Spark Window Functions for DataFrames and SQL

转载自http://xinhstechblog.blogspot.com/2016/04/spark-window-functions-for-dataframes.html

Introduced in Spark 1.4, Spark window functions improved the expressiveness of Spark DataFrames and Spark SQL. With window functions, you can easily calculate a moving average or cumulative sum, or reference a value in a previous row of a table. Window functions allow you to do many common calculations with DataFrames, without having to resort to RDD manipulation.

 

Aggregates, UDFs vs. Window functions

Window functions are complementary to existing DataFrame operations: aggregates, such as sumand avg, and UDFs. To review, aggregates calculate one result, a sum or average, for each group of rows, whereas UDFs calculate one result for each row based on only data in that row. In contrast, window functions calculate one result for each row based on a window of rows. For example, in a moving average, you calculate for each row the average of the rows surrounding the current row; this can be done with window functions.

 

Moving Average Example

Let us dive right into the moving average example. In this example dataset, there are two customers who have spent different amounts of money each day.

 

// Building the customer DataFrame. All examples are written in Scala with Spark 1.6.1, but the same can be done in Python or SQL.

val customers = sc.parallelize(List(("Alice", "2016-05-01", 50.00),

                                    ("Alice", "2016-05-03", 45.00),

                                    ("Alice", "2016-05-04", 55.00),

                                    ("Bob", "2016-05-01", 25.00),

                                    ("Bob", "2016-05-04", 29.00),

                                    ("Bob", "2016-05-06", 27.00))).

                               toDF("name", "date", "amountSpent")

 

// Import the window functions.

import org.apache.spark.sql.expressions.Window

import org.apache.spark.sql.functions._

 

// Create a window spec.

val wSpec1 = Window.partitionBy("name").orderBy("date").rowsBetween(-1, 1)

 

In this window spec, the data is partitioned by customer. Each customer’s data is ordered by date. And, the window frame is defined as starting from -1 (one row before the current row) and ending at 1 (one row after the current row), for a total of 3 rows in the sliding window.

 

// Calculate the moving average

customers.withColumn( "movingAvg",

                                             avg(customers("amountSpent")).over(wSpec1)  ).show()

 

This code adds a new column, “movingAvg”, by applying the avg function on the sliding window defined in the window spec:

 

name	date	amountSpent	movingAvg
Alice	5/1/2016	50	47.5
Alice	5/3/2016	45	50
Alice	5/4/2016	55	50
Bob	5/1/2016	25	27
Bob	5/4/2016	29	27
Bob	5/6/2016	27	28

 

Window function and Window Spec definition

As shown in the above example, there are two parts to applying a window function: (1) specifying the window function, such as avg in the example, and (2) specifying the window spec, or wSpec1 in the example. For (1), you can find a full list of the window functions here:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$
 You can use functions listed under “Aggregate Functions” and “Window Functions”.

 

For (2) specifying a window spec, there are three components: partition by, order by, and frame.

1.     “Partition by” defines how the data is grouped; in the above example, it was by customer. You have to specify a reasonable grouping because all data within a group will be collected to the same machine. Ideally, the DataFrame has already been partitioned by the desired grouping.
2.       “Order by” defines how rows are ordered within a group; in the above example, it was by date.
3.       “Frame” defines the boundaries of the window with respect to the current row; in the above example, the window ranged between the previous row and the next row.

 

Cumulative Sum

Next, let us calculate the cumulative sum of the amount spent per customer.

 

// Window spec: the frame ranges from the beginning (Long.MinValue) to the current row (0).

val wSpec2 = Window.partitionBy("name").orderBy("date").rowsBetween(Long.MinValue, 0)

 

// Create a new column which calculates the sum over the defined window frame.

customers.withColumn( "cumSum",

  sum(customers("amountSpent")).over(wSpec2)  ).show()

 

name	date	amountSpent	cumSum
Alice	5/1/2016	50	50
Alice	5/3/2016	45	95
Alice	5/4/2016	55	150
Bob	5/1/2016	25	25
Bob	5/4/2016	29	54
Bob	5/6/2016	27	81

 

Data from previous row

In the next example, we want to see the amount spent by the customer in their previous visit.

 

// Window spec. No need to specify a frame in this case.

val wSpec3 = Window.partitionBy("name").orderBy("date")

 

// Use the lag function to look backwards by one row.

customers.withColumn("prevAmountSpent",

 lag(customers("amountSpent"), 1).over(wSpec3) ).show()

 

name	date	amountSpent	prevAmountSpent
Alice	5/1/2016	50	null
Alice	5/3/2016	45	50
Alice	5/4/2016	55	45
Bob	5/1/2016	25	null
Bob	5/4/2016	29	25
Bob	5/6/2016	27	29

 

Rank

In this example, we want to know the order of a customer’s visit (whether this is their first, second, or third visit).

 

// The rank function returns what we want.

customers.withColumn( "rank", rank().over(wSpec3) ).show()

 

name	date	amountSpent	rank
Alice	5/1/2016	50	1
Alice	5/3/2016	45	2
Alice	5/4/2016	55	3
Bob	5/1/2016	25	1
Bob	5/4/2016	29	2
Bob	5/6/2016	27	3

 

Conclusion

 

I hope these examples have helped you understand Spark’s window functions. There is more functionality that was not covered here. To learn more, please see the Databricks article on this topic: https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html

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