spark-rdd

rdd Resilient Distributed DataSets 容错的 并行的数据结果

transform 和 action 算子

https://blog.csdn.net/zzh118/article/details/52048521

transfrom操作：

• parallelize， mkRDD:

  sc.makeRDD(Array((1,"A"),(2,"B"),(3,"C"),(4,"D")),2) 

• map
• flatMap
• flatMapValues:
  def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)]
• filter
• mapValues:
  def mapValues[U](f: V => U): RDD[(K, U)]
• distinct(numPartitions: Int) numPartitions 可缺省
• glom：
  将每个分区中的元素转换成Array,这样每个分区就只有一个数组元素，最终返回一个RDD def glom(): RDD[Array[T]]
• groupByKey:
  返回 (K, Seq[V])的RDD
• reduceByKey:
  (_ + _)
• combineByKey
  使用用户设置好的聚合函数对每个key中得value进行组合（combine）,可以将输入类型为RDD[(k, v)]转成RDD[(k, c)]。
• sortByKey()
• sortBy()

def sortBy[K](
      f: (T) => K,
      ascending: Boolean = true,
      numPartitions: Int = this.partitions.length)
      (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope {
    this.keyBy[K](f)
        .sortByKey(ascending, numPartitions)
        .values
  }

def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)
      : RDD[(K, V)] = self.withScope
  {
    val part = new RangePartitioner(numPartitions, self, ascending)
    new ShuffledRDD[K, V, V](self, part)
      .setKeyOrdering(if (ascending) ordering else ordering.reverse)
  }

val rdd = spark.sparkContext.makeRDD(1 to 10 zip (11 to 20))
val f = (x: (Int,Int)) => x._1%3
rdd.sortBy(f, false, 2)
rdd.sortBy
rdd.sortByKey(false, 2)

• zip ()

• zipWithUniqueId()

• zipWithIndex()

• zipPartitions()

• cogroup
  相当于SQL中的全外关联full outer join，返回左右RDD中的记录，关联不上的为空。

• join, leftOuterJoin、rightOuterJoin操作.

• sample:

def sample(
      withReplacement: Boolean,  // 是否有放回采样，可以做降采样或者升采样
      fraction: Double,
      seed: Long = Utils.random.nextLong)

• cartesian 笛卡儿积
  def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)]
• union，++操作 并集
• subtract 差集
• intersection 交集
• groupByKey: def groupByKey(): RDD[(K, Iterable[V])]
• partitionBy: 重新分区

action操作： 输出结果非RDD， 将触发依赖的transform操作

• reduce
• collect
• count
• first
• take
• takeSample(withReplacecment, num, seed) 返回数组
• countBykey() : 返回Map(K, Int)
• foreach
• foreachPartition
• saveAsTextFile
• saveAsSequenceFile
• flod:
  折叠(fold)操作和reduce（归约）操作比较类似。fold操作需要从一个初始的“种子”值开始，并以该值作为上下文，处理集合中的每个元素。

rdd.map(_._1).fold(0)(_ + _)

• aggregate
  def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U):

官网给的列表：

Transformations

The following table lists some of the common transformations supported by Spark. Refer to the RDD API doc (Scala, Java, Python, R) and pair RDD functions doc (Scala, Java) for details.

Transformation	Meaning
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 funcreturns true.
flatMap(func)	Similar to map, but each input item can be mapped to 0 or more output items (so funcshould 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 => Iterator 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) => Iterator 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.
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([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) 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.
这里也提到了， 用reduceByKey和aggregateByKey而非groupByKey， 减少shuffle， 数据倾斜的处理中也可以作为一个两步聚合的方案
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) | 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) | 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. |
上面这些都是会导致重新分区的操作， 即宽依赖， 是stage的分割点， 带来shuffle

Actions

The following table lists some of the common actions supported by Spark. Refer to the RDD API doc (Scala, Java, Python, R)

and pair RDD functions doc (Scala, Java) for details.

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()	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 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.
这个编程中要注意： 即使foreach， 对于其中的变量也要用累加器Accumulators(map类操作就不用讲了)

The Spark RDD API also exposes asynchronous versions of some actions, like foreachAsync for foreach, which immediately return a FutureAction to the caller instead of blocking on completion of the action. This can be used to manage or wait for the asynchronous execution of the action.