DataFrame和Parquet

Apache Parquet作为文件格式最近获得了显著关注，假设你有一个100列的表，大部分时间你只需要访问3-10列，行存储，不管你需要不需要它们，你必须扫描所有。Apache Parquet是列存储，如果需要3列，那么只有这3列被load。并且datatype、compression和quality非常好。下面我们来介绍如何把一个表存储为Parquet和如何加载。首先建立一个表格：

Spark SQL:

val hc = new org.apache.spark.sql.hive.HiveContext(sc)
import hc.implicits._
case class Person(firstName: String, lastName: String, gender: String)
val personRDD = sc.textFile("person").map(_.split("\t")).map(p => Person(p(0),p(1),p(2)))
val person = personRDD.toDFperson.registerTempTable("person")
val males = hc.sql("select * from person where gender='M'")
males.collect.foreach(println)

保存DF为Parquet格式:

person.write.parquet("person.parquet")

Hive中建立Parquet格式的表:

create table person_parquet like person stored as parquet;
insert overwrite table person_parquet select * from person;

加载Parquet文件不再需要case class。

val personDF = hc.read.parquet("person.parquet")personDF.registerAsTempTable("pp")
val males = hc.sql("select * from pp where gender='M'")
males.collect.foreach(println)

Sometimes Parquet files pulled from other sources like Impala save String as binary. To fix that issue, add the following line right after creating SqlContext:

sqlContext.setConf("spark.sql.parquet.binaryAsString","true")

参考