Example Spark submit command used in very complex etl Jobs

In a complex ETL (Extract, Transform, Load) environment, the spark-submit command can be customized with various options to optimize performance, handle large datasets, and configure the execution environment. Here’s a detailed example of a spark-submit command used in such a scenario, along with explanations for each option:

Example spark-submit Command

spark-submit \
  --master yarn \
  --deploy-mode cluster \
  --name complex-etl-job \
  --conf spark.executor.memory=8g \
  --conf spark.executor.cores=4 \
  --conf spark.driver.memory=4g \
  --conf spark.driver.cores=2 \
  --conf spark.dynamicAllocation.enabled=true \
  --conf spark.dynamicAllocation.minExecutors=2 \
  --conf spark.dynamicAllocation.maxExecutors=10 \
  --conf spark.sql.shuffle.partitions=200 \
  --conf spark.sql.broadcastTimeout=1200 \
  --conf spark.sql.autoBroadcastJoinThreshold=104857600 \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.kryo.classesToRegister=org.example.SomeClass,org.example.AnotherClass \
  --conf spark.speculation=true \
  --conf spark.sql.files.maxPartitionBytes=134217728 \
  --conf spark.sql.files.openCostInBytes=4194304 \
  --files /path/to/external/config.properties \
  --jars /path/to/external/dependency.jar \
  --py-files /path/to/external/python.zip \
  --archives /path/to/external/archive.zip \
  your_etl_script.py \
  --input /path/to/input/data \
  --output /path/to/output/data \
  --config /path/to/job/config.yaml

Explanation of Options

1. --master yarn: Specifies YARN as the cluster manager. YARN manages resources and scheduling for Spark applications.
2. --deploy-mode cluster: Runs the Spark driver on the cluster rather than on the local machine. Suitable for production environments.
3. --name complex-etl-job: Sets the name of the Spark application for easier identification and tracking.
4. --conf spark.executor.memory=8g: Allocates 8 GB of memory for each executor. Adjust based on the memory requirements of your ETL job.
5. --conf spark.executor.cores=4: Assigns 4 CPU cores to each executor. Determines the parallelism for each executor.
6. --conf spark.driver.memory=4g: Allocates 4 GB of memory for the driver program. Increase if the driver is handling large amounts of data.
7. --conf spark.driver.cores=2: Assigns 2 CPU cores to the driver. Ensures sufficient resources for the driver to manage tasks.
8. --conf spark.dynamicAllocation.enabled=true: Enables dynamic allocation of executors. Allows Spark to automatically scale the number of executors based on workload.
9. --conf spark.dynamicAllocation.minExecutors=2: Sets the minimum number of executors to 2. Ensures that there are always at least 2 executors.
10. --conf spark.dynamicAllocation.maxExecutors=10: Sets the maximum number of executors to 10. Limits the number of executors to avoid over-provisioning.
11. --conf spark.sql.shuffle.partitions=200: Sets the number of partitions to use when shuffling data for joins or aggregations. Adjust based on the size of the data.
12. --conf spark.sql.broadcastTimeout=1200: Sets the timeout for broadcasting large datasets to 1200 seconds (20 minutes). Helps in handling large broadcast joins.
13. --conf spark.sql.autoBroadcastJoinThreshold=104857600: Sets the threshold for automatic broadcasting of tables to 100 MB. Large tables above this threshold will not be broadcasted.
14. --conf spark.serializer=org.apache.spark.serializer.KryoSerializer: Uses Kryo serialization for better performance with complex objects. Replace with the appropriate serializer based on your needs.
15. --conf spark.kryo.classesToRegister=org.example.SomeClass,org.example.AnotherClass: Registers specific classes with Kryo for optimized serialization. Replace with classes relevant to your application.
16. --conf spark.speculation=true: Enables speculative execution. Helps in handling straggler tasks by re-running them if they are slow.
17. --conf spark.sql.files.maxPartitionBytes=134217728: Sets the maximum size of a single file partition to 128 MB. Helps in controlling the partition size for file-based sources.
18. --conf spark.sql.files.openCostInBytes=4194304: Sets the cost of opening a file to 4 MB. Used for partitioning logic in file sources.
19. --files /path/to/external/config.properties: Specifies additional files to be distributed to the executors. Use this for configuration files or other resources.
20. --jars /path/to/external/dependency.jar: Specifies additional JAR files to be included in the classpath. Use this for external dependencies.
21. --py-files /path/to/external/python.zip: Specifies Python files or ZIP archives to be distributed to the executors. Use this for custom Python modules.
22. --archives /path/to/external/archive.zip: Specifies archives (e.g., ZIP files) to be extracted and distributed to the executors. Use this for additional resources.
23. your_etl_script.py: The path to the Python script to be executed. Replace with the path to your ETL script.
24. --input /path/to/input/data: Command-line argument for the input data path. Use this for passing input parameters to the script.
25. --output /path/to/output/data: Command-line argument for the output data path. Use this for passing output parameters to the script.
26. --config /path/to/job/config.yaml: Command-line argument for additional configuration parameters. Use this for passing custom configuration files.

Summary of Key Options

• Resource Configuration: --conf spark.executor.memory, --conf spark.executor.cores, --conf spark.driver.memory, --conf spark.driver.cores
• Dynamic Allocation: --conf spark.dynamicAllocation.enabled, --conf spark.dynamicAllocation.minExecutors, --conf spark.dynamicAllocation.maxExecutors
• Performance Tuning: --conf spark.sql.shuffle.partitions, --conf spark.sql.broadcastTimeout, --conf spark.sql.autoBroadcastJoinThreshold
• Serialization: --conf spark.serializer, --conf spark.kryo.classesToRegister
• Execution: --conf spark.speculation
• File Handling: --conf spark.sql.files.maxPartitionBytes, --conf spark.sql.files.openCostInBytes
• Dependencies and Files: --files, --jars, --py-files, --archives

These options help you fine-tune your Spark job to handle complex ETL tasks efficiently and are essential for optimizing performance and resource utilization in a big data environment.

spark-submit \
  --master yarn \
  --deploy-mode cluster \
  --num-executors 10 \
  --executor-cores 4 \
  --executor-memory 16g \
  --driver-memory 16g \
  --conf spark.executor.memoryOverhead=2048 \
  --conf spark.driver.memoryOverhead=2048 \
  --conf spark.shuffle.service.enabled=true \
  --conf spark.dynamicAllocation.enabled=true \
  --conf spark.dynamicAllocation.minExecutors=5 \
  --conf spark.dynamicAllocation.maxExecutors=15 \
  --conf spark.sql.shuffle.partitions=200 \
  --conf spark.sql.broadcastTimeout=36000 \
  --conf spark.sql.autoBroadcastJoinThreshold=100000000 \
  --conf spark.sql.join.preferSortMergeJoin=true \
  --conf spark.sql.join.preferBroadCastHashJoin=true \
  --conf spark.sql.join.broadcastHashJoinThreshold=100000000 \
  --conf spark.sql.join.sortMergeJoinThreshold=100000000 \
  --conf spark.sql.optimizer.maxIterations=100 \
  --conf spark.sql.optimizer.useMetadataOnly=true \
  --conf spark.sql.parquet.compression.codec=snappy \
  --conf spark.sql.parquet.mergeSchema=true \
  --conf spark.sql.hive.convertMetastoreParquet=true \
  --conf spark.sql.hive.convertMetastoreOrc=true \
  --conf spark.kryo.registrationRequired=true \
  --conf spark.kryo.unsafe=false \
  --conf spark.rdd.compress=true \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.ui.showConsoleProgress=true \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=/path/to/event/log \
  --conf spark.history.fs.logDirectory=/path/to/history/log \
  --conf spark.history.ui.port=18080 \
  --class com.example.MyETLJob \
  --jars /path/to/jar1.jar,/path/to/jar2.jar \
  --files /path/to/file1.txt,/path/to/file2.txt \
  --py-files /path/to/python_file1.py,/path/to/python_file2.py \
  --properties-file /path/to.properties \
  my_etl_job.jar \
  --input-path /path/to/input \
  --output-path /path/to/output

Here is a comprehensive list of Spark submit options in Excel format:

Spark Submit Options

Here is a comprehensive list of Spark submit options in Excel format, including all available options, especially in --conf:

Spark Submit Options

–conf options