spark optimization for big cluster

1. Increase Shuffle Partitions

Given the size of your cluster, you can increase the shuffle partitions significantly to leverage the parallelism.

spark.conf.set("spark.sql.shuffle.partitions", 1500)  // Adjust as necessary

2. Increase Executor Memory and Cores

With ample memory per node, allocate more memory to each executor to improve performance.

spark.conf.set("spark.executor.memory", "200g")

spark.conf.set("spark.executor.cores", 8)  // Adjust based on your node's CPU capacity

spark.conf.set("spark.executor.instances", 90)  // Adjust to use the available nodes effectively

3. Increase Driver Memory

Ensure the driver has sufficient memory to handle the coordination tasks.

spark.conf.set("spark.driver.memory", "200g")

4. Enable Dynamic Allocation

Allow Spark to dynamically allocate executor resources based on the workload.

spark.conf.set("spark.dynamicAllocation.enabled", true)

spark.conf.set("spark.dynamicAllocation.minExecutors", 50)

spark.conf.set("spark.dynamicAllocation.maxExecutors", 750)  // Use all nodes if necessary

5. Optimize Broadcast Joins

Given the large memory, increase the threshold for broadcast joins.

scala

spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 209715200)  // 200MB or adjust as necessary

6. Use Adaptive Query Execution (AQE)

Ensure AQE is enabled to optimize query plans based on runtime statistics.

spark.conf.set("spark.sql.adaptive.enabled", true)

spark.conf.set("spark.sql.adaptive.skewJoin.enabled", true)

7. Use Off-Heap Memory

Utilize off-heap memory to further enhance performance.

spark.conf.set("spark.memory.offHeap.enabled", true)

spark.conf.set("spark.memory.offHeap.size", "50g")

8. Optimize Shuffle Operations

Ensure optimal shuffling by tuning the shuffle configurations.

spark.conf.set("spark.shuffle.service.enabled", true)

spark.conf.set("spark.shuffle.service.port", 7337)

spark.conf.set("spark.shuffle.compress", true)

spark.conf.set("spark.shuffle.spill.compress", true)

9. Enable In-Memory Columnar Storage

Optimize query execution by compressing and batching data in memory.

spark.conf.set("spark.sql.inMemoryColumnarStorage.compressed", true)

spark.conf.set("spark.sql.inMemoryColumnarStorage.batchSize", 100000)

10. Enable Dynamic Partition Pruning

Improve join performance by dynamically pruning partitions.

spark.conf.set("spark.sql.dynamicPartitionPruning.enabled", true)

Example Configuration Code:

Here’s an example of how you can configure these settings in your Spark application:

// Increase shuffle partitions

spark.conf.set("spark.sql.shuffle.partitions", 1500)
// Increase executor memory and cores

spark.conf.set("spark.executor.memory", "200g")

spark.conf.set("spark.executor.cores", 8)

spark.conf.set("spark.executor.instances", 90)
// Increase driver memory

spark.conf.set("spark.driver.memory", "200g")
// Enable dynamic allocation

spark.conf.set("spark.dynamicAllocation.enabled", true)

spark.conf.set("spark.dynamicAllocation.minExecutors", 50)

spark.conf.set("spark.dynamicAllocation.maxExecutors", 750)
// Optimize broadcast joins

spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 209715200)
// Enable Adaptive Query Execution

spark.conf.set("spark.sql.adaptive.enabled", true)

spark.conf.set("spark.sql.adaptive.skewJoin.enabled", true)
// Use off-heap memory

spark.conf.set("spark.memory.offHeap.enabled", true)

spark.conf.set("spark.memory.offHeap.size", "50g")
// Optimize shuffle operations

spark.conf.set("spark.shuffle.service.enabled", true)

spark.conf.set("spark.shuffle.service.port", 7337)

spark.conf.set("spark.shuffle.compress", true)

spark.conf.set("spark.shuffle.spill.compress", true)
// Enable in-memory columnar storage

spark.conf.set("spark.sql.inMemoryColumnarStorage.compressed", true)

spark.conf.set("spark.sql.inMemoryColumnarStorage.batchSize", 100000)

// Enable dynamic partition pruning spark.conf.set("spark.sql.dynamicPartitionPruning.enabled", true)

You can pass all the configuration settings during runtime using the --conf flag in the spark-submit command. Here’s an example of how you can set these configurations: