Bug: Oracle JDBC - java.lang.ArithmeticException: Decimal precision 49 exceeds max precision 38
Status: Open | Priority: Major | Resolution: 
Created: 20/Nov/21 23:54
Affects Version/s: 3.2.0

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. I checked in plain JDBC connection that ID columns has values 39 wide, but why can't spark read them in as STRING with some conf?