spark中rdd的操作

2018年6月22日10:19:06 发表评论 394 views

1、RDDs的操作

支持两种类型的操作:transformations,从一个存在的数据集创建一个新的数据集;另一个是actions,返回一个基于数据集计算后的值,也肯能是一个新的数据集。下面列出一些常用的RDDS操作

transformations:

Transformation Meaning
map(func) 返回一个被func处理后的新的分布式数据集。
filter(func) 返回一个新的满足筛选条件的数据集。
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(withReplacementfractionseed) Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed.
union(otherDataset) Return a new dataset that contains the union of the elements in the source dataset and the argument.
intersection(otherDataset) Return a new RDD that contains the intersection of elements in the source dataset and the argument.
distinct([numTasks])) Return a new dataset that contains the distinct elements of the source dataset.
groupByKey([numTasks]) 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 numTasks argument to set a different number of tasks.
reduceByKey(func, [numTasks]) 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)(seqOpcombOp, [numTasks]) 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], [numTasks]) 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, [numTasks]) 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 leftOuterJoinrightOuterJoin, and fullOuterJoin.
cogroup(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) 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. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings.
coalesce(numPartitions) 减少RDD中patitions的数量,一般用在过滤了大的数据集后。
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.

actions:

Action Meaning
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() 将所有的子数据集聚集到driver合成一个大的数据集。
count() 统计数据集数据的数量。
first() Return the first element of the dataset (similar to take(1)).
take(n) 从数据集中取出n条数据。
takeSample(withReplacementnum, [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 usingSparkContext.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.

2、closures(闭包)

为了执行job,Spark会将处理RDD的操作分成许多tasks,每一个task被一个executor执行。在执行executor之前,Spark会计算task的闭包(包含task所需的变量和方法等),然后将闭包序列化并发送到每一个executor。所以每个闭包就是driver的一个拷贝,这样不管executor中的closures怎么变,都不会对driver中原始值起作用,这和java中的jmm相似。

3、RDD打印输出

常规的rdd.foreach(println) or rdd.map(println)在cluster模式下是打印不出来预期的数据,因为在cluster下stdout是被executors调用而不是driver,所以stdout在driver将不会展现;可以使用rdd.collect().foreach(println)和 rdd.take(100).foreach(println)代替。

  • 我的微信
  • 这是我的微信扫一扫
  • weinxin
  • 我的微信公众号
  • 我的微信公众号扫一扫
  • weinxin

发表评论

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen: