We have problems connecting to a MonetDB Cluster via Apache Spark using JDBC.
Connection to a database that is not clustered works. But when we try to connect to a clustered MonetDB database via Apache Spark, it fails with a "unhandled result type error". The entire query is given below
We have tried plain JDBC to connect to a monetdb cluster and it works. Only Spark fails.
val v1 = hiveContext.load("jdbc",Map("url" -> "jdbc:monetdb://1.1.1.1/tpch1?user=monetdb&password=monetdb","dbtable" -> "(select count(*) from customer)v1"))
**java.sql.SQLException: node */tpch/1/monet returned unhandled result type**
java.sql.SQLException: node */tpch/2/monet returned unhandled result type
at nl.cwi.monetdb.jdbc.MonetConnection$ResponseList.executeQuery(MonetConnection.java:2536)
at nl.cwi.monetdb.jdbc.MonetConnection$ResponseList.processQuery(MonetConnection.java:2284)
at nl.cwi.monetdb.jdbc.MonetStatement.internalExecute(MonetStatement.java:508)
at nl.cwi.monetdb.jdbc.MonetStatement.execute(MonetStatement.java:349)
at nl.cwi.monetdb.jdbc.MonetPreparedStatement.<init>(MonetPreparedStatement.java:118)
at nl.cwi.monetdb.jdbc.MonetConnection.prepareStatement(MonetConnection.java:901)
at nl.cwi.monetdb.jdbc.MonetConnection.prepareStatement(MonetConnection.java:825)
at org.apache.spark.sql.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:96)
at org.apache.spark.sql.jdbc.JDBCRelation.<init>(JDBCRelation.scala:125)
at org.apache.spark.sql.jdbc.DefaultSource.createRelation(JDBCRelation.scala:114)
at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:290)
at org.apache.spark.sql.SQLContext.load(SQLContext.scala:679)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:23)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:28)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:30)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:32)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:34)
at $iwC$$iwC$$iwC.<init>(<console>:36)
at $iwC$$iwC.<init>(<console>:38)
at $iwC.<init>(<console>:40)
at <init>(<console>:42)
at .<init>(<console>:46)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:856)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:901)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:813)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:656)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:664)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:669)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:996)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:944)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1058)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
RD 1429856293844: read final block: 67 bytes RX 1429856293845:
bwnthqobupI3CPY:merovingian:9:RIPEMD160,SHA256,SHA1,MD5:LIT:SHA512: RD
1429856293845: inserting prompt TD 1429856293846: write final block:
99 bytes TX 1429856293846:
BIG:merovingian:{SHA256}7070477fd396595c929386453a32ac5059a9b76976e54dab3fbb7a8f5299bd25:sql:tpch1:
RD 1429856293847: read final block: 0 bytes RX 1429856293847: RD
1429856293847: inserting prompt TD 1429856293847: write final block:
49 bytes TX 1429856293847: sSET TIME ZONE INTERVAL '+05:30' HOUR TO
MINUTE ; RD 1429856293848: read final block: 3 bytes RX 1429856293848:
&3
RD 1429856293848: inserting prompt TD 1429856293855: write final
block: 15 bytes TX 1429856293855: Xreply_size 250 RD 1429856293855:
read final block: 0 bytes RX 1429856293855: RD 1429856293855:
inserting prompt TD 1429856293855: write final block: 68 bytes TX
1429856293855: sPREPARE SELECT * FROM (select count(*) from
customer)v1 WHERE 1=0 ; RD 1429856293856: read final block: 52 bytes
RX 1429856293856: !node */tpch/2/monet returned unhandled result type
RD 1429856293856: inserting prompt
The problem lies not with JDBC, but with the merovingian funnel. It seems the funnel does not like the statement PREPARE SELECT * FROM (select count(*) from customer)v1 WHERE 1=0 ; See whether you can keep your application from using prepared statements. Feel free to file a bug report on this at http://bugs.monetdb.org
Related
I'm trying to submit a simple spark job in an Amazon EMR cluster. My cluster has 5 M4.2xlarge instances (1 master, 4 slaves), each with 16 vCPU, and 32 gigs of memory.
This is my code:
def main(args : Array[String]): Unit = {
val sparkConfig = new SparkConf()
.set("hive.exec.dynamic.partition", "true")
.set("hive.exec.dynamic.partition.mode", "nonstrict")
.set("hive.s3.max-client-retries", "50")
.set("hive.s3.max-error-retries", "50")
.set("hive.s3.max-connections", "100")
.set("hive.s3.connect-timeout", "5m")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.set("spark.kryo.registrationRequired", "true")
.set("spark.kryo.classesToRegister", "org.apache.spark.graphx.impl.VertexAttributeBlock")
.set("spark.broadcast.compress", "true")
val spark = SparkSession.builder()
.appName("Spark Hive Example")
.enableHiveSupport()
.config(sparkConfig)
.getOrCreate()
// Set Kryo for serializing
GraphXUtils.registerKryoClasses(sparkConfig)
val res = spark.sql("SELECT col1, col2, col3 FROM table1 limit 10000")
val edgesRDD = res.rdd.map(row => Edge(row.getString(0).hashCode, row.getString(1).hashCode, row(2).asInstanceOf[String]))
val res_two = spark.sql("SELECT col1 FROM table2 where col1 is not NULL and col1 != '' limit 100000")
val vertexRDD: RDD[(VertexId, String)] = res_two.rdd.map(row => (row.getString(0).hashCode, row(0).asInstanceOf[String]))
val graph = Graph(vertexRDD, edgesRDD)
val connectedComponents = graph.connectedComponents().vertices
Both table1, and table2 are S3 backed external tables on hive. When I run this program, my job fails with the following error:
Job aborted due to stage failure: Task 827 in stage 0.0 failed 4 times, most recent failure: Lost task 827.3 in stage 0.0 (TID 921, xxx.internal, executor 3): com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.SdkClientException: Unable to execute HTTP request: Timeout waiting for connection from pool
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleRetryableException(AmazonHttpClient.java:1069)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1035)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:742)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:716)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:699)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:667)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:649)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:513)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4169)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:4116)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:1237)
at com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectMetadataCall.perform(GetObjectMetadataCall.java:24)
at com.amazon.ws.emr.hadoop.fs.s3.lite.call.GetObjectMetadataCall.perform(GetObjectMetadataCall.java:10)
at com.amazon.ws.emr.hadoop.fs.s3.lite.executor.GlobalS3Executor.execute(GlobalS3Executor.java:82)
at com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.invoke(AmazonS3LiteClient.java:176)
at com.amazon.ws.emr.hadoop.fs.s3.lite.AmazonS3LiteClient.getObjectMetadata(AmazonS3LiteClient.java:94)
at com.amazon.ws.emr.hadoop.fs.s3.lite.AbstractAmazonS3Lite.getObjectMetadata(AbstractAmazonS3Lite.java:39)
at com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrieveMetadata(Jets3tNativeFileSystemStore.java:211)
at sun.reflect.GeneratedMethodAccessor26.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy35.retrieveMetadata(Unknown Source)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:768)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.open(S3NativeFileSystem.java:1194)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:773)
at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.open(EmrFileSystem.java:166)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:355)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:316)
at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:237)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getReader(OrcInputFormat.java:1204)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getRecordReader(OrcInputFormat.java:1113)
at org.apache.spark.rdd.HadoopRDD$$anon$1.liftedTree1$1(HadoopRDD.scala:246)
at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:245)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:203)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:94)
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.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.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
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: com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.conn.ConnectionPoolTimeoutException: Timeout waiting for connection from pool
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.conn.PoolingHttpClientConnectionManager.leaseConnection(PoolingHttpClientConnectionManager.java:286)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.conn.PoolingHttpClientConnectionManager$1.get(PoolingHttpClientConnectionManager.java:263)
at sun.reflect.GeneratedMethodAccessor19.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.conn.ClientConnectionRequestFactory$Handler.invoke(ClientConnectionRequestFactory.java:70)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.conn.$Proxy37.get(Unknown Source)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:190)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:184)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:184)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:82)
at com.amazon.ws.emr.hadoop.fs.shaded.org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:55)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1190)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1030)
... 59 more
Not sure if it is coming from hadoop or when reading from hive, but I saw a similar issue here, so I added the following params in my spark-submit command:
--conf "spark.driver.extraJavaOptions=-Djavax.net.ssl.sessionCacheSize=1000 -Djavax.net.ssl.sessionCacheTimeout=60" --conf "spark.executor.extraJavaOptions=-Djavax.net.ssl.sessionCacheSize=1000 -Djavax.net.ssl.sessionCacheTimeout=60"
Still doesn't work. Does anyone know what's going on?
TLDR: The property you need to set is fs.s3.maxConnections in the emrfs-site.xml configuration file. It defaults to 50. We were getting exactly the same error/stack trace as you, so I set it to 5000, which fixed the problem and had no ill effects.
From what I can tell, the root cause is InputFormat implementations that do not properly use try...finally to ensure that connections get closed when an exceptions are thrown. Notably, older versions of Hive, including v1.2.1 that Spark is compiled against, exhibit this bug. Hive 2.x massively refactors OrcInputFormat, though I haven't verified that the bug is fixed, nor do I know if/when/how you can compile Spark against Hive 2.x.
The workaround increases the size of the connection pool, as suggested in another answer, but both the property and its location are quite different than in the "classic" S3 filesystems (s3/s3a/s3n). Of course, this isn't documented anywhere and required decompilation of the emrfs jar to tease out...
I don't use EMRFS, but I do know the other spark/hadoop S3 clients all use a pool of http connections for their requests to S3, and "timeout waiting for pool" messages invariably means "pool isn't big enough". See if you can find out what the emrfs options are for increasing that pool size. You will need at least one for every worker thread running in your process, and I'd double it in the hope that emrfs parallelises block uploads the way the s3a client does.
I did a join of two dataframes on one common column and then ran a show method:
df= df1.join(df2, df1.col1== df2.col2, 'inner')
df.show()
Then join ran very slow and finally raise an error: slave lost.
Py4JJavaError: An error occurred while calling o109.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 8.0 failed 4 times, most recent failure: Lost task 0.3 in stage 8.0 : ExecutorLostFailure (executor 1 exited caused by one of the running tasks) Reason: Slave lost
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212)
at
org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:165)
at
org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174)
at
org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
at
org.apache.spark.sql.DataFrame$$anonfun$org$apache$spark$sql$DataFrame$$execute$1$1.apply(DataFrame.scala:1499)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
at
org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)
at
org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)
at
org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)
at
org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)
at
org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)
at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)
at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374) at
org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456) at
org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170) at
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) at
py4j.Gateway.invoke(Gateway.java:259) at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79) at
py4j.GatewayConnection.run(GatewayConnection.java:209) at
java.lang.Thread.run(Thread.java:745)
After some search, it seems this is a memory related issue. Then I increased repartition to 3000, increased executor memory,increased memoryOverhead, but still no luck, I got the same slave lost error. During df.show(), I found one of the execuctor shuffle write size is very high, the others were not so high.
Any clue?
Thanks
If using scala try
val df = df1.join(df2,Seq("column name"))
if pyspark
df = df1.join(df2,["columnname"])
or
df = df1.join(df2,df1.columnname == df2.columnname)
display(df)
If trying to do same in pyspark - sql
df1.createOrReplaceTempView("left_test_table")
df2..createOrReplaceTempView("right_test_table")
left <- sql(sqlContext, "SELECT * FROM left_test_table")
right <- sql(sqlContext, "SELECT * FROM right_test_table")
head(drop(join(left, right), left$name))
I am using EMR-4.3.0, Spark 1.6.0, Hive 1.0.0.
I write a table like so (pseudocode) -
val df = <a dataframe>
df.registerTempTable("temptable")
sqlContext.setConf("hive.exec.dynamic.partition.mode","true")
sqlContext.sql("create external table exttable ( some columns ... )
partitioned by (partkey int) stored as parquet location 's3://some.bucket'")
sqlContext.sql("insert overwrite exttable partition(partkey) select columns from
temptable")
The write works fine and I can read the table back using -
sqlContext.sql("select * from exttable")
However, when I try to read the table using Hive as -
hive -e 'select * from exttable'
Hive throws a NullPointerException with the stack trace below. Any help appreciated! -
questTime=[0.008], ResponseProcessingTime=[0.295], HttpClientSendRequestTime=[0.313],
2016-05-19 03:08:02,537 ERROR [main()]: CliDriver (SessionState.java:printError(833)) - Failed with exception java.io.IOException:java.lang.NullPointerException
java.io.IOException: java.lang.NullPointerException
at org.apache.hadoop.hive.ql.exec.FetchOperator.getNextRow(FetchOperator.java:663)
at org.apache.hadoop.hive.ql.exec.FetchOperator.pushRow(FetchOperator.java:561)
at org.apache.hadoop.hive.ql.exec.FetchTask.fetch(FetchTask.java:138)
at org.apache.hadoop.hive.ql.Driver.getResults(Driver.java:1619)
at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(CliDriver.java:221)
at org.apache.hadoop.hive.cli.CliDriver.processCmd(CliDriver.java:153)
at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:364)
at org.apache.hadoop.hive.cli.CliDriver.executeDriver(CliDriver.java:712)
at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:631)
at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:570)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: java.lang.NullPointerException
at parquet.format.converter.ParquetMetadataConverter.fromParquetStatistics(ParquetMetadataConverter.java:247)
at parquet.format.converter.ParquetMetadataConverter.fromParquetMetadata(ParquetMetadataConverter.java:368)
at parquet.format.converter.ParquetMetadataConverter.readParquetMetadata(ParquetMetadataConverter.java:346)
at parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:296)
at parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:254)
at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.getSplit(ParquetRecordReaderWrapper.java:200)
at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:79)
at org.apache.hadoop.hive.ql.io.parquet.read.ParquetRecordReaderWrapper.<init>(ParquetRecordReaderWrapper.java:66)
at org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat.getRecordReader(MapredParquetInputFormat.java:72)
at org.apache.hadoop.hive.ql.exec.FetchOperator.getRecordReader(FetchOperator.java:498)
at org.apache.hadoop.hive.ql.exec.FetchOperator.getNextRow(FetchOperator.java:588)
... 15 more
UPDATE - After messing around for a bit, it seems like null values in the data mess Hive up. How do I avoid this?
I have multiple compressed files and each compressed file contains 8 xml files of size 5-10kb. I took this data for testing purpose otherwise live data has 1000s of xml files. I wrote map only program to uncompress the compressed file
for(FileStatus status : status_list){
this.unzip(status.getPath().toString() , DestPath, fs);
}
This method will create file and read uncompressed data
FSDataOutputStream out = fs.create(new Path(filePath));
byte[] bytesIn = new byte[BUFFER_SIZE];
int read = 0;
while ((read = zipIn.read(bytesIn)) != -1) {
out.write(bytesIn, 0, read);
}
out.flush();
out.close();
When mapper tried to write multiple files each of different name, hadoop returns LeaseExpiredException.
15/09/26 19:53:46 INFO mapreduce.Job: Task Id : attempt_1443265405944_0005_m_000000_0, Status : FAILED
Error: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.hdfs.server.namenode.LeaseExpiredException): No lease on /test_poc/x/injection_00001 (163rd copy).xml (inode 400006): File does not exist. [Lease. Holder: DFSClient_attempt_1443265405944_0005_m_000000_0_-657768289_1, pendingcreates: 1]
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkLease(FSNamesystem.java:3431)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.analyzeFileState(FSNamesystem.java:3236)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getNewBlockTargets(FSNamesystem.java:3074)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:3034)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.addBlock(NameNodeRpcServer.java:723)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.addBlock(ClientNamenodeProtocolServerSideTranslatorPB.java:492)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:616)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:969)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2049)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2045)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2043)
at org.apache.hadoop.ipc.Client.call(Client.java:1476)
at org.apache.hadoop.ipc.Client.call(Client.java:1407)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)
at com.sun.proxy.$Proxy12.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.addBlock(ClientNamenodeProtocolTranslatorPB.java:418)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy13.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.locateFollowingBlock(DFSOutputStream.java:1430)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1226)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:449)
I don't know how to handle multiple compressed files without using the loop. I wrote map reduce program using MR2 API and using Hadoop 2.7.1 in psuedo distributed mode. Any pointers?
Assuming that zipIn is a java.util.zip.ZipInputStream, shouldn't you be iteratively calling getNextEntry instead of reading bytes?
I resolved this issue after doing some changes in my code. In the first part of code, I was trying to unzip all the zip files whereas I should have access the spilts. Hadoop basic, which I forgot during implementation.
Whenever I am running a hive query - select * from table. It is running fine. But when i m trying to run a MR job for query - select count(*) from table. It is failing with the following error -
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapred.reduce.tasks=<number>
java.io.IOException: cannot find dir = hdfs://hostname:9000/tmp in pathToPartitionInfo: [hdfs://hostname:9000/data1/]
at org.apache.hadoop.hive.ql.io.HiveFileFormatUtils.getPartitionDescFromPathRecursively(HiveFileFormatUtils.java:298)
at org.apache.hadoop.hive.ql.io.HiveFileFormatUtils.getPartitionDescFromPathRecursively(HiveFileFormatUtils.java:260)
at org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.getSplits(CombineHiveInputFormat.java:293)
at org.apache.hadoop.mapred.JobClient.writeOldSplits(JobClient.java:1051)
at org.apache.hadoop.mapred.JobClient.writeSplits(JobClient.java:1043)
at org.apache.hadoop.mapred.JobClient.access$700(JobClient.java:179)
at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:959)
at org.apache.hadoop.mapred.JobClient$2.run(JobClient.java:912)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:416)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1149)
at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:912)
at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:886)
at org.apache.hadoop.hive.ql.exec.ExecDriver.execute(ExecDriver.java:447)
at org.apache.hadoop.hive.ql.exec.MapRedTask.execute(MapRedTask.java:138)
at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:144)
at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:57)
at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:1355)
at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1139)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:945)
at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(CliDriver.java:259)
at org.apache.hadoop.hive.cli.CliDriver.processCmd(CliDriver.java:216)
at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:413)
at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:756)
at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:614)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
Job Submission failed with exception 'java.io.IOException(cannot find dir = hdfs://hostname:9000/tmp in pathToPartitionInfo: [hdfs://hostname:9000/data1/])'
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask