pyspark 常用action 算子

from pyspark import SparkConf, SparkContext

conf = SparkConf().setAppName("test_SamShare").setMaster("local[4]")
sc = SparkContext(conf=conf)

# 1. collect 指的是把数据汇集到driver端 ，便于后续操作

rdd = sc.parallelize(range(0, 5))
rdd_collect = rdd.collect()
print(rdd_collect)

# [0, 1, 2, 3, 4]

# 2.first 取第一个元素

rdd = sc.parallelize(['a', 'b', 'c'])
print(rdd.first())

# a

# 3. collectAsMap :转换为dict,使用这个要注意不要对大数据使用，不然全部加载到driver端会爆内存

rdd = sc.parallelize([(1, 2), (3, 4)])
print(rdd.collectAsMap())

# {1: 2, 3: 4}

# 4. reduce 逐步对两个元素进行操作
rdd = sc.parallelize(range(10), 5)
print(rdd.collect())
print(rdd.reduce(lambda x, y: x+y))

# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# 45

# 5.countByKey/countByValue

rdd = sc.parallelize([("a", 1), ("b", 2), ("a", 1)])
print(rdd.countByKey().items())
print(rdd.countByValue().items())


# dict_items([('a', 2), ('b', 1)])
# dict_items([(('a', 1), 2), (('b', 2), 1)])

# 6.take 相当于取几个数据到driver端

rdd = sc.parallelize([("a", 1), ("b", 2), ("a", 1)])
print(rdd.take(2))

# [('a', 1), ('b', 2)]

# 7.saveAsTextFile 保存rdd成text文件到本地

text_file = "./rdd.txt"
rdd = sc.parallelize(range(5))
rdd.saveAsTextFile(text_file)

# 8.takesample 随机取数

rdd = sc.textFile("./word.txt", 4) # 4代表分区数量
rdd_sample = rdd.takeSample(True, 2, 0)  # withReplacement 参数1：代表是否是有放回抽样
print(rdd_sample)

# 9.foreach 对每一个元素执行某种操作，不生成新的RDD

rdd = sc.parallelize(range(5))
accum = sc.accumulator(0)
rdd.foreach(lambda x : accum.add(x))
print(accum.value)

# 10