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https://issues.apache.org/jira/browse/SPARK-32317?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17160207#comment-17160207
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Chen Zhang edited comment on SPARK-32317 at 7/19/20, 6:10 PM:
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The DECIMAL type in Parquet is stored by INT32, INT64, FIXED_LEN_BYTE_ARRAY, or
BINARY.
Taking 19500.00 stored as INT64 as an example, the parquet file is stored as
DECIMAL(15,2) -> 1950000, DECIMAL(15,6) -> 19500000000.
Spark uses requiredSchema to convert the read parquet file data. It does not
consider that the requiredSchema and the schema stored in the file may be
different. This may potentially cause data correctness problems.
I think it is necessary to consider the mapping between the requiredSchema and
the schema stored in the file. I will try to modify the code, and if it goes
well, I can submit a PR.
was (Author: chen zhang):
The DECIMAL type in Parquet is stored by INT32, INT64, FIXED_LEN_BYTE_ARRAY, or
BINARY.
Taking 19500.00 stored as INT64 as an example, the parquet file is stored as
DECIMAL(15,2) -> 1950000, DECIMAL(15,6) -> 19500000000.
I see that the spark source code
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter use
catalystType to convert the decimal type. Maybe we should use the schema in the
parquet file to convert.
something like:
{code:java}
//case t: DecimalType if parquetType.asPrimitiveType().getPrimitiveTypeName ==
INT64 =>
// new ParquetIntDictionaryAwareDecimalConverter(t.precision, t.scale, updater)
case t: DecimalType if parquetType.asPrimitiveType().getPrimitiveTypeName ==
INT64 =>
val mate = parquetType.asPrimitiveType().getDecimalMetadata()
new ParquetLongDictionaryAwareDecimalConverter(mate.getPrecision,
mate.getScale, updater)
{code}
I will do some validation later.
> Parquet file loading with different schema(Decimal(N, P)) in files is not
> working as expected
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-32317
> URL: https://issues.apache.org/jira/browse/SPARK-32317
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.0
> Environment: Its failing in all environments that I tried.
> Reporter: Krish
> Priority: Major
> Labels: easyfix
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> Hi,
>
> We generate parquet files which are partitioned on Date on a daily basis, and
> we send updates to historical data some times, what we noticed is due to some
> configuration error the patch data schema is inconsistent to earlier files.
> Assuming we had files generated with schema having ID and Amount as fields.
> Historical data is having schema like ID INT, AMOUNT DECIMAL(15,6) and the
> files we send as updates has schema like DECIMAL(15,2).
>
> Having two different schema in a Date partition and when we load the data of
> a Date into spark, it is loading the data but the amount is getting
> manipulated.
>
> file1.snappy.parquet
> ID: INT
> AMOUNT: DECIMAL(15,6)
> Content:
> 1,19500.00
> 2,198.34
> file2.snappy.parquet
> ID: INT
> AMOUNT: DECIMAL(15,2)
> Content:
> 1,19500.00
> 3,198.34
> Load these two files togeather
> df3 = spark.read.parquet("output/")
> df3.show() #-we can see amount getting manipulated here,
> +-----+---------------+
> |ID| AMOUNT|
> +-----+---------------+
> |1| 1.950000|
> |3| 0.019834|
> |1|19500.000000|
> |2| 198.340000|
> +-----+---------------+
> x
> Options Tried:
> We tried to give schema as String for all fields, but that didt work
> df3 = spark.read.format("parquet").schema(schema).load("output/")
> Error: "org.apache.spark.sql.execution.QueryExecutionException: Parquet
> column cannot be converted in file file*****.snappy.parquet. Column:
> [AMOUNT], Expected: string, Found: INT64"
>
> I know merge schema works if it finds few extra columns in one file but the
> fileds which are in common needs to have same schema. That might nort work
> here.
>
> Looking for some work around solution here. Or if there is an option which I
> havent tried you can point me to that.
>
> With schema merging I got below eeror:
> An error occurred while calling o2272.parquet. :
> org.apache.spark.SparkException: Failed merging schema: root |-- ID: string
> (nullable = true) |-- AMOUNT: decimal(15,6) (nullable = true) at
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.$anonfun$mergeSchemasInParallel$5(SchemaMergeUtils.scala:100)
> at
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.$anonfun$mergeSchemasInParallel$5$adapted(SchemaMergeUtils.scala:95)
> at
> scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36) at
> scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198) at
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.mergeSchemasInParallel(SchemaMergeUtils.scala:95)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.mergeSchemasInParallel(ParquetFileFormat.scala:485)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetUtils$.inferSchema(ParquetUtils.scala:107)
> at
> org.apache.spark.sql.execution.datasources.v2.parquet.ParquetTable.inferSchema(ParquetTable.scala:44)
> at
> org.apache.spark.sql.execution.datasources.v2.FileTable.$anonfun$dataSchema$4(FileTable.scala:69)
> at scala.Option.orElse(Option.scala:447) at
> org.apache.spark.sql.execution.datasources.v2.FileTable.dataSchema$lzycompute(FileTable.scala:69)
> at
> org.apache.spark.sql.execution.datasources.v2.FileTable.dataSchema(FileTable.scala:63)
> at
> org.apache.spark.sql.execution.datasources.v2.FileTable.schema$lzycompute(FileTable.scala:82)
> at
> org.apache.spark.sql.execution.datasources.v2.FileTable.schema(FileTable.scala:80)
> at
> org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation$.create(DataSourceV2Relation.scala:141)
> at
> org.apache.spark.sql.DataFrameReader.$anonfun$load$1(DataFrameReader.scala:225)
> at scala.Option.map(Option.scala:230) at
> org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:206) at
> org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:674) at
> sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498) at
> py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
> py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at
> py4j.Gateway.invoke(Gateway.java:282) at
> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at
> py4j.commands.CallCommand.execute(CallCommand.java:79) at
> py4j.GatewayConnection.run(GatewayConnection.java:238) at
> java.lang.Thread.run(Thread.java:748) Caused by:
> org.apache.spark.SparkException: Failed to merge fields 'AMOUNT' and
> 'AMOUNT'. Failed to merge decimal types with incompatible scala 2 and 6 at
> org.apache.spark.sql.types.StructType$.$anonfun$merge$2(StructType.scala:559)
> at scala.Option.map(Option.scala:230) at
> org.apache.spark.sql.types.StructType$.$anonfun$merge$1(StructType.scala:551)
> at
> org.apache.spark.sql.types.StructType$.$anonfun$merge$1$adapted(StructType.scala:548)
> at
> scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36) at
> scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198) at
> org.apache.spark.sql.types.StructType$.merge(StructType.scala:548) at
> org.apache.spark.sql.types.StructType.merge(StructType.scala:458) at
> org.apache.spark.sql.execution.datasources.SchemaMergeUtils$.$anonfun$mergeSchemasInParallel$5(SchemaMergeUtils.scala:97)
> ... 30 more
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