大数据下的Distinct Count(一):序
< 返回列表时间: 2020-03-02来源:OSCHINA
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大数据(big data),IT行业术语,是指无法在一定时间范围内用常规软件工具进行捕捉、管理和处理的数据集合,是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。


Hive
在大数据场景下,报表很重要一项是UV(Unique Visitor)统计,即某时间段内用户人数。例如,查看一周内app的用户分布情况,Hive中写HiveQL实现: select app, count(distinct uid) as uv from log_table where week_cal = '2016-03-27'
Pig
与之类似,Pig的写法: -- all users define DISTINCT_COUNT(A, a) returns dist { B = foreach $A generate $a; unique_B = distinct B; C = group unique_B all; $dist = foreach C generate SIZE(unique_B); } A = load '/path/to/data' using PigStorage() as (app, uid); B = DISTINCT_COUNT(A, uid); -- A = load '/path/to/data' using PigStorage() as (app, uid); B = distinct A; C = group B by app; D = foreach C generate group as app, COUNT($1) as uv; -- suitable for small cardinality scenarios D = foreach C generate group as app, SIZE($1) as uv;
DataFu 为pig提供基数估计的UDF datafu.pig.stats.HyperLogLogPlusPlus,其采用HyperLogLog++算法,更为快速地Distinct Count: define HyperLogLogPlusPlus datafu.pig.stats.HyperLogLogPlusPlus(); A = load '/path/to/data' using PigStorage() as (app, uid); B = group A by app; C = foreach B generate group as app, HyperLogLogPlusPlus($1) as uv;
Spark
在Spark中,Load数据后通过RDD一系列的转换——map、distinct、reduceByKey进行Distinct Count: rdd.map { row => (row.app, row.uid) } .distinct() .map { line => (line._1, 1) } .reduceByKey(_ + _) // or rdd.map { row => (row.app, row.uid) } .distinct() .mapValues{ _ => 1 } .reduceByKey(_ + _) // or rdd.map { row => (row.app, row.uid) } .distinct() .map(_._1) .countByValue()
同时,Spark提供近似Distinct Count的API: rdd.map { row => (row.app, row.uid) } .countApproxDistinctByKey(0.001)
实现是基于HyperLogLog算法: The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
或者,将Schema化的RDD转成DataFrame后,registerTempTable然后执行sql命令亦可: val sqlContext = new SQLContext(sc) val df = rdd.toDF() df.registerTempTable("app_table") val appUsers = sqlContext.sql("select app, count(distinct uid) as uv from app_table group by app") 本文地址: https://www.linuxprobe.com/big-data-sequence.html
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