Skip to main content

Spark HBase Connector CDP Issue - java.lang.ClassNotFoundException: org.apache.hadoop.hbase.spark.SparkSQLPushDownFilter

 

We wrote the Spark code to read data from using HBase-Connector as below - 

val sql = spark.sqlContext
val df = sql.read.format("org.apache.hadoop.hbase.spark")
 .option("hbase.columns.mapping",
   "name STRING :key, email STRING c:email, " +
     "birthDate DATE p:birthDate, height FLOAT p:height")
 .option("hbase.table", "person")
 .option("hbase.spark.use.hbasecontext", false)
 .load()
df.createOrReplaceTempView("personView")
val results = sql.sql("SELECT * FROM personView")
results.show()

Above code works fine. But, if we add a where clause to SQL above, it gives error as below - 

val results = sql.sql("SELECT * FROM personView where name='Jaiganesh'")
results.show()


Error - 

Caused by: org.apache.hadoop.hbase.ipc.RemoteWithExtrasException(org.apache.hadoop.hbase.DoNotRetryIOException): org.apache.hadoop.hbase.DoNotRetryIOException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.spark.SparkSQLPushDownFilter
        at org.apache.hadoop.hbase.shaded.protobuf.ProtobufUtil.toFilter(ProtobufUtil.java:1612)
        at org.apache.hadoop.hbase.shaded.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:1157)
        at org.apache.hadoop.hbase.regionserver.RSRpcServices.newRegionScanner(RSRpcServices.java:3039)
        at org.apache.hadoop.hbase.regionserver.RSRpcServices.scan(RSRpcServices.java:3369)
        at org.apache.hadoop.hbase.shaded.protobuf.generated.ClientProtos$ClientService$2.callBlockingMethod(ClientProtos.java:42278)
        at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:413)
        at org.apache.hadoop.hbase.ipc.CallRunner.run(CallRunner.java:133)
        at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor.java:338)
        at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor.java:318)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.spark.SparkSQLPushDownFilter
        at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:355)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
        at org.apache.hadoop.hbase.util.DynamicClassLoader.loadClass(DynamicClassLoader.java:147)
        at java.lang.Class.forName0(Native Method)
        at java.lang.Class.forName(Class.java:348)
        at org.apache.hadoop.hbase.shaded.protobuf.ProtobufUtil.toFilter(ProtobufUtil.java:1603)
        ... 8 more


This seems a problem with HBase-Spark Connector. Refer - https://issues.apache.org/jira/browse/HBASE-22769


The possible resolution that we could find was to set 

.option("hbase.spark.pushdown.columnfilter", false)

But, this will disable pushdown of column filters which is needed for efficiency and performance.

Comments

Popular posts

Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),...




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.dataso...




Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...




Hadoop Distcp Error Duplicate files in input path

  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.




Scala Spark building Jar leads java.lang.StackOverflowError

  Exception -  [Thread-3] ERROR scala_maven.ScalaCompileMojo - error: java.lang.StackOverflowError [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.TraversableForwarder$class.isEmpty(TraversableForwarder.scala:36) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.isEmpty(ListBuffer.scala:45) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.toList(ListBuffer.scala:306) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.result(ListBuffer.scala:300) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:31) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:27) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.GenericCompanion.apply(GenericCompanion.scala:50) [Thread-3] INFO scala_maven.ScalaCompile...