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Questions and Answers
With Delta Lake, you can use Delta tables as both streaming sources and sinks, but only for batch processing.
False
Schema enforcement is not available when streaming into Delta Lake.
False
A streaming query is a combination of reading a stream from a source and writing the stream to a target, but only for Delta Lake.
False
You can perform a count() or a sort() operation on a streaming DataFrame.
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The checkpoint file is not necessary for fault tolerance and query recovery in case of failure.
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The QPL is a JSON log generated by every single micro-batch, but it does not provide execution details on the micro-batch.
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The stream unique id is not displayed above the streaming dashboard header.
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The isStartingVersion boolean field is set to false if the reservoirVersion is set to the version of the Delta table at which the current stream was started.
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Spark Structured Streaming is running as a real-time streaming service.
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The model of doing batch updates to the source table is economical in a real-world application.
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Spark Structured Streaming was first introduced in Apache Spark 1.0.
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The main goal of Structured Streaming is to build batch processing applications on Spark.
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Structured Streaming is based on the old Spark RDD model.
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Delta Lake is integrated with Spark Structured Streaming through its three major operators: readstream, writeStream, and upsertStream.
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Delta tables can only be used as streaming sources.
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The AvailableNow stream triggering mode is used for building batch pipelines.
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Spark Structured Streaming is a batch processing engine built on top of Apache Spark.
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Spark Structured Streaming only supports reading and writing data from Kafka.
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Delta Lake will only pick up new records from the source table since the last run.
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The ignoreChanges option will prevent the rewriting of all files in the Delta table to the stream.
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The recentProgress property will print out the same output as the raw data section from the streaming output in the notebook.
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Deleting the checkpoint file and running the streaming query again will start from the current version of the source table.
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Setting readChangeFeed to false will allow us to efficiently stream changes from a source table to a downstream target table.
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Using .option('startingVersion', 0) will start the Delta table streaming source from the current version of the table.
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Using .option('readChangeFeed', 'true') will return table changes with the regular table schema.
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The rate limit options can be used to increase the processing resources when there is an influx of new data files.
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The awaitTermination() method will immediately stop the streaming query.
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Study Notes
Spark Structured Streaming
- Introduced in Apache Spark 2.0, replacing the older DStreams (Discretized Streams) API
- Goals: build near-real-time streaming applications on Spark
Integration with Delta Lake
- Integrated through two major operators: readStream and writeStream
- Delta tables can be used as both streaming sources and sinks
- Overcomes limitations of traditional streaming systems:
- Coalescing small files produced by low-latency ingestion
- Maintaining "exactly-once" processing with concurrent batch jobs
- Leveraging the Delta transaction log for efficient discovery of new files
Key Features
- AvailableNow stream triggering mode enables incremental pipelines without maintaining state variables
- Scalable, fault-tolerant, and low-latency processing of continuous data streams
- High-level API for building end-to-end streaming applications with various sources and sinks
- Treats data streams as boundless table-like structures, allowing SQL-like operations
Delta Lake and Structured Streaming
- Combines transactional guarantees of Delta Lake with the powerful programming model of Apache Spark Structured Streaming
- Enables continuous processing through Raw, Bronze, Silver, and Gold data lake layers
- Offers schema enforcement, ensuring incoming data streams are validated against predefined schema
Streaming Queries and Checkpoints
- Streaming query: combination of reading a stream from a source and writing to a target
- Checkpoint file maintains metadata and state of the streaming query for fault tolerance and recovery
- Query process log and checkpoint files provide execution details and state information
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Description
Learn about Apache Spark's Structured Streaming feature, introduced in Spark 2.0, and its integration with Delta Lake for near-real-time streaming applications. Explore the benefits of this integration, including overcoming limitations of traditional streaming systems.
![(Spark)[Medium] Chapter 4. Structured API Overview](https://assets.quizgecko.com/cdn-cgi/image/width=800,quality=90,format=webp/quiz/12ff353fa4ae889a77ecad5a604659aa.jpg)
