RDD的基本命令

1 创建RDD

intRDD=sc.parallelize([3,1,2,5,6])
intRDD.collect()
[4, 2, 3, 6, 7]

2 单RDD转换

(1) MAP

def addone(x):
return (x+1)
intRDD.map(addone).collect()
[4, 2, 3, 6, 7]

intRDD.map(lambda x: x+1).collect()
[4, 2, 3, 6, 7]

stringRDD.map(lambda x:'fruit:'+x).collect()
['fruit:Apple', 'fruit:Orange', 'fruit:Banana', 'fruit:Grape', 'fruit:Apple']

(2) filter

intRDD.filter(lambda x: x<3).collect()
[1, 2]
intRDD.filter(lambda x:1<x and x<5).collect()
[3, 2]
stringRDD.filter(lambda x: "ra" in x).collect()
['Orange', 'Grape']

(3) distinct

intRDD.distinct().collect()
[1, 5, 2, 6, 3]
stringRDD.distinct().collect()
['Orange', 'Apple', 'Banana', 'Grape']

(4) randomSplit

sRDD=intRDD.randomSplit([0.4,0.6])
sRDD[0].collect()
[1, 2]
sRDD[1].collect()
[3, 5, 6]

(5) groupby

gRDD=intRDD.groupBy(lambda x:'even' if (x%2==0) else 'odd').collect()
print('even') 
print(list(gRDD[0][1])) 
print('odd') 
print(gRDD[1][1])

even 
[2, 6] 
odd 
<pyspark.resultiterable.ResultIterable object at 0x7f9ba805d438>

3  多个RDD转换运算

intRDD1=sc.parallelize([3,1,2,5,5])
intRDD2=sc.parallelize([5,6])
intRDD3=sc.parallelize([2,7])

并集union

intRDD1.union(intRDD2).union(intRDD3).collect()

[3, 1, 2, 5, 5, 5, 6, 2, 7]

交集intersection

intRDD1.intersection(intRDD2).collect()

[5]

差集 subtract

intRDD1.subtract(intRDD2).collect()

[1, 2, 3]

笛卡尔积乘积 cartesian

intRDD1.cartesian(intRDD2).collect()

[(3, 5),

(3, 6),

(1, 5),

(1, 6),

(2, 5),

(2, 6),

(5, 5),

(5, 5),

(5, 6),

(5, 6)]

动作 运算

first() 读取第一项数据
take(2) 取出前两项数据
takeOrdered(3) 从小到大排序，取出前三项数据
takeOrdered(3,key=lambda x:-x) 从大到小排序，取出前三项

统计功能

stats()
min()
max()
stdev()
count()
sum()
mean()

RDD key-value transformation

kvRDD1=sc.parallelize([(3,4),(3,6),(5,6),(1,2)])
kvRDD2=sc.parallelize([(3,8)])

kvRDD1.collect()
[(3, 4), (3, 6), (5, 6), (1, 2)]
kvRDD2.collect()
[(3, 8)]

join

kvRDD1.join(kvRDD2).collect()
[(3, (4, 8)), (3, (6, 8))]

leftOuterJoin

kvRDD1.leftOuterJoin(kvRDD2).collect()

[(1, (2, None)), (3, (4, 8)), (3, (6, 8)), (5, (6, None))]

rightOuterJoin

kvRDD1.rightOuterJoin(kvRDD2).collect()

[(3, (4, 8)), (3, (6, 8))]

subtractByKey

kvRDD1.subtractByKey(kvRDD2).collect()

[(1, 2), (5, 6)]

RDD key-value Action

key-value first

kvFirst=kvRDD1.first()
print(kvFirst[0])
print(kvFirst[1])

3 
4

key count

kvRDD1.countByKey()

defaultdict(int, {1: 1, 3: 2, 5: 1})

create key-value map –>collectAsMap

KV=kvRDD1.collectAsMap()
KV

{1: 2, 3: 6, 5: 6}

print(type(KV)) 
print(KV[3])
<class 'dict'> 6

input key to get value

kvRDD1.lookup(3)

[4, 6]