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Streaming Transformations

Transformation Gems

Transform Gems that generate code but do not work within Streaming Applications include OrderBy, Limit, and Window (partitionBy). Although Window would work with a watermarked column (explained below) as part of the partitioning, it is advised to use window() or session_window() from the pyspark.sql.functions package (link).


Watermarking is a technique that enables aggregations on streaming data by limiting the state over which the aggregation is performed. In order to prevent out-of-memory errors, we have introduced support for watermarking. More information on watermarking is available in the Spark documentation here

We have added a Watermarking Gem in the Transform Section that allows a user to add a Watermark to a DataFrame. Example usage of Watermark - Watermark Table

In this example, we add Watermarking to the timestamp column. A user may enter the column name or select one from the Schema Table on the left. The text box is editable. Finally, define the Watermark Duration. It is recommended to use Watermarking on a Streaming DataFrame in case you're planning to use any of the following operations on it: