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spark-sql-multi-dimensional-aggregation.html
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<!doctype html>
<html lang="en">
<head>
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<title>Apache Spark™ Workshop | Spark SQL | Multi-Dimensional Aggregation</title>
<meta name="description" content="Apache Spark™ Workshop | Spark SQL | Multi-Dimensional Aggregation">
<meta name="author" content="Jacek Laskowski">
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© <a href="https://medium.com/@jaceklaskowski">Jacek Laskowski</a> 2019 / <a href="https://twitter.com/jaceklaskowski">@JacekLaskowski</a>
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<section class="intro" data-transition="zoom" id="home">
<p>
<img width="12%" style="background:none; border:none; box-shadow:none;" data-src="images/spark-logo.png">
<img width="6%" src="images/jacek_laskowski_20141201_512px.png" style="border: 0">
</p>
<h1 style="font-size: 3.07em;">Multi-Dimensional Aggregation</h1>
<h3>Apache Spark 2.4.4 / Spark SQL</h3>
<hr />
<h4 style="font-size: smaller;">
<a href="https://twitter.com/jaceklaskowski">@jaceklaskowski</a> / <a href="https://stackoverflow.com/users/1305344/jacek-laskowski">StackOverflow</a> / <a href="https://github.com/jaceklaskowski">GitHub</a>
<br>
The "Internals" Books: <a href="https://bit.ly/apache-spark-internals">Apache Spark</a> / <a href="https://bit.ly/spark-sql-internals">Spark SQL</a> / <a href="https://bit.ly/spark-structured-streaming">Spark Structured Streaming</a>
</h4>
</section>
<section id="agenda" data-markdown>
<textarea data-template>
## Agenda
1. [Multi-dimensional rollup Operator](#/rollup)
1. [Multi-dimensional cube Operator](#/cube)
1. [GROUPING SETS SQL clause](#/grouping-sets)
1. [grouping Aggregate Function](#/grouping)
1. [grouping_id Aggregate Function](#/grouping-id)
</textarea>
</section>
<section id="rollup" data-markdown style="font-size: 85%">
<textarea data-template>
## Multi-dimensional rollup Operator
1. **rollup** calculates subtotals and totals over (ordered) combination of groups
```scala
val inventory =
Seq(("t1", 2015, 100), ("t1", 2016, 50), ("t2", 2016, 40))
.toDF("name", "year", "amount")
inventory.rollup("name", "year").sum("amount")
```
* **Quiz**: How many records in result set?
1. Advanced variant of **groupBy** with higher efficiency
1. Creates **RelationalGroupedDataset**
* Supports untyped, Row-based **agg**
* Shortcuts for _the usual suspects_, e.g. **avg**, **count**, **pivot**
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [rollup Aggregation Operator](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-multi-dimensional-aggregation.html#rollup)
</textarea>
</section>
<section>
<section id="cube" data-markdown style="font-size: 90%">
<textarea data-template>
## Multi-dimensional cube Operator
1. **cube** is similar to **rollup** but calculates subtotals and totals over **all combinations** of groups
```scala
val inventory =
Seq(("t1", 2015, 100), ("t1", 2016, 50), ("t2", 2016, 40))
.toDF("name", "year", "amount")
inventory.cube("name", "year").sum("amount") // note cube (not rollup)
```
* **Quiz**: How many records in result set?
1. Advanced variant of **groupBy** with higher efficiency
1. Creates **RelationalGroupedDataset**
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [cube Aggregation Operator](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-multi-dimensional-aggregation.html#cube)
</textarea>
</section>
</section>
<section id="grouping-sets" data-markdown style="font-size: 90%">
<textarea data-template>
## GROUPING SETS SQL clause
1. Spark SQL supports **GROUPING SETS** in SQL only
1. Used in GROUP BY allows to specify more than one GROUP BY option in the same record set
* Equivalent to several GROUP BYs connected by UNION
```scala
Seq(("a1", "b1", 3), ("a1", "b2", 7), ("a2", "b2", 5))
.toDF("a", "b", "c")
.createOrReplaceTempView("t1")
val q = sql(
"SELECT a,b, SUM(c) FROM t1 GROUP BY a, b GROUPING SETS (a,b)"
)
```
**Quiz**: How many records in result set?
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [GROUPING SETS SQL Clause](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-multi-dimensional-aggregation.html#grouping-sets)
</textarea>
</section>
<section id="grouping" data-markdown>
<textarea data-template>
## grouping Aggregate Function
1. **grouping** - aggregate function that indicates whether a specified column in a GROUP BY list is aggregated or not
```scala
workshops
.cube("city", "year")
.agg(grouping("city"), grouping("year"))
.sort($"city".desc_nulls_last, $"year".desc_nulls_last)
```
* returns 1 for aggregated or 0 for not aggregated
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [grouping Aggregate Function](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-functions.html#grouping)
</textarea>
</section>
<section id="grouping-id" data-markdown>
<textarea data-template>
## grouping_id Aggregate Function
1. **Exercise** (hard): Can you guess yourself?
1. Switch to [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* [grouping_id Aggregate Function](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-functions.html#grouping_id)
</textarea>
</section>
<section id="recap" data-markdown>
<textarea data-template>
## Recap
1. [Multi-dimensional rollup Operator](#/rollup)
1. [Multi-dimensional cube Operator](#/cube)
1. [GROUPING SETS SQL clause](#/grouping-sets)
1. [grouping Aggregate Function](#/grouping)
1. [grouping_id Aggregate Function](#/grouping-id)
</textarea>
</section>
<section style="text-align: left" data-markdown id="questions">
<textarea data-template>
# Questions?
* Read [The Internals of Apache Spark](https://bit.ly/apache-spark-internals)
* Read [The Internals of Spark SQL](https://bit.ly/spark-sql-internals)
* Read [The Internals of Spark Structured Streaming](https://bit.ly/spark-structured-streaming)
* Follow [@jaceklaskowski](https://twitter.com/jaceklaskowski) on twitter
* Upvote [my questions and answers on StackOverflow](http://stackoverflow.com/users/1305344/jacek-laskowski)
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