores: 1
    instances: 1
    memory: 512m
    labels:
      version: 3.1.1
    serviceAccount: spark

Specifying Extra Java Options

A SparkApplication can specify extra Java options for the driver or executors, using the optional field .spec.driver.javaOptions for the driver and .spec.executor.javaOptions for executors. Below is an example:

spec:
  executor:
    javaOptions: "-XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap"

Values specified using those two fields get converted to Spark configuration properties spark.driver.extraJavaOptions and spark.executor.extraJavaOptions, respectively. Prefer using the above two fields over configuration properties spark.driver.extraJavaOptions and spark.executor.extraJavaOptions as the fields work well with other fields that might modify what gets set for spark.driver.extraJavaOptions or spark.executor.extraJavaOptions.

Specifying Environment Variables

There are two fields for specifying environment variables for the driver and/or executor containers, namely .spec.driver.env (or .spec.executor.env for the executor container) and .spec.driver.envFrom (or .spec.executor.envFrom for the executor container). Specifically, .spec.driver.env (and .spec.executor.env) takes a list of EnvVar, each of which specifies an environment variable or the source of an environment variable, e.g., a name-value pair, a ConfigMap key, a Secret key, etc. Alternatively, .spec.driver.envFrom (and .spec.executor.envFrom) takes a list of EnvFromSource and allows using all key-value pairs in a ConfigMap or Secret as environment variables. The SparkApplication snippet below shows the use of both fields:

spec:
  driver:
    env:
      - name: ENV1
        value: VAL1
      - name: ENV2
        value: VAL2
      - name: ENV3
        valueFrom:
          configMapKeyRef:
            name: some-config-map
            key: env3-key
      - name: AUTH_KEY
        valueFrom:
          secretKeyRef:
            name: some-secret
            key: auth-key
    envFrom:
      - configMapRef:
          name: env-config-map
      - secretRef:
          name: env-secret
  executor:
    env:
      - name: ENV1
        value: VAL1
      - name: ENV2
        value: VAL2
      - name: ENV3
        valueFrom:
          configMapKeyRef:
            name: some-config-map
            key: env3-key
      - name: AUTH_KEY
        valueFrom:
          secretKeyRef:
            name: some-secret
            key: auth-key
    envFrom:
      - configMapRef:
          name: my-env-config-map
      - secretRef:
          name: my-env-secret

Note: legacy field envVars that can also be used for specifying environment variables is deprecated and will be removed in a future API version.

Requesting GPU Resources

A SparkApplication can specify GPU resources for the driver or executor pod, using the optional field .spec.driver.gpu or .spec.executor.gpu. Below is an example:

spec:
  driver:
    cores: 0.1
    coreLimit: "200m"
    memory: "512m"
    gpu:
      name: "amd.com/gpu"   # GPU resource name
      quantity: 1           # number of GPUs to request
    labels:
      version: 3.1.1
    serviceAccount: spark
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    serviceAccount: spark
    gpu:
      name: "nvidia.com/gpu"
      quantity: 1

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Host Network

A SparkApplication can specify hostNetwork for the driver or executor pod, using the optional field .spec.driver.hostNetwork or .spec.executor.hostNetwork. When hostNetwork is true, the operator sets pods’ spec.hostNetwork to true and sets pods’ spec.dnsPolicy to ClusterFirstWithHostNet. Below is an example:

spec:
  driver:
    cores: 0.1
    coreLimit: "200m"
    memory: "512m"
    hostNetwork: true
    labels:
      version: 3.1.1
    serviceAccount: spark
  executor:
    cores: 1
    instances: 1
    memory: "512m"

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Mounting Secrets

As mentioned above, both the driver specification and executor specification have an optional field secrets for configuring the list of Kubernetes Secrets to be mounted into the driver and executors, respectively. The field is a map with the names of the Secrets as keys and values specifying the mount path and type of each Secret. For instance, the following example shows a driver specification with a Secret named gcp-svc-account of type GCPServiceAccount to be mounted to /mnt/secrets in the driver pod.

spec:
  driver:
    secrets:
      - name: gcp-svc-account
        path: /mnt/secrets
        secretType: GCPServiceAccount

The type of a Secret as specified by the secretType field is a hint to the operator on what extra configuration it needs to take care of for the specific type of Secrets. For example, if a Secret is of type GCPServiceAccount, the operator additionally sets the environment variable GOOGLE_APPLICATION_CREDENTIALS to point to the JSON key file stored in the secret. Please refer to Getting Started with Authentication for more information on how to authenticate with GCP services using a service account JSON key file. Note that the operator assumes that the key of the service account JSON key file in the Secret data map is key.json so it is able to set the environment variable automatically. Similarly, if the type of a Secret is HadoopDelegationToken, the operator additionally sets the environment variable HADOOP_TOKEN_FILE_LOCATION to point to the file storing the Hadoop delegation token. In this case, the operator assumes that the key of the delegation token file in the Secret data map is hadoop.token. The secretType field should have the value Generic if no extra configuration is required.

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Mounting ConfigMaps

Both the driver specification and executor specifications have an optional field for configuring the list of Kubernetes ConfigMaps to be mounted into the driver and executors, respectively. The field is a map with keys being the names of the ConfigMaps and values specifying the mount path of each ConfigMap. For instance, the following example shows a driver specification with a ConfigMap named configmap1 to be mounted to /mnt/config-maps in the driver pod.

spec:
  driver:
    configMaps:
      - name: configmap1
        path: /mnt/config-maps

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Mounting a ConfigMap storing Spark Configuration Files

A SparkApplication can specify a Kubernetes ConfigMap storing Spark configuration files such as spark-env.sh or spark-defaults.conf using the optional field .spec.sparkConfigMap whose value is the name of the ConfigMap. The ConfigMap is assumed to be in the same namespace as that of the SparkApplication. The operator mounts the ConfigMap onto path /etc/spark/conf in both the driver and executors. Additionally, it also sets the environment variable SPARK_CONF_DIR to point to /etc/spark/conf in the driver and executors.

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Mounting a ConfigMap storing Hadoop Configuration Files

A SparkApplication can specify a Kubernetes ConfigMap storing Hadoop configuration files such as core-site.xml using the optional field .spec.hadoopConfigMap whose value is the name of the ConfigMap. The ConfigMap is assumed to be in the same namespace as that of the SparkApplication. The operator mounts the ConfigMap onto path /etc/hadoop/conf in both the driver and executors. Additionally, it also sets the environment variable HADOOP_CONF_DIR to point to /etc/hadoop/conf in the driver and executors.

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Mounting Volumes

The operator also supports mounting user-specified Kubernetes volumes into the driver and executors. A SparkApplication has an optional field .spec.volumes for specifying the list of volumes the driver and the executors need collectively. Then both the driver and executor specifications have an optional field volumeMounts that specifies the volume mounts for the volumes needed by the driver and executors, respectively. The following is an example showing a SparkApplication with both driver and executor volume mounts.

spec:
  volumes:
    - name: spark-data
      persistentVolumeClaim:
        claimName: my-pvc
    - name: spark-work
      emptyDir:
        sizeLimit: 5Gi
  driver:
    volumeMounts:
      - name: spark-work
        mountPath: /mnt/spark/work
  executor:
    volumeMounts:
      - name: spark-data
        mountPath: /mnt/spark/data
      - name: spark-work
        mountPath: /mnt/spark/work

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Secrets As Environment Variables

Note: envSecretKeyRefs is deprecated and will be removed in a future API version.

A SparkApplication can use secrets as environment variables, through the optional field .spec.driver.envSecretKeyRefs for the driver pod and the optional field .spec.executor.envSecretKeyRefs for the executor pods. A envSecretKeyRefs is a map from environment variable names to pairs consisting of a secret name and a secret key. Below is an example:

spec:
  driver:
    envSecretKeyRefs:
      SECRET_USERNAME:
        name: mysecret
        key: username
      SECRET_PASSWORD:
        name: mysecret
        key: password

Using Image Pull Secrets

Note that this feature requires an image based on the latest Spark master branch.

For images that need image-pull secrets to be pulled, a SparkApplication has an optional field .spec.imagePullSecrets for specifying a list of image-pull secrets. Below is an example:

spec:
  imagePullSecrets:
    - secret1
    - secret2

Using Pod Affinity

A SparkApplication can specify an Affinity for the driver or executor pod, using the optional field .spec.driver.affinity or .spec.executor.affinity. Below is an example:

spec:
  driver:
    affinity:
      podAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          ...
  executor:
    affinity:
      podAntiAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          ...

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Tolerations

A SparkApplication can specify an Tolerations for the driver or executor pod, using the optional field .spec.driver.tolerations or .spec.executor.tolerations. Below is an example:

spec:
  driver:
    tolerations:
    - key: Key
      operator: Exists
      effect: NoSchedule

  executor:
    tolerations:
    - key: Key
      operator: Equal
      value: Value
      effect: NoSchedule

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Security Context

A SparkApplication can specify a SecurityContext for the driver or executor containers, using the optional field .spec.driver.securityContext or .spec.executor.securityContext. SparkApplication can also specify a PodSecurityContext for the driver or executor pod, using the optional field .spec.driver.podSecurityContext or .spec.executor.podSecurityContext. Below is an example:

spec:
  driver:
    podSecurityContext:
      runAsUser: 1000
    securityContext:
      allowPrivilegeEscalation: false
      runAsUser: 2000
  executor:
    podSecurityContext:
      runAsUser: 1000
    securityContext:
      allowPrivilegeEscalation: false
      runAsUser: 2000

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Sidecar Containers

A SparkApplication can specify one or more optional sidecar containers for the driver or executor pod, using the optional field .spec.driver.sidecars or .spec.executor.sidecars. The specification of each sidecar container follows the Container API definition. Below is an example:

spec:
  driver:
    sidecars:
    - name: "sidecar1"
      image: "sidecar1:latest"
      ...
  executor:
    sidecars:
    - name: "sidecar1"
      image: "sidecar1:latest"
      ...

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Init-Containers

A SparkApplication can optionally specify one or more init-containers for the driver or executor pod, using the optional field .spec.driver.initContainers or .spec.executor.initContainers, respectively. The specification of each init-container follows the Container API definition. Below is an example:

spec:
  driver:
    initContainers:
    - name: "init-container1"
      image: "init-container1:latest"
      ...
  executor:
    initContainers:
    - name: "init-container1"
      image: "init-container1:latest"
      ...

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using DNS Settings

A SparkApplication can define DNS settings for the driver and/or executor pod, by adding the standard DNS kubernetes settings. Fields to add such configuration are .spec.driver.dnsConfig and .spec.executor.dnsConfig. Example:

spec:
  driver:
    dnsConfig:
      nameservers:
        - 1.2.3.4
      searches:
        - ns1.svc.cluster.local
        - my.dns.search.suffix
      options:
        - name: ndots
          value: "2"
        - name: edns0

Note that the mutating admission webhook is needed to use this feature. Please refer to the Getting Started on how to enable the mutating admission webhook.

Using Volume For Scratch Space

By default, Spark uses temporary scratch space to spill data to disk during shuffles and other operations. The scratch directory defaults to /tmp of the container. If that storage isn’t enough or you want to use a specific path, you can use one or more volumes. The volume names should start with spark-local-dir-.

spec:
  volumes:
    - name: "spark-local-dir-1"
      hostPath:
        path: "/tmp/spark-local-dir"
  executor:
    volumeMounts:
      - name: "spark-local-dir-1"
        mountPath: "/tmp/spark-local-dir"
    ...

Then you will get SPARK_LOCAL_DIRS set to /tmp/spark-local-dir in the pod like below.

Environment:
  SPARK_USER:                 root
  SPARK_DRIVER_BIND_ADDRESS:  (v1:status.podIP)
  SPARK_LOCAL_DIRS:           /tmp/spark-local-dir
  SPARK_CONF_DIR:             /opt/spark/conf

Note: Multiple volumes can be used together

spec:
  volumes:
    - name: "spark-local-dir-1"
      hostPath:
        path: "/mnt/dir1"
    - name: "spark-local-dir-2"
      hostPath:
        path: "/mnt/dir2"
  executor:
    volumeMounts:
      - name: "spark-local-dir-1"
        mountPath: "/tmp/dir1"
      - name: "spark-local-dir-2"
        mountPath: "/tmp/dir2"
    ...

Note: Besides hostPath, persistentVolumeClaim can be used as well.

spec:
  volumes:
    - name: "spark-local-dir-1"
      persistentVolumeClaim:
        claimName: network-file-storage
  executor:
    volumeMounts:
      - name: "spark-local-dir-1"
        mountPath: "/tmp/dir1"

Using Termination Grace Period

A Spark Application can optionally specify a termination grace Period seconds to the driver and executor pods. More info

spec:
  driver:
    terminationGracePeriodSeconds: 60

Using Container LifeCycle Hooks

A Spark Application can optionally specify a Container Lifecycle Hooks for a driver. It is useful in cases where you need a PreStop or PostStart hooks to driver and executor.

spec:
  driver:
    lifecycle:
      preStop:
        exec:
          command:
          - /bin/bash
          - -c
          - touch /var/run/killspark && sleep 65

In cases like Spark Streaming or Spark Structured Streaming applications, you can test if a file exists to start a graceful shutdown and stop all streaming queries manually.

Python Support

Python support can be enabled by setting .spec.mainApplicationFile with path to your python application. Optionally, the .spec.pythonVersion field can be used to set the major Python version of the docker image used to run the driver and executor containers. Below is an example showing part of a SparkApplication specification:

spec:
  type: Python
  pythonVersion: 2
  mainApplicationFile: local:///opt/spark/examples/src/main/python/pyfiles.py

Some PySpark applications need additional Python packages to run. Such dependencies are specified using the optional field .spec.deps.pyFiles, which translates to the --py-files option of the spark-submit command.

spec:
  deps:
    pyFiles:
       - local:///opt/spark/examples/src/main/python/py_container_checks.py
       - gs://spark-data/python-dep.zip

In order to use the dependencies that are hosted remotely, the following PySpark code can be used in Spark 2.4.

python_dep_file_path = SparkFiles.get("python-dep.zip")
spark.sparkContext.addPyFile(dep_file_path)

Note that Python binding for PySpark is available in Apache Spark 2.4.

Monitoring

The operator supports using the Spark metric system to expose metrics to a variety of sinks. Particularly, it is able to automatically configure the metric system to expose metrics to Prometheus. Specifically, the field .spec.monitoring specifies how application monitoring is handled and particularly how metrics are to be reported. The metric system is configured through the configuration file metrics.properties, which gets its content from the field .spec.monitoring.metricsProperties. The content of metrics.properties will be used by default if .spec.monitoring.metricsProperties is not specified. .spec.monitoring.metricsPropertiesFile overwrite the value spark.metrics.conf in spark.properties, and will not use content from .spec.monitoring.metricsProperties. You can choose to enable or disable reporting driver and executor metrics using the fields .spec.monitoring.exposeDriverMetrics and .spec.monitoring.exposeExecutorMetrics, respectively.

Further, the field .spec.monitoring.prometheus specifies how metrics are exposed to Prometheus using the Prometheus JMX exporter. When .spec.monitoring.prometheus is specified, the operator automatically configures the JMX exporter to run as a Java agent. The only required field of .spec.monitoring.prometheus is jmxExporterJar, which specified the path to the Prometheus JMX exporter Java agent jar in the container. If you use the image gcr.io/spark-operator/spark:v3.1.1-gcs-prometheus, the jar is located at /prometheus/jmx_prometheus_javaagent-0.11.0.jar. The field .spec.monitoring.prometheus.port specifies the port the JMX exporter Java agent binds to and defaults to 8090 if not specified. The field .spec.monitoring.prometheus.configuration specifies the content of the configuration to be used with the JMX exporter. The content of prometheus.yaml will be used by default if .spec.monitoring.prometheus.configuration is not specified.

Below is an example that shows how to configure the metric system to expose metrics to Prometheus using the Prometheus JMX exporter. Note that the JMX exporter Java agent jar is listed as a dependency and will be downloaded to where .spec.dep.jarsDownloadDir points to in Spark 2.3.x, which is /var/spark-data/spark-jars by default. Things are different in Spark 2.4 as dependencies will be downloaded to the local working directory instead in Spark 2.4. A complete example can be found in examples/spark-pi-prometheus.yaml.

spec:
  deps:
    jars:
    - http://central.maven.org/maven2/io/prometheus/jmx/jmx_prometheus_javaagent/0.11.0/jmx_prometheus_javaagent-0.11.0.jar
  monitoring:
    exposeDriverMetrics: true
    prometheus:
      jmxExporterJar: "/var/spark-data/spark-jars/jmx_prometheus_javaagent-0.11.0.jar"

The operator automatically adds the annotations such as prometheus.io/scrape=true on the driver and/or executor pods (depending on the values of .spec.monitoring.exposeDriverMetrics and .spec.monitoring.exposeExecutorMetrics) so the metrics exposed on the pods can be scraped by the Prometheus server in the same cluster.

Dynamic Allocation

The operator supports a limited form of Spark Dynamic Resource Allocation through the shuffle tracking enhancement introduced in Spark 3.0.0 without needing an external shuffle service (not available in the Kubernetes mode). See this issue for details on the enhancement. To enable this limited form of dynamic allocation, follow the example below:

spec:
  dynamicAllocation:
    enabled: true
    initialExecutors: 2
    minExecutors: 2
    maxExecutors: 10

Note that if dynamic allocation is enabled, the number of executors to request initially is set to the bigger of .spec.dynamicAllocation.initialExecutors and .spec.executor.instances if both are set.

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Last modified June 22, 2024: Add docs for spark-operator (#3767) (b622672)