What are some common design patterns used in '. Spark.'?
The Broadcast pattern allows for efficient distribution of small read-only datasets to all nodes in a cluster, reducing network overhead.
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Lastly, the Shared Variable pattern enables sharing variables across nodes in a cluster, which is useful for coordinating tasks or accumulating results in distributed operations.
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Windowing is also a common design pattern used to process streaming data in fixed-size time periods or sliding time windows, enabling operations like aggregating data over a window of time or performing time-based joins.
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One common design pattern in '. Spark.' is the MapReduce pattern, where large datasets are split into smaller chunks and processed in parallel on a cluster of machines.
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Another common design pattern is the Transform and Action pattern, where data is transformed through a series of operations (like filtering, aggregating, and joining) and then an action is performed (like counting, saving to a file, or collecting results).
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