Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/22880#discussion_r229449812
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetReadSupport.scala
---
@@ -49,34 +49,82 @@ import org.apache.spark.sql.types._
* Due to this reason, we no longer rely on [[ReadContext]] to pass
requested schema from [[init()]]
* to [[prepareForRead()]], but use a private `var` for simplicity.
*/
-private[parquet] class ParquetReadSupport(val convertTz: Option[TimeZone])
+private[parquet] class ParquetReadSupport(val convertTz: Option[TimeZone],
+ usingVectorizedReader: Boolean)
extends ReadSupport[UnsafeRow] with Logging {
private var catalystRequestedSchema: StructType = _
def this() {
// We need a zero-arg constructor for SpecificParquetRecordReaderBase.
But that is only
// used in the vectorized reader, where we get the convertTz value
directly, and the value here
// is ignored.
- this(None)
+ this(None, usingVectorizedReader = true)
}
/**
* Called on executor side before [[prepareForRead()]] and instantiating
actual Parquet record
* readers. Responsible for figuring out Parquet requested schema used
for column pruning.
*/
override def init(context: InitContext): ReadContext = {
+ val conf = context.getConfiguration
catalystRequestedSchema = {
- val conf = context.getConfiguration
val schemaString =
conf.get(ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
assert(schemaString != null, "Parquet requested schema not set.")
StructType.fromString(schemaString)
}
- val caseSensitive =
context.getConfiguration.getBoolean(SQLConf.CASE_SENSITIVE.key,
+ val schemaPruningEnabled =
conf.getBoolean(SQLConf.NESTED_SCHEMA_PRUNING_ENABLED.key,
+ SQLConf.NESTED_SCHEMA_PRUNING_ENABLED.defaultValue.get)
+ val caseSensitive = conf.getBoolean(SQLConf.CASE_SENSITIVE.key,
SQLConf.CASE_SENSITIVE.defaultValue.get)
- val parquetRequestedSchema = ParquetReadSupport.clipParquetSchema(
- context.getFileSchema, catalystRequestedSchema, caseSensitive)
-
+ val parquetFileSchema = context.getFileSchema
+ val parquetClippedSchema =
ParquetReadSupport.clipParquetSchema(parquetFileSchema,
+ catalystRequestedSchema, caseSensitive)
+
+ // As part of schema clipping, we add fields in
catalystRequestedSchema which are missing
+ // from parquetFileSchema to parquetClippedSchema. However, nested
schema pruning requires
+ // we ignore unrequested field data when reading from a Parquet file.
Therefore we pass two
+ // schema to ParquetRecordMaterializer: the schema of the file data we
want to read
+ // (parquetRequestedSchema), and the schema of the rows we want to
return
+ // (catalystRequestedSchema). The reader is responsible for
reconciling the differences between
+ // the two.
+ //
+ // Aside from checking whether schema pruning is enabled
(schemaPruningEnabled), there
+ // is an additional complication to constructing
parquetRequestedSchema. The manner in which
+ // Spark's two Parquet readers reconcile the differences between
parquetRequestedSchema and
+ // catalystRequestedSchema differ. Spark's vectorized reader does not
(currently) support
+ // reading Parquet files with complex types in their schema. Further,
it assumes that
+ // parquetRequestedSchema includes all fields requested in
catalystRequestedSchema. It includes
+ // logic in its read path to skip fields in parquetRequestedSchema
which are not present in the
+ // file.
+ //
+ // Spark's parquet-mr based reader supports reading Parquet files of
any kind of complex
+ // schema, and it supports nested schema pruning as well. Unlike the
vectorized reader, the
+ // parquet-mr reader requires that parquetRequestedSchema include only
those fields present in
+ // the underlying parquetFileSchema. Therefore, in the case where we
use the parquet-mr reader
+ // we intersect the parquetClippedSchema with the parquetFileSchema to
construct the
+ // parquetRequestedSchema set in the ReadContext.
--- End diff --
> For vectorized reader, even we do this additional
`intersectParquetGroups`, will it cause any problem?
Yes. The relevant passage being
```
Further, [the vectorized reader] assumes that parquetRequestedSchema
includes all fields requested in
catalystRequestedSchema. It includes logic in its read path to skip fields
in parquetRequestedSchema
which are not present in the file.
```
If we break this assumption by giving the vectorized reader a Parquet
requested schema which does not include all of the fields in the Catalyst
requested schema, then it will fail with an exception. This scenario is covered
by the tests. (Comment out the relevant code below and run the tests to see.)
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