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RDD Operations

The RDD provides the two types of operations:

  • Transformation
  • Action

Transformation

In Spark, the role of transformation is to create a new dataset from an existing one. The transformations are considered lazy as they only computed when an action requires a result to be returned to the driver program.

Let's see some of the frequently used RDD Transformations.

Transformation Description
map(func) It returns a new distributed dataset formed by passing each element of the source through a function func.
filter(func) It returns a new dataset formed by selecting those elements of the source on which func returns true.
flatMap(func) Here, each input item can be mapped to zero or more output items, so func should return a sequence rather than a single item.
mapPartitions(func) It is similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator<T> => Iterator<U> when running on an RDD of type T.
mapPartitionsWithIndex(func) It is similar to mapPartitions that provides func with an integer value representing the index of the partition, so func must be of type (Int, Iterator<T>) => Iterator<U> when running on an RDD of type T.
sample(withReplacement, fraction, seed) It samples the fraction fraction of the data, with or without replacement, using a given random number generator seed.
union(otherDataset) It returns a new dataset that contains the union of the elements in the source dataset and the argument.
intersection(otherDataset) It returns a new RDD that contains the intersection of elements in the source dataset and the argument.
distinct([numPartitions])) It returns a new dataset that contains the distinct elements of the source dataset.
groupByKey([numPartitions]) It returns a dataset of (K, Iterable) pairs when called on a dataset of (K, V) pairs.
reduceByKey(func, [numPartitions]) When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V.
aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions]) When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value.
sortByKey([ascending], [numPartitions]) It returns a dataset of key-value pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument.
join(otherDataset, [numPartitions]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin.
cogroup(otherDataset, [numPartitions]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable, Iterable)) tuples. This operation is also called groupWith.
cartesian(otherDataset) When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements).
pipe(command, [envVars]) Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script.
coalesce(numPartitions) It decreases the number of partitions in the RDD to numPartitions.
repartition(numPartitions) It reshuffles the data in the RDD randomly to create either more or fewer partitions and balance it across them.
repartitionAndSortWithinPartitions(partitioner) It repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys.

Action

In Spark, the role of action is to return a value to the driver program after running a computation on the dataset.

Let's see some of the frequently used RDD Actions.

Action Description
reduce(func) It aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel.
collect() It returns all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data.
count() It returns the number of elements in the dataset.
first() It returns the first element of the dataset (similar to take(1)).
take(n) It returns an array with the first n elements of the dataset.
takeSample(withReplacement, num, [seed]) It returns an array with a random sample of num elements of the dataset, with or without replacement, optionally pre-specifying a random number generator seed.
takeOrdered(n, [ordering]) It returns the first n elements of the RDD using either their natural order or a custom comparator.
saveAsTextFile(path) It is used to write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark calls toString on each element to convert it to a line of text in the file.
saveAsSequenceFile(path)
(Java and Scala)
It is used to write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system.
saveAsObjectFile(path)
(Java and Scala)
It is used to write the elements of the dataset in a simple format using Java serialization, which can then be loaded usingSparkContext.objectFile().
countByKey() It is only available on RDDs of type (K, V). Thus, it returns a hashmap of (K, Int) pairs with the count of each key.
foreach(func) It runs a function func on each element of the dataset for side effects such as updating an Accumulator or interacting with external storage systems.

Next TopicRDD Persistence




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