AI - Spark Streaming
Empowering real-time data processing with efficient connection pooling and aggregation operations through Spark Streaming.
- Name
- Spark Streaming -https://spark.apache.org/docs/latest/streaming-programming-guide.html
- Last Audited At
About Spark Streaming
Spark Streaming is a component of Apache Spark designed for real-time processing of live data streams. It develops and provides a static, lazily initialized pool of connections named ConnectionPool, which is used to efficiently send records in iterations, and then return them back to the pool for future reuse. This mechanism helps reduce the overhead of creating new connections for each record.
To utilize Spark Streaming, users need to create a StreamingContext object from a SparkContext, serving as the primary interface for Spark Streaming functionality. It supports various programming languages such as Python, Scala, and Java. The ConnectionPool is employed within the StreamingContext, enabling users to process streaming data efficiently.
Spark Streaming offers several window operations like window, countByWindow, and reduceByWindow. These operations help perform aggregations, counting elements, and applying reduce functions on batches of the source DStream over a sliding interval. Spark Streaming also supports associative and commutative functions for parallel computation.
Additionally, it allows users to monitor files in specific directories using a POSIX glob pattern. Spark Streaming processes all files that match the pattern under directories. All monitored files must be in the same data format and are considered part of a time period based on their modification time. Once processed, any updates within the current window will not cause the file to be reread, as changes are ignored during processing. However, the more files under a directory, the longer it takes to scan for changes. Renaming an entire directory to match the path adds it to the list of monitored directories.
