📅 最后修改于: 2020-12-27 02:37:12 🧑 作者: Mango
RDD提供两种类型的操作：
在Spark中，转换的作用是从现有数据集中创建新数据集。转换被认为是惰性的，因为它们仅在动作需要将结果返回给驱动程序时才计算。
让我们看一些常用的RDD转换。
| 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 |
| 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 |
| 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 |
| 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 |
| 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. |
在Spark中，操作的作用是在数据集上运行计算后将值返回给驱动程序。
让我们看看一些常用的RDD动作。
| 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. |