rdd Resilient Distributed DataSets 容錯的 并行的數(shù)據(jù)結(jié)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：
將每個分區(qū)中的元素轉(zhuǎn)換成Array,這樣每個分區(qū)就只有一個數(shù)組元素派任，最終返回一個RDD def glom(): RDD[Array[T]] - groupByKey:
返回 (K, Seq[V])的RDD - reduceByKey:
(_ + _) - combineByKey
使用用戶設(shè)置好的聚合函數(shù)對每個key中得value進(jìn)行組合（combine）,可以將輸入類型為RDD[(k, v)]轉(zhuǎn)成RDD[(k, c)]粪摘。 - 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
相當(dāng)于SQL中的全外關(guān)聯(lián)full outer join熏纯，返回左右RDD中的記錄，關(guān)聯(lián)不上的為空。join, leftOuterJoin唐瀑、rightOuterJoin操作.
sample:
def sample(
withReplacement: Boolean, // 是否有放回采樣团南，可以做降采樣或者升采樣
fraction: Double,
seed: Long = Utils.random.nextLong)
- cartesian 笛卡兒積
def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] - union噪沙，++操作 并集
- subtract 差集
- intersection 交集
- groupByKey: def groupByKey(): RDD[(K, Iterable[V])]
- partitionBy: 重新分區(qū)
action操作： 輸出結(jié)果非RDD， 將觸發(fā)依賴的transform操作
- reduce
- collect
- count
- first
- take
- takeSample(withReplacecment, num, seed) 返回數(shù)組
- countBykey() : 返回Map(K, Int)
- foreach
- foreachPartition
- saveAsTextFile
- saveAsSequenceFile
- flod:
折疊(fold)操作和reduce（歸約）操作比較類似吐根。fold操作需要從一個初始的“種子”值開始正歼，并以該值作為上下文，處理集合中的每個元素拷橘。
rdd.map(_._1).fold(0)(_ + _)
- aggregate
def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U):
官網(wǎng)給的列表：
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<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. |
| 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<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.
這里也提到了局义， 用reduceByKey和aggregateByKey而非groupByKey， 減少shuffle冗疮， 數(shù)據(jù)傾斜的處理中也可以作為一個兩步聚合的方案
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<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) | 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. |
上面這些都是會導(dǎo)致重新分區(qū)的操作萄唇， 即寬依賴， 是stage的分割點术幔， 帶來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.
這個編程中要注意： 即使foreach另萤， 對于其中的變量也要用累加器Accumulators(map類操作就不用講了)
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.
