文章目录

Spark2.x常用算子

[TOC]

Spark2.x常用算子

下面是2.x文档中的内容，但是有些api还需要调用SparkContext进行操作。

1. Transformations

map(func)

Return a new distributed dataset formed by passing each element of the source through a function func.

filter(func)

Return a new dataset formed by selecting those elements of the source on which func returns true.

flatMap(func)

Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).

mapPartitions(func)

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)

Similar to mapPartitions, but also 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)

Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed.

* withReplacement: 元素是否可以被多次抽样
* fraction: 
	withReplacement=false，抽取元素的概率，分数必须在[0,1]
	withReplacement=true，抽取每个元素希望的次数，分数必须大于0
* seed: 随机数生成器的种子

union(otherDataset)

Return a new dataset that contains the union of the elements in the source dataset and the argument.

intersection(otherDataset)

交集。Return a new RDD that contains the intersection of elements in the source dataset and the argument.

distinct([numPartitions]))

Return a new dataset that contains the distinct elements of the source dataset.

groupByKey([numPartitions])

When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs. 
* Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, 
using reduceByKey or aggregateByKey will yield much better performance. 
* Note: By default, the level of parallelism in the output depends on the number of partitions of the 
parent RDD. You can pass an optional numPartitions argument to set a different number of tasks.

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. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.

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. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.

sortByKey([ascending], [numPartitions])

When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) 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)

笛卡尔积。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. RDD elements are written to the process’s stdin and lines output to its stdout are returned as an RDD of strings.

coalesce(numPartitions)

合并partition。Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.

repartition(numPartitions)

Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.

repartitionAndSortWithinPartitions(partitioner)

Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.

2. Actions

reduce(func)

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()

Return 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()

Return the number of elements in the dataset.

first()

Return the first element of the dataset (similar to take(1)).

take(n)

Return an array with the first n elements of the dataset.

takeSample(withReplacement, num, [seed])

Return 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])

Return the first n elements of the RDD using either their natural order or a custom comparator.

saveAsTextFile(path)

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 will call toString on each element to convert it to a line of text in the file.

saveAsSequenceFile(path)

(Java and Scala)

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. This is available on RDDs of key-value pairs that implement Hadoop’s Writable interface. In Scala, it is also available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc).

saveAsObjectFile(path)

(Java and Scala)

Write the elements of the dataset in a simple format using Java serialization, which can then be loaded using SparkContext.objectFile().

countByKey()

Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key.

foreach(func)

Run a function func on each element of the dataset. This is usually done for side effects such as updating an Accumulator or interacting with external storage systems.
Note: modifying variables other than Accumulators outside of the foreach() may result in undefined behavior. See Understanding closures for more details.

3. Shuffle operations

Certain operations within Spark trigger an event known as the shuffle. The shuffle is Spark’s mechanism for re-distributing data so that it’s grouped differently across partitions. This typically involves copying data across executors and machines, making the shuffle a complex and costly operation.

3.1 Background

概念

In Spark, data is generally not distributed across partitions to be in the necessary place for a specific operation. During computations, a single task will operate on a single partition - thus, to organize all the data for a single reduceByKey reduce task to execute, Spark needs to perform an all-to-all operation. It must read from all partitions to find all the values for all keys, and then bring together values across partitions to compute the final result for each key - this is called the shuffle.

Although the set of elements in each partition of newly shuffled data will be deterministic, and so is the ordering of partitions themselves, the ordering of these elements is not. If one desires predictably ordered data following shuffle then it’s possible to use:

mapPartitions to sort each partition using, for example, .sorted
repartitionAndSortWithinPartitions to efficiently sort partitions while simultaneously repartitioning
sortBy to make a globally ordered RDD

Operations which can cause a shuffle include repartition operations like repartition and coalesce, ‘ByKey operations (except for counting) like groupByKey and reduceByKey, and join operations like cogroup and join.

3.2 Performance Impact

The Shuffle is an expensive operation since it involves disk I/O, data serialization, and network I/O.To organize data for the shuffle, Spark generates sets of tasks - map tasks to organize the data, and a set of reduce tasks to aggregate it. This nomenclature comes from MapReduce and does not directly relate to Spark’s map and reduce operations.

Internally, results from individual map tasks are kept in memory until they can’t fit. Then, these are sorted based on the target partition and written to a single file. On the reduce side, tasks read the relevant sorted blocks.

Certain shuffle operations can consume significant amounts of heap memory since they employ in-memory data structures to organize records before or after transferring them. Specifically, reduceByKey and aggregateByKey create these structures on the map side, and 'ByKey operations generate these on the reduce side. When data does not fit in memory Spark will spill these tables to disk, incurring the additional overhead of disk I/O and increased garbage collection.

Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files are preserved until the corresponding RDDs are no longer used and are garbage collected. This is done so the shuffle files don’t need to be re-created if the lineage is re-computed. Garbage collection may happen only after a long period of time, if the application retains references to these RDDs or if GC does not kick in frequently. This means that long-running Spark jobs may consume a large amount of disk space. The temporary storage directory is specified by the spark.local.dir configuration parameter when configuring the Spark context.

Shuffle behavior can be tuned by adjusting a variety of configuration parameters. See the ‘Shuffle Behavior’ section within the Spark Configuration Guide.