[jira] [Updated] (SPARK-36803) ClassCastException: optional int32 col-0 is not a group when reading legacy Parquet files
[
https://issues.apache.org/jira/browse/SPARK-36803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan updated SPARK-36803:
-
Description:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
The issue is only reproducible with schema evolution with parquet-mr reader and
when there are two schemas like this:
File 1:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
{code}
File 2:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
|||-- col-1: integer (nullable = true){code}
When ParquetRowConverter tries to unwrap ArrayType, it checks if the underlying
types between Parquet and Spark match. However, in the case above since the
actual schema would include both fields, resulting in mismatch and failure to
read File 1:
{noformat}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in
stage 11.0 failed 1 times, most recent failure: Lost task 1.0 in stage 11.0
(TID 18) (ip-1-2-3-4.us-west-2.compute.internal executor driver):
java.lang.ClassCastException: optional int32 col-0 is not a group
at org.apache.parquet.schema.Type.asGroupType(Type.java:248)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:424)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter$ElementConverter.(ParquetRowConverter.scala:633)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter.(ParquetRowConverter.scala:616)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:390)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.$anonfun$fieldConverters$1(ParquetRowConverter.scala:214)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.(ParquetRowConverter.scala:210){noformat}
This happens due to L606 in ParquetRowConverter:
{code:java}
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
{code}
The code assumes that we are working with 3 level lists and would incorrectly
remove the “dummy” level from the Parquet schema.
The actual error varies depending on column names - in this case struct type
name matches primitive type name so we end up with "optional int32 col-0 is not
a group". In other case, it could fail with IndexOutOfBoundException or
NoSuchElementException when the column name is not found in the struct.
The reason it works with 3-level list, that
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
always evaluates to false, we remove the “dummy” level and perform struct match
which takes into account schema evolution.
Repro:
{code:java}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val schema1 = StructType(
StructField("col-0", ArrayType(
StructType(
StructField("col-0", IntegerType, true) :: Nil
),
containsNull = false
)) :: Nil
)
val rdd1 = sc.parallelize(Row(Array(Row(1))) :: Nil, 1)
val df1 = spark.createDataFrame(rdd1, schema1)
df1.write.parquet("/tmp/legacy-parquet")
val schema2 = StructType(
StructField("col-0", ArrayType(
StructType(
StructField("col-0", IntegerType, true) :: StructField("col-1",
IntegerType, true) :: Nil
),
containsNull = false
)) :: Nil
)
val rdd2 = sc.parallelize(Row(Array(Row(1, 2))) :: Nil, 1)
val df2 = spark.createDataFrame(rdd2, schema2)
df2.write.mode("append").parquet("/tmp/legacy-parquet")
// Fails with: Caused by: ClassCastException: optional int32 col-0 is not a
group
display(spark.read.schema(schema2).parquet("/tmp/legacy-parquet"))
{code}
was:
[jira] [Updated] (SPARK-36803) ClassCastException: optional int32 col-0 is not a group when reading legacy Parquet files
[
https://issues.apache.org/jira/browse/SPARK-36803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan updated SPARK-36803:
-
Description:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
The issue is only reproducible with schema evolution when there are two schemas
like this:
File 1:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
{code}
File 2:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
|||-- col-1: integer (nullable = true){code}
When ParquetRowConverter tries to unwrap ArrayType, it checks if the underlying
types between Parquet and Spark match. However, in the case above since the
actual schema would include both fields, resulting in mismatch and failure to
read File 1:
{noformat}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in
stage 11.0 failed 1 times, most recent failure: Lost task 1.0 in stage 11.0
(TID 18) (ip-1-2-3-4.us-west-2.compute.internal executor driver):
java.lang.ClassCastException: optional int32 col-0 is not a group
at org.apache.parquet.schema.Type.asGroupType(Type.java:248)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:424)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter$ElementConverter.(ParquetRowConverter.scala:633)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter.(ParquetRowConverter.scala:616)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:390)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.$anonfun$fieldConverters$1(ParquetRowConverter.scala:214)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.(ParquetRowConverter.scala:210){noformat}
This happens due to L606 in ParquetRowConverter:
{code:java}
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
{code}
The code assumes that we are working with 3 level lists and would incorrectly
remove the “dummy” level from the Parquet schema.
The actual error varies depending on column names - in this case struct type
name matches primitive type name so we end up with "optional int32 col-0 is not
a group". In other case, it could fail with IndexOutOfBoundException or
NoSuchElementException when the column name is not found in the struct.
The reason it works with 3-level list, that
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
always evaluates to false, we remove the “dummy” level and perform struct match
which takes into account schema evolution.
Repro:
{code:java}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val schema1 = StructType(
StructField("col-0", ArrayType(
StructType(
StructField("col-0", IntegerType, true) :: Nil
),
containsNull = false
)) :: Nil
)
val rdd1 = sc.parallelize(Row(Array(Row(1))) :: Nil, 1)
val df1 = spark.createDataFrame(rdd1, schema1)
df1.write.parquet("/tmp/legacy-parquet")
val schema2 = StructType(
StructField("col-0", ArrayType(
StructType(
StructField("col-0", IntegerType, true) :: StructField("col-1",
IntegerType, true) :: Nil
),
containsNull = false
)) :: Nil
)
val rdd2 = sc.parallelize(Row(Array(Row(1, 2))) :: Nil, 1)
val df2 = spark.createDataFrame(rdd2, schema2)
df2.write.mode("append").parquet("/tmp/legacy-parquet")
// Fails with: Caused by: ClassCastException: optional int32 col-0 is not a
group
display(spark.read.schema(schema2).parquet("/tmp/legacy-parquet"))
{code}
was:
When reading Parquet files
[jira] [Updated] (SPARK-36803) ClassCastException: optional int32 col-0 is not a group when reading legacy Parquet files
[
https://issues.apache.org/jira/browse/SPARK-36803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan updated SPARK-36803:
-
Description:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
The issue is only reproducible with schema evolution when there are two schemas
like this:
File 1:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
{code}
File 2:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
|||-- col-1: integer (nullable = true){code}
When ParquetRowConverter tries to unwrap ArrayType, it checks if the underlying
types between Parquet and Spark match. However, in the case above since the
actual schema would include both fields, resulting in mismatch and failure to
read File 1:
{noformat}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in
stage 11.0 failed 1 times, most recent failure: Lost task 1.0 in stage 11.0
(TID 18) (ip-1-2-3-4.us-west-2.compute.internal executor driver):
java.lang.ClassCastException: optional int32 col-0 is not a group
at org.apache.parquet.schema.Type.asGroupType(Type.java:248)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:424)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter$ElementConverter.(ParquetRowConverter.scala:633)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter.(ParquetRowConverter.scala:616)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:390)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.$anonfun$fieldConverters$1(ParquetRowConverter.scala:214)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.(ParquetRowConverter.scala:210){noformat}
This happens due to L606 in ParquetRowConverter:
{code:java}
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
{code}
The code assumes that we are working with 3 level lists and would incorrectly
remove the “dummy” level from the Parquet schema.
The actual error varies depending on column names - in this case struct type
name matches primitive type name so we end up with "optional int32 col-0 is not
a group". In other case, it could fail with IndexOutOfBoundException or
NoSuchElementException when the column name is not found in the struct.
The reason it works with 3-level list, that
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
always evaluates to false, we remove the “dummy” level and perform struct match
which takes into account schema evolution.
was:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
The issue is only reproducible with schema evolution when there are two schemas
like this:
File 1:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
{code}
File 2:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
|||-- col-1: integer (nullable = true){code}
When
[jira] [Updated] (SPARK-36803) ClassCastException: optional int32 col-0 is not a group when reading legacy Parquet files
[
https://issues.apache.org/jira/browse/SPARK-36803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan updated SPARK-36803:
-
Description:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
The issue is only reproducible with schema evolution when there are two schemas
like this:
File 1:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
{code}
File 2:
{code:java}
root
|-- col-0: array (nullable = true)
||-- element: struct (containsNull = false)
|||-- col-0: integer (nullable = true)
|||-- col-1: integer (nullable = true){code}
When ParquetRowConverter tries to unwrap ArrayType, it checks if the underlying
types between Parquet and Spark match. However, in the case above since the
actual schema would include both fields, resulting in mismatch and failure to
read File 1:
{noformat}
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in
stage 11.0 failed 1 times, most recent failure: Lost task 1.0 in stage 11.0
(TID 18) (ip-1-2-3-4.us-west-2.compute.internal executor driver):
java.lang.ClassCastException: optional int32 col-0 is not a group
at org.apache.parquet.schema.Type.asGroupType(Type.java:248)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:424)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter$ElementConverter.(ParquetRowConverter.scala:633)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetArrayConverter.(ParquetRowConverter.scala:616)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.org$apache$spark$sql$execution$datasources$parquet$ParquetRowConverter$$newConverter(ParquetRowConverter.scala:390)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.$anonfun$fieldConverters$1(ParquetRowConverter.scala:214)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at
org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter.(ParquetRowConverter.scala:210){noformat}
Now the L606 in ParquetRowConverter:
{code:java}
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
{code}
would return false because the check matches the types exactly and in this case
they don’t match. The code assumes that we are working with 3 level lists and
would incorrectly remove the “dummy” level from the Parquet schema.
The actual error varies depending on column names - in this case struct type
name matches primitive type name so we end up with "optional int32 col-0 is not
a group". In other case, it could fail with IndexOutOfBoundException or
NoSuchElementException when the column name is not found in the struct.
The reason it works with 3-level list, that
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
always evaluates to false, we remove the “dummy” level and perform struct match
which takes into account schema evolution.
was:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606.|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
When we write arrays in legacy mode, we use 2-level wrapping like this:
{{optional group col-0 (LIST) \{
repeated group array {
optional group col-0 {
optional float col-0;
}
}
}}}
It works just fine if the corresponding Spark
[jira] [Updated] (SPARK-36803) ClassCastException: optional int32 col-0 is not a group when reading legacy Parquet files
[
https://issues.apache.org/jira/browse/SPARK-36803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ivan updated SPARK-36803:
-
Description:
When reading Parquet files that have been written in legacy mode and schema
evolution, we observed that 2-level LIST annotated types are traversed
incorrectly.
The root cause is the imprecise check on the underlying element type for Array
types (and potentially Map types but I have not checked those yet) that happens
here:
[https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606.|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
When we write arrays in legacy mode, we use 2-level wrapping like this:
{{optional group col-0 (LIST) \{
repeated group array {
optional group col-0 {
optional float col-0;
}
}
}}}
It works just fine if the corresponding Spark schema for all of the Parquet
files is like this:
{{ArrayType(StructType(
StructField(col-0, StructType(
StructField(col-0, FloatType, true)
))
))}}
When ParquetRowConverter tries to unwrap ArrayType, it checks if the underlying
types between Parquet and Spark match. In this case, they do, so it is all good.
The problem arises when (due to schema evolution), parquet schema does not
match the Spark one, for example:
{{ArrayType(StructType(
StructField(col-1, LongType, true), <-- added field
StructField(col-0, StructType(
StructField(col-0, FloatType, true)
))
))}}
Now the L606 in ParquetRowConverter:
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
would return false because the check matches the types exactly and in this case
they don’t match. The code assumes that we are working with 3 level lists and
would incorrectly remove the “dummy” level from the Parquet schema, leaving us
with the following:
{{optional float col-0
StructType(StructField(col-0, FloatType, true))}}
The actual error varies depending on column names - in this case struct type
name matches primitive type name so we end up with optional float col-0 is not
a group. In other case, it could fail with IndexOutOfBoundException or
NoSuchElementException when the column name is not found in the struct.
The reason it works with 3-level list, that
DataType.equalsIgnoreCompatibleNullability(guessedElementType, elementType)
always evaluates to false, we remove the “dummy” level and perform struct match
which takes into account schema evolution here:
[https://github.com/databricks/runtime/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L210].
So the check should DataType.equalsIgnoreCompatibleNullability should probably
be something like DataType.partiallyContainsSchema, where we can check that the
guessedElementType is a subset of the elementType.
IMHO, this creates an impression that the code works rather incidentally for
legacy mode due to the check.
Logs for converting the offending type for different cases:
> ClassCastException: optional int32 col-0 is not a group when reading legacy
> Parquet files
> --
>
> Key: SPARK-36803
> URL: https://issues.apache.org/jira/browse/SPARK-36803
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.1.2
>Reporter: Ivan
>Priority: Major
>
> When reading Parquet files that have been written in legacy mode and schema
> evolution, we observed that 2-level LIST annotated types are traversed
> incorrectly.
> The root cause is the imprecise check on the underlying element type for
> Array types (and potentially Map types but I have not checked those yet) that
> happens here:
> [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606.|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala#L606]
> When we write arrays in legacy mode, we use 2-level wrapping like this:
>
> {{optional group col-0 (LIST) \{
> repeated group array {
> optional group col-0 {
> optional float col-0;
> }
> }
> }}}
> It works just fine if the corresponding Spark schema for all of the Parquet
> files is like this:
>
> {{ArrayType(StructType(
> StructField(col-0, StructType(
> StructField(col-0, FloatType, true)
> ))
> ))}}
> When ParquetRowConverter tries to unwrap ArrayType, it checks if the
> underlying types between Parquet and Spark match. In this case, they do, so
> it is all good.
> The problem
