Re: OptionalDataException in spark
Hi, We are encountering java OptionalDataException in one of our spark jobs. All the tasks of a stage passes (or atleast we do not see any error), but stage fails with above exception while getting task result. And and this exception gets printed on driver. Any pointers in this regard would be helpful. Here is the stack-trace: java.io.OptionalDataException at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1555) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:422) at scala.collection.mutable.HashMap$$anonfun$readObject$1.apply(HashMap.scala:174) at scala.collection.mutable.HashMap$$anonfun$readObject$1.apply(HashMap.scala:174) at scala.collection.mutable.HashTable$class.init(HashTable.scala:109) at scala.collection.mutable.HashMap.init(HashMap.scala:40) at scala.collection.mutable.HashMap.readObject(HashMap.scala:174) at sun.reflect.GeneratedMethodAccessor22.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1058) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2136) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2027) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1535) at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2245) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2169) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2027) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1535) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:422) at org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1$$anonfun$apply$mcV$sp$2.apply(TaskResult.scala:67) at org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1$$anonfun$apply$mcV$sp$2.apply(TaskResult.scala:66) at scala.collection.immutable.Range.foreach(Range.scala:160) at org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1.apply$mcV$sp(TaskResult.scala:66) at org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1.apply(TaskResult.scala:55) at org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1.apply(TaskResult.scala:55) at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1326) at org.apache.spark.scheduler.DirectTaskResult.readExternal(TaskResult.scala:55) at java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:2076) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2025) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1535) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:422) at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75) at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:108) at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:64) at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:63) at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:63) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945) at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:62) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Thanks [Image removed by sender.]<http://adobe.com/> Ravi Aggarwal 408.536.6719 (tel) E11-355 Computer Scientist 669.214.1491 (cell) San Jose, CA, 95110, US Adobe. Make It an Experience. raagg...@adobe.com Adobe.com<http://www.adobe.com/> Any additional, business necessary information, such as legal requirements for your region can go here – Arial, size 8, italicized. Otherwise, delete this text.
RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2
Hi Ian,
Thanks for the information.
I think you are referring to post
http://apache-spark-user-list.1001560.n3.nabble.com/How-spark-decides-whether-to-do-BroadcastHashJoin-or-SortMergeJoin-td27369.html.
Yeah I could solve above issue of mine using
spark.sql.autoBroadcastJoinThreshold=-1, so that it always results in
Sort-Merge join instead of BroadcastHashJoin, Rather ideal fix for me is to
calculate size of my custom default source (BaseRelation’s sizeInBytes) in
right manner, to make spark planner take appropriate decision for me.
Thanks
Ravi
From: ianoconn...@gmail.com [mailto:ianoconn...@gmail.com] On Behalf Of Ian
O'Connell
Sent: Wednesday, July 20, 2016 11:05 PM
To: Ravi Aggarwal <raagg...@adobe.com>
Cc: Ted Yu <yuzhih...@gmail.com>; user <user@spark.apache.org>
Subject: Re: OutOfMemory when doing joins in spark 2.0 while same code runs
fine in spark 1.5.2
Ravi did your issue ever get solved for this?
I think i've been hitting the same thing, it looks like the
spark.sql.autoBroadcastJoinThreshold stuff isn't kicking in as expected, if I
set that to -1 then the computation proceeds successfully.
On Tue, Jun 14, 2016 at 12:28 AM, Ravi Aggarwal
<raagg...@adobe.com<mailto:raagg...@adobe.com>> wrote:
Hi,
Is there any breakthrough here?
I had one more observation while debugging the issue
Here are the 4 types of data I had:
Da -> stored in parquet
Di -> stored in parquet
Dl1 -> parquet version of lookup
Dl2 -> hbase version of lookup
Joins performed and type of join done by spark:
Da and Di Sort-merge failed (OOM)
Da and Dl1 B-H passed
Da and Dl2 Sort-Mergepassed
Di and Dl1B-H passed
Di and Dl2Sort-Mergefailed (OOM)
From entries I can deduce that problem is with sort-merge join involving Di.
So the hbase thing is out of equation, that is not the culprit.
In physical plan I could see there are only two operations that are done
additionally in sort-merge as compared to Broadcast-hash.
==> Exchange Hashpartitioning
==> Sort
And finally sort-merge join.
Can we deduce anything from this?
Thanks
Ravi
From: Ravi Aggarwal
Sent: Friday, June 10, 2016 12:31 PM
To: 'Ted Yu' <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>>
Cc: user <user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: RE: OutOfMemory when doing joins in spark 2.0 while same code runs
fine in spark 1.5.2
Hi Ted,
Thanks for the reply.
Here is the code
Btw – df.count is running fine on dataframe generated from this default source.
I think it is something in the combination of join and hbase data source that
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and
s3a/hbase join, In case you want that let me know.
import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory,
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory
class DefaultSource extends SchemaRelationProvider with FileFormat {
override def createRelation(sqlContext: SQLContext, parameters: Map[String,
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
}
def inferSchema(sparkSession: SparkSession,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = ???
def prepareWrite(sparkSession: SparkSession,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = ???
}
object HBaseConfigurationUtil {
lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
}
}
class HBaseRelation(val schema: StructType, parameters: Map[String, String])
(@transient val sqlContext: SQLContext) extends BaseRelation
with TableScan {
import sqlContext.sparkContext
override def buildScan(): RDD[Row] = {
val bcDataSchema = sparkContext.broadcast(schema)
val tableName = parameters.get("path") match
RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2
Hi,
Is there any breakthrough here?
I had one more observation while debugging the issue
Here are the 4 types of data I had:
Da -> stored in parquet
Di -> stored in parquet
Dl1 -> parquet version of lookup
Dl2 -> hbase version of lookup
Joins performed and type of join done by spark:
Da and Di Sort-merge failed (OOM)
Da and Dl1 B-H passed
Da and Dl2 Sort-Mergepassed
Di and Dl1B-H passed
Di and Dl2Sort-Mergefailed (OOM)
From entries I can deduce that problem is with sort-merge join involving Di.
So the hbase thing is out of equation, that is not the culprit.
In physical plan I could see there are only two operations that are done
additionally in sort-merge as compared to Broadcast-hash.
è Exchange Hashpartitioning
è Sort
And finally sort-merge join.
Can we deduce anything from this?
Thanks
Ravi
From: Ravi Aggarwal
Sent: Friday, June 10, 2016 12:31 PM
To: 'Ted Yu' <yuzhih...@gmail.com>
Cc: user <user@spark.apache.org>
Subject: RE: OutOfMemory when doing joins in spark 2.0 while same code runs
fine in spark 1.5.2
Hi Ted,
Thanks for the reply.
Here is the code
Btw – df.count is running fine on dataframe generated from this default source.
I think it is something in the combination of join and hbase data source that
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and
s3a/hbase join, In case you want that let me know.
import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory,
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory
class DefaultSource extends SchemaRelationProvider with FileFormat {
override def createRelation(sqlContext: SQLContext, parameters: Map[String,
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
}
def inferSchema(sparkSession: SparkSession,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = ???
def prepareWrite(sparkSession: SparkSession,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = ???
}
object HBaseConfigurationUtil {
lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
}
}
class HBaseRelation(val schema: StructType, parameters: Map[String, String])
(@transient val sqlContext: SQLContext) extends BaseRelation
with TableScan {
import sqlContext.sparkContext
override def buildScan(): RDD[Row] = {
val bcDataSchema = sparkContext.broadcast(schema)
val tableName = parameters.get("path") match {
case Some(t) => t
case _ => throw new RuntimeException("Table name (path) not provided in
parameters")
}
val hbaseQuorum = parameters.get("hbaseQuorum") match {
case Some(s: String) => s
case _ => throw new RuntimeException("hbaseQuorum not provided in
options")
}
val rdd = sparkContext.newAPIHadoopRDD(
HBaseConfigurationUtil.hbaseConfiguration(tableName, hbaseQuorum),
classOf[TableInputFormat],
classOf[ImmutableBytesWritable],
classOf[Result]
)
val rowRdd = rdd
.map(tuple => tuple._2)
.map { record =>
val cells: java.util.List[Cell] = record.listCells()
val splitRec =
cells.toArray.foldLeft(Array(CellUtil.cloneRow(cells.get(0 {(a, b) =>
a :+ CellUtil.cloneValue(b.asInstanceOf[Cell])
}
val keyFieldName = bcDataSchema.value.fields.filter(e =>
e.metadata.contains("isPrimary") && e.metadata.getBoolean("isPrimary"))(0).name
val schemaArr = cells.toArray.foldLeft(Array(keyFieldName)) {(a, b) => {
val fieldCell = b.asInstanceOf[Cell]
a :+ new
String(fieldCell.getQualifierArray).substring(fieldCell.getQualifierOffset,
fieldCell.getQualifierLength + fieldCell.getQualifierOffset)
RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2
Hi Ted,
Thanks for the reply.
Here is the code
Btw – df.count is running fine on dataframe generated from this default source.
I think it is something in the combination of join and hbase data source that
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and
s3a/hbase join, In case you want that let me know.
import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory,
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory
class DefaultSource extends SchemaRelationProvider with FileFormat {
override def createRelation(sqlContext: SQLContext, parameters: Map[String,
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
}
def inferSchema(sparkSession: SparkSession,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = ???
def prepareWrite(sparkSession: SparkSession,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = ???
}
object HBaseConfigurationUtil {
lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
}
}
class HBaseRelation(val schema: StructType, parameters: Map[String, String])
(@transient val sqlContext: SQLContext) extends BaseRelation
with TableScan {
import sqlContext.sparkContext
override def buildScan(): RDD[Row] = {
val bcDataSchema = sparkContext.broadcast(schema)
val tableName = parameters.get("path") match {
case Some(t) => t
case _ => throw new RuntimeException("Table name (path) not provided in
parameters")
}
val hbaseQuorum = parameters.get("hbaseQuorum") match {
case Some(s: String) => s
case _ => throw new RuntimeException("hbaseQuorum not provided in
options")
}
val rdd = sparkContext.newAPIHadoopRDD(
HBaseConfigurationUtil.hbaseConfiguration(tableName, hbaseQuorum),
classOf[TableInputFormat],
classOf[ImmutableBytesWritable],
classOf[Result]
)
val rowRdd = rdd
.map(tuple => tuple._2)
.map { record =>
val cells: java.util.List[Cell] = record.listCells()
val splitRec =
cells.toArray.foldLeft(Array(CellUtil.cloneRow(cells.get(0 {(a, b) =>
a :+ CellUtil.cloneValue(b.asInstanceOf[Cell])
}
val keyFieldName = bcDataSchema.value.fields.filter(e =>
e.metadata.contains("isPrimary") && e.metadata.getBoolean("isPrimary"))(0).name
val schemaArr = cells.toArray.foldLeft(Array(keyFieldName)) {(a, b) => {
val fieldCell = b.asInstanceOf[Cell]
a :+ new
String(fieldCell.getQualifierArray).substring(fieldCell.getQualifierOffset,
fieldCell.getQualifierLength + fieldCell.getQualifierOffset)
}
}
val res = Map(schemaArr.zip(splitRec).toArray: _*)
val recordFields = res.map(value => {
val colDataType =
try {
bcDataSchema.value.fields.filter(_.name == value._1)(0).dataType
} catch {
case e: ArrayIndexOutOfBoundsException => throw new
RuntimeException("Schema doesn't contain the fieldname")
}
CatalystTypeConverters.convertToScala(
Cast(Literal(value._2), colDataType).eval(),
colDataType)
}).toArray
Row(recordFields: _*)
}
rowRdd
}
}
Thanks
Ravi
From: Ted Yu [mailto:yuzhih...@gmail.com]
Sent: Thursday, June 9, 2016 7:56 PM
To: Ravi Aggarwal <raagg...@adobe.com>
Cc: user <user@spark.apache.org>
Subject: Re: OutOfMemory when doing joins in spark 2.0 while same code runs
fine in spark 1.5.2
bq. Read data from hbase using custom DefaultSource (implemented using
TableScan)
Did you use the DefaultSource from hbase-spark module in hbase master branch ?
If you wrote your own, mind sharing related code ?
Thanks
On Thu, Jun 9, 2016 at 2:53 AM, raaggarw
<raagg...@adobe.com<mailto:raagg...@adobe.com>> wrote:
Hi,
I was
