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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2020/07/03 17:49:39 UTC
[GitHub] [hudi] zuyanton opened a new issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
zuyanton opened a new issue #1790:
URL: https://github.com/apache/hudi/issues/1790
It looks like Hudi does not handle DecimalType properly. one of the symptoms is when we try to use decimal column as partition, Hudi creates folders that look like this '[0, 0, 0, 0, 0, 0, 0, 0, 27, -63, 109, 103, 78, -56, 0, 0]' instead of expected '2'. The other symptom is that querying MoR tables fails when table contain Decimal columns. It looks like failure happens when Hudi tries to digest decimals coming from log (avro) files. When all data is in the parquet file, spark sql works just fine.
**To Reproduce**
consider following example:
Step 1 - create table
```
spark.sql("drop table if exists testTable_ro")
spark.sql("drop table if exists testTable_rt")
var df = Seq((1, 2, 3)).toDF("pk", "partition", "sort_key")
df = df.withColumn("decimal_column", df.col("pk").cast("decimal(38,18)"))
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://someBucket/testTable")
```
since its a firs time we wrote to the table, there is no log files as of yet, Hudi created only one parquet file. Therefore simple select query works as expected:
```
scala> spark.sql("select * from testTable_rt").show
+-------------------+--------------------+------------------+----------------------+--------------------+---+--------+--------------------+---------+
|_hoodie_commit_time|_hoodie_commit_seqno|_hoodie_record_key|_hoodie_partition_path| _hoodie_file_name| pk|sort_key| decimal_column|partition|
+-------------------+--------------------+------------------+----------------------+--------------------+---+--------+--------------------+---------+
| 20200703164509| 20200703164509_0_8| pk:1| 2|cd1293b4-3876-426...| 1| 3|1.000000000000000000| 2|
+-------------------+--------------------+------------------+----------------------+--------------------+---+--------+--------------------+---------+
```
Step 2 - update table with the same datafarame
```
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://someBucket/testTable")
```
since we updating existing record , Hudi this time creates log (avro) file on top of existing parquet file. Running the same query as in step one will result in exception
```
scala> spark.sql("select * from testTable_rt").show
java.lang.ClassCastException: org.apache.hadoop.io.BytesWritable cannot be cast to org.apache.hadoop.hive.serde2.io.HiveDecimalWritable
at org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableHiveDecimalObjectInspector.getPrimitiveWritableObject(WritableHiveDecimalObjectInspector.java:41)
at org.apache.spark.sql.hive.HiveShim$.toCatalystDecimal(HiveShim.scala:107)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$11.apply(TableReader.scala:414)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$11.apply(TableReader.scala:413)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:442)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:433)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:291)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:283)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
running query that does not pull Decimal column works just fine:
```
scala> spark.sql("select pk from testTable_rt").show
+---+
| pk|
+---+
| 1|
+---+
```
running any query on this table via hive, produces different exception:
```
hive> select * from testTable_rt;
TaskAttempt 3 failed, info=[Error: Error while running task ( failure ) : attempt_1593458427178_1028_1_00_000000_3:java.lang.RuntimeException: java.lang.RuntimeException: java.io.IOException: org.apache.hudi.exception.HoodieIOException: IOException when reading log file
at org.apache.hadoop.hive.ql.exec.tez.TezProcessor.initializeAndRunProcessor(TezProcessor.java:211)
at org.apache.hadoop.hive.ql.exec.tez.TezProcessor.run(TezProcessor.java:168)
at org.apache.tez.runtime.LogicalIOProcessorRuntimeTask.run(LogicalIOProcessorRuntimeTask.java:374)
at org.apache.tez.runtime.task.TaskRunner2Callable$1.run(TaskRunner2Callable.java:73)
at org.apache.tez.runtime.task.TaskRunner2Callable$1.run(TaskRunner2Callable.java:61)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1844)
at org.apache.tez.runtime.task.TaskRunner2Callable.callInternal(TaskRunner2Callable.java:61)
at org.apache.tez.runtime.task.TaskRunner2Callable.callInternal(TaskRunner2Callable.java:37)
at org.apache.tez.common.CallableWithNdc.call(CallableWithNdc.java:36)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.RuntimeException: java.io.IOException: org.apache.hudi.exception.HoodieIOException: IOException when reading log file
at org.apache.hadoop.mapred.split.TezGroupedSplitsInputFormat$TezGroupedSplitsRecordReader.initNextRecordReader(TezGroupedSplitsInputFormat.java:206)
at org.apache.hadoop.mapred.split.TezGroupedSplitsInputFormat$TezGroupedSplitsRecordReader.<init>(TezGroupedSplitsInputFormat.java:145)
at org.apache.hadoop.mapred.split.TezGroupedSplitsInputFormat.getRecordReader(TezGroupedSplitsInputFormat.java:111)
at org.apache.tez.mapreduce.lib.MRReaderMapred.setupOldRecordReader(MRReaderMapred.java:157)
at org.apache.tez.mapreduce.lib.MRReaderMapred.setSplit(MRReaderMapred.java:83)
at org.apache.tez.mapreduce.input.MRInput.initFromEventInternal(MRInput.java:703)
at org.apache.tez.mapreduce.input.MRInput.initFromEvent(MRInput.java:662)
at org.apache.tez.mapreduce.input.MRInputLegacy.checkAndAwaitRecordReaderInitialization(MRInputLegacy.java:150)
at org.apache.tez.mapreduce.input.MRInputLegacy.init(MRInputLegacy.java:114)
at org.apache.hadoop.hive.ql.exec.tez.MapRecordProcessor.getMRInput(MapRecordProcessor.java:525)
at org.apache.hadoop.hive.ql.exec.tez.MapRecordProcessor.init(MapRecordProcessor.java:171)
at org.apache.hadoop.hive.ql.exec.tez.TezProcessor.initializeAndRunProcessor(TezProcessor.java:184)
... 14 more
Caused by: java.io.IOException: org.apache.hudi.exception.HoodieIOException: IOException when reading log file
at org.apache.hadoop.hive.io.HiveIOExceptionHandlerChain.handleRecordReaderCreationException(HiveIOExceptionHandlerChain.java:97)
at org.apache.hadoop.hive.io.HiveIOExceptionHandlerUtil.handleRecordReaderCreationException(HiveIOExceptionHandlerUtil.java:57)
at org.apache.hadoop.hive.ql.io.HiveInputFormat.getRecordReader(HiveInputFormat.java:379)
at org.apache.hadoop.mapred.split.TezGroupedSplitsInputFormat$TezGroupedSplitsRecordReader.initNextRecordReader(TezGroupedSplitsInputFormat.java:203)
... 25 more
Caused by: org.apache.hudi.exception.HoodieIOException: IOException when reading log file
at org.apache.hudi.common.table.log.AbstractHoodieLogRecordScanner.scan(AbstractHoodieLogRecordScanner.java:242)
at org.apache.hudi.common.table.log.HoodieMergedLogRecordScanner.<init>(HoodieMergedLogRecordScanner.java:79)
at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.getMergedLogRecordScanner(RealtimeCompactedRecordReader.java:67)
at org.apache.hudi.hadoop.realtime.RealtimeCompactedRecordReader.<init>(RealtimeCompactedRecordReader.java:50)
at org.apache.hudi.hadoop.realtime.HoodieRealtimeRecordReader.constructRecordReader(HoodieRealtimeRecordReader.java:67)
at org.apache.hudi.hadoop.realtime.HoodieRealtimeRecordReader.<init>(HoodieRealtimeRecordReader.java:45)
at org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat.getRecordReader(HoodieParquetRealtimeInputFormat.java:222)
at org.apache.hadoop.hive.ql.io.HiveInputFormat.getRecordReader(HiveInputFormat.java:376)
... 26 more
```
Querying read optimized table (testTable_ro) works as expected all the time.
Things get even messier when we try to use decimal column as primary key.
full code snippet:
```
import org.apache.spark.sql.functions._
import org.apache.hudi.hive.MultiPartKeysValueExtractor
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.keygen.ComplexKeyGenerator
import org.apache.hadoop.hive.conf.HiveConf
val hiveConf = new HiveConf()
val hiveMetastoreURI = hiveConf.get("hive.metastore.uris").replaceAll("thrift://", "")
val hiveServer2URI = hiveMetastoreURI.substring(0, hiveMetastoreURI.lastIndexOf(":"))
var hudiOptions = Map[String,String](
HoodieWriteConfig.TABLE_NAME → "testTable",
"hoodie.consistency.check.enabled"->"true",
"hoodie.compact.inline.max.delta.commits"->"100",
"hoodie.compact.inline"->"true",
DataSourceWriteOptions.STORAGE_TYPE_OPT_KEY -> "MERGE_ON_READ",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "pk",
DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY -> classOf[ComplexKeyGenerator].getName,
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY ->"partition",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "sort_key",
DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY → "true",
DataSourceWriteOptions.HIVE_TABLE_OPT_KEY → "testTable",
DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY → "partition",
DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY → classOf[MultiPartKeysValueExtractor].getName,
DataSourceWriteOptions.HIVE_URL_OPT_KEY ->s"jdbc:hive2://$hiveServer2URI:10000"
)
spark.sql("drop table if exists testTable_ro")
spark.sql("drop table if exists testTable_rt")
var df = Seq((1, 2, 3)).toDF("pk", "partition", "sort_key")
df = df.withColumn("decimal_column", df.col("pk").cast("decimal(38,18)"))
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://someBucket/testTable")
spark.sql("select * from testTable_rt").show
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://someBucket/testTable")
spark.sql("select * from testTable_rt").show
```
**Environment Description**
* Hudi version :0.5.3
* Spark version :2.4.4
* Hive version :2.3.6
* Hadoop version :2.8.5
* Storage (HDFS/S3/GCS..) :S3
* Running on Docker? (yes/no) :no
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[GitHub] [hudi] zhedoubushishi commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
zhedoubushishi commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657720726
I think it's this pr that fix the bug for your exception: https://github.com/apache/hudi/pull/1677/files.
For the ```hive-exec-1.2.1-spark2-amzn-1.jar``` issue, I don't think it's specifically related to EMR because all the Spark 2.x depends on ```1.2.1.spark2``` hive version: https://github.com/apache/spark/blob/v2.4.6/pom.xml#L129.
To solve this issue, I would suggest using ```HiveDecimalUtils.enforcePrecisionScale(HiveDecimalWritable writable, DecimalTypeInfo typeInfo)``` instead of https://github.com/apache/hudi/blob/2603cfb33e272632d7f36a53e1b13fe86dbb8627/hudi-hadoop-mr/src/main/java/org/apache/hudi/hadoop/utils/HoodieRealtimeRecordReaderUtils.java#L213. Because ```HiveDecimalUtils.enforcePrecisionScale(HiveDecimalWritable writable, DecimalTypeInfo typeInfo)``` is compatible with both Hive 2.3.1 and Hive 1.2.0.
It this makes sense, I can create a pr for this.
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[GitHub] [hudi] bvaradar commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bvaradar commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-658245237
Closing this ticket as we have a jira to track this issue.
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[GitHub] [hudi] garyli1019 commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
garyli1019 commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657726718
@zhedoubushishi makes sense to me!
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[GitHub] [hudi] zuyanton commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
zuyanton commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-656228790
I was able to confirm that master branch fixed the issue however not without difficulties. More specifically:
When I deploy master branch to EMR and run select query in spark-shell, I get ```NoSuchMethodException HiveDecimalWritable.enforcePrecisionScale``` at [this line](https://github.com/apache/hudi/blob/master/hudi-hadoop-mr/src/main/java/org/apache/hudi/hadoop/utils/HoodieRealtimeRecordReaderUtils.java#L213). I tried to lookup where spark shell grabs that class definition via
```
scala> classOf[org.apache.hadoop.hive.serde2.io.HiveDecimalWritable].getProtectionDomain
res3: java.security.ProtectionDomain =
ProtectionDomain (file:/usr/lib/spark/jars/hive-exec-1.2.1-spark2-amzn-1.jar <no signer certificates>)
sun.misc.Launcher$AppClassLoader@36f6e879
<no principals>
java.security.Permissions@49e335a5 (
("java.io.FilePermission" "/usr/lib/spark/jars/hive-exec-1.2.1-spark2-amzn-1.jar" "read")
("java.lang.RuntimePermission" "exitVM")
)
```
and it looks like it comes from outdated hive-exec-1.2.1-spark2-amzn-1.jar which indeed does not have "enforcePrecisionScale" method. I tried uploading hive-storage-api (dependency of hive-exec and actual package containing HiveDecimalWritable definition) to the cluster and referencing it to spark-shell via ```--jar /some_temp_location/hive-storage-api-2.3.1.jar``` - didn't help. The only thing that worked for me is adding hive-storage-api-2.3.1.jar directly to ```/usr/lib/spark/jars``` folder, which does not seems right to me. @garyli1019 have you seen similar issues with [your change](https://github.com/apache/hudi/commit/37838cea6094ddc66191df42e8b2c84f132d1623#diff-68b6e6f1a2c961fea254a2fc3b93ac23R213) ? is it specific to AWS EMR Spark ?
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[GitHub] [hudi] bhasudha commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bhasudha commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-654257840
@zuyanton thanks for reporting this. Will try to reproduce and get back soon. This looks like some deserialization issue involving Parquet/Avro. @bvaradar @umehrot2 any idea ?
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[GitHub] [hudi] bhasudha commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bhasudha commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657296109
@garyli1019 could you help with this question ?
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[GitHub] [hudi] bvaradar commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bvaradar commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657597244
@umehrot2 @zhedoubushishi @bschell : Is this a known issue with EMR ?
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[GitHub] [hudi] zuyanton commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
zuyanton commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-654566959
@bhasudha Thank you for your reply . If I read code correctly ,I believe that handling decimal is missing here https://github.com/apache/hudi/blob/release-0.5.3/hudi-hadoop-mr/src/main/java/org/apache/hudi/hadoop/realtime/AbstractRealtimeRecordReader.java#L303
and it looks like it was fixed in master with this PR: https://github.com/apache/hudi/commit/37838cea6094ddc66191df42e8b2c84f132d1623#diff-68b6e6f1a2c961fea254a2fc3b93ac23R209 . ... let me checkout masters and verify
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[GitHub] [hudi] bvaradar commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bvaradar commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-658804172
@zuyanton : Thanks for reporting this issue. We have merged @zhedoubushishi changes to master.
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[GitHub] [hudi] garyli1019 commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
garyli1019 commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657332648
Hi @zuyanton , thanks for report this issue.
https://github.com/apache/hudi/commit/37838cea6094ddc66191df42e8b2c84f132d1623#diff-68b6e6f1a2c961fea254a2fc3b93ac23R213 is a refactoring work that doesn't change any code behavior.
Based on your description, I believe this issue was related to EMR specifically. If `hive-exec-1.2.1-spark2-amzn-1.jar` is being used then it's reasonable for me that this issue happened. Would you able to update the HIVE related components to Hudi compatible version if it's possible on EMR?
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[GitHub] [hudi] bvaradar commented on issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bvaradar commented on issue #1790:
URL: https://github.com/apache/hudi/issues/1790#issuecomment-657736191
@zhedoubushishi : Thanks for the jumping into this. I have assigned the jira : https://issues.apache.org/jira/browse/HUDI-1087 to you. Please go ahead and create a PR.
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[GitHub] [hudi] bvaradar closed issue #1790: [SUPPORT] Querying MoR tables with DecimalType columns via Spark SQL fails
Posted by GitBox <gi...@apache.org>.
bvaradar closed issue #1790:
URL: https://github.com/apache/hudi/issues/1790
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