In this article, we will delve into aggregations using groupBy in Spark. We will explore the key concepts and functions commonly used for aggregations in Spark SQL.
Here are the key concepts and functions we will cover in this article:
Grouping Data
When working with data in Spark, we often use groupBy to group data based on a specific key. Other similar functions include rollup and cube.
# Example of using groupBy
data_frame.groupBy('key').count().show()
Aggregating Data
For performing aggregations on grouped data, we can use functions like count, sum, avg, min, and max.
# Example of performing aggregation with count
data_frame.groupBy('key').agg(count(lit(1)).alias('Count')).show()
Hands-On Tasks
Here are some hands-on tasks for you to practice the concepts discussed in the article:
- Get number of flights scheduled each day for the month of January 2008.
- Get count of flights departed, total departure delay, and average departure delay for each day in January 2008.
- Calculate revenue for each order from the order items dataset.
- Get the minimum and maximum order_item_subtotal for each order ID.
Conclusion
In this article, we explored the aggregation capabilities of Spark SQL using groupBy. We covered key concepts and functions that are essential for performing aggregations on grouped data. We encourage you to practice these tasks and engage with the community for further learning.
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