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[SPARK-16632][SQL] Use Spark requested schema to guide vectorized Parquet reader initialization #14278

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[SPARK-16632][SQL] Use Spark requested schema to guide vectorized Parquet reader initialization #14278

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f1e487b
Fixes SPARK-16632
liancheng Jul 20, 2016
d532e5e
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liancheng Jul 20, 2016
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5 changes: 4 additions & 1 deletion ...a/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
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Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,7 @@
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.Types;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.sql.types.StructType$;

/**
* Base class for custom RecordReaders for Parquet that directly materialize to `T`.
Expand Down Expand Up @@ -136,7 +137,9 @@ public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptCont
ReadSupport.ReadContext readContext = readSupport.init(new InitContext(
taskAttemptContext.getConfiguration(), toSetMultiMap(fileMetadata), fileSchema));
this.requestedSchema = readContext.getRequestedSchema();
this.sparkSchema = new ParquetSchemaConverter(configuration).convert(requestedSchema);
String sparkRequestedSchemaString =
configuration.get(ParquetReadSupport$.MODULE$.SPARK_ROW_REQUESTED_SCHEMA());
this.sparkSchema = StructType$.MODULE$.fromString(sparkRequestedSchemaString);
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@yhuai yhuai Jul 20, 2016

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We still have the requestedSchema in parquet's form, which does not contain the correct annotation. It may still be a potential issue when correct annotations in requestedSchema matters?

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@liancheng liancheng Jul 21, 2016

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Actually it's safer when the Parquet requested schema conforms to the actual physical file to be read. Normally, we shouldn't care about logical types (those with annotations) at the level of Parquet record reader. It's the upper level engine's responsibility to convert basic types like int32 into logical types like INT_8 and INT_16. The vectorized reader has to mix them up because we need to construct value vectors of proper types at this level.

this.reader = new ParquetFileReader(configuration, file, blocks, requestedSchema.getColumns());
for (BlockMetaData block : blocks) {
this.totalRowCount += block.getRowCount();
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24 changes: 24 additions & 0 deletions ...src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
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Expand Up @@ -680,6 +680,30 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
)
}
}

test("SPARK-16632: read Parquet int32 as ByteType and ShortType") {
withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> "true") {
withTempPath { dir =>
val path = dir.getCanonicalPath

// When being written to Parquet, `TINYINT` and `SMALLINT` should be converted into
// `int32 (INT_8)` and `int32 (INT_16)` respectively. However, Hive doesn't add the `INT_8`
// and `INT_16` annotation properly (HIVE-14294). Thus, when reading files written by Hive
// using Spark with the vectorized Parquet reader enabled, we may hit error due to type
// mismatch.
//
// Here we are simulating Hive's behavior by writing a single `INT` field and then read it
// back as `TINYINT` and `SMALLINT` in Spark to verify this issue.
Seq(1).toDF("f").write.parquet(path)

val withByteField = new StructType().add("f", ByteType)
checkAnswer(spark.read.schema(withByteField).parquet(path), Row(1: Byte))

val withShortField = new StructType().add("f", ShortType)
checkAnswer(spark.read.schema(withShortField).parquet(path), Row(1: Short))
}
}
}
}

object TestingUDT {
Expand Down