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如何在Spark中结合合并列的两个DataFrame？

[英]How to join two DataFrame with combined columns in Spark?

 原文  2019-01-18 08:39:48  9   2    java/ scala/ apache-spark/ apache-spark-sql

问题描述

我不明白如何将这样的两个DataFrame彼此连接。

第一DataFrame存储有关用户到服务中心的请求时间的信息。

我们将此称为DataFrame df1 ： 

+-----------+---------------------+
| USER_NAME | REQUEST_DATE        |
+-----------+---------------------+
| Alex      | 2018-03-01 00:00:00 |
| Alex      | 2018-09-01 00:00:00 |
| Bob       | 2018-03-01 00:00:00 |
| Mark      | 2018-02-01 00:00:00 |
| Mark      | 2018-07-01 00:00:00 |
| Kate      | 2018-02-01 00:00:00 |
+-----------+---------------------+

第二个DataFrame存储有关用户可以使用服务中心的服务的可能期限（许可期限）的信息。

让我们称之为df2 。 

+-----------+---------------------+---------------------+------------+
| USER_NAME | START_SERVICE       | END_SERVICE         | STATUS     |
+-----------+---------------------+---------------------+------------+
| Alex      | 2018-01-01 00:00:00 | 2018-06-01 00:00:00 | Active     |
| Bob       | 2018-01-01 00:00:00 | 2018-02-01 00:00:00 | Not Active |
| Mark      | 2018-01-01 00:00:00 | 2018-05-01 23:59:59 | Active     |
| Mark      | 2018-05-01 00:00:00 | 2018-08-01 23:59:59 | VIP        |
+-----------+---------------------+---------------------+------------+

如何加入这两个DataFrame并返回这样的结果？ 治疗时如何获得用户许可证类型列表？ 

+-----------+---------------------+----------------+
| USER_NAME | REQUEST_DATE        | STATUS         |
+-----------+---------------------+----------------+
| Alex      | 2018-03-01 00:00:00 | Active         |
| Alex      | 2018-09-01 00:00:00 | No information |
| Bob       | 2018-03-01 00:00:00 | Not Active     |
| Mark      | 2018-02-01 00:00:00 | Active         |
| Mark      | 2018-07-01 00:00:00 | VIP            |
| Kate      | 2018-02-01 00:00:00 | No information |
+-----------+---------------------+----------------+

码： 

import org.apache.spark.sql.DataFrame

val df1: DataFrame  = Seq(
    ("Alex", "2018-03-01 00:00:00"),
    ("Alex", "2018-09-01 00:00:00"),
    ("Bob", "2018-03-01 00:00:00"),
    ("Mark", "2018-02-01 00:00:00"),
    ("Mark", "2018-07-01 00:00:00"),
    ("Kate", "2018-07-01 00:00:00")
).toDF("USER_NAME", "REQUEST_DATE")

df1.show()

val df2: DataFrame  = Seq(
    ("Alex", "2018-01-01 00:00:00", "2018-06-01 00:00:00", "Active"),
    ("Bob", "2018-01-01 00:00:00", "2018-02-01 00:00:00", "Not Active"),
    ("Mark", "2018-01-01 00:00:00", "2018-05-01 23:59:59", "Active"),
    ("Mark", "2018-05-01 00:00:00", "2018-08-01 23:59:59", "Active")
).toDF("USER_NAME", "START_SERVICE", "END_SERVICE", "STATUS")

df1.show()

val total = df1.join(df2, df1("USER_NAME")===df2("USER_NAME"), "left").filter(df1("REQUEST_DATE") >= df2("START_SERVICE") && df1("REQUEST_DATE") <= df2("END_SERVICE")).select(df1("*"), df2("STATUS"))

total.show()

错误 ： 

org.apache.spark.SparkException: Exception thrown in awaitResult:
  at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
  at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:136)
  at org.apache.spark.sql.execution.InputAdapter.doExecuteBroadcast(WholeStageCodegenExec.scala:367)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:144)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:140)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.SparkPlan.executeBroadcast(SparkPlan.scala:140)
  at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.prepareBroadcast(BroadcastHashJoinExec.scala:135)
  at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.codegenInner(BroadcastHashJoinExec.scala:232)
  at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.doConsume(BroadcastHashJoinExec.scala:102)
  at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
  at org.apache.spark.sql.execution.ProjectExec.consume(basicPhysicalOperators.scala:35)
  at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:65)
  at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
  at org.apache.spark.sql.execution.FilterExec.consume(basicPhysicalOperators.scala:85)
  at org.apache.spark.sql.execution.FilterExec.doConsume(basicPhysicalOperators.scala:206)
  at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:181)
  at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:354)
  at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:383)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:354)
  at org.apache.spark.sql.execution.FilterExec.doProduce(basicPhysicalOperators.scala:125)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.FilterExec.produce(basicPhysicalOperators.scala:85)
  at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:45)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:83)
  at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:35)
  at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.doProduce(BroadcastHashJoinExec.scala:97)
  at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:88)

2 个解决方案

解决方案1
 2  2019-01-18 08:44:27

如何加入这两个DataFrame并返回这样的结果？ 

df_joined = df1.join(df2, Seq('USER_NAME'), 'left' )

如何获得仍与许可证相关的所有用户的列表？ 

df_relevant = df_joined
.withColumn('STATUS', when(col('REQUEST_DATE') > col('START_SERVICE') and col('REQUEST_DATE') < col('END_SERVICE'), col('STATUS')).otherwise('No information') 
.select('USER_NAME', 'REQUEST_DATE', 'STATUS' )

解决方案2
 0  2019-01-18 10:19:11

您正在比较<=和> =在不正确的字符串值上。 在进行此类比较之前，应将它们转换为时间戳。 下面的代码为我工作。

顺便说一句，您使用的过滤条件没有给出您在问题中发布的结果。 请再次检查。 

scala> val df= Seq(("Alex","2018-03-01 00:00:00"),("Alex","2018-09-01 00:00:00"),("Bob","2018-03-01 00:00:00"),("Mark","2018-02-01 00:00:00"),("Mark","2018-07-01 00:00:00"),("Kate","2018-02-01 00:00:00")).toDF("USER_NAME","REQUEST_DATE").withColumn("REQUEST_DATE",to_timestamp('REQUEST_DATE))
df: org.apache.spark.sql.DataFrame = [USER_NAME: string, REQUEST_DATE: timestamp]

scala> df.printSchema
root
 |-- USER_NAME: string (nullable = true)
 |-- REQUEST_DATE: timestamp (nullable = true)


scala> df.show(false)
+---------+-------------------+
|USER_NAME|REQUEST_DATE       |
+---------+-------------------+
|Alex     |2018-03-01 00:00:00|
|Alex     |2018-09-01 00:00:00|
|Bob      |2018-03-01 00:00:00|
|Mark     |2018-02-01 00:00:00|
|Mark     |2018-07-01 00:00:00|
|Kate     |2018-02-01 00:00:00|
+---------+-------------------+


scala> val df2 = Seq(( "Alex","2018-01-01 00:00:00","2018-06-01 00:00:00","Active"),("Bob","2018-01-01 00:00:00","2018-02-01 00:00:00","Not Active"),("Mark","2018-01-01 00:00:00","2018-05-01 23:59:59","Active"),("Mark","2018-05-01 00:00:00","2018-08-01 23:59:59","VIP")).toDF("USER_NAME","START_SERVICE","END_SERVICE","STATUS").withColumn("START_SERVICE",to_timestamp('START_SERVICE)).withColumn("END_SERVICE",to_timestamp('END_SERVICE))
df2: org.apache.spark.sql.DataFrame = [USER_NAME: string, START_SERVICE: timestamp ... 2 more fields]

scala> df2.printSchema
root
 |-- USER_NAME: string (nullable = true)
 |-- START_SERVICE: timestamp (nullable = true)
 |-- END_SERVICE: timestamp (nullable = true)
 |-- STATUS: string (nullable = true)


scala> df2.show(false)
+---------+-------------------+-------------------+----------+
|USER_NAME|START_SERVICE      |END_SERVICE        |STATUS    |
+---------+-------------------+-------------------+----------+
|Alex     |2018-01-01 00:00:00|2018-06-01 00:00:00|Active    |
|Bob      |2018-01-01 00:00:00|2018-02-01 00:00:00|Not Active|
|Mark     |2018-01-01 00:00:00|2018-05-01 23:59:59|Active    |
|Mark     |2018-05-01 00:00:00|2018-08-01 23:59:59|VIP       |
+---------+-------------------+-------------------+----------+


scala> df.join(df2,Seq("USER_NAME"),"leftOuter").filter(" REQUEST_DATE >= START_SERVICE and REQUEST_DATE <= END_SERVICE").select(df("*"),df2("status")).show(false)
+---------+-------------------+------+
|USER_NAME|REQUEST_DATE       |status|
+---------+-------------------+------+
|Alex     |2018-03-01 00:00:00|Active|
|Mark     |2018-02-01 00:00:00|Active|
|Mark     |2018-07-01 00:00:00|VIP   |
+---------+-------------------+------+


scala>

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