Data Engineering Spark SQL - Windowing Functions - Filtering Window Function Results

Data Engineering Spark
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In this article, we will explore the concept of filtering based on the results of Window Functions in Spark SQL. We will discuss the limitations of Window Functions and how to overcome them using Sub Queries. By the end of this article, you will have a clear understanding of how to apply filters on Window Function results to extract meaningful insights from your data.

Initializing Spark Context

Before we begin, let’s initialize the Spark context for this Notebook to execute the provided code. If you are interested in learning Spark SQL using our state-of-the-art cluster, you can sign up for our labs.

from pyspark.sql import SparkSession

spark = SparkSession. \
    builder. \
    config('spark.ui.port', '0'). \
    config("spark.sql.warehouse.dir", f"/user/{username}/warehouse"). \
    enableHiveSupport(). \
    appName(f'{username} | Python - Data Processing - Overview'). \
    master('yarn'). \
    getOrCreate()

Window Functions Limitations:

  • Window Functions can only be used in the SELECT clause.
  • Filtering based on Window Function results requires the use of Sub Queries.
  • Filters can be applied using aliases provided for the Window Functions.

Watch the video tutorial here

Hands-On Tasks

  1. Initialize Spark Session and configure it for usage in Spark SQL.
  2. Use Sub Queries to filter data based on Window Function results.
  3. Apply filters on Window Function results to get desired outcomes.

Conclusion

In this article, we explored the process of filtering data based on Window Function results in Spark SQL. By using Sub Queries and applying filters strategically, we can obtain meaningful insights from our dataset. Remember to practice these concepts and engage with the community for further learning.