本文介绍了从PySpark的RDD中的数据中找到最小和最大日期的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我正在将 Spark
与 Ipython
一起使用,并具有 RDD
在打印时包含此格式的数据:
I am using Spark
with Ipython
and have a RDD
which contains data in this format when printed:
print rdd1.collect()
[u'2010-12-08 00:00:00', u'2010-12-18 01:20:00', u'2012-05-13 00:00:00',....]
每个数据都是 datetimestamp
,我想在此 RDD
中找到最小值和最大值。我该怎么做?
Each data is a datetimestamp
and I want to find the minimum and the maximum in this RDD
. How can I do that?
推荐答案
例如,您可以使用 aggregate
函数(有关其工作原理的解释,请参见:)
You can for example use aggregate
function (for an explanation how it works see: What is the equivalent implementation of RDD.groupByKey() using RDD.aggregateByKey()?)
from datetime import datetime
rdd = sc.parallelize([
u'2010-12-08 00:00:00', u'2010-12-18 01:20:00', u'2012-05-13 00:00:00'])
def seq_op(acc, x):
""" Given a tuple (min-so-far, max-so-far) and a date string
return a tuple (min-including-current, max-including-current)
"""
d = datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
return (min(d, acc[0]), max(d, acc[1]))
def comb_op(acc1, acc2):
""" Given a pair of tuples (min-so-far, max-so-far)
return a tuple (min-of-mins, max-of-maxs)
"""
return (min(acc1[0], acc2[0]), max(acc1[1], acc2[1]))
# (initial-min <- max-date, initial-max <- min-date)
rdd.aggregate((datetime.max, datetime.min), seq_op, comb_op)
## (datetime.datetime(2010, 12, 8, 0, 0), datetime.datetime(2012, 5, 13, 0, 0))
或 DataFrames
:
from pyspark.sql import Row
from pyspark.sql.functions import from_unixtime, unix_timestamp, min, max
row = Row("ts")
df = rdd.map(row).toDF()
df.withColumn("ts", unix_timestamp("ts")).agg(
from_unixtime(min("ts")).alias("min_ts"),
from_unixtime(max("ts")).alias("max_ts")
).show()
## +-------------------+-------------------+
## | min_ts| max_ts|
## +-------------------+-------------------+
## |2010-12-08 00:00:00|2012-05-13 00:00:00|
## +-------------------+-------------------+
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