CODEWITHSUNDEEP
apache-sparkapache-icebergspark-thriftserver
Bill Goldberg
Bill Goldberg

Reputation: 1829

Getting error when querying iceberg table via Spark thrift server using beeline client?

I am trying to query iceberg table (External table with data in S3 & Metadata in Hivemetastore) using spark thrift server coming as part of Spark. I am able to query non iceberg tables but when I query iceberg table I am getting below error. Can we not query iceberg table via spark thrift server?

Version details

I have started the thrift server with following command

start-thriftserver.sh \
--hiveconf hive.metastore.uris=thrift://$ip:$port \
--conf spark.hadoop.fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider \
--conf spark.hadoop.fs.s3a.access.key=$key \
--conf spark.hadoop.fs.s3a.secret.key=$secret \
--conf spark.sql.catalog.iceberg_catalog.uri=thrift://$ip:$port \
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
--conf spark.sql.catalog.iceberg_catalog=org.apache.iceberg.spark.SparkSessionCatalog \
--conf spark.sql.catalog.iceberg_catalog.type=hive \
--conf spark.sql.catalog.iceberg_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
--conf iceberg.engine.hive.enabled=true \

Error in beeline when querying iceberg table select count(*) from $table_name

Error: org.apache.hive.service.cli.HiveSQLException: Error running query: java.lang.RuntimeException: java.lang.InstantiationException
        at org.apache.spark.sql.hive.thriftserver.HiveThriftServerErrors$.runningQueryError(HiveThriftServerErrors.scala:44)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:325)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.$anonfun$run$2(SparkExecuteStatementOperation.scala:230)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
        at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties(SparkOperation.scala:79)
        at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties$(SparkOperation.scala:63)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.withLocalProperties(SparkExecuteStatementOperation.scala:43)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:230)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:225)
        at java.base/java.security.AccessController.doPrivileged(Native Method)
        at java.base/javax.security.auth.Subject.doAs(Subject.java:423)
        at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1878)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2.run(SparkExecuteStatementOperation.scala:239)
        at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
        at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
        at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
        at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
        at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.RuntimeException: java.lang.InstantiationException
        at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:137)
        at org.apache.spark.rdd.HadoopRDD.getInputFormat(HadoopRDD.scala:191)
        at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:205)
        at org.apache.spark.rdd.RDD.$anonfun$partitions$2(RDD.scala:300)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:296)
        at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
        at org.apache.spark.rdd.RDD.$anonfun$partitions$2(RDD.scala:300)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:296)
        at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
        at org.apache.spark.rdd.RDD.$anonfun$partitions$2(RDD.scala:300)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:296)
        at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
        at org.apache.spark.rdd.RDD.$anonfun$partitions$2(RDD.scala:300)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:296)
        at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
        at org.apache.spark.rdd.RDD.$anonfun$partitions$2(RDD.scala:300)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:296)
        at org.apache.spark.rdd.RDD.getNumPartitions(RDD.scala:316)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture$lzycompute(ShuffleExchangeExec.scala:140)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture(ShuffleExchangeExec.scala:139)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeLike.$anonfun$submitShuffleJob$1(ShuffleExchangeExec.scala:68)
        at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:219)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeLike.submitShuffleJob(ShuffleExchangeExec.scala:68)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeLike.submitShuffleJob$(ShuffleExchangeExec.scala:67)
        at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.submitShuffleJob(ShuffleExchangeExec.scala:115)
        at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.shuffleFuture$lzycompute(QueryStageExec.scala:170)
        at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.shuffleFuture(QueryStageExec.scala:170)
        at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.doMaterialize(QueryStageExec.scala:172)
        at org.apache.spark.sql.execution.adaptive.QueryStageExec.materialize(QueryStageExec.scala:82)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5(AdaptiveSparkPlanExec.scala:256)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5$adapted(AdaptiveSparkPlanExec.scala:254)
        at scala.collection.Iterator.foreach(Iterator.scala:943)
        at scala.collection.Iterator.foreach$(Iterator.scala:943)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
        at scala.collection.IterableLike.foreach(IterableLike.scala:74)
        at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
        at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$1(AdaptiveSparkPlanExec.scala:254)
        at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.getFinalPhysicalPlan(AdaptiveSparkPlanExec.scala:226)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.withFinalPlanUpdate(AdaptiveSparkPlanExec.scala:365)
        at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.executeCollect(AdaptiveSparkPlanExec.scala:338)
        at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3715)
        at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2971)
        at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3706)
        at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
        at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
        at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
        at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
        at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3704)
        at org.apache.spark.sql.Dataset.collect(Dataset.scala:2971)
        at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:300)
        ... 16 more
Caused by: java.lang.InstantiationException
        at java.base/jdk.internal.reflect.InstantiationExceptionConstructorAccessorImpl.newInstance(InstantiationExceptionConstructorAccessorImpl.java:48)
        at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
        at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:135)
        ... 75 more (state=,code=0)

Upvotes: 3

Views: 8908

Answers (2)

Giri
Giri

Reputation: 1634

If you create a table via Spark/Beeline and you can see that table, but not the table that exist within Hive, that typically means Spark isn't configured to use Hive metastore.

In Spark 1.6, by default the Thrift server runs in multi-session mode. Which means each JDBC/ODBC connection owns a copy of their own SQL configuration and temporary function registry. Cached tables are still shared. You are registering a temp table and so in order to see the temp table, you need to run the Thrift server in single-session mode. In spark-default.conf set spark.sql.hive.thriftServer.singleSession to true. When you call for an instance of the Thrift server in you code, it should start up in single session mode. When you initialize and register the temp table, it should show up when you connect and issue show tables command. You can create a permanent table in which case it should show up in multi session mode and from Hive (You have the code to do that but it's commented out). Under /usr/hdp/current/spark-client/conf on the sandbox:

[root@sandbox conf]# cat hive-site.xml 
  <configuration> 
    <property> 
      <name>hive.metastore.uris</name> 
      <value>thrift://sandbox.hortonworks.com:9083</value> 
    </property> 
  </configuration>

Upvotes: 0

Kyle Bendickson
Kyle Bendickson

Reputation: 318

Based on your configuration, it looks like you’re trying to use a catalog named iceberg_catalog, but it’s being configured as Iceberg’s SparkSessionCatalog.

However, SparkSessionCatalog is reserved for the default catalog that Spark uses, which allows that catalog to be used with both Iceberg tables and other formats.

The session catalog must be named spark_catalog. This is a requirement imposed by Spark.

So you need to either use org.apache.iceberg.SparkCatalog with your current configuration of a separate catalog named iceberg_catalog (where the name is up to you), or if you want to override the default catalog so that Iceberg tables and non-Iceberg tables can live within one catalog, you’d need to change the catalog name to spark_catalog and keep your current configuration.

Refer to the docs on adding catalogs. In the configuration used there the session catalog, spark_catalog, is overridden and then there is also a catalog named local that is distinct and can only have Iceberg tables.

Upvotes: 4

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