PySpark Structured Streaming is a stream processing engine built on top of Spark SQL. It enables the processing of real-time data streams in a fault-tolerant and scalable manner. Structured Streaming allows you to write streaming queries using the same DataFrame and SQL APIs that you would use for batch processing, making it easier to transition from batch processing to streaming processing. In this series we look at a variety of PySpark streaming worked examples.
So far the background of PySpark structured streaming and the motivation for using applyInPandasWithState along with a notebook to generate streaming files has been covered. In part 3 of this tutorial on how to use applyInPandasWithState, the CSV files will be streamed, data will be grouped by flight id and custom logic to maintain the
PySpark structured streaming with applyInPandasWithState worked example Read More »
The demo is an example of how to set up a simulated streaming source of csv files. It is the second part in a 3 part series on streaming data with applyInPandasWithState. If you just want an example of how to create test stream files and have come directly to this page then you are
How to generate PySpark streaming files Read More »
This tutorial will cover a complete worked example of how to stream data in PySpark using the applyInPandasWithState function and foreachBatch. Spark structured streaming does not always come with the tools needed out-of-the-box and by using applyInPandasWithState and foreachBatch the streaming functionality can be customised. The example will use the scenario of streaming data from
Supercharge PySpark steaming with applyInPandasWithState Read More »
