在 'pyspark.resultiterable.ResultIterable' 上调用 Distinct
Call Distinct on 'pyspark.resultiterable.ResultIterable'
我正在写一些 spark 代码,我有一个看起来像
的 RDD
[(4, <pyspark.resultiterable.ResultIterable at 0x9d32a4c>),
(1, <pyspark.resultiterable.ResultIterable at 0x9d32cac>),
(5, <pyspark.resultiterable.ResultIterable at 0x9d32bac>),
(2, <pyspark.resultiterable.ResultIterable at 0x9d32acc>)]
我需要做的是在 pyspark.resultiterable.ResultIterable
上调用一个 distinct
我试过了
def distinctHost(a, b):
p = sc.parallelize(b)
return (a, p.distinct())
mydata.map(lambda x: distinctHost(*x))
但是我得到一个错误:
Exception: It appears that you are attempting to reference
SparkContext from a broadcast variable, action, or transforamtion.
SparkContext can only be used on the driver, not in code that it run
on workers. For more information, see SPARK-5063.
错误不言自明,我无法使用 sc。但是我需要找到一种方法将 pyspark.resultiterable
.ResultIterable
覆盖到 RDD 以便我可以调用它。
直接的方法是使用集合:
from numpy.random import choice, seed
seed(323)
keys = (4, 1, 5, 2)
hosts = [
u'in24.inetnebr.com',
u'ix-esc-ca2-07.ix.netcom.com',
u'uplherc.upl.com',
u'slppp6.intermind.net',
u'piweba4y.prodigy.com'
]
pairs = sc.parallelize(zip(choice(keys, 20), choice(hosts, 20))).groupByKey()
pairs.map(lambda (k, v): (k, set(v))).take(3)
结果:
[(1, {u'ix-esc-ca2-07.ix.netcom.com', u'slppp6.intermind.net'}),
(2,
{u'in24.inetnebr.com',
u'ix-esc-ca2-07.ix.netcom.com',
u'slppp6.intermind.net',
u'uplherc.upl.com'}),
(4, {u'in24.inetnebr.com', u'piweba4y.prodigy.com', u'uplherc.upl.com'})]
如果有特殊原因需要使用 rdd.disinct
,您可以尝试这样的操作:
def distinctHost(pairs, key):
return (pairs
.filter(lambda (k, v): k == key)
.flatMap(lambda (k, v): v)
.distinct())
[(key, distinctHost(pairs, key).collect()) for key in pairs.keys().collect()]
我正在写一些 spark 代码,我有一个看起来像
的 RDD[(4, <pyspark.resultiterable.ResultIterable at 0x9d32a4c>),
(1, <pyspark.resultiterable.ResultIterable at 0x9d32cac>),
(5, <pyspark.resultiterable.ResultIterable at 0x9d32bac>),
(2, <pyspark.resultiterable.ResultIterable at 0x9d32acc>)]
我需要做的是在 pyspark.resultiterable.ResultIterable
我试过了
def distinctHost(a, b):
p = sc.parallelize(b)
return (a, p.distinct())
mydata.map(lambda x: distinctHost(*x))
但是我得到一个错误:
Exception: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforamtion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.
错误不言自明,我无法使用 sc。但是我需要找到一种方法将 pyspark.resultiterable
.ResultIterable
覆盖到 RDD 以便我可以调用它。
直接的方法是使用集合:
from numpy.random import choice, seed
seed(323)
keys = (4, 1, 5, 2)
hosts = [
u'in24.inetnebr.com',
u'ix-esc-ca2-07.ix.netcom.com',
u'uplherc.upl.com',
u'slppp6.intermind.net',
u'piweba4y.prodigy.com'
]
pairs = sc.parallelize(zip(choice(keys, 20), choice(hosts, 20))).groupByKey()
pairs.map(lambda (k, v): (k, set(v))).take(3)
结果:
[(1, {u'ix-esc-ca2-07.ix.netcom.com', u'slppp6.intermind.net'}),
(2,
{u'in24.inetnebr.com',
u'ix-esc-ca2-07.ix.netcom.com',
u'slppp6.intermind.net',
u'uplherc.upl.com'}),
(4, {u'in24.inetnebr.com', u'piweba4y.prodigy.com', u'uplherc.upl.com'})]
如果有特殊原因需要使用 rdd.disinct
,您可以尝试这样的操作:
def distinctHost(pairs, key):
return (pairs
.filter(lambda (k, v): k == key)
.flatMap(lambda (k, v): v)
.distinct())
[(key, distinctHost(pairs, key).collect()) for key in pairs.keys().collect()]