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https://issues.apache.org/jira/browse/SPARK-19711?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15882475#comment-15882475
]
Luis Felipe Sant Ana edited comment on SPARK-19711 at 2/24/17 11:23 AM:
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The problem seems to be in using the string type in the schema. I converted the
CNPJ field to double
ds <- withColumn (ds, "CNPJ", cast (ds$CNPJ, "double"))
PrintSchema(ds)
And I modified the schema to return a double:
#gapply function
schema <- structType (structField ("CNPJ", "double"))
result <- gapply(
ds,
c("CNPJ", "PID"),
function(key, x) {
data.frame(CNPJ = x$CNPJ)
},
schema)
This works.
was (Author: luisfsantana_20):
The problem seems to be in using the string type in the schema. I converted the
CNPJ field to double
ds <- withColumn (ds, "CNPJ", cast (ds $ CNPJ, "double"))
PrintSchema (ds)
And I modified the schema to return a double:
#gapply function
schema <- structType (structField ("CNPJ", "double"))
Result <- gapply (
Ds,
C ("CNPJ", "PID"),
Function (key, x) {
Data.frame (CNPJ = x$CNPJ)
},
Schema)
This works.
> Bug in gapply function
> ----------------------
>
> Key: SPARK-19711
> URL: https://issues.apache.org/jira/browse/SPARK-19711
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Affects Versions: 2.1.0
> Environment: Using Databricks plataform.
> Reporter: Luis Felipe Sant Ana
> Attachments: mv_demand_20170221.csv, resume.R
>
>
> I have a dataframe in SparkR like
> CNPJ PID DATA N
> 1 10140281000131 10000000000021 2015-04-23 1
> 2 10140281000131 10000000000021 2015-04-27 1
> 3 10140281000131 10000000000021 2015-04-02 1
> 4 10140281000131 10000000000021 2015-11-10 1
> 5 10140281000131 10000000000021 2016-11-14 1
> 6 10140281000131 10000000000021 2015-04-03 1
> And, I want to group by columns CNPJ and PID using gapply() function, filling
> in the column DATA with date. Then I fill in the missing dates with zeros.
> The code:
> schema <- structType(structField("CNPJ", "string"),
> structField("PID", "string"),
> structField("DATA", "date"),
> structField("N", "double"))
> result <- gapply(
> ds_filtered,
> c("CNPJ", "PID"),
> function(key, x) {
> dts <- data.frame(key, DATA = seq(min(as.Date(x$DATA)), as.Date(e_date),
> "days"))
> colnames(dts)[c(1, 2)] <- c("CNPJ", "PID")
>
> y <- data.frame(key, DATA = as.Date(x$DATA), N = x$N)
> colnames(y)[c(1, 2)] <- c("CNPJ", "PID")
>
> y <- dplyr::left_join(dts,
> y,
> by = c("CNPJ", "PID", "DATA"))
>
> y[is.na(y$N), 4] <- 0
>
> data.frame(CNPJ = as.character(y$CNPJ),
> PID = as.character(y$PID),
> DATA = y$DATA,
> N = y$N)
> },
> schema)
> Error:
> Error in handleErrors(returnStatus, conn) :
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
> in stage 92.0 failed 4 times, most recent failure: Lost task 0.3 in stage
> 92.0 (TID 7032, 10.93.243.111, executor 0): org.apache.spark.SparkException:
> R computation failed with
> Error in writeType(con, serdeType) :
> Unsupported type for serialization factor
> Calls: outputResult ... serializeRow -> writeList -> writeObject -> writeType
> Execution halted
> at org.apache.spark.api.r.RRunner.compute(RRunner.scala:108)
> at
> org.apache.spark.sql.execution.FlatMapGroupsInRExec$$anonfun$12.apply(objects.scala:404)
> at
> org.apache.spark.sql.execution.FlatMapGroupsInRExec$$anonfun$12.apply(objects.scala:386)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:333)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2784)
> at
> org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2354)
> at
> org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2354)
> at org.apache.spark.sql.Dataset$$anonfun$59.apply(Dataset.scala:2768)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2767)
> at org.apache.spark.sql.Dataset.collect(Dataset.scala:2354)
> at org.apache.spark.sql.api.r.SQLUtils$.dfToCols(SQLUtils.scala:208)
> at org.apache.spark.sql.api.r.SQLUtils.dfToCols(SQLUtils.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:167)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:108)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:40)
> at
> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:293)
> at
> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:267)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
> at
> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:652)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:575)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:489)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:451)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
> at
> io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.spark.SparkException: R computation failed with
> Error in writeType(con, serdeType) :
> Unsupported type for serialization factor
> Calls: outputResult ... serializeRow -> writeList -> writeObject -> writeType
> Execution halted
> at org.apache.spark.api.r.RRunner.compute(RRunner.scala:108)
> at
> org.apache.spark.sql.execution.FlatMapGroupsInRExec$$anonfun$12.apply(objects.scala:404)
> at
> org.apache.spark.sql.execution.FlatMapGroupsInRExec$$anonfun$12.apply(objects.scala:386)
> at org.apache.spark.rdd.RDD$$an
> With gapplyCollect() function this work.
> Thank you!
>
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