How to handle the `OutOfMemoryError` in a Spark streaming application using Scala?
I've been researching this but I'm working through a tutorial and I'm writing unit tests and I've been struggling with this for a few days now and could really use some help. I'm currently developing a real-time data processing application using Apache Spark (version 3.1.2) and Scala (version 2.13.6). The application reads data from Kafka and processes it using a Spark streaming job. However, I keep working with an `OutOfMemoryError` during the processing of a important volume of messages, especially when there's a spike in traffic. I've tried increasing the executor memory using the following configuration in my Spark submit command: ```bash --executor-memory 4g ``` And I've also set the driver memory to 2g: ```bash --driver-memory 2g ``` Despite these adjustments, the question continues, and the behavior log indicates the following: ``` Exception in thread "streaming-start-thread" java.lang.OutOfMemoryError: Java heap space ``` I've also considered the size of the RDD I'm working with, which accumulates records over time. I attempted to optimize the transformations by using `reduceByKey` instead of `groupByKey`, since I read that it could help reduce memory consumption. Here's a snippet of my streaming code: ```scala val kafkaStream = KafkaUtils.createStream(streamingContext, kafkaParams, fromOffsets, StorageLevel.MEMORY_AND_DISK) val processedStream = kafkaStream.map(record => (record.key, record.value)) .reduceByKey((a, b) => a + b) ``` Additionally, I've enabled checkpointing to prevent data loss, but it doesn't seem to alleviate the memory issues. ```scala streamingContext.checkpoint("/path/to/checkpoint") ``` I'm wondering if there are any best practices or debugging techniques specific to handling memory issues in Spark streaming applications. Are there better strategies for managing stateful transformations or optimizing memory usage in this context? Any insights would be greatly appreciated! For context: I'm using Scala on Ubuntu. What am I doing wrong? The stack includes Scala and several other technologies. I recently upgraded to Scala 3.11. Any pointers in the right direction?