Spark Operations|groupByKey() & reduceByKey()

One of the common question asked in Data Engineering interviwers to check the knowledge of operations in Pyspark is, what is the difference between groupByKey & reduceByKey.

First we understand the groupByKey() & reduceByKey() operation

• groupByKey and reduceByKey are two transformation operations in Apache Spark, used for processing data in parallel.
• They are both used to group data by a key, but they have different characteristics and performance implications.

groupByKey:

• Operation: This operation groups the data in a RDD by a key, creating a new RDD where each key is associated with an iterable collection of values.
• Example: If we have an RDD of (key, value) pairs and we use groupByKey, we'll get an RDD of (key, Iterable[values]) pairs.

rdd = sc.parallelize([(1, 'apple'), (2, 'banana'), (1, 'cherry')])

grouped_rdd = rdd.groupByKey()

# Result: [(1, ['apple', 'cherry']), (2, ['banana'])]        

• Performance: groupByKey can be expensive in terms of performance, as it shuffles all data with the same key to a single partition. This can lead to a lot of data movement across the network, especially when dealing with large datasets.

reduceByKey:

• Operation: This operation groups the data by key and then applies a specified reduction function to the values associated with each key. The result is an RDD with unique keys and a reduced value for each key.
• Example: If we have an RDD of (key, value) pairs and we use reduceByKey, we can apply a reduction function (e.g., addition) to the values for each key, resulting in an RDD of (key, reduced_value) pairs.

?rdd = sc.parallelize([(1, 2), (2, 3), (1, 4)])

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

# Result: [(1, 6), (2, 3)]        

• Performance: reduceByKey is more efficient than groupByKey for certain operations because it reduces data shuffling. It performs the reduction locally on each partition before shuffling only the reduced values across the network.

?

Considering some parameters we can compare groupByKey and reduceByKey as follows,

Simplicity:

• groupByKey() is a simple operation that groups data by key and returns a pair RDD where each key is associated with a list of values. It does not require a reduce function and is easy to understand and use.
• While reduceByKey() operation group the data by applying some reduce function on the values of key and return a pair of RDD of (key, reduced_value).

Use Cases:

• GroupByKey is typically used when we need to group data by key and process all the values associated with each key together. It is common in scenarios like word count or grouping data for further analysis.
• ReduceByKey is used when we need to perform aggregations or computations on grouped values based on their keys. It is suitable for scenarios like calculating sum, average, maximum, or minimum values for each key.

Performance:

• GroupByKey operation can be expensive in terms of performance since it shuffles and transfers all the values associated with each key across the cluster. This can cause significant data movement and can be a bottleneck when dealing with large datasets.
• ReduceByKey operation reduces data movement compared to GroupByKey. It performs the reduction locally on each partition and then shuffles and combines the reduced values across the cluster. This reduces the amount of data transfer and improves performance.

Data Set Size:

• For small datasets, the difference in performance between reduceByKey() and groupByKey() may not be significant. In fact, for datasets where the number of keys is small and the values associated with each key are relatively small, groupByKey() may even be faster than reduceByKey() due to the overhead of serialization and deserialization in the reduce function.

Non-associative operations:

• groupByKey() can be used for non-associative operations, where the order of application of the operation matters. For example, if we want to calculate the median of a set of values for each key, we cannot use reduceByKey(), since median is not an associative operation. In this case, we can use groupByKey() to group the data by key and then apply a custom function to calculate the median for each key.

?

?