Troubleshooting Spark Streaming with Delta Lake

Understanding Offset Files in Spark Streaming with Delta Lake

Q: What do the Offset folder and files contain, and how can you understand their content?

• Checkpoint Metadata: Information about the state of the streaming query.
• Write-Ahead Logs (WAL): Logs ensuring exactly-once processing semantics.
• Offset Files: JSON-encoded files tracking the progress of the streaming query.

Breaking Down Offset File Content

Q: Can you explain the content of an Offset file?

{
    "batchWatermarkMs": 0,
    "batchTimestampMs": 1735351100082,
    "conf": {
        "spark.sql.streaming.stateStore.providerClass": "org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider",
        "spark.sql.streaming.join.stateFormatVersion": "2",
        "spark.sql.streaming.stateStore.compression.codec": "lz4",
        "spark.sql.streaming.stateStore.rocksdb.formatVersion": "5",
        "spark.sql.streaming.statefulOperator.useStrictDistribution": "true",
        "spark.sql.streaming.flatMapGroupsWithState.stateFormatVersion": "2",
        "spark.sql.streaming.multipleWatermarkPolicy": "min",
        "spark.sql.streaming.aggregation.stateFormatVersion": "2",
        "spark.sql.shuffle.partitions": "72"
    }
}
{
    "sourceVersion": 1,
    "reservoirId": "q5376b00-73yh-4125-8676-e344e7d643ez",
    "reservoirVersion": 1607,
    "index": -1,
    "isStartingVersion": false
}        

• batchWatermarkMs: Event-time watermark.
• batchTimestampMs: Processing timestamp.
• conf: Configuration settings.
• sourceVersion: Version of the source.
• reservoirId: Unique identifier.
• reservoirVersion: Version of the reservoir.
• index: Batch index.
• isStartingVersion: Indicates starting version.

Handling Data Exceptions in Streaming Jobs

Q: I had a scenario where there was a data exception in my streaming Spark job. After fixing the data issue, how can I ensure the data update is considered and the Spark job runs without exception?

One effective approach is to delete the specific problematic offset file and restart the Spark job. This forces Spark to reprocess the data starting from that point, considering the updated data. For example, if the issue was with offset file 4583, deleting this file and restarting the job would result in Spark creating a new offset file 4583 with the updated data.

Q: How can I ensure the data update is considered and the Spark job runs without exception?

• Delete the specific problematic offset file.
• Restart the Spark job.
• Ensure the job reprocesses the data starting from the updated point.

Understanding Commit Files in Spark Streaming

Q: Why do commit files have minimal content?

• Commit files store essential state information.
• nextBatchWatermarkMs helps manage late data and track event-time progress.

The Role of Watermarks in Streaming

Q: What does it mean if nextBatchWatermarkMs has a value like 1735351100082?

If nextBatchWatermarkMs has a value like 1735351100082, it represents the event-time watermark for the next batch. This timestamp ensures that Spark processes data with event times greater than or equal to the watermark. Data with event times before this timestamp is considered late.

For example, if the watermark is 22:45:00 on December 27, 2024, Spark will process data with event times after 22:45:00 and consider earlier data as late.

Q: What does it mean if nextBatchWatermarkMs has a specific value?

• Represents the event-time watermark for the next batch.
• Ensures Spark processes data after the specified timestamp.

Processing Batches with No Watermark

Q: If nextBatchWatermarkMs is 0, how does Spark identify the next batch of data?

• Uses system processing time and ingestion time.
• Sets a new watermark as new event-time data arrives.

Internal State Management in Spark Streaming

Q: Where is system time and event-time data stored?

• Managed through Spark's internal mechanisms.
• In-memory state and state store providers.
• Stream execution plan.