Apache Spark 3.4.1 - Reading Data from Kafka with Incorrect Offsets Causes Data Skew
I've hit a wall trying to This might be a silly question, but I'm currently using Apache Spark 3.4.1 to read streaming data from Kafka, but I've encountered a question with data skew when processing messages. Initially, I set up my Spark application to read from a specific Kafka topic like this: ```scala val kafkaDF = spark.readStream .format("kafka") .option("kafka.bootstrap.servers", "localhost:9092") .option("subscribe", "my-topic") .option("startingOffsets", "earliest") .load() ``` However, I've noticed that the records are unevenly distributed across my partitions, leading to some tasks taking significantly longer to complete than others. After some investigation, I found that the offsets being read sometimes skip over messages, leading to inconsistent data being processed. I've tried adjusting the `maxOffsetsPerTrigger` option to control the number of records being fetched per batch, but that hasn’t resolved the skew scenario. My current configuration looks like this: ```scala val kafkaDF = spark.readStream .format("kafka") .option("kafka.bootstrap.servers", "localhost:9092") .option("subscribe", "my-topic") .option("startingOffsets", "earliest") .option("maxOffsetsPerTrigger", "1000") .load() ``` I also implemented a simple aggregation on the data: ```scala val processedStream = kafkaDF.selectExpr("CAST(value AS STRING)") .groupBy("value") .count() .writeStream .outputMode("complete") .format("console") .start() ``` Although the processing works, some partitions are getting stalled as they wait for the few partitions with a large volume of data. I keep seeing this warning in the logs: ``` WARN org.apache.spark.scheduler.TaskSetManager: Lost task 0.0 in stage 1.0 (TID 1, localhost, executor 1): org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, executor 1): java.lang.RuntimeException: There was an behavior while fetching offsets. ``` Does anyone have any insights on how to address this skewed data scenario or ensure that all Kafka messages are being processed evenly? Any recommendations on configurations or coding practices would be greatly appreciated! How would you solve this? The project is a desktop app built with Scala. Is there a simpler solution I'm overlooking? Thanks, I really appreciate it!