codeIn [Spark]

spark-submit #

spark-submit is the command used to submit applications to a Spark cluster. It is a powerful tool that allows you to configure various settings for your Spark jobs, including memory and CPU allocation, cluster modes, and application-specific parameters. Properly configuring spark-submit is essential for optimizing Spark jobs for performance and resource usage.

Key Configurations in spark-submit #

Below are the most important configurations you can use with spark-submit, along with their purposes and examples:

1. Application Resource Configuration #

• --master: Specifies the cluster manager to connect to. It can be local for local mode, yarn for Hadoop YARN, mesos for Apache Mesos, or k8s for Kubernetes.
  • Example: --master yarn
• --deploy-mode: Defines whether to launch the driver on the worker nodes (cluster) or locally on the machine submitting the application (client).
  • Example: --deploy-mode cluster
• --num-executors: Sets the number of executors to use for the job. This is applicable in cluster modes like YARN.
  • Example: --num-executors 5
• --executor-cores: Specifies the number of CPU cores per executor. Higher values increase parallelism.
  • Example: --executor-cores 4
• --executor-memory: Allocates memory for each executor process. Proper sizing can prevent out-of-memory errors.
  • Example: --executor-memory 8G
• --driver-memory: Sets the amount of memory allocated for the driver process.
  • Example: --driver-memory 4G

2. Configuration for Dynamic Resource Allocation #

• --conf spark.dynamicAllocation.enabled=true: Enables dynamic allocation of executors. Spark will scale the number of executors up and down based on workload.
  • Example: --conf spark.dynamicAllocation.enabled=true
• --conf spark.dynamicAllocation.minExecutors: Minimum number of executors to be allocated when dynamic allocation is enabled.
  • Example: --conf spark.dynamicAllocation.minExecutors=2
• --conf spark.dynamicAllocation.maxExecutors: Maximum number of executors Spark can allocate.
  • Example: --conf spark.dynamicAllocation.maxExecutors=10

3. Resource Management and Scheduling #

• --conf spark.yarn.executor.memoryOverhead: Extra memory to be allocated per executor for JVM overheads. This is useful for managing memory more effectively.
  • Example: --conf spark.yarn.executor.memoryOverhead=1024
• --conf spark.scheduler.mode: Configures the scheduling mode (FIFO or FAIR). FAIR scheduling allows jobs to share resources more evenly.
  • Example: --conf spark.scheduler.mode=FAIR
• --conf spark.locality.wait: Adjusts the amount of time Spark waits to launch tasks on preferred nodes before scheduling elsewhere. Helps in managing locality.
  • Example: --conf spark.locality.wait=3s

4. Spark Logging and Debugging #

• --conf spark.eventLog.enabled=true: Enables Spark event logging. This helps in monitoring and debugging by storing event information.
  • Example: --conf spark.eventLog.enabled=true
• --conf spark.eventLog.dir: Specifies the directory where the event logs should be stored.
  • Example: --conf spark.eventLog.dir=hdfs:///logs/
• --conf spark.executor.logs.rolling.strategy=time: Sets the rolling strategy for executor logs. Useful for managing log file sizes and retention.
  • Example: --conf spark.executor.logs.rolling.strategy=time
• --conf spark.executor.logs.rolling.time.interval=daily: Defines the interval for rolling executor logs.
  • Example: --conf spark.executor.logs.rolling.time.interval=daily

5. Application Specific Configurations #

• --conf spark.sql.shuffle.partitions: Configures the number of partitions to use when shuffling data during Spark SQL operations. Tweaking this number can optimize shuffling.
  • Example: --conf spark.sql.shuffle.partitions=200
• --conf spark.serializer: Specifies the serializer for RDDs. The default is Java serialization, but Kryo serialization can be more efficient.
  • Example: --conf spark.serializer=org.apache.spark.serializer.KryoSerializer
• --conf spark.executor.extraJavaOptions: Passes additional JVM options for executors. Useful for setting system properties or managing JVM memory.
  • Example: --conf spark.executor.extraJavaOptions="-XX:+UseG1GC"

6. Security Configurations #

• --conf spark.authenticate=true: Enables authentication for Spark communication to enhance security.
  • Example: --conf spark.authenticate=true
• --conf spark.authenticate.secret: Defines the secret key for Spark authentication.
  • Example: --conf spark.authenticate.secret=mySecretKey
• --conf spark.ssl.enabled=true: Enables SSL for all Spark communication.
  • Example: --conf spark.ssl.enabled=true

Example spark-submit Command #

Below is an example of a spark-submit command using several of these configurations:

spark-submit \
  --master yarn \
  --deploy-mode cluster \
  --num-executors 5 \
  --executor-cores 4 \
  --executor-memory 8G \
  --driver-memory 4G \
  --conf spark.dynamicAllocation.enabled=true \
  --conf spark.dynamicAllocation.minExecutors=2 \
  --conf spark.dynamicAllocation.maxExecutors=10 \
  --conf spark.yarn.executor.memoryOverhead=1024 \
  --conf spark.scheduler.mode=FAIR \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=hdfs:///logs/ \
  --conf spark.sql.shuffle.partitions=200 \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.executor.extraJavaOptions="-XX:+UseG1GC" \
  --conf spark.authenticate=true \
  --conf spark.authenticate.secret=mySecretKey \
  --class com.example.MySparkApp \
  /path/to/my-spark-app.jar