spark算子深度解析

“以道佐人主者，不以兵强天下。
其事好远。
师之所处，荆棘生焉；大军之后，必有凶年。
善有果而已，不敢以取强。
果而勿矜，果而勿伐，果而勿骄。
果而不得已，果而勿强。
物壮则老，是谓不道，不道早已。”¹

spark内置了非常多有用的算子（方法），通过对这些算子的组合就可以完成业务需要的功能，spark的编程归根结底就是对spark算子的使用，因此非常有必要对这些内置算子进行详细的归纳。
spark算子在大的方向上可以分为两类：

名称	说明
Transformation	`变换、转换算子`：不触发提交作业，只是完成作业中间过程处理；Transformation 操作是延迟计算的，也就是说从一个RDD 转换生成另一个 RDD 的转换操作不是马上执行，需要等到有 Action 操作的时候才会真正触发运算。Transformation参数类型为value或者key-value的形式。
Action	`行动算子`：触发SparkContext提交job作业。Action 算子会触发 Spark 提交作业（Job），并将数据输出 Spark系统。

上表中提到Transformation的参数类型分为两类：value和key-value形式，对其归纳如下：

value 类型

细类型	算子
输入分区与输出分区一对一型	map flatMap mapPartitions glom
输入分区与输出分区多对一型	union cartesain
输入分区与输出分区多对多型	groupBy
输出分区为输入分区子集型	filter distinct substract sample takeSample
Cache型	cache persist
#### key-value类型
细类型	算子
:——	:——
输入分区与输出分区一对一	mapValues
对单个RDD或两个RDD聚集	单个RDD聚集: combineByKey reduceByKey partitionBy 两个RDD聚集: Cogroup
连接	join leftOutJoin和 rightOutJoin

Action算子

细类型	算子
无输出	foreach
HDFS	saveAsTextFile saveAsObjectFile
Scala集合和数据类型	collect collectAsMap reduceByKeyLocally lookup count top reduce fold aggregate

接下来，以pyspark为编译环境对上述算子(不限于)进行详细解析。

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.3.0.cloudera2
      /_/

Using Python version 2.7.5 (default, Aug  4 2017 00:39:18)
SparkSession available as 'spark'.

map

map(f, preservesPartitioning=False)

map是对RDD中的每个元素都执行一个指定的函数来产生一个新的RDD，RDD之间的元素是一对一关系。

1	>>> x = sc.parallelize([1,2,3,4])
2	>>> y = x.map(lambda x:(x**3))
3	>>> y.collect()
4	[1, 8, 27, 64]

filter

filter(f)

对RDD元素进行过滤，返回一个新的数据集，由经过func函数后返回值为true的原元素组成。

1	>>> y = x.filter(lambda x:x>2)
2	>>> y.collect()
3	[3, 4]

flatMap

flatMap(f, preservesPartitioning=False)

类似于map，但是每一个输入元素会被映射为0到多个输入元素，RDD之间的元素是一对多关系。

1	>>> y = x.flatMap(lambda x:(x,x100,x*2))
2	>>> y.collect()
3	[1, 100, 1, 2, 200, 4, 3, 300, 9, 4, 400, 16]

glom

glom()

返回一个RDD，它将每个分区中的所有元素合并到一个列表中。

1	>>> a = sc.parallelize([1,2,3,4],2) //第二个参数2，表示数据集切分的份数(slices)。Spark将会在集群上为每一份数据起一个任务。
2	>>> y = a.glom()
3	>>> y.collect()
4	[[1, 2], [3, 4]]

mapPartitions

mapPartitions(f, preservesPartitioning=False)

Return a new RDD by applying a function to each partition of this RDD.

1	>>> xx = sc.parallelize([1,2,3,4], 2)
2	>>> def f(iter):
3	... yield sum(iter)
4	...
5	>>> yy = xx.mapPartitions(f)
6	>>> print 'xx原来分区信息：{0}'.format(xx.glom().collect())
7	xx原来分区信息：[[1, 2], [3, 4]]
8	>>> print 'xx经过f计算后的结果：{}'.format(yy.glom().collect())
9	xx经过f计算后的结果：[[3], [7]]

关于yield的使用，请参考我的另一篇笔记《Python易筋经-yield when data is bigger》

mapPartitionsWithIndex

mapPartitionsWithIndex(f, preservesPartitioning=False)

Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
和mapPartitions类似，也是针对每个分区处理，但是func函数需要两个入参，第一个表示partition分区索引，第二个入参表示每个分区的迭代器。

1	>>> x = sc.parallelize([1, 2, 3, 4], 2)
2	>>> def f(splitIndex, iterator): yield (splitIndex, sum(iterator))
3	...
4	>>> y = x.mapPartitionsWithIndex(f)
5	>>> print 'x原来分区信息：{0}'.format(x.glom().collect())
6	x原来分区信息：[[1, 2], [3, 4]]
7	>>> print 'x经过f计算后的结果：{}'.format(y.glom().collect())
8	x经过f计算后的结果：[[(0, 3)], [(1, 7)]]

getNumsPartitions

getNumPartitions()

Returns the number of partitions in RDD

1	>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
2	>>> print '分区有{}个'.format(rdd.getNumPartitions())
3	分区有2

distinct

distinct(numPartitions=None)

Return a new RDD containing the distinct elements in this RDD.

1	>>> res = sorted(sc.parallelize([1, 1, 1, 2, 3, 2, 3]).distinct().collect())
2	>>> print '去重后的结果：{}'.format(res)
3	去重后的结果：[1, 2, 3]

sample

sample(withReplacement, fraction, seed=None)

Return a sampled subset of this RDD.
Parameters:
withReplacement – can elements be sampled multiple times (replaced when sampled out)

fraction – expected size of the sample as a fraction of this RDD’s size without replacement: probability that each element is chosen; fraction must be [0, 1] with replacement: expected number of times each element is chosen; fraction must be >= 0

seed – seed for the random number generator
对原RDD进行采样，其中withReplacement表示是否有放回的抽样，fraction表示采样大小是原RDD的百分比，seed表示随机数生成器。fraction和seed相同，则每次返回的数据相同。

1	>>> rdd = sc.parallelize(range(100), 4)
2	>>> y = rdd.sample(False, 0.1, 81)
3	>>> y.collect()
4	[4, 27, 40, 42, 43, 60, 76, 80, 86, 97]
5	>>> y.count()
6	10

union 并集

union(other)

Return the union of this RDD and another one.

1	>>> rdd = sc.parallelize([1, 1, 2, 3])
2	>>> rdd1 = sc.parallelize([5, 3, 4, 6])
3	>>> print rdd.union(rdd1).collect()
4	[1, 1, 2, 3, 5, 3, 4, 6]

intersection 交集

intersection(other)

Note that this method performs a shuffle internally.

1	>>> rdd = sc.parallelize([1, 1, 2, 3])
2	>>> rdd1 = sc.parallelize([5, 3, 4, 6])
3	>>> print rdd.intersection(rdd1).collect()
4	[3]

sortByKey

sortByKey(ascending=True, numPartitions=None, keyfunc=func)

Sorts this RDD, which is assumed to consist of (key, value) pairs.

1	>>> tmp = [('a', 1), ('f', 2), ('d', 3), ('c', 4), ('b', 5)]
2	>>> rdd = sc.parallelize(tmp, 2)
3	>>> print rdd.glom().collect()
4	[[('a', 1), ('f', 2)], [('d', 3), ('c', 4), ('b', 5)]]
5	>>> sort1 = rdd.sortByKey(True,1).glom().collect()
6	>>> sort2 = rdd.sortByKey(True,3).glom().collect()
7	>>> print sort1
8	[[('a', 1), ('b', 5), ('c', 4), ('d', 3), ('f', 2)]]
9	>>> print sort2
10	[[('a', 1), ('b', 5)], [('c', 4), ('d', 3)], [('f', 2)]]

sortBy

sortBy(keyfunc, ascending=True, numPartitions=None)

跟sortByKey类似，用索引的方式指定根据什么sort

1	>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
2	>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
3	[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
4	>>> sc.parallelize(tmp).sortBy(lambda x: x[1]).collect()
5	[('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]

cartesian

cartesian(other)

返回两个rdd的笛卡尔积。

1	>>> rdd = sc.parallelize([1, 2])
2	>>> rdd_1 = sc.parallelize([3,4])
3	>>> rdd.cartesian(rdd_1).collect()
4	[(1, 3), (1, 4), (2, 3), (2, 4)]

groupBy

groupBy(f, numPartitions=None, partitionFunc=)

根据f逻辑结果进行分组。

1	>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
2	>>> result = rdd.groupBy(lambda x: x % 2).collect()
3	>>> sorted([(x, sorted(y)) for (x, y) in result])
4	[(0, [2, 8]), (1, [1, 1, 3, 5])]

groupByKey

groupByKey(numPartitions=None, partitionFunc=)

groupByKey([numTasks])是数据分组操作，在一个由(K, V)键值对组成的数据集上调用，返回一个(K, Seq[V])对的数据集。

注意，如果要对每个键执行聚合(比如求和或平均值)，使用reduceByKey或aggregateByKey将提供更好的性能。

1	>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
2	>>> sorted(rdd.groupByKey().mapValues(len).collect())
3	[('a', 2), ('b', 1)]
4	>>> sorted(rdd.groupByKey().mapValues(list).collect())
5	[('a', [1, 1]), ('b', [1])]

pipe

pipe(command, env=None, checkCode=False

将由管道元素创建的RDD返回到一个forked外部进程。
参数:checkCode——是否检查shell命令的返回值。

1	>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
2	[u'1', u'2', u'', u'3']
3
4	>>> rdd = sc.parallelize(['spark2.3.0', 'kafka', 'hbase'])
5	>>> rdd2 = rdd.pipe('grep -i "ar"')
6	>>> print '经过pipe处理过后的数据：{}'.format(rdd2.collect())
7	经过pipe处理过后的数据：[u'spark2.3.0']

foreach

foreach(f)

Applies a function to all elements of this RDD.

1	>>> def f(x): print(x)
2	...
3	>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)

max, min, sum, count

1	>>> x = sc.parallelize(range(10))
2	>>> x.collect()
3	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
4	>>> print '最大值：{}'.format(x.max())
5	最大值：9
6	>>> print '最小值：{}'.format(x.min())
7	最小值：0
8	>>> print '总和：{}'.format(x.sum())
9	总和：45
10	>>> print '总个数：{}'.format(x.count())
11	总个数：10

mean, variance, sampleVariance, stdev, sampleStdev

1	>>> x.collect()
2	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3	>>> print '平均值：{}'.format(x.mean())
4	平均值：4.5
5	>>> print '方差：{}'.format(x.variance())
6	方差：8.25
7	>>> print '样本方差：{}'.format(x.sampleVariance())
8	样本方差：9.16666666667
9	>>> print '总体标准偏差：{}'.format(x.stdev())
10	总体标准偏差：2.87228132327
11	>>> print '样本标准偏差：{}'.format(x.sampleStdev())
12	样本标准偏差：3.0276503541

countByKey, countByValue

countByKey()

Count the number of elements for each key, and return the result to the master as a dictionary.

countByValue()

Return the count of each unique value in this RDD as a dictionary of (value, count) pairs.

1	>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
2	>>> sorted(rdd.countByKey().items())
3	[('a', 2), ('b', 1)]
4
5	>>> sorted(sc.parallelize([1, 2, 1, 2, 2], 2).countByValue().items())
6	[(1, 2), (2, 3)]

first, top, take, takeOrdered

first()
top(num, key=None)

从按降序排列的RDD中获取前N个元素，或者有可选的key函数决定顺序。

take(num)

Take the first num elements of the RDD.

akeOrdered(num, key=None)

从按升序排列的RDD中获取N个元素，或者由可选key函数指定。

1	>>> rdd = sc.parallelize([10, 4, 2, 12, 3])
2	>>> rdd.first()
3	10
4	>>> rdd.top(1)
5	[12]
6	>>> rdd.top(2)
7	[12, 10]
8	>>> rdd.top(2,key=str)
9	[4, 3]
10	>>> rdd.take(3)
11	[10, 4, 2]

subtract

subtract(other, numPartitions=None)

Return each value in self that is not contained in other.

1	>>> x = sc.parallelize([("a", 1), ("b", 4), ("b", 5), ("a", 3)])
2	>>> y = sc.parallelize([("a", 3), ("c", None)])
3	>>> sorted(x.subtract(y).collect())
4	[('a', 1), ('b', 4), ('b', 5)]

cache

cache()

Persist this RDD with the default storage level (MEMORY_ONLY).

presist

persist()

Set this RDD’s storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. If no storage level is specified defaults to (MEMORY_ONLY).

cache只有一个默认的缓存级别MEMORY_ONLY ，而persist可以根据情况设置其它的缓存级别。

reduce

reduce(f)

Reduces the elements of this RDD using the specified commutative and associative binary operator. Currently reduces partitions locally.

>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 for _ in range(10))).map(lambda x: 1).cache().reduce(add)
10

reduceByKey

reduceByKey(func, numPartitions=None, partitionFunc=)

Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before sending results to a reducer, similarly to a “combiner” in MapReduce.
Output will be partitioned with numPartitions partitions, or the default parallelism level if numPartitions is not specified. Default partitioner is hash-partition.

把一个函数作用在一个RDD上，这个函数必须接收两个参数，reduce把结果继续和RDD的下一个元素做累积计算。注意：reduceByKey的函数是针对具有相同key的二元组

1	>>> from operator import add
2	>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
3	>>> sorted(rdd.reduceByKey(add).collect())
4	[('a', 2), ('b', 1)]
5
6	>>> num = sc.parallelize([1, 4, 2, 3, 4, 4, 2, 4])
7	>>> pairs = num.map(lambda x: (x, 1))
8	>>> pairs.collect()
9	[(1, 1), (4, 1), (2, 1), (3, 1), (4, 1), (4, 1), (2, 1), (4, 1)]
10	>>> a = pairs.reduceByKey(lambda x, y: x+y+1)
11	>>> a.collect()
12	[(2, 3), (4, 7), (1, 1), (3, 1)]
13	>>> b = pairs.reduceByKey(lambda x, y: x+y+2)
14	>>> b.collect()
15	[(2, 4), (4, 10), (1, 1), (3, 1)

1:老子《道德经》第三十章，老子故里，中国鹿邑。

1	Welcome to
2	____ __
3	/ __/__ ___ _____/ /__
4	_\ \/ _ \/ _ `/ __/ '_/
5	/__ / .__/\_,_/_/ /_/\_\ version 2.3.0.cloudera2
6	/_/
7
8	Using Python version 2.7.5 (default, Aug 4 2017 00:39:18)
9	SparkSession available as 'spark'.

1	>>> from operator import add
2	>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
3	15
4	>>> sc.parallelize((2 for _ in range(10))).map(lambda x: 1).cache().reduce(add)
5	10