Scaling Spark Structured Streaming applications can be challenging, particularly when managing data processing in AWS. This post discusses best practices for implementing custom autoscaling logic for Spark applications, specifically focusing on batch processing duration and executor management.

Q: Why doesn’t Spark Dynamic Allocation work well for streaming?

Spark’s built-in dynamic allocation is not well-suited for streaming applications due to the nature of batch processing. Executors are only removed based on idle time, which conflicts with continuous data streaming, leading to inefficient scaling.

Q: What is the recommended approach for Spark autoscaling in streaming applications?

A more reliable method involves building custom autoscaling logic using Spark Developer APIs. The logic can be based on batch duration and controlled using the requestExecutors and killExecutor methods in the SparkContext class. Here’s a basic structure:

val sparkContext = spark.sparkContext

val batchDurationThreshold = 30  // in seconds

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def autoscale(executorCount: Int): Unit = {

  val currentBatchDuration = getCurrentBatchDuration()

  if (currentBatchDuration > batchDurationThreshold) {

    sparkContext.requestExecutors(executorCount + 2)

  } else if (currentBatchDuration < batchDurationThreshold && executorCount > 1) {

    sparkContext.killExecutor("executor-id")

  }

}

Q: What alternatives exist for real-time processing and scaling?

• AWS Kinesis Data Analytics: Serverless stream processing with Flink integration. It automatically scales based on the throughput of the Kinesis stream.
  
  
• AWS EMR with Auto Scaling: Allows Spark workloads to scale based on CPU, memory, and other metrics.
  
  

Q: How does Snowplow handle autoscaling in real-time pipelines?

Internally, Snowplow leverages AWS Kinesis Data Analytics for real-time aggregation. Flink libraries are used to write streaming jobs, allowing for near real-time processing while maintaining auto-scaling capabilities.

Example dependencies for Flink streaming:

"com.amazonaws"     %  "aws-kinesisanalytics-runtime" % "1.1.0"

"com.amazonaws"     %  "aws-kinesisanalytics-flink"   % "1.1.0"

"org.apache.flink"  %% "flink-streaming-scala"        % "1.8.1" % Provided

Q: Are there other best practices for scaling Spark streaming jobs?

• Optimize Batch Duration: Tune batch intervals to balance latency and resource consumption.
  
  
• Data Partitioning: Adjust the number of partitions to align with the number of executors.
  
  
• Monitoring and Alerts: Integrate AWS CloudWatch for real-time monitoring and alerting based on batch duration and resource usage.
  
  

Final Thoughts

While Spark’s native dynamic allocation is not optimal for streaming, custom autoscaling logic offers a practical solution. By leveraging SparkContext’s executor management methods and AWS’s managed services, teams can achieve efficient scaling while maintaining data processing performance. For advanced use cases, consider integrating Flink with Kinesis Data Analytics for more granular control over stream processing.