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https://issues.apache.org/jira/browse/SPARK-21063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16196354#comment-16196354
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Andreas Weise commented on SPARK-21063:
---------------------------------------
Seems like a bug IMHO. Same problem here with Spark 2.2.0, Hive on MySQL and
default HiveThriftServer2.
{code:title=Creating Table}
spark.range(10).write.mode('overwrite').format("parquet").saveAsTable("test1")
{code}
{code:title=beeline is fine}
$ bin/beeline
Beeline version 1.2.1.spark2 by Apache Hive
beeline> !connect jdbc:hive2://localhost:10000
Connecting to jdbc:hive2://localhost:10000
Connected to: Spark SQL (version 2.2.0)
Driver: Hive JDBC (version 1.2.1.spark2)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://localhost:10000> select * from test1;
+-----+--+
| id |
+-----+--+
| 5 |
| 6 |
| 7 |
| 8 |
| 9 |
| 0 |
| 1 |
| 2 |
| 3 |
| 4 |
+-----+--+
10 rows selected (0.213 seconds)
{code}
{code:title=spark+jdbc gives 0 results}
spark.read.jdbc("jdbc:hive2://localhost:10000/default","test1",
properties={"driver": "org.apache.hive.jdbc.HiveDriver"}).collect()
[]
{code}
Whats interesting now is the SQL Statements fired during JDBC call from Spark
in contrast to the one from beeline. They are all logged in HiveThriftServer2:
{code:title=SQL from Spark+JDBC}
SELECT * FROM test1 WHERE 1=0
SELECT "id" FROM test1
{code}
{code:title=SQL from beeline}
select * from test1
{code}
Now tried Spark+JDBC with fetchsize option:
{code:title=Spark+JDBC with fetchsize}
spark.read.jdbc("jdbc:hive2://localhost:10000/default","test1",
properties={"driver": "org.apache.hive.jdbc.HiveDriver", "fetchsize":
"10"}).collect()
{code}
This results in an error.
{noformat}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 28.0 failed 4 times, most recent failure: Lost task 0.3 in stage 28.0
(TID 52, 192.168.0.210, executor 10): java.sql.SQLException: Cannot convert
column 1 to long: java.lang.NumberFormatException: For input string: "id"
at
org.apache.hive.jdbc.HiveBaseResultSet.getLong(HiveBaseResultSet.java:372)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$8.apply(JdbcUtils.scala:409)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$8.apply(JdbcUtils.scala:408)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:330)
at
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:312)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
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$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
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)
Caused by: java.lang.NumberFormatException: For input string: "id"
at
java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
at java.lang.Long.parseLong(Long.java:589)
at java.lang.Long.valueOf(Long.java:803)
at
org.apache.hive.jdbc.HiveBaseResultSet.getLong(HiveBaseResultSet.java:368)
... 23 more
{noformat}
> Spark return an empty result from remote hadoop cluster
> -------------------------------------------------------
>
> Key: SPARK-21063
> URL: https://issues.apache.org/jira/browse/SPARK-21063
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 2.1.0, 2.1.1
> Reporter: Peter Bykov
>
> Spark returning empty result from when querying remote hadoop cluster.
> All firewall settings removed.
> Querying using JDBC working properly using hive-jdbc driver from version 1.1.1
> Code snippet is:
> {code:java}
> val spark = SparkSession.builder
> .appName("RemoteSparkTest")
> .master("local")
> .getOrCreate()
> val df = spark.read
> .option("url", "jdbc:hive2://remote.hive.local:10000/default")
> .option("user", "user")
> .option("password", "pass")
> .option("dbtable", "test_table")
> .option("driver", "org.apache.hive.jdbc.HiveDriver")
> .format("jdbc")
> .load()
>
> df.show()
> {code}
> Result:
> {noformat}
> +-------------------+
> |test_table.test_col|
> +-------------------+
> +-------------------+
> {noformat}
> All manipulations like:
> {code:java}
> df.select(*).show()
> {code}
> returns empty result too.
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