我创建了一个如下所示的DataFrame，我想对列'title'应用map-reduce算法，但是当我使用reduceByKey函数时，我遇到了一些问题。
+-------+--------------------+------------+-----------+
|project| title|requests_num|return_size|
+-------+--------------------+------------+-----------+
| aa|%CE%92%CE%84_%CE%...| 1| 4854|
| aa|%CE%98%CE%B5%CF%8...| 1| 4917|
| aa|%CE%9C%CF%89%CE%A...| 1| 4832|
| aa|%CE%A0%CE%B9%CE%B...| 1| 4828|
| aa|%CE%A3%CE%A4%CE%8...| 1| 4819|
| aa|%D0%A1%D0%BE%D0%B...| 1| 4750|
| aa| 271_a.C| 1| 4675|
| aa|Battaglia_di_Qade...| 1| 4765|
| aa| Category:User_th| 1| 4770|
| aa| Chiron_Elias_Krase| 1| 4694|
| aa|County_Laois/en/Q...| 1| 4752|
| aa| Dassault_rafaele| 2| 9372|
| aa|Dyskusja_wikiproj...| 1| 4824|
| aa| E.Desv| 1| 4662|
| aa|Enclos-apier/fr/E...| 1| 4772|
| aa|File:Wiktionary-l...| 1| 10752|
| aa|Henri_de_Sourdis/...| 1| 4748|
| aa|Incentive_Softwar...| 1| 4777|
| aa|Indonesian_Wikipedia| 1| 4679|
| aa| Main_Page| 5| 266946|
+-------+--------------------+------------+-----------+
我试试这个，但它不起作用：
dataframe.select("title").map(word => (word,1)).reduceByKey(_+_);
似乎我应首先将数据帧传输到列表，然后使用map函数生成键值对（word，1），最后总结键值。 我是一种从stackoverflow将数据帧传输到列表的方法， 例如
val text =dataframe.select("title").map(r=>r(0).asInstanceOf[String]).collect()
但发生错误
scala> val text = dataframe.select("title").map(r=>r(0).asInstanceOf[String]).collect()
2018-04-08 21:49:35 WARN NettyRpcEnv:66 - Ignored message: HeartbeatResponse(false)
2018-04-08 21:49:35 WARN Executor:87 - Issue communicating with driver in heartbeater
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [10 seconds]. This timeout is controlled by spark.executor.heartbeatInterval
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:47)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:62)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:58)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:785)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply$mcV$sp(Executor.scala:814)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:814)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:814)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988)
at org.apache.spark.executor.Executor$$anon$2.run(Executor.scala:814)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [10 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:201)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
... 14 more
java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.sql.execution.SparkPlan$$anon$1.next(SparkPlan.scala:280)
at org.apache.spark.sql.execution.SparkPlan$$anon$1.next(SparkPlan.scala:276)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.sql.execution.SparkPlan$$anon$1.foreach(SparkPlan.scala:276)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeCollect$1.apply(SparkPlan.scala:298)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeCollect$1.apply(SparkPlan.scala:297)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:297)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3272)
at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722)
at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3253)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3252)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2722)
... 16 elided
scala> val text = dataframe.select("title").map(r=>r(0).asInstanceOf[String]).collect()
java.lang.OutOfMemoryError: GC overhead limit exceeded
at org.apache.spark.sql.execution.SparkPlan$$anon$1.next(SparkPlan.scala:280)
at org.apache.spark.sql.execution.SparkPlan$$anon$1.next(SparkPlan.scala:276)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.sql.execution.SparkPlan$$anon$1.foreach(SparkPlan.scala:276)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeCollect$1.apply(SparkPlan.scala:298)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeCollect$1.apply(SparkPlan.scala:297)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:297)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3272)
at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722)
at org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2722)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3253)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3252)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2722)
... 16 elided
答案 0 :(得分：0)
Collect - 将您的DataFrame添加到Scala集合会对数据集大小施加约束。相反，您可以将DataFrame转换为RDD，然后应用map和reduceByKey，如下所示：
val df = Seq(
("aa", "271_a.C", 1, 4675),
("aa", "271_a.C", 1, 4400),
("aa", "271_a.C", 1, 4600),
("aa", "Chiron_Elias_Krase", 1, 4694),
("aa", "Chiron_Elias_Krase", 1, 4500)
).toDF("project", "title", "request_num", "return_size")
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Row
val rdd = df.rdd.
map{ case Row(_, title: String, _, _) => (title, 1) }.
reduceByKey(_ + _)
rdd.collect
// res1: Array[(String, Int)] = Array((Chiron_Elias_Krase,2), (271_a.C,3))
您还可以使用groupBy直接转换数据框：
df.groupBy($"title").agg(count($"title").as("count")).
show
// +------------------+-----+
// | title|count|
// +------------------+-----+
// | 271_a.C| 3|
// |Chiron_Elias_Krase| 2|
// +------------------+-----+