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]
