Dynamic executor allocation on Kubernetes for Spark Streaming means Spark can automatically scale the number of executors it uses up and down based on the workload, rather than you having to pre-configure a fixed number.
Here’s what it looks like in action. Imagine a Spark Streaming job processing incoming Kafka messages.
val spark = SparkSession.builder
.appName("DynamicExecutorAllocationExample")
.master("k8s://https://your-k8s-api-server:6443") // Replace with your K8s API server
.config("spark.executor.instances", "2") // Initial instances, can be dynamic
.config("spark.dynamicAllocation.enabled", "true")
.config("spark.dynamicAllocation.minExecutors", "1")
.config("spark.dynamicAllocation.maxExecutors", "10")
.config("spark.dynamicAllocation.initialExecutors", "2") // If not set, uses spark.executor.instances
.config("spark.dynamicAllocation.shuffleTracking.enabled", "true") // Crucial for streaming
.getOrCreate()
val df = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "your-kafka-brokers:9092") // Replace
.option("subscribe", "your-topic") // Replace
.load()
val processedDf = df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") // Example transformation
val query = processedDf.writeStream
.outputMode("append")
.format("console") // Or your actual sink
.start()
query.awaitTermination()
When this job starts, Spark will request 2 executors. If the incoming data rate spikes, Spark notices that tasks are queuing up and it can’t process them fast enough. It then asks the Kubernetes cluster manager (via the Spark on K8s operator or scheduler plugin) to provision more executor pods, up to the maxExecutors limit. Conversely, if the data rate drops to a trickle, Spark will observe that executors are idle and will start de-provisioning them, scaling down to minExecutors to save resources.
The core problem dynamic allocation solves is resource waste and under-utilization. In a fixed-allocation world, you either over-provision to handle peak loads, leaving resources idle most of the time, or under-provision and miss processing deadlines during peaks. Dynamic allocation lets Spark adapt. For streaming, this is critical because data arrival rates are rarely constant.
Internally, Spark monitors two key metrics to drive scaling decisions: task pending time and executor utilization. When tasks wait too long for an available executor, it’s a signal to add more. When executors are consistently underutilized (e.g., CPU is mostly idle), it’s a signal to remove them. The spark.dynamicAllocation.shuffleTracking.enabled setting is particularly important for streaming. It tells Spark to also consider the amount of data being shuffled across executors. If shuffle data is high, it implies active computation and data movement, which is a reason to keep executors around, even if their CPU isn’t maxed out.
The spark.dynamicAllocation.executorIdleTimeout setting is another lever. If an executor has been idle for this duration (default 10 minutes), and the total number of executors is above minExecutors, Spark will consider removing it. For streaming, you might tune this down slightly if you expect rapid fluctuations, but be careful not to set it too low, or you might end up with thrashing as executors are added and removed too frequently.
The "executor-adds-timeout" and "executor-removes-timeout" configurations are also worth noting. If Spark requests more executors but they don’t show up within "executor-adds-timeout" (default 3 minutes), it might back off and try again later. Similarly, if it tries to remove an executor but it doesn’t shut down cleanly within "executor-removes-timeout" (default 1 minute), it might be kept around longer. These timeouts help handle transient issues with Kubernetes or the Spark scheduler.
The most subtle aspect of dynamic allocation, especially with streaming, is how it interacts with spark.streaming.backpressure.enabled. When backpressure is on, Spark will intentionally slow down the rate at which it pulls data from the source if it detects that processing is falling behind. Dynamic allocation then reacts to this controlled slowdown. If the slowdown is significant and sustained, dynamic allocation might decide to scale down executors, which could paradoxically make the backpressure last longer. It’s a delicate dance between slowing ingestion and adjusting capacity.
The next challenge you’ll face is ensuring your Spark Streaming application can gracefully handle executor failures and restarts within this dynamic environment.