carbondata-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Chetan Bhat (JIRA)" <j...@apache.org>
Subject [jira] [Closed] (CARBONDATA-1783) (Carbon1.3.0 - Streaming) Error "Failed to filter row in vector reader" when filter query executed on streaming data
Date Fri, 22 Dec 2017 16:26:00 GMT

     [ https://issues.apache.org/jira/browse/CARBONDATA-1783?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chetan Bhat closed CARBONDATA-1783.
-----------------------------------
       Resolution: Fixed
    Fix Version/s: 1.3.0

The defect is fixed in the latest 1.3.0 build and closed.

> (Carbon1.3.0 - Streaming) Error "Failed to filter row in vector reader" when filter query
executed on streaming data
> --------------------------------------------------------------------------------------------------------------------
>
>                 Key: CARBONDATA-1783
>                 URL: https://issues.apache.org/jira/browse/CARBONDATA-1783
>             Project: CarbonData
>          Issue Type: Bug
>          Components: data-query
>    Affects Versions: 1.3.0
>         Environment: 3 node ant cluster
>            Reporter: Chetan Bhat
>              Labels: DFX
>             Fix For: 1.3.0
>
>
> Steps :-
> Spark submit thrift server is started using the command - bin/spark-submit --master yarn-client
--executor-memory 10G --executor-cores 5 --driver-memory 5G --num-executors 3 --class org.apache.carbondata.spark.thriftserver.CarbonThriftServer
/srv/spark2.2Bigdata/install/spark/sparkJdbc/carbonlib/carbondata_2.11-1.3.0-SNAPSHOT-shade-hadoop2.7.2.jar
"hdfs://hacluster/user/hive/warehouse/carbon.store"
> Spark shell is launched using the command - bin/spark-shell --master yarn-client --executor-memory
10G --executor-cores 5 --driver-memory 5G --num-executors 3 --jars /srv/spark2.2Bigdata/install/spark/sparkJdbc/carbonlib/carbondata_2.11-1.3.0-SNAPSHOT-shade-hadoop2.7.2.jar
> From Spark shell user creates table and loads data in the table as shown below.
> import java.io.{File, PrintWriter}
> import java.net.ServerSocket
> import org.apache.spark.sql.{CarbonEnv, SparkSession}
> import org.apache.spark.sql.hive.CarbonRelation
> import org.apache.spark.sql.streaming.{ProcessingTime, StreamingQuery}
> import org.apache.carbondata.core.constants.CarbonCommonConstants
> import org.apache.carbondata.core.util.CarbonProperties
> import org.apache.carbondata.core.util.path.{CarbonStorePath, CarbonTablePath}
> CarbonProperties.getInstance().addProperty(CarbonCommonConstants.CARBON_TIMESTAMP_FORMAT,
"yyyy/MM/dd")
> import org.apache.spark.sql.CarbonSession._
> val carbonSession = SparkSession.
>   builder().
>   appName("StreamExample").
>   getOrCreateCarbonSession("hdfs://hacluster/user/hive/warehouse/carbon.store")
>    
> carbonSession.sparkContext.setLogLevel("INFO")
> def sql(sql: String) = carbonSession.sql(sql)
> def writeSocket(serverSocket: ServerSocket): Thread = {
>   val thread = new Thread() {
>     override def run(): Unit = {
>       // wait for client to connection request and accept
>       val clientSocket = serverSocket.accept()
>       val socketWriter = new PrintWriter(clientSocket.getOutputStream())
>       var index = 0
>       for (_ <- 1 to 1000) {
>         // write 5 records per iteration
>         for (_ <- 0 to 100) {
>           index = index + 1
>           socketWriter.println(index.toString + ",name_" + index
>                                + ",city_" + index + "," + (index * 10000.00).toString
+
>                                ",school_" + index + ":school_" + index + index + "$"
+ index)
>         }
>         socketWriter.flush()
>         Thread.sleep(2000)
>       }
>       socketWriter.close()
>       System.out.println("Socket closed")
>     }
>   }
>   thread.start()
>   thread
> }
>   
> def startStreaming(spark: SparkSession, tablePath: CarbonTablePath, tableName: String,
port: Int): Thread = {
>     val thread = new Thread() {
>       override def run(): Unit = {
>         var qry: StreamingQuery = null
>         try {
>           val readSocketDF = spark.readStream
>             .format("socket")
>             .option("host", "10.18.98.34")
>             .option("port", port)
>             .load()
>           qry = readSocketDF.writeStream
>             .format("carbondata")
>             .trigger(ProcessingTime("5 seconds"))
>             .option("checkpointLocation", tablePath.getStreamingCheckpointDir)
>             .option("tablePath", tablePath.getPath).option("tableName", tableName)
>             .start()
>           qry.awaitTermination()
>         } catch {
>           case ex: Throwable =>
>             ex.printStackTrace()
>             println("Done reading and writing streaming data")
>         } finally {
>           qry.stop()
>         }
>       }
>     }
>     thread.start()
>     thread
> }
> val streamTableName = "all_datatypes_2048"
> sql(s"create table all_datatypes_2048 (imei string,deviceInformationId int,MAC string,deviceColor
string,device_backColor string,modelId string,marketName string,AMSize string,ROMSize string,CUPAudit
string,CPIClocked string,series string,productionDate timestamp,bomCode string,internalModels
string, deliveryTime string, channelsId string, channelsName string , deliveryAreaId string,
deliveryCountry string, deliveryProvince string, deliveryCity string,deliveryDistrict string,
deliveryStreet string, oxSingleNumber string, ActiveCheckTime string, ActiveAreaId string,
ActiveCountry string, ActiveProvince string, Activecity string, ActiveDistrict string, ActiveStreet
string, ActiveOperatorId string, Active_releaseId string, Active_EMUIVersion string, Active_operaSysVersion
string, Active_BacVerNumber string, Active_BacFlashVer string, Active_webUIVersion string,
Active_webUITypeCarrVer string,Active_webTypeDataVerNumber string, Active_operatorsVersion
string, Active_phonePADPartitionedVersions string, Latest_YEAR int, Latest_MONTH int, Latest_DAY
Decimal(30,10), Latest_HOUR string, Latest_areaId string, Latest_country string, Latest_province
string, Latest_city string, Latest_district string, Latest_street string, Latest_releaseId
string, Latest_EMUIVersion string, Latest_operaSysVersion string, Latest_BacVerNumber string,
Latest_BacFlashVer string, Latest_webUIVersion string, Latest_webUITypeCarrVer string, Latest_webTypeDataVerNumber
string, Latest_operatorsVersion string, Latest_phonePADPartitionedVersions string, Latest_operatorId
string, gamePointDescription string,gamePointId double,contractNumber BigInt) STORED BY 'org.apache.carbondata.format'
TBLPROPERTIES('streaming'='true','table_blocksize'='2048')")
> sql(s"LOAD DATA INPATH 'hdfs://hacluster/chetan/100_olap_C20.csv' INTO table all_datatypes_2048
options ('DELIMITER'=',', 'BAD_RECORDS_ACTION'='FORCE','FILEHEADER'='imei,deviceInformationId,MAC,deviceColor,device_backColor,modelId,marketName,AMSize,ROMSize,CUPAudit,CPIClocked,series,productionDate,bomCode,internalModels,deliveryTime,channelsId,channelsName,deliveryAreaId,deliveryCountry,deliveryProvince,deliveryCity,deliveryDistrict,deliveryStreet,oxSingleNumber,contractNumber,ActiveCheckTime,ActiveAreaId,ActiveCountry,ActiveProvince,Activecity,ActiveDistrict,ActiveStreet,ActiveOperatorId,Active_releaseId,Active_EMUIVersion,Active_operaSysVersion,Active_BacVerNumber,Active_BacFlashVer,Active_webUIVersion,Active_webUITypeCarrVer,Active_webTypeDataVerNumber,Active_operatorsVersion,Active_phonePADPartitionedVersions,Latest_YEAR,Latest_MONTH,Latest_DAY,Latest_HOUR,Latest_areaId,Latest_country,Latest_province,Latest_city,Latest_district,Latest_street,Latest_releaseId,Latest_EMUIVersion,Latest_operaSysVersion,Latest_BacVerNumber,Latest_BacFlashVer,Latest_webUIVersion,Latest_webUITypeCarrVer,Latest_webTypeDataVerNumber,Latest_operatorsVersion,Latest_phonePADPartitionedVersions,Latest_operatorId,gamePointId,gamePointDescription')")
> val carbonTable = CarbonEnv.getInstance(carbonSession).carbonMetastore.
>   lookupRelation(Some("default"), streamTableName)(carbonSession).asInstanceOf[CarbonRelation].carbonTable
> val tablePath = CarbonStorePath.getCarbonTablePath(carbonTable.getAbsoluteTableIdentifier)
> val port = 8007
> val serverSocket = new ServerSocket(port)
> val socketThread = writeSocket(serverSocket)
> val streamingThread = startStreaming(carbonSession, tablePath, streamTableName, port)
> While the streaming load is in progress from Beeline user executes the below select filter
query 
>  select imei,gamePointId, channelsId,series  from all_datatypes_2048 where channelsId
>=10 OR channelsId <=1 and series='7Series';
> *Issue : The select filter query fails with exception as shown below.*
> 0: jdbc:hive2://10.18.98.34:23040> select imei,gamePointId, channelsId,series  from
all_datatypes_2048 where channelsId >=10 OR channelsId <=1 and series='7Series';
> Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage
773.0 failed 4 times, most recent failure: Lost task 6.3 in stage 773.0 (TID 33727, BLR1000014269,
executor 14): java.io.IOException: Failed to filter row in vector reader
>         at org.apache.carbondata.hadoop.streaming.CarbonStreamRecordReader.scanBlockletAndFillVector(CarbonStreamRecordReader.java:423)
>         at org.apache.carbondata.hadoop.streaming.CarbonStreamRecordReader.nextColumnarBatch(CarbonStreamRecordReader.java:317)
>         at org.apache.carbondata.hadoop.streaming.CarbonStreamRecordReader.nextKeyValue(CarbonStreamRecordReader.java:298)
>         at org.apache.carbondata.spark.rdd.CarbonScanRDD$$anon$1.hasNext(CarbonScanRDD.scala:298)
>         at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown
Source)
>         at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
>         at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
>         at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
>         at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
>         at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
>         at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
>         at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
>         at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>         at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
>         at org.apache.spark.scheduler.Task.run(Task.scala:99)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
>         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)
> Caused by: org.apache.carbondata.core.scan.expression.exception.FilterUnsupportedException:
[B cannot be cast to org.apache.spark.unsafe.types.UTF8String
>         at org.apache.spark.sql.SparkUnknownExpression.evaluate(SparkUnknownExpression.scala:50)
>         at org.apache.carbondata.core.scan.expression.conditional.GreaterThanEqualToExpression.evaluate(GreaterThanEqualToExpression.java:38)
>         at org.apache.carbondata.core.scan.filter.executer.RowLevelFilterExecuterImpl.applyFilter(RowLevelFilterExecuterImpl.java:272)
>         at org.apache.carbondata.core.scan.filter.executer.OrFilterExecuterImpl.applyFilter(OrFilterExecuterImpl.java:49)
>         at org.apache.carbondata.hadoop.streaming.CarbonStreamRecordReader.scanBlockletAndFillVector(CarbonStreamRecordReader.java:418)
>         ... 20 more
> Driver stacktrace: (state=,code=0)
> Expected : The select filter query should be success without error/exception.
> The issue also occurs with the below queries
> select imei,gamePointId, channelsId,series  from all_datatypes_2048 where channelsId
>=10 OR channelsId <=1 or series='7Series';
> select imei,gamePointId, channelsId,series  from all_datatypes_2048 where channelsId
>=10 OR (channelsId <=1 and series='1Series');
> select sum(gamePointId) a from all_datatypes_2048 where channelsId >=10 OR (channelsId
<=1 and series='1Series');
> select * from (select imei,if(imei='1AA100060',NULL,imei) a from all_datatypes_2048)
aa  where a IS NULL;
>  select imei from all_datatypes_2048 where  (contractNumber == 5281803) and (gamePointId==2738.562);
>  select deliveryCity from all_datatypes_2048 where  (deliveryCity == 'yichang') and (
deliveryStreet=='yichang');
>  select channelsId  from all_datatypes_2048 where  (channelsId == '4') and (gamePointId==2738.562);
>  select imei from all_datatypes_2048 where  (contractNumber == 5281803) OR (gamePointId==2738.562)
order by contractNumber ;
>   select channelsId from all_datatypes_2048 where  (channelsId == '4') OR (gamePointId==2738.562)
order by channelsId ;
>  select deliveryCity  from all_datatypes_2048 where  (deliveryCity == 'yichang') OR (
deliveryStreet=='yichang') order by deliveryCity;



--
This message was sent by Atlassian JIRA
(v6.4.14#64029)

Mime
View raw message