You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "t oo (Jira)" <ji...@apache.org> on 2021/11/21 00:03:00 UTC
[jira] [Updated] (SPARK-37420) Oracle JDBC - java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
[ https://issues.apache.org/jira/browse/SPARK-37420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
t oo updated SPARK-37420:
-------------------------
Description:
reading oracle jdbc is not working as expected, i thought a simple df show should work.
{code:java}
/usr/local/bin/pyspark --driver-class-path "/home/user/extra_jar_spark/*" --jars "/home/user/extra_jar_spark/*"
jdbc2DF = spark.read \
.format("jdbc") \
.option("url", "jdbc:oracle:thin:@redact") \
.option("driver", "oracle.jdbc.OracleDriver") \
.option("dbtable", "s.t") \
.option("user", "redact") \
.option("password", "redact") \
.option("fetchsize", 10000) \
.load()
jdbc2DF.printSchema()
root
|-- ID: decimal(38,10) (nullable = true)
|-- OBJECT_VERSION_NUMBER: decimal(9,0) (nullable = true)
|-- START_DATE: timestamp (nullable = true)
|-- END_DATE: timestamp (nullable = true)
|-- CREATED_BY: decimal(15,0) (nullable = true)
|-- CREATION_DATE: timestamp (nullable = true)
|-- LAST_UPDATED_BY: decimal(15,0) (nullable = true)
|-- LAST_UPDATE_DATE: timestamp (nullable = true)
|-- LAST_UPDATE_LOGIN: decimal(15,0) (nullable = true)
|-- CONTINGENCY: string (nullable = true)
|-- CONTINGENCY_ID: decimal(38,10) (nullable = true)
jdbc2DF.show()
21/11/20 23:42:00 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
21/11/20 23:42:00 WARN TaskSetManager: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)21/11/20 23:42:00 ERROR TaskSetManager: Task 0 in stage 2.0 failed 1 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pyspark/sql/dataframe.py", line 494, in show
print(self._jdf.showString(n, 20, vertical))
File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/java_gateway.py", line 1310, in __call__
File "/usr/local/lib/python3.7/site-packages/pyspark/sql/utils.py", line 111, in deco
return f(*a, **kw)
File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o109.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
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)Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2403)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2352)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2351)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2351)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1109)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2591)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2533)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2522)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:898)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:476)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:429)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3715)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2728)
at org.apache.spark.sql.Dataset.$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.head(Dataset.scala:2728)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2935)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:287)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:326)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more {code}
as you can see oracle type is 38,10 so not sure why spark thinks precision is greater than 38. It seems to be adding scale to precision
was:
reading oracle jdbc is not working as expected, i thought a simple df show should work.
{code:java}
/usr/local/bin/pyspark --driver-class-path "/home/user/extra_jar_spark/*" --jars "/home/user/extra_jar_spark/*"
jdbc2DF = spark.read \
.format("jdbc") \
.option("url", "jdbc:oracle:thin:@redact") \
.option("driver", "oracle.jdbc.OracleDriver") \
.option("dbtable", "s.t") \
.option("user", "redact") \
.option("password", "redact") \
.option("fetchsize", 10000) \
.load()
jdbc2DF.printSchema()
root
|-- ID: decimal(38,10) (nullable = true)
|-- OBJECT_VERSION_NUMBER: decimal(9,0) (nullable = true)
|-- START_DATE: timestamp (nullable = true)
|-- END_DATE: timestamp (nullable = true)
|-- CREATED_BY: decimal(15,0) (nullable = true)
|-- CREATION_DATE: timestamp (nullable = true)
|-- LAST_UPDATED_BY: decimal(15,0) (nullable = true)
|-- LAST_UPDATE_DATE: timestamp (nullable = true)
|-- LAST_UPDATE_LOGIN: decimal(15,0) (nullable = true)
|-- CONTINGENCY: string (nullable = true)
|-- CONTINGENCY_ID: decimal(38,10) (nullable = true)
jdbc2DF.show()
21/11/20 23:42:00 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
21/11/20 23:42:00 WARN TaskSetManager: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
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)21/11/20 23:42:00 ERROR TaskSetManager: Task 0 in stage 2.0 failed 1 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pyspark/sql/dataframe.py", line 494, in show
print(self._jdf.showString(n, 20, vertical))
File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/java_gateway.py", line 1310, in __call__
File "/usr/local/lib/python3.7/site-packages/pyspark/sql/utils.py", line 111, in deco
return f(*a, **kw)
File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o109.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2403)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2352)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2351)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2351)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1109)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2591)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2533)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2522)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:898)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:476)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:429)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3715)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2728)
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.head(Dataset.scala:2728)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2935)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:287)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:326)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more {code}
> Oracle JDBC - java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-37420
> URL: https://issues.apache.org/jira/browse/SPARK-37420
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.2.0
> Reporter: t oo
> Priority: Major
>
> reading oracle jdbc is not working as expected, i thought a simple df show should work.
>
> {code:java}
> /usr/local/bin/pyspark --driver-class-path "/home/user/extra_jar_spark/*" --jars "/home/user/extra_jar_spark/*"
> jdbc2DF = spark.read \
> .format("jdbc") \
> .option("url", "jdbc:oracle:thin:@redact") \
> .option("driver", "oracle.jdbc.OracleDriver") \
> .option("dbtable", "s.t") \
> .option("user", "redact") \
> .option("password", "redact") \
> .option("fetchsize", 10000) \
> .load()
>
> jdbc2DF.printSchema()
> root
> |-- ID: decimal(38,10) (nullable = true)
> |-- OBJECT_VERSION_NUMBER: decimal(9,0) (nullable = true)
> |-- START_DATE: timestamp (nullable = true)
> |-- END_DATE: timestamp (nullable = true)
> |-- CREATED_BY: decimal(15,0) (nullable = true)
> |-- CREATION_DATE: timestamp (nullable = true)
> |-- LAST_UPDATED_BY: decimal(15,0) (nullable = true)
> |-- LAST_UPDATE_DATE: timestamp (nullable = true)
> |-- LAST_UPDATE_LOGIN: decimal(15,0) (nullable = true)
> |-- CONTINGENCY: string (nullable = true)
> |-- CONTINGENCY_ID: decimal(38,10) (nullable = true)
> jdbc2DF.show()
> 21/11/20 23:42:00 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
> java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
> at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
> at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
> at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
> at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
> 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)
> 21/11/20 23:42:00 WARN TaskSetManager: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
> at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
> at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
> at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
> at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)21/11/20 23:42:00 ERROR TaskSetManager: Task 0 in stage 2.0 failed 1 times; aborting job
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/usr/local/lib/python3.7/site-packages/pyspark/sql/dataframe.py", line 494, in show
> print(self._jdf.showString(n, 20, vertical))
> File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/java_gateway.py", line 1310, in __call__
> File "/usr/local/lib/python3.7/site-packages/pyspark/sql/utils.py", line 111, in deco
> return f(*a, **kw)
> File "/usr/local/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.9.2-src.zip/py4j/protocol.py", line 328, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o109.showString.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2) (localhost executor driver): java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
> at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
> at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
> at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
> at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
> 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)Driver stacktrace:
> at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2403)
> at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2352)
> at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2351)
> at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2351)
> at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1109)
> at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1109)
> at scala.Option.foreach(Option.scala:407)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1109)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2591)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2533)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2522)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:898)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:476)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:429)
> at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
> at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3715)
> at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2728)
> at org.apache.spark.sql.Dataset.$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.head(Dataset.scala:2728)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2935)
> at org.apache.spark.sql.Dataset.getRows(Dataset.scala:287)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:326)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:282)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
> at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
> at org.apache.spark.sql.errors.QueryExecutionErrors$.decimalPrecisionExceedsMaxPrecisionError(QueryExecutionErrors.scala:847)
> at org.apache.spark.sql.types.Decimal.set(Decimal.scala:123)
> at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:572)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$4(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.nullSafeConvert(JdbcUtils.scala:546)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3(JdbcUtils.scala:418)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$makeGetter$3$adapted(JdbcUtils.scala:416)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:367)
> at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:349)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
> at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:131)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> ... 1 more {code}
>
> as you can see oracle type is 38,10 so not sure why spark thinks precision is greater than 38. It seems to be adding scale to precision
--
This message was sent by Atlassian Jira
(v8.20.1#820001)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org