Pair RDDs

Key/value RDDs are commonly used to perform aggregations, and often we will do some initial ETL (extract, transform, and load) to get our data into a key/value format. Key/value RDDs expose new operations (e.g., counting up reviews for each product, grouping together data with the same key, and grouping together two different RDDs).

Spark provides special operations on RDDs containing key/value pairs. These RDDs are called pair RDDs. Pair RDDs are a useful building block in many programs, as they expose operations that allow you to act on each key in parallel or regroup data across the network. For example, pair RDDs have a reduceByKey() method that can aggregate data separately for each key, and a join() method that can merge two RDDs together by grouping elements with the same key. It is common to extract fields from an RDD (representing, for instance, an event time, customer ID, or other identifier) and use those fields as keys in pair RDD operations.

Follow this section on Jupyter notebook

Running map() changes a regular RDD into a pair RDD:

# Create a pair of first word in a string and the string.
lines = sc.parallelize(["Hello World", "KBTU Cambridge"])
pairs = x: (x.split(" ")[0], x))


[('Hello', 'Hello World'), ('KBTU', 'KBTU Cambridge')]

Transformations on one pair RDD

PairRDD: {(1, 2), (3, 4), (3, 6)}

Function name





Combine values with the same key.

rdd.reduceByKey(lambda x, y: x + y)

{(1,2), (3,10)}


Group values with the same key.


{(1, [2]), (3, [4, 6])}


Apply a function to each value of a pair RDD without changing the key.

rdd.mapValues(lambda x: x + 1)

{(1, 3), (3, 5), (3, 7)}


Apply a function to each value of a pair RDD without changing the key.

rdd.flatMapValues(lambda x: range(x, 5))

{(1, 2), (1, 3), (1, 4), (1, 5), (3, 4), (3, 5)}


Return an RDD of just the keys.


{1, 3, 3}


Return an RDD of just the values.


{2, 4, 6}


Return an RDD sorted by the key.


{(1, 2), (3, 4), (3, 6)}

Transformation of two-pair RDDs

rdd = {(1, 2), (3, 4), (3, 6)} other = {(3, 9)}

Pair RDDs are also still RDDs (of tuples), and thus support the same functions as RDDs. For instance, we can take our pair RDD from the previous section and filter out lines longer than 20 characters.

pairs = sc.parallelize([("gru", "likes coffee"), ("minons", "like long strings and coffee")])
result = pairs.filter(lambda keyValue: len(keyValue[1]) < 20)

[('gru', 'likes coffee')]


When datasets are described in terms of key/value pairs, it is common to want to aggregate statistics across all elements with the same key. We have looked at the fold() , combine() , and reduce() actions on basic RDDs, and similar per-key transformations exist on pair RDDs. Spark has a similar set of operations that combines values that have the same key.

  • reduceByKey() takes a function and use it to combine values.

    reduceByKey() runs several parallel reduce operations, one

    for each key in the dataset, where each operation combines values that have the

    same key. reduceByKey() does not return a value to the user program, instead,

    it returns a new RDD consisting of each key and the reduced value for that key.

Per-key average with reduceByKey() and mapValues() in Python

rdd = sc.parallelize([("panda", 0), ("pink", 3), ("pirate", 3), ("panda", 1), ("pink", 4)])
rdd.mapValues(lambda x: (x, 1)).reduceByKey(lambda x, y: (x[0] + y[0], x[1] + y[1])).collect()

[('panda', (1, 2)), ('pink', (7, 2)), ('pirate', (3, 1))]

  • foldByKey() use a zero value of the same type of the data in our RDD and combination function.

  • combineByKey() is the most general of the per-key aggregation functions. combineByKey() allows the user to return values that are not the same type as our input data. As combineByKey() goes through the elements in a partition, each element either has a key it hasn’t seen before or has the same key as a previous element. If it’s a new element, combineByKey() uses a function called createCombiner(), to create the initial value for the accumulator on that key. It’s important to note that this happens the first time a key is found in each partition, rather than only the first time the key is found in the RDD. If it is a value we have seen before while processing that partition, it will instead use the provided function, mergeValue(), with the current value for the accumulator for that key and the new value. Since each partition is processed independently, we can have multiple accumulators for the same key. When we are merging the results from each partition, if two or more partitions have an accumulator for the same key we merge the accumulators using the user-supplied mergeCombiners() function.

Follow this section on combineByKey in Jupyter notebook

data = sc.parallelize([(0, 2.), (0, 4.), (1, 0.), (1, 10.), (1, 20.), (1, 30)])

# Using combineByKey to get the sum and the count of each key
sumcount = data.combineByKey(lambda value: (value, 1),
                             lambda x, value: (x[0] + value, x[1] + 1),
                             lambda x, y: (x[0] + y[0], x[1] + y[1]))
# Display sum and count of each key

Create a PairRDD of {(0, 2.), (0, 4.), (1, 0.), (1, 10.), (1, 20.), (1, 30)}. Assume the data is partitioned into two. Partition #1 has (0, 2.), (0, 4.), (1, 20.) and Partition #2 has (1, 0.), (1, 10.), (1, 30.). In this example, we will look only at key 1. As combineByKey() goes through the elements in a partition 1, a key of 1 has never been encountered before and createCombiner() is called. This creates a new pair of (value, 1) from lambda value: (value, 1). Similarly, lets say in Parition #2, we encounter (1, 0) and a (value, 1) pair is created.

`lambda value: (value, 1)`
# Partition 1
(1, 20) => (20, 1)

# Partition 2
(1, 0) => (0, 1)

Since there are no more keys of 1 in partition #1, no mergeValue() calls are executed. In partition #2, as it encounters (1, 10), there is an accumulated value (0, 1), hence, it executes mergeValue(): lambda x, value: (x[0] + value, x[1] + 1).

# mergeValue()
# lambda x, value: (x[0] + value, x[1] + 1)
# Partition #2
(1, 10), (1, 30)
# for (1, 10)
# where, x refers to the accumulated value of (0, 1) in partition #2
# and vale refers to 10 for a key of 1.
# (lambda (0,1), 10: 0 + 10, 1 + 1) => (10, 2)

# for (1, 30)
# where, x refers to the accumulated value of (10, 2) in partition #2
# and vale refers to 30 for a key of 1.
# (lambda (10,2), 30: 10 + 30, 2 + 1) => (40, 3)

Finally, as the two partitions are merged using mergeCombiners():

# Partition 1
(20, 1)
# Partition 2
(40, 3)

# mergeCombiners()
# lambda x, y: (x[0] + y[0], x[1] + y[1])
# lambda (20, 1), (40, 3): (20 + 40, 1 + 3)
(60, 4)

This gives a combineByKey() value of (1, (60, 4)). A typical combineByKey() has the following syntax .combineByKey(createCombiner(), mergeValue(), mergeCombiners).

combineByKey workflow

Data partitioning

Spark’s partitioning is available on all RDDs of key/value pairs. Although Spark does not give explicit control of assigning specific worker nodes for each key, it lets the program ensure that a set of keys will appear together on some node. For example, you might choose to hash-partition an RDD into 100 partitions so that keys that have the same hash value modulo 100 appear on the same node. Or you might range-partition the RDD into sorted ranges of keys so that elements with keys in the same range appear on the same node.

In Python, the number of partitions can be specified using partitionBy().

rdd.partitionBy(npartitions, function)

Challenge: Computing Pi with partitions

  • Compute the value of Pi with range partitioning (hint: use a large n like

    10 million).

  • Vary the parition numbers and see the effect.

count = sc.parallelize(range(1, n + 1), npartitions).map(f).reduce(add)

Download Jupyter notebook for computing Pi with partitions

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