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Big data, an IT industry term, refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time frame. It is a massive, high-growth, and diverse information asset that requires new processing models to have stronger decision-making power, insight, and process optimization capabilities.

Hive

In a big data scenario, an important item in the report is UV (Unique Visitor) statistics, that is, the number of users in a certain period of time. For example, to view the distribution of app users in a week, Hive writes HiveQL implementation:

select app, count(distinct uid) as uvfrom log_tablewhere week_cal = '2016-03-27'

Pig

Similarly, Pig is written:

-- all usersdefine 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 scenariosD = foreach C generate group as app, SIZE($1) as uv;

DataFu UDF datafu.pig.stats. HyperLogPlusPlus that provides cardinality estimates for pig, which uses HyperLogLog++ algorithm to more quickly distinguish 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

In Spark, Load data is transformed through a series of RDD transformations--map, distinct, reduceByKey Distinct Count:

rdd.map { row => (row.app, row.uid) } .distinct() .map { line => (line._ 1, 1) } .reduceByKey(_ + _)// orrdd.map { row => (row.app, row.uid) } .distinct() .mapValues{ _ => 1 } .reduceByKey(_ + _)// or rdd.map { row => (row.app, row.uid) } .distinct() .map(_._ 1) .countByValue()

Meanwhile, Spark provides an API that approximates Distinct Count:

rdd.map { row => (row.app, row.uid) } .countApproxDistinctByKey(0.001)

The implementation is based on the HyperLogLog algorithm:

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.

Alternatively, after converting the Schemaized RDD to DataFrame, registerTempTable and then executing sql command can also be:

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") The above is the content of this article about "Distinct Count has what role". I believe everyone has a certain understanding. I hope the content shared by Xiaobian will be helpful to everyone. If you want to know more relevant knowledge content, please pay attention to the industry information channel.

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