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Questions and Answers
What is the primary processing model used by Structured Streaming?
What is the primary processing model used by Structured Streaming?
Which mode of operation in Structured Streaming outputs only new rows appended to the result table?
Which mode of operation in Structured Streaming outputs only new rows appended to the result table?
How are data streams characterized in the context of Structured Streaming?
How are data streams characterized in the context of Structured Streaming?
What is an advantage of using Structured Streaming over traditional Spark Streaming?
What is an advantage of using Structured Streaming over traditional Spark Streaming?
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Which of the following statements correctly describes the abstraction used in Structured Streaming?
Which of the following statements correctly describes the abstraction used in Structured Streaming?
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Study Notes
Structured Streaming (Spark Streams)
- Structured Streaming is an advanced streaming engine built on Spark SQL.
- It processes real-time data streams using high-level abstractions like DataFrames and Datasets.
- It differs from Spark Streaming, which uses micro-batches, by working on a continuous data model.
Key Concepts
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Unbounded Data Streams: Data streams are considered continuous and flow into the system continuously.
- Examples include logs from web servers, financial transactions, IoT device sensor data.
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Streaming as an Append-Only Table: Streams are conceptualized as continuously growing tables where new data rows are appended as they arrive.
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Data streams are depicted as continuously flowing into an unbounded table. New data in the stream results in new rows being appended to the unbounded table.
Modes of Operations
- Complete Mode: The entire result table is updated whenever it changes.
- Update Mode: Only updated rows in the result table are output.
- Append Mode: Only newly appended rows are output to the result table.
Advantages
- Unified API: Uses the same APIs for batch and streaming processing (DataFrame/Dataset). You can write a query for batch processing and adjust it to handle streaming data.
- Declarative Query Language: High-level SQL-like queries are written to transform streams.
- Optimized Performance: Spark Catalyst Optimizer improves performance, and resource usage is more efficient than older systems like Spark Streaming (DStream).
Differences Between Structured Streaming and Spark Streaming
| Feature | Structured Streaming | Spark Streaming |
|---|---|---|
| API | DataFrame/Dataset | DStream API (RDD-based) |
| Processing Model | Continuous (row-by-row) | Micro-batches |
| Abstraction | Infinite Table | Batch RDDs |
| Performance | More optimized | Less optimized |
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Description
This quiz examines the key concepts and operations of Structured Streaming in Spark, a cutting-edge engine for real-time data processing. Learn about unbounded data streams, append-only tables, and different operational modes like Complete and Update mode. Test your understanding of how Structured Streaming differs from traditional Spark Streaming.
