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https://issues.apache.org/jira/browse/SPARK-21183?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16063448#comment-16063448
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Matthew Walton commented on SPARK-21183:
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Hi Sean,
I think the issue is Spark is not handling the integer data types properly and
this causes a conversion error. Running a simple select against the same data
with a tool like SQuirrel works fine to fetch back integer data but Spark seems
to be using a different method to fetch. I'm not sure if you have access to
Google BigQuery but you can easily reproduce this issue. If not, please take a
look at the other ticket for Hive JDBC drivers as I think you probably have
access to Hive for testing. If this is asking too much I guess I'll try to log
this and the other issue with Simba and let them pursue the issues more.
Essentially, I would not expect an error on handling the integer type with
Spark especially with the driver that Google expects its customers to use.
Thanks,
Matt
> Unable to return Google BigQuery INTEGER data type into Spark via google
> BigQuery JDBC driver: java.sql.SQLDataException: [Simba][JDBC](10140) Error
> converting value to long.
> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-21183
> URL: https://issues.apache.org/jira/browse/SPARK-21183
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell, SQL
> Affects Versions: 1.6.0, 2.0.0, 2.1.1
> Environment: OS: Linux
> Spark version 2.1.1
> JDBC: Download the latest google BigQuery JDBC Driver from Google
> Reporter: Matthew Walton
>
> I'm trying to fetch back data in Spark using a JDBC connection to Google
> BigQuery. Unfortunately, when I try to query data that resides in an INTEGER
> column I get the following error:
> java.sql.SQLDataException: [Simba][JDBC](10140) Error converting value to
> long.
> Steps to reproduce:
> 1) On Google BigQuery console create a simple table with an INT column and
> insert some data
> 2) Copy the Google BigQuery JDBC driver to the machine where you will run
> Spark Shell
> 3) Start Spark shell loading the GoogleBigQuery JDBC driver jar files
> ./spark-shell --jars
> /home/ec2-user/jdbc/gbq/GoogleBigQueryJDBC42.jar,/home/ec2-user/jdbc/gbq/google-api-client-1.22.0.jar,/home/ec2-user/jdbc/gbq/google-api-services-bigquery-v2-rev320-1.22.0.jar,/home/ec2-user/jdbc/gbq/google-http-client-1.22.0.jar,/home/ec2-user/jdbc/gbq/google-http-client-jackson2-1.22.0.jar,/home/ec2-user/jdbc/gbq/google-oauth-client-1.22.0.jar,/home/ec2-user/jdbc/gbq/jackson-core-2.1.3.jar
> 4) In Spark shell load the data from Google BigQuery using the JDBC driver
> val gbq = spark.read.format("jdbc").options(Map("url" ->
> "jdbc:bigquery://https://www.googleapis.com/bigquery/v2;ProjectId=your-project-name-here;OAuthType=0;OAuthPvtKeyPath=/usr/lib/spark/YourProjectPrivateKey.json;OAuthServiceAcctEmail=YourEmail@gmail.comAllowLargeResults=1;LargeResultDataset=_bqodbc_temp_tables;LargeResultTable=_matthew;Timeout=600","dbtable";
> ->
> "test.lu_test_integer")).option("driver","com.simba.googlebigquery.jdbc42.Driver").option("user","").option("password","").load()
> 5) In Spark shell try to display the data
> gbq.show()
> At this point you should see the error:
> scala> gbq.show()
> 17/06/22 19:34:57 WARN TaskSetManager: Lost task 0.0 in stage 3.0 (TID 6,
> ip-172-31-37-165.ec2.internal, executor 3): java.sql.SQLDataException:
> [Simba][JDBC](10140) Error converting value to long.
> at com.simba.exceptions.ExceptionConverter.toSQLException(Unknown
> Source)
> at com.simba.utilities.conversion.TypeConverter.toLong(Unknown Source)
> at com.simba.jdbc.common.SForwardResultSet.getLong(Unknown Source)
> at
> org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$8.apply(JdbcUtils.scala:365)
> at
> org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$8.apply(JdbcUtils.scala:364)
> at
> org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:286)
> at
> org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:268)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:827)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> 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:748)
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