I have written a function which takes the elastic query as input, executes on Elastic cluster and return back with the result.
When I pass simple query like this
val query = {
"query" = {
"match" : {
"healthSystemId.keyword":"XYZ"
}
}
}
it return back the executed query results.
But when I pass aggregation query like this
val query = {
"size": 0,
"aggregations": {
"corr_agg": {
"terms": {
"field": "healthSystemId.keyword",
"size": 5000
}
}
}
}
it throws the following error.
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 122.0 failed 4 times, most recent failure: Lost task 0.3 in stage 122.0 (TID 594) (10.139.64.24 executor 4): org.elasticsearch.hadoop.rest.EsHadoopInvalidRequest: org.elasticsearch.hadoop.rest.EsHadoopRemoteException: parsing_exception: [size] query malformed, no start_object after query name
{"query":{"size":0,"aggregations":{"corr_agg":{"terms":{"field":"healthSystemId.keyword","size":5000}}}},"_source":["healthSystemId"]}
at org.elasticsearch.hadoop.rest.RestClient.checkResponse(RestClient.java:477)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:434)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:428)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:408)
at org.elasticsearch.hadoop.rest.RestRepository.scroll(RestRepository.java:311)
at org.elasticsearch.hadoop.rest.ScrollQuery.hasNext(ScrollQuery.java:93)
at org.elasticsearch.spark.rdd.AbstractEsRDDIterator.hasNext(AbstractEsRDDIterator.scala:61)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:761)
at org.apache.spark.sql.execution.collect.UnsafeRowBatchUtils$.encodeUnsafeRows(UnsafeRowBatchUtils.scala:80)
at org.apache.spark.sql.execution.collect.Collector.$anonfun$processFunc$1(Collector.scala:186)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$3(ResultTask.scala:75)
at com.databricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.$anonfun$runTask$1(ResultTask.scala:75)
at com.databricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:55)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:169)
at org.apache.spark.scheduler.Task.$anonfun$run$4(Task.scala:137)
at com.databricks.unity.EmptyHandle$.runWithAndClose(UCSHandle.scala:104)
at org.apache.spark.scheduler.Task.$anonfun$run$1(Task.scala:137)
at com.databricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.scheduler.Task.run(Task.scala:96)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$13(Executor.scala:902)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1696)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:905)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at com.databricks.spark.util.ExecutorFrameProfiler$.record(ExecutorFrameProfiler.scala:110)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:760)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
But when I execute this query in Elastic, it return me back with results. This is the helper method I am using to read from Elastic
def readFromElastic(mappingId:String,arrayFields:String,fieldsToInclude:String,index:String,query:String) = {
spark.read
.format("org.elasticsearch.spark.sql")
.option("es.port", <ES_PORT> )
.option("es.nodes", <ES_NODE> )
.option("es.nodes.wan.only", <ES_WAN_ONLY> )
.option("es.net.ssl", sys.env("ES_NET_SSL"))
.option("es.net.http.auth.user", <ES_USER> )
.option("es.net.http.auth.pass", <ES_PASSWORD> )
.option("es.field.read.empty.as.null", "no")
.option("es.mapping.id",mappingId)
.option("es.read.field.as.array.include",arrayFields)
.option("es.read.field.include", fieldsToInclude)
.option("pushdown", "true")
.option("es.query", query )
.load(index)
}
Any idea on what possibly could be wrong here
Related
I am raising a custom exception to test failure in my structured streaming job as below. I see the query gets terminated but not able to understand why driver script is not failing with a non zero exit code
streamingDF.writeStream
.trigger(Trigger.ProcessingTime(10000L))
.foreachBatch {
(batchDF: DataFrame, batchId: Long) => {
val transformedDF: DataFrame = DoSomeProcessing(batchDF)
if (batchId == 1) {
throw new Exception("Custom Exception as batchId is 1")
}
I get below trace on my console but the driver script is not exiting and no new logs are printed on console.
Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: Custom Exception as batchId is 1
=== Streaming Query ===
Identifier: [id = 6f4c3b4c-bc30-46fe-93ef-8378c23380ab, runId = 1241cb37-493b-4882-ab28-9df8a8c6fb1a]
Current Committed Offsets: ...
Current Available Offsets: ...
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
RepartitionByExpression [timestamp#12], 10
...
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.Exception: Custom Exception as batchId is 1
at MySteamingApp$$anonfun$startSparkStructuredStreaming$1.apply(MySteamingApp.scala:61)
at MySteamingApp$$anonfun$startSparkStructuredStreaming$1.apply(MySteamingApp.scala:57)
at org.apache.spark.sql.execution.streaming.sources.ForeachBatchSink.addBatch(ForeachBatchSink.scala:35)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5$$anonfun$apply$17.apply(MicroBatchExecution.scala:534)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5.apply(MicroBatchExecution.scala:532)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:531)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:198)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:166)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:160)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
... 1 more
I think number of task failures were configured more
spark.task.maxFailures default 4 Number of failures of any particular task before giving up on the job. The total number of failures spread across different tasks will not cause the job to fail; a particular task has to fail this number of attempts. Should be greater than or equal to 1. Number of allowed retries = this value - 1.
Further have a look at Is there a way to dynamically stop Spark Structured Streaming?
I have orc files in my hdfs. One of the fields is Map(String, String). Somehow there are some rows with value Map(null,null) in this field. null in the map keys is a critical error for java. So, when I'm trying to access this field, I got NullPointer exeption.
I want to read these files and change this fields to emty map.
I tried to do it this way:
val df = spark.read.format("orc").load("/tmp/bad_orc")
def func(s: org.apache.spark.sql.Row): String = {
try
{
if ( s(14) == null ) // the 14'th column is the column with Map(String,String) type
{
return "Ok"
}
else
{
return "Zero"
}
}
catch
{
case x: Exception => return "Erro"
}
}
df.rdd.map(func).take(20)
I got this exception, when I run this script.
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 417.0 failed 4 times, most recent failure: Lost task 0.3 in stage 417.0 (TID 97094, srvg1076.local.odkl.ru, executor 86): java.lang.NullPointerException
at java.util.TreeMap.compare(TreeMap.java:1294)
at java.util.TreeMap.put(TreeMap.java:538)
at org.apache.orc.mapred.OrcMapredRecordReader.nextMap(OrcMapredRecordReader.java:507)
at org.apache.orc.mapred.OrcMapredRecordReader.nextValue(OrcMapredRecordReader.java:554)
at org.apache.orc.mapreduce.OrcMapreduceRecordReader.nextKeyValue(OrcMapreduceRecordReader.java:104)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
And when I'm trying to acces any other column in this orc - everything is Ok.
How catch this exception and how fix these files? Help me, please
I am trying to apply string indexer on multiple columns. Here is my code
val stringIndexers = Categorical_Model.map { colName =>new StringIndexer().setInputCol(colName).setOutputCol(colName + "_indexed")}
var dfStringIndexed = stringIndexers(0).fit(df3).transform(df3) // 'fit's a model then 'transform's data
for(x<-1 to stringIndexers.length-1)
{dfStringIndexed = stringIndexers(x).fit(dfStringIndexed).transform(dfStringIndexed)
}
dfStringIndexed = dfStringIndexed.drop(Categorical_Model: _*)
The Schema shows up with all columns having nullable as false
The stringIndexers array shows up like this
stringIndexers: Array[org.apache.spark.ml.feature.StringIndexer] = Array(strIdx_c53c3bdf464c, strIdx_61e685c520f7, strIdx_d6e59b2fc69d, ......)
dfStringIndexed.show(10)
This throws the following error
org.apache.spark.SparkException: Failed to execute user defined function($anonfun$9: (string) => double)
Why is it that print schema is showing up but no data is available .
Update: If I loop the string Indexers manually like so instead of the loop. This code works. Which is wierd.
var dfStringIndexed = stringIndexers(0).fit(df3).transform(df3) // 'fit's a model then 'transform's data
dfStringIndexed = stringIndexers(1).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(2).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(3).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(4).fit(dfStringIndexed).transform(dfStringIndexed)
Adding Stacktrace on request
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:363)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
at org.apache.spark.sql.Dataset.show(Dataset.scala:723)
at org.apache.spark.sql.Dataset.show(Dataset.scala:682)
... 63 elided
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$9: (string) => double)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
... 3 more
Caused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.
at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$9.apply(StringIndexer.scala:251)
at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$9.apply(StringIndexer.scala:246)
... 19 more
I have also been getting a similar issue, even on a tiny subset of 50 rows, none of which have nulls in the column I am string indexing. But it didn't work even when I ran it manually.
I can avoid the error by including .setHandleInvalid("keep"), and I've checked the outputs and it's not doing anything strange like setting everything to be 0 or the same value or anything. I'm still unhappy about that being the resolution as it seems quite unsafe. Would be very interested to know if you found a more reasonable answer and resolution!
dfStringIndexed = stringIndexers(1).setHandleInvalid("keep").fit(dfStringIndexed).transform(dfStringIndexed)
I think it might also be fixed by changing the nullability of your column, even if it doesn't contain nulls in it, which I did as per here
Can I change the nullability of a column in my Spark dataframe?
I'm new in scala and I'm looking for solving this error.
The scenario I'm working on is this. I've 3 tables:
user: containing ID and name
business: containing ID and name
reviews: containing user.ID and business.ID
Only users make a review and only business receive a review. The graph will be something like this:
What I'm looking for is:
For each user I want to know the other users that made a review to the same business
I did this actions to create the graph:
val users = sqlContext.sql("Select user_id as ID from user")
val business= sqlContext.sql("Select business_id as ID from business")
users.write.mode(SaveMode.Append).saveAsTable("user_busin_db")
business.write.mode(SaveMode.Append).saveAsTable("user_busin_db")
val user_bus = sqlContext.sql("Select ID from user_busin_db")
val reviews = sqlContext.sql("Select user_id, business_id from review")
The table user_bus will be used for vertexs creation.
After that I created the graph with GraphX with this code:
def str2Long(s: String) = s.##.toLong
val vertex: RDD[(VertexId, String)] = user_bus.rdd.map(x => (str2Long(x(0).asInstanceOf[String]),(x(0).asInstanceOf[String])))
val edge:RDD[Edge[String]] = reviews.rdd.map(row => Edge(str2Long(row(0).asInstanceOf[String]), str2Long(row(1).asInstanceOf[String]), "review"))
val default = "missing"
val myGraph = Graph(vertex, edge, default)
myGraph.cache()
Now to answer my question I tried to do a aggregateMessages for eaither users and business with this code:
val userAggregate: VertexRDD[(List[Long])] = myGraph.aggregateMessages[(List[Long])](triplet => {
triplet.sendToSrc((List(triplet.dstId)))
},
(a,b) => (a.union(b))
)
val businessAggregate: VertexRDD[(List[Long])] = myGraph.aggregateMessages[(List[Long])](triplet => {
triplet.sendToDst((List(triplet.srcId)))
},
(a,b) => (a.union(b))
)
And then the code that gives me the error. To collect for each user what are the other users that made a reviews at same business I wrote this:
userAggregate.map(userAggr =>
(userAggr._1, userAggr._2.flatMap(userAggrListElem =>
userAggr._2.patch(0,businessAggregate.filter(busAggr => busAggr._1 == userAggrListElem).map(row => row._2).take(1)(0),userAggr._2.size+1))))
If I try to use .collect or .count on it i got this error:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 138.0 failed 1 times, most recent failure: Lost task 1.0 in stage 138.0 (TID 2807, localhost): java.lang.NullPointerException
at org.apache.spark.graphx.impl.VertexRDDImpl.mapVertexPartitions(VertexRDDImpl.scala:94)
at org.apache.spark.graphx.VertexRDD.filter(VertexRDD.scala:98)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5$$anonfun$apply$1.apply(<console>:102)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5$$anonfun$apply$1.apply(<console>:101)
at scala.collection.immutable.List.flatMap(List.scala:327)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5.apply(<console>:101)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5.apply(<console>:100)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1769)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:314)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1930)
at org.apache.spark.rdd.RDD.count(RDD.scala:1134)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:105)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:115)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:117)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:119)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:121)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:123)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:125)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:127)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:129)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:131)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:133)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:135)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:137)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:139)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:141)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:143)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw.<init>(<console>:145)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw.<init>(<console>:147)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw.<init>(<console>:149)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw.<init>(<console>:151)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw.<init>(<console>:153)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw.<init>(<console>:155)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$eval$.$print$lzycompute(<console>:7)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$eval$.$print(<console>:6)
Caused by: java.lang.NullPointerException
at org.apache.spark.graphx.impl.VertexRDDImpl.mapVertexPartitions(VertexRDDImpl.scala:94)
at org.apache.spark.graphx.VertexRDD.filter(VertexRDD.scala:98)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5$$anonfun$apply$1.apply(<console>:102)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5$$anonfun$apply$1.apply(<console>:101)
at scala.collection.immutable.List.flatMap(List.scala:327)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5.apply(<console>:101)
at linea6ec9c0b0ced4184a0288c57eb3bdda585.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$5.apply(<console>:100)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1769)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:314)
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:748)
The algorithm works well if I use a subset of userAggregate, indeed if I use take(1) I get this result:
Array[(org.apache.spark.graphx.VertexId, List[Long])] = Array((-1324024017,List(-1851582020, -1799460264, -1614007919, -1573604682, ...)))
Which is: (user_ID, List(user_id that made a review to the same business,...)
Now I think there is a problem with the Vertexs, there is somewhere an unconnected vertex that gives me NullPointer error, but I'm not able to find it and delete from my grapf. What can I do for solving this problem?
TL;DR It is not a valid Spark code.
This is an expected outcome. It is not allowed to nest transformations in Apache Spark, hence you cannot access businessAggregate inside the closure of userAggregate.map.
In Spark 1.6.0 I have a data frame with a column that holds a job description, like:
Description
bartender
bartender
employee
taxi-driver
...
I retrieve a list of unique values from that column with:
val jobs = people.select("Description").distinct().rdd.map(r => r(0).asInstanceOf[String]).repartition(4)
I then try, for each job description, to retrieve people with that job and do something, but I get a NullPointerException:
jobs.foreach {
ajob =>
var peoplewithjob = people.filter($"Description" === ajob)
// ... do stuff
}
I don't understand why this happens, because every job has been extracted from the people data frame, so there should be at least one with that job... any hint more that welcome! Here's the stack trace:
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 4.0 failed 1 times, most recent failure: Lost task 3.0 in stage 4.0 (TID 206, localhost): java.lang.NullPointerException
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:131)
at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$withPlan(DataFrame.scala:2165)
at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:799)
at jago.Run$$anonfun$main$1.apply(Run.scala:89)
at jago.Run$$anonfun$main$1.apply(Run.scala:82)
at scala.collection.Iterator$class.foreach(Iterator.scala:742)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$32.apply(RDD.scala:912)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$32.apply(RDD.scala:912)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
It happens because Spark doesn't support nested actions or transformations. If you want to operate on distinct values extracted from the DataFrame you have to fetch the results to the driver and iterate locally:
// or toLocalIterator
jobs.collect.foreach {
ajob =>
var peoplewithjob = people.filter($"Description" === ajob)
}
Depending on what kind of transformations you apply as "do stuff" it can be a better idea to simply grouBy and aggregate:
people.groupBy($"Description").agg(...)