Data Jobs Monitoring for Spark on Kubernetes
Data Jobs Monitoring gives visibility into the performance and reliability of Apache Spark applications on Kubernetes.
Setup
Follow these steps to enable Data Jobs Monitoring for Spark on Kubernetes.
- Install the Datadog Agent on your Kubernetes cluster.
- Inject Spark instrumentation.
Install the Datadog Agent on your Kubernetes cluster
If you have already installed the Datadog Agent on your Kubernetes cluster, ensure that you have enabled the Datadog Admission Controller. You can then go to the next step, Inject Spark instrumentation.
You can install the Datadog Agent using the Datadog Operator or Helm.
Prerequisites
Installation
Install the Datadog Operator by running the following commands:
helm repo add datadog https://helm.datadoghq.com
helm install my-datadog-operator datadog/datadog-operator
Create a Kubernetes Secret to store your Datadog API key.
kubectl create secret generic datadog-secret --from-literal api-key=<DATADOG_API_KEY> --from-literal app-key=<DATADOG_APP_KEY>
Create a file, datadog-agent.yaml, that contains the following configuration:
kind: DatadogAgent
apiVersion: datadoghq.com/v2alpha1
metadata:
name: datadog
spec:
features:
apm:
enabled: true
hostPortConfig:
enabled: true
hostPort: 8126
admissionController:
enabled: true
mutateUnlabelled: false
global:
tags:
- 'data_workload_monitoring_trial:true'
site: <DATADOG_SITE>
credentials:
apiSecret:
secretName: datadog-secret
keyName: api-key
appSecret:
secretName: datadog-secret
keyName: app-key
override:
nodeAgent:
env:
- name: DD_DJM_CONFIG_ENABLED
value: "true"
Replace <DATADOG_SITE> with your Datadog site. Your site is
Deploy the Datadog Agent with the above configuration file:
kubectl apply -f /path/to/your/datadog-agent.yaml
Create a Kubernetes Secret to store your Datadog API key.
kubectl create secret generic datadog-secret --from-literal api-key=<DATADOG_API_KEY> --from-literal app-key=<DATADOG_APP_KEY>
Create a file, datadog-values.yaml, that contains the following configuration:
datadog:
apiKeyExistingSecret: datadog-secret
appKeyExistingSecret: datadog-secret
site: <DATADOG_SITE>
apm:
portEnabled: true
port: 8126
tags:
- 'data_workload_monitoring_trial:true'
env:
- name: DD_DJM_CONFIG_ENABLED
value: "true"
clusterAgent:
admissionController:
enabled: true
muteUnlabelled: false
Replace <DATADOG_SITE> with your Datadog site. Your site is
Run the following command:
helm install <RELEASE_NAME> \
-f datadog-values.yaml \
--set targetSystem=<TARGET_SYSTEM> \
datadog/datadog
Replace <RELEASE_NAME> with your release name. For example, datadog-agent.
Replace <TARGET_SYSTEM> with the name of your OS. For example, linux or windows.
Inject Spark instrumentation
When you run your Spark job, use the following configurations:
spark.kubernetes.driver.label.admission.datadoghq.com/enabled (Required)truespark.kubernetes.driver.annotation.admission.datadoghq.com/java-lib.version (Required)latestspark.driver.extraJavaOptions-Ddd.integration.spark.enabled (Required)true-Ddd.integrations.enabled (Required)false-Ddd.service (Optional)- Your service name. Because this option sets the job name in Datadog, it is recommended that you use a human-readable name.
-Ddd.env (Optional)- Your environment, such as
prod or dev. -Ddd.version (Optional)- Your version.
-Ddd.tags (Optional)- Other tags you wish to add, in the format
<KEY_1>:<VALUE_1>,<KEY_2:VALUE_2>. -Ddd.trace.experimental.long-running.enabled (Optional)true To view jobs while they are still running
Example: spark-submit
spark-submit \
--class org.apache.spark.examples.SparkPi \
--master k8s://<CLUSTER_ENDPOINT> \
--conf spark.kubernetes.container.image=895885662937.dkr.ecr.us-west-2.amazonaws.com/spark/emr-6.10.0:latest \
--deploy-mode cluster \
--conf spark.kubernetes.namespace=<NAMESPACE> \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=<SERVICE_ACCOUNT> \
--conf spark.kubernetes.driver.label.admission.datadoghq.com/enabled=true \
--conf spark.kubernetes.driver.annotation.admission.datadoghq.com/java-lib.version=latest \
--conf spark.driver.extraJavaOptions="-Ddd.integration.spark.enabled=true -Ddd.integrations.enabled=false -Ddd.service=<JOB_NAME> -Ddd.env=<ENV> -Ddd.version=<VERSION> -Ddd.tags=<KEY_1>:<VALUE_1>,<KEY_2:VALUE_2> -Ddd.trace.experimental.long-running.enabled=true" \
local:///usr/lib/spark/examples/jars/spark-examples.jar 20
Example: AWS start-job-run
aws emr-containers start-job-run \
--virtual-cluster-id <EMR_CLUSTER_ID> \
--name myjob \
--execution-role-arn <EXECUTION_ROLE_ARN> \
--release-label emr-6.10.0-latest \
--job-driver '{
"sparkSubmitJobDriver": {
"entryPoint": "s3://BUCKET/spark-examples.jar",
"sparkSubmitParameters": "--class <MAIN_CLASS> --conf spark.kubernetes.driver.label.admission.datadoghq.com/enabled=true --conf spark.kubernetes.driver.annotation.admission.datadoghq.com/java-lib.version=latest --conf spark.driver.extraJavaOptions=\"-Ddd.integration.spark.enabled=true -Ddd.integrations.enabled=false -Ddd.service=<JOB_NAME> -Ddd.env=<ENV> -Ddd.version=<VERSION> -Ddd.tags=<KEY_1>:<VALUE_1>,<KEY_2:VALUE_2> -Ddd.trace.experimental.long-running.enabled=true\""
}
}
Validation
In Datadog, view the Data Jobs Monitoring page to see a list of all your data processing jobs.
Advanced Configuration
Tag spans at runtime
You can set tags on Spark spans at runtime. These tags are applied only to spans that start after the tag is added.
// Add tag for all next Spark computations
sparkContext.setLocalProperty("spark.datadog.tags.key", "value")
spark.read.parquet(...)
To remove a runtime tag:
// Remove tag for all next Spark computations
sparkContext.setLocalProperty("spark.datadog.tags.key", null)
Further Reading
Additional helpful documentation, links, and articles:
