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Posted to issues@carbondata.apache.org by "Ramakrishna S (JIRA)" <ji...@apache.org> on 2017/11/21 13:46:00 UTC
[jira] [Created] (CARBONDATA-1790) (Carbon1.3.0 - Streaming) Data
load in Stream Segment fails if batch load is performed in between the
streaming
Ramakrishna S created CARBONDATA-1790:
-----------------------------------------
Summary: (Carbon1.3.0 - Streaming) Data load in Stream Segment fails if batch load is performed in between the streaming
Key: CARBONDATA-1790
URL: https://issues.apache.org/jira/browse/CARBONDATA-1790
Project: CarbonData
Issue Type: Bug
Components: data-query
Affects Versions: 1.3.0
Environment: 3 node ant cluster
Reporter: Ramakrishna S
Steps :
User starts the thrift server 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"
User connects to spark shell 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
In spark shell User creates a table and does streaming load in the table as per the below socket streaming script.
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/david")
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 = "stream_table"
sql(s"CREATE TABLE $streamTableName (id INT,name STRING,city STRING,salary FLOAT) STORED BY 'carbondata' TBLPROPERTIES('streaming'='true', 'sort_columns'='name')")
sql(s"LOAD DATA LOCAL INPATH 'hdfs://hacluster/tmp/streamSample.csv' INTO TABLE $streamTableName OPTIONS('HEADER'='true')")
sql(s"select * from $streamTableName").show
val carbonTable = CarbonEnv.getInstance(carbonSession).carbonMetastore.
lookupRelation(Some("default"), streamTableName)(carbonSession).asInstanceOf[CarbonRelation].carbonTable
val tablePath = CarbonStorePath.getCarbonTablePath(carbonTable.getAbsoluteTableIdentifier)
val port = 7995
val serverSocket = new ServerSocket(port)
val socketThread = writeSocket(serverSocket)
val streamingThread = startStreaming(carbonSession, tablePath, streamTableName, port)
While load is in progress user executes select query on the streaming table from beeline.
0: jdbc:hive2://10.18.98.34:23040> select * from stream_table;
*Issue : The Select query fails with java.io.EOFException when socket streaming is in progress.*
0: jdbc:hive2://10.18.98.34:23040> select * from stream_table;
Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 1.0 failed 4 times, most recent failure: Lost task 3.3 in stage 1.0 (TID 38, BLR1000014278, executor 7): java.io.EOFException
at org.apache.carbondata.hadoop.streaming.StreamBlockletReader.readBytesFromStream(StreamBlockletReader.java:182)
at org.apache.carbondata.hadoop.streaming.StreamBlockletReader.readBlockletData(StreamBlockletReader.java:116)
at org.apache.carbondata.hadoop.streaming.CarbonStreamRecordReader.scanBlockletAndFillVector(CarbonStreamRecordReader.java:406)
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$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace: (state=,code=0)
*Also when user checks the spark shell terminal there are exceptions thrown.*
scala> org.apache.spark.sql.streaming.StreamingQueryException: Offsets committed out of order: 100999 followed by 100 scala.sys.package$.error(package.scala:27)
org.apache.spark.sql.execution.streaming.TextSocketSource.commit(socket.scala:151)
org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2$$anonfun$apply$mcV$sp$4.apply(StreamExecution.scala:421)
org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2$$anonfun$apply$mcV$sp$4.apply(StreamExecution.scala:420)
scala.collection.Iterator$class.foreach(Iterator.scala:893)
scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2.apply$mcV$sp(StreamExecution.scala:420)
org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2.apply(StreamExecution.scala:404)
=== Streaming Query ===
Identifier: [id = d23c5633-e747-457e-a5c0-69ec09bb183f, runId = 2db93553-fe97-4fa6-b425-278128a42f50]
Current Offsets: {org.apache.spark.sql.execution.streaming.TextSocketSource@750267f5: 100}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
org.apache.spark.sql.execution.streaming.TextSocketSource@750267f5
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:284)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:177)
Caused by: java.lang.RuntimeException: Offsets committed out of order: 100999 followed by 100
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.execution.streaming.TextSocketSource.commit(socket.scala:151)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2$$anonfun$apply$mcV$sp$4.apply(StreamExecution.scala:421)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2$$anonfun$apply$mcV$sp$4.apply(StreamExecution.scala:420)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2.apply$mcV$sp(StreamExecution.scala:420)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2.apply(StreamExecution.scala:404)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch$2.apply(StreamExecution.scala:404)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:262)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:46)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$constructNextBatch(StreamExecution.scala:404)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$1.apply$mcV$sp(StreamExecution.scala:250)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$1.apply(StreamExecution.scala:244)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$1.apply(StreamExecution.scala:244)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:262)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:46)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:244)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:43)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:239)
... 1 more
Done reading and writing streaming data
*Expected Output : select query should be successful from beeline on the streaming table.*
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