Top 30 Spark Structured Streaming Details and Links
Here are 30 important details and concepts related to Apache Spark Structured Streaming, along with relevant links to the official Spark documentation.
1. Unified Batch and Streaming API
Details: Structured Streaming provides a high-level API that is consistent with Spark’s batch processing API (DataFrames and Datasets), making it easier for developers familiar with batch processing to work with streaming data.
2. Incremental Computation
Details: Structured Streaming processes data in small batches (micro-batches) or continuously, incrementally updating the result as new data arrives, rather than reprocessing the entire dataset.
3. Fault Tolerance
Details: Built on top of Spark’s fault-tolerant engine, Structured Streaming ensures that even in the event of failures, the system can recover and continue processing data with exactly-once or at-least-once guarantees.
4. Sources
Details: Structured Streaming supports various data sources for ingestion, including Apache Kafka, Apache Flume, Kinesis, files (text, CSV, JSON, Parquet), and network sockets.
5. Sinks
Details: Processed streaming data can be written to various sinks, such as files, Apache Kafka, databases, and for each batch processing (foreachSink).
6. Transformations
Details: You can apply a wide range of transformations on streaming DataFrames/Datasets, similar to batch processing, including map, filter, groupBy, windowing, and joins.
7. Windowing Operations
Details: Structured Streaming supports windowing operations (Tumbling, Sliding, Session windows) to aggregate data over a specific time interval.
8. Watermarking
Details: Watermarking is a mechanism to handle late-arriving data in windowed aggregations and joins, allowing the system to know when to stop waiting for potentially delayed events.
9. Joins
Details: Structured Streaming supports various types of joins between streaming DataFrames/Datasets and between streaming and static DataFrames/Datasets (stream-static joins).
10. Output Modes
Details: Structured Streaming offers different output modes to specify what gets written to the sink after each micro-batch: Append, Complete, and Update.
11. Triggers
Details: Triggers define when the streaming query should process new data. Common triggers include `ProcessingTime`, `Once`, and `Continuous` (experimental for some sources).
12. State Management
Details: For stateful operations like aggregations and joins, Structured Streaming manages the state efficiently and provides mechanisms for state persistence and recovery.
13. Checkpointing
Details: Checkpointing is crucial for fault tolerance. Structured Streaming periodically saves the progress of the streaming query to a reliable storage system (e.g., HDFS, S3, Azure Blob Storage), allowing it to resume from the last successful state upon failure.
14. Exactly-Once vs. At-Least-Once Semantics
Details: Structured Streaming aims for exactly-once processing semantics, ensuring that each record is processed exactly once. However, the actual guarantee depends on the source and sink being idempotent.
15. Performance Tuning
Details: Optimizing Structured Streaming applications involves considerations like batch interval, parallelism, state management, and data serialization.
16. Monitoring and Debugging
Details: Spark provides tools and metrics for monitoring the progress and performance of Structured Streaming queries, including the Spark UI.
17. Integration with Spark SQL
Details: You can register streaming DataFrames/Datasets as temporary views and query them using Spark SQL.
18. ForEachSink
Details: A powerful output sink that allows you to define custom logic to be executed on each micro-batch of data.
19. Rate Source
Details: A built-in source for generating data at a specified rate, useful for testing and development.
20. Memory Management
Details: Understanding how Spark manages memory for stateful streaming operations is crucial for preventing out-of-memory errors.
21. Backpressure Handling
Details: Structured Streaming can handle situations where the rate of incoming data exceeds the processing capacity of the system using backpressure mechanisms.
22. Schema Evolution
Details: Handling changes in the schema of the streaming data source over time is an important consideration.
23. Exactly-Once Output to External Systems
Details: Achieving exactly-once output to external systems often requires careful design and potentially idempotent writes or transactional updates.
24. Continuous Processing Mode (Experimental)
Details: An experimental processing mode that aims for lower latency compared to micro-batching for some sources.
25. Trigger.Once
Details: A trigger that processes all available data in the source and then stops.
26. Trigger.ProcessingTime
Details: A trigger that processes data at a fixed interval, regardless of when the data arrives.
27. Event Time vs. Processing Time
Details: Understanding the difference between event time (timestamp in the data) and processing time (when the data is processed by Spark) is crucial for correct windowing and watermarking.
28. Streaming Deduplication
Details: Techniques for removing duplicate records in a streaming pipeline, often using watermarking and stateful operations.
29. Integration with External Databases
Details: Connecting to and writing streaming data to external databases often requires careful consideration of connection management and write strategies.
30. Use Cases
Details: Structured Streaming is used in various real-time applications, including fraud detection, real-time dashboards, IoT data processing, and stream analytics.
This list provides a foundation for understanding Spark Structured Streaming. For in-depth knowledge, refer to the official Apache Spark documentation.
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