PySpark RDD Transformations are lazy evaluation and is used to transform/update from one RDD into another. When executed on RDD, it results in a single or multiple new RDD.
Since RDD are immutable in nature, transformations always create a new RDD without updating an existing one hence, a chain of RDD transformations creates an RDD lineage.
RDD Lineage is also known as the RDD operator graph or RDD dependency graph.
In this tutorial, you will learn lazy transformations, types of transformations, a complete list of transformation functions using wordcount example.
- What is a lazy transformation
- Transformation types
- Transformation functions
- Transformation functions with word count examples
RDD Transformations are Lazy
RDD Transformations are lazy operations meaning none of the transformations get executed until you call an action on PySpark RDD. Since RDD’s are immutable, any transformations on it result in a new RDD leaving the current one unchanged.
RDD Transformation Types
There are two types of transformations.
Narrow transformations are the result of map() and filter() functions and these compute data that live on a single partition meaning there will not be any data movement between partitions to execute narrow transformations.
Functions such as
union() are some examples of narrow transformation
Wider transformations are the result of groupByKey() and reduceByKey() functions and these compute data that live on many partitions meaning there will be data movements between partitions to execute wider transformations. Since these shuffles the data, they also called shuffle transformations.
Functions such as
repartition() are some examples of a wider transformations.
Note: When compared to Narrow transformations, wider transformations are expensive operations due to shuffling.
PySpark RDD Transformation functions
|Timestamp Function Syntax||Timestamp Function Description|
|current_timestamp () : Column||Returns the current timestamp as a timestamp column|
|hour(e: Column): Column||Extracts the hours as an integer from a given date/timestamp/string.|
|minute(e: Column): Column||Extracts the minutes as an integer from a given date/timestamp/string.|
|second(e: Column): Column||Extracts the seconds as an integer from a given date/timestamp/string.|
|to_timestamp(s: Column): Column||Converts to a timestamp by casting rules to `TimestampType`.|
|to_timestamp(s: Column, fmt: String): Column||Converts time string with the given pattern to timestamp.|
PySpark RDD Transformations with Examples
In this section, I will explain a few RDD Transformations with word count example in scala, before we start first, let’s create an RDD by reading a text file. The text file used here is available at the GitHub and, the scala example is available at GitHub project for reference.
from pyspark.sql import SparkSession spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate() rdd = spark.sparkContext.textFile("/apps/sparkbyexamples/src/pyspark-examples/data.txt")
for element in rdd.collect(): print(element)
printing RDD after collect results in.
flatMap() transformation flattens the RDD after applying the function and returns a new RDD. On the below example, first, it splits each record by space in an RDD and finally flattens it. Resulting RDD consists of a single word on each record.
rdd2=rdd.flatMap(lambda x: x.split(" "))
Yields below output
Project Gutenberg’s Alice’s Adventures in Wonderland Project Gutenberg’s Adventures in Wonderland Project Gutenberg’s
map() transformation is used the apply any complex operations like adding a column, updating a column e.t.c, the output of map transformations would always have the same number of records as input.
In our word count example, we are adding a new column with value 1 for each word, the result of the RDD is PairRDDFunctions which contains key-value pairs, word of type String as Key and 1 of type Int as value.
rdd3=rdd2.map(lambda x: (x,1))
Collecting and Printing rdd3 yields below output.
reduceByKey() merges the values for each key with the function specified. In our example, it reduces the word string by applying the sum function on value. The result of our RDD contains unique words and their count.
rdd4=rdd3.reduceByKey(lambda a,b: a+b)
Collecting and Printing rdd4 yields below output.
sortByKey() transformation is used to sort RDD elements on key. In our example, first, we convert RDD[(String,Int]) to RDD[(Int,String]) using map transformation and later apply sortByKey which ideally does sort on an integer value. And finally, foreach with println statement prints all words in RDD and their count as key-value pair to console.
rdd5 = rdd4.map(lambda x: (x,x)).sortByKey()
Collecting and Printing rdd5 yields below output. Note the columns order has changed.
filter() transformation is used to filter the records in an RDD. In our example we are filtering all words starts with “a”.
rdd6 = rdd5.filter(lambda x : 'a' in x)
This above statement yields “
(2, 'Wonderland')” that has a value ‘a’.
PySpark RDD Transformations complete example
package com.sparkbyexamples.spark.rdd from pyspark.sql import SparkSession spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate() rdd = spark.sparkContext.textFile("/apps/sparkbyexamples/src/pyspark-examples/data.txt") for element in rdd.collect(): print(element) #Flatmap rdd2=rdd.flatMap(lambda x: x.split(" ")) for element in rdd2.collect(): print(element) #map rdd3=rdd2.map(lambda x: (x,1)) for element in rdd3.collect(): print(element) #reduceByKey rdd4=rdd3.reduceByKey(lambda a,b: a+b) for element in rdd4.collect(): print(element) #map rdd5 = rdd4.map(lambda x: (x,x)).sortByKey() for element in rdd5.collect(): print(element) #filter rdd6 = rdd5.filter(lambda x : 'a' in x) for element in rdd6.collect(): print(element)
In this PySpark RDD Transformations article, you have learned different transformation functions and their usage with Python examples and GitHub project for quick reference.
Happy Learning !!