GpuInsertIntoHiveTable supports parquet

integration_tests/src/main/python/hive_parquet_write_test.py

@@ -0,0 +1,172 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+from asserts import assert_gpu_and_cpu_sql_writes_are_equal_collect
+from data_gen import *
+from hive_write_test import _restricted_timestamp
+from marks import allow_non_gpu, ignore_order
+from spark_session import with_cpu_session, is_before_spark_320
+
+# Disable the meta conversion from Hive write to FrameData write in Spark, to test
+# "GpuInsertIntoHiveTable" for Parquet write.
+_write_to_hive_conf = {"spark.sql.hive.convertMetastoreParquet": False}
+
+_hive_basic_gens = [
+    byte_gen, short_gen, int_gen, long_gen, float_gen, double_gen, string_gen, boolean_gen,
+    DateGen(start=date(1590, 1, 1)), _restricted_timestamp(),
+    DecimalGen(precision=19, scale=1, nullable=True),
+    DecimalGen(precision=23, scale=5, nullable=True),
+    DecimalGen(precision=36, scale=3, nullable=True)]
+
+_hive_basic_struct_gen = StructGen(
+    [['c'+str(ind), c_gen] for ind, c_gen in enumerate(_hive_basic_gens)])
+
+_hive_struct_gens = [
+    _hive_basic_struct_gen,
+    StructGen([['child0', byte_gen], ['child1', _hive_basic_struct_gen]]),
+    StructGen([['child0', ArrayGen(short_gen)], ['child1', double_gen]])]
+
+_hive_array_gens = [ArrayGen(sub_gen) for sub_gen in _hive_basic_gens] + [
+    ArrayGen(ArrayGen(short_gen, max_length=10), max_length=10),
+    ArrayGen(ArrayGen(string_gen, max_length=10), max_length=10),
+    ArrayGen(StructGen([['child0', byte_gen], ['child1', string_gen], ['child2', float_gen]]))]
+
+_hive_map_gens = [simple_string_to_string_map_gen] + [MapGen(f(nullable=False), f()) for f in [
+    BooleanGen, ByteGen, ShortGen, IntegerGen, LongGen, FloatGen, DoubleGen,
+    lambda nullable=True: _restricted_timestamp(nullable=nullable),
+    lambda nullable=True: DateGen(start=date(1590, 1, 1), nullable=nullable),
+    lambda nullable=True: DecimalGen(precision=19, scale=1, nullable=nullable),
+    lambda nullable=True: DecimalGen(precision=36, scale=5, nullable=nullable)]]
+
+_hive_write_gens = [_hive_basic_gens, _hive_struct_gens, _hive_array_gens, _hive_map_gens]
+
+
+@allow_non_gpu(*non_utc_allow)
+@ignore_order(local=True)
+@pytest.mark.parametrize("is_ctas", [True, False], ids=['CTAS', 'CTTW'])
+@pytest.mark.parametrize("gens", _hive_write_gens, ids=idfn)
+def test_write_parquet_into_hive_table(spark_tmp_table_factory, is_ctas, gens):
+
+    def gen_table(spark):
+        gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)]
+        types_sql_str = ','.join('{} {}'.format(
+            name, gen.data_type.simpleString()) for name, gen in gen_list)
+        data_table = spark_tmp_table_factory.get()
+        gen_df(spark, gen_list).createOrReplaceTempView(data_table)
+        return data_table, types_sql_str
+
+    (input_table, input_schema) = with_cpu_session(gen_table)
+
+    def write_to_hive_sql(spark, output_table):
+        if is_ctas:
+            # Create Table As Select
+            return [
+                "CREATE TABLE {} STORED AS PARQUET AS SELECT * FROM {}".format(
+                    output_table, input_table)
+            ]
+        else:
+            # Create Table Then Write
+            return [
+                "CREATE TABLE {} ({}) STORED AS PARQUET".format(output_table, input_schema),
+                "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table)
+            ]
+
+    assert_gpu_and_cpu_sql_writes_are_equal_collect(
+        spark_tmp_table_factory,
+        write_to_hive_sql,
+        _write_to_hive_conf)
+
+
+@allow_non_gpu(*non_utc_allow)
+@ignore_order(local=True)
+@pytest.mark.parametrize("is_static", [True, False], ids=['Static_Partition', 'Dynamic_Partition'])
+def test_write_parquet_into_partitioned_hive_table(spark_tmp_table_factory, is_static):
+    # Generate hive table in Parquet format
+    def gen_table(spark):
+        # gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)]
+        dates = [date(2024, 2, 28), date(2024, 2, 27), date(2024, 2, 26)]
+        gen_list = [('a', int_gen),
+                    ('b', long_gen),
+                    ('c', short_gen),
+                    ('d', string_gen),
+                    ('part', SetValuesGen(DateType(), dates))]
+        data_table = spark_tmp_table_factory.get()
+        gen_df(spark, gen_list).createOrReplaceTempView(data_table)
+        return data_table
+
+    input_table = with_cpu_session(gen_table)
+
+    def partitioned_write_to_hive_sql(spark, output_table):
+        sql_create_part_table = (
+            "CREATE TABLE {} (a INT, b LONG, c SHORT, d STRING) "
+            "PARTITIONED BY (part DATE) STORED AS PARQUET"
+        ).format(output_table)
+        if is_static:
+            return [
+                # sql_1: Create partitioned hive table
+                sql_create_part_table,
+                # sql_2: Static partition write only to partition 'par2'
+                "INSERT OVERWRITE TABLE {} PARTITION (part='2024-02-25') "
+                "SELECT a, b, c, d FROM {}".format(output_table, input_table)
+            ]
+        else:
+            return [
+                # sql_1: Create partitioned hive table
+                sql_create_part_table,
+                # sql_2: Dynamic partition write
+                "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table)
+            ]
+    all_confs = copy_and_update(_write_to_hive_conf, {
+        "hive.exec.dynamic.partition.mode": "nonstrict"})
+    assert_gpu_and_cpu_sql_writes_are_equal_collect(
+        spark_tmp_table_factory,
+        partitioned_write_to_hive_sql,
+        all_confs)
+
+
+zstd_param = pytest.param('ZSTD',
+    marks=pytest.mark.skipif(is_before_spark_320(), reason="zstd is not supported before 320"))
+
+@allow_non_gpu(*non_utc_allow)
+@ignore_order(local=True)
+@pytest.mark.parametrize("comp_type", ['UNCOMPRESSED', 'SNAPPY', zstd_param])
+def test_write_compressed_parquet_into_hive_table(spark_tmp_table_factory, comp_type):
+    # Generate hive table in Parquet format
+    def gen_table(spark):
+        gens = _hive_basic_gens + _hive_struct_gens + _hive_array_gens + _hive_map_gens
+        gen_list = [('_c' + str(i), gen) for i, gen in enumerate(gens)]
+        types_sql_str = ','.join('{} {}'.format(
+            name, gen.data_type.simpleString()) for name, gen in gen_list)
+        data_table = spark_tmp_table_factory.get()
+        gen_df(spark, gen_list).createOrReplaceTempView(data_table)
+        return data_table, types_sql_str
+
+    input_table, schema_str = with_cpu_session(gen_table)
+
+    def write_to_hive_sql(spark, output_table):
+        return [
+            # Create table with compression type
+            "CREATE TABLE {} ({}) STORED AS PARQUET "
+            "TBLPROPERTIES ('parquet.compression'='{}')".format(
+                output_table, schema_str, comp_type),
+            # Insert into table
+            "INSERT OVERWRITE TABLE {} SELECT * FROM {}".format(output_table, input_table)
+        ]
+
+    assert_gpu_and_cpu_sql_writes_are_equal_collect(
+        spark_tmp_table_factory,
+        write_to_hive_sql,
+        _write_to_hive_conf)

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuParquetFileFormat.scala

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -321,7 +321,7 @@ class GpuParquetWriter(
         new GpuColumnVector(cv.dataType, deepTransformColumn(cv.getBase, cv.dataType))
             .asInstanceOf[org.apache.spark.sql.vectorized.ColumnVector]
       }
-      new ColumnarBatch(transformedCols)
+      new ColumnarBatch(transformedCols, batch.numRows())
     }
   }
 

sql-plugin/src/main/scala/org/apache/spark/sql/hive/rapids/GpuHiveTextFileFormat.scala → sql-plugin/src/main/scala/org/apache/spark/sql/hive/rapids/GpuHiveFileFormat.scala

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,8 +17,9 @@
 package org.apache.spark.sql.hive.rapids
 
 import java.nio.charset.Charset
+import java.util.Locale
 
-import ai.rapids.cudf.{CSVWriterOptions, DType, QuoteStyle, Scalar, Table, TableWriter => CudfTableWriter}
+import ai.rapids.cudf.{CompressionType, CSVWriterOptions, DType, ParquetWriterOptions, QuoteStyle, Scalar, Table, TableWriter => CudfTableWriter}
 import com.google.common.base.Charsets
 import com.nvidia.spark.rapids._
 import com.nvidia.spark.rapids.Arm.withResource
@@ -27,14 +28,88 @@ import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.sql.SparkSession
-import org.apache.spark.sql.hive.rapids.GpuHiveTextFileUtils._
+import org.apache.spark.sql.execution.datasources.parquet.ParquetOptions
 import org.apache.spark.sql.hive.rapids.shims.GpuInsertIntoHiveTableMeta
-import org.apache.spark.sql.types.{DataType, StringType, StructType}
+import org.apache.spark.sql.rapids.execution.TrampolineUtil
+import org.apache.spark.sql.types.{DataType, Decimal, DecimalType, StringType, StructType}
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
-object GpuHiveTextFileFormat extends Logging {
+object GpuHiveFileFormat extends Logging {
+  private val parquetOutputFormatClass =
+    "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"
+  private val parquetSerdeClass =
+    "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"
 
-  private def checkIfEnabled(meta: GpuInsertIntoHiveTableMeta): Unit = {
+  def tagGpuSupport(meta: GpuInsertIntoHiveTableMeta): Option[ColumnarFileFormat] = {
+    val insertCmd = meta.wrapped
+    // Bucketing write
+    if (insertCmd.table.bucketSpec.isDefined) {
+      meta.willNotWorkOnGpu("bucketed tables are not supported yet")
+    }
+
+    // Infer the file format from the serde string, similar as what Spark does in
+    // RelationConversions for Hive.
+    val serde = insertCmd.table.storage.serde.getOrElse("").toLowerCase(Locale.ROOT)
+    val tempFileFormat = if (serde.contains("parquet")) {
+      // Parquet specific tagging
+      tagGpuSupportForParquet(meta)
+    } else {
+      // Default to text file format
+      tagGpuSupportForText(meta)
+    }
+
+    if (meta.canThisBeReplaced) {
+      Some(tempFileFormat)
+    } else {
+      None
+    }
+  }
+
+  private def tagGpuSupportForParquet(meta: GpuInsertIntoHiveTableMeta): ColumnarFileFormat = {
+    val insertCmd = meta.wrapped
+    val storage = insertCmd.table.storage
+    // Configs check for Parquet write enabling/disabling
+
+    // FIXME Need to check serde and output format classes ?
+    if (storage.outputFormat.getOrElse("") != parquetOutputFormatClass) {
+      meta.willNotWorkOnGpu(s"unsupported output-format found: ${storage.outputFormat}, " +
+        s"only $parquetOutputFormatClass is currently supported for Parquet")
+    }
+    if (storage.serde.getOrElse("") != parquetSerdeClass) {
+      meta.willNotWorkOnGpu(s"unsupported serde found: ${storage.serde}, " +
+        s"only $parquetSerdeClass is currently supported for Parquet")
+    }
+
+    // Decimal type check
+    val hasIntOrLongBackedDec = insertCmd.query.schema.exists { field =>
+      TrampolineUtil.dataTypeExistsRecursively(field.dataType, {
+        case dec: DecimalType if dec.precision <= Decimal.MAX_LONG_DIGITS => true
+        case _ => false
+      })
+    }
+    if (hasIntOrLongBackedDec) {
+      meta.willNotWorkOnGpu("decimal can fit inside an int or a long is not supported " +
+        s"for Parquet. Hive always writes decimal as binary array but GPU writes it " +
+        s"as an int or a long")
+    }
+
+    // FIXME Need a new format type for Hive Parquet write ?
+    FileFormatChecks.tag(meta, insertCmd.table.schema, ParquetFormatType, WriteFileOp)
+
+    // Compression type
+    val parquetOptions = new ParquetOptions(insertCmd.table.properties, insertCmd.conf)
+    val compressionType =
+      GpuParquetFileFormat.parseCompressionType(parquetOptions.compressionCodecClassName)
+        .getOrElse {
+          meta.willNotWorkOnGpu("compression codec " +
+            s"${parquetOptions.compressionCodecClassName} is not supported for Parquet")
+          CompressionType.NONE
+        }
+    new GpuHiveParquetFileFormat(compressionType)
+  }
+
+  private def tagGpuSupportForText(meta: GpuInsertIntoHiveTableMeta): ColumnarFileFormat = {
+    import org.apache.spark.sql.hive.rapids.GpuHiveTextFileUtils._
     if (!meta.conf.isHiveDelimitedTextEnabled) {
       meta.willNotWorkOnGpu("Hive text I/O has been disabled. To enable this, " +
         s"set ${RapidsConf.ENABLE_HIVE_TEXT} to true")
@@ -43,21 +118,16 @@ object GpuHiveTextFileFormat extends Logging {
       meta.willNotWorkOnGpu("writing Hive delimited text tables has been disabled, " +
         s"to enable this, set ${RapidsConf.ENABLE_HIVE_TEXT_WRITE} to true")
     }
-  }
-
-  def tagGpuSupport(meta: GpuInsertIntoHiveTableMeta)
-  : Option[ColumnarFileFormat] = {
-    checkIfEnabled(meta)
 
     val insertCommand = meta.wrapped
     val storage  = insertCommand.table.storage
     if (storage.outputFormat.getOrElse("") != textOutputFormat) {
       meta.willNotWorkOnGpu(s"unsupported output-format found: ${storage.outputFormat}, " +
-        s"only $textOutputFormat is currently supported")
+        s"only $textOutputFormat is currently supported for text")
     }
     if (storage.serde.getOrElse("") != lazySimpleSerDe) {
       meta.willNotWorkOnGpu(s"unsupported serde found: ${storage.serde}, " +
-        s"only $lazySimpleSerDe is currently supported")
+        s"only $lazySimpleSerDe is currently supported for text")
     }
 
     val serializationFormat = storage.properties.getOrElse(serializationKey, "1")
@@ -86,28 +156,60 @@ object GpuHiveTextFileFormat extends Logging {
       meta.willNotWorkOnGpu("only UTF-8 is supported as the charset")
     }
 
-    if (insertCommand.table.bucketSpec.isDefined) {
-      meta.willNotWorkOnGpu("bucketed tables are not supported")
-    }
-
-    if (insertCommand.conf.getConfString("hive.exec.compress.output", "false").toLowerCase
-          != "false") {
+    if (insertCommand.conf.getConfString("hive.exec.compress.output", "false").toBoolean) {
       meta.willNotWorkOnGpu("compressed output is not supported, " +
         "set hive.exec.compress.output to false to enable writing Hive text via GPU")
     }
 
-    FileFormatChecks.tag(meta,
-                         insertCommand.table.schema,
-                         HiveDelimitedTextFormatType,
-                         WriteFileOp)
+    FileFormatChecks.tag(meta, insertCommand.table.schema, HiveDelimitedTextFormatType,
+      WriteFileOp)
+
+    new GpuHiveTextFileFormat()
+  }
+}
+
+class GpuHiveParquetFileFormat(compType: CompressionType) extends ColumnarFileFormat {
+
+  override def prepareWrite(sparkSession: SparkSession, job: Job,
+      options: Map[String, String], dataSchema: StructType): ColumnarOutputWriterFactory = {
+
+    // Avoid referencing the outer object.
+    val compressionType = compType
+    new ColumnarOutputWriterFactory {
+      override def getFileExtension(context: TaskAttemptContext): String =
+        compressionType match {
+          case CompressionType.NONE => ".parquet"
+          case ct => s".${ct.name().toLowerCase(Locale.ROOT)}.parquet"
+        }
+
+      override def newInstance(path: String,
+          dataSchema: StructType,
+          context: TaskAttemptContext): ColumnarOutputWriter = {
+        new GpuHiveParquetWriter(path, dataSchema, context, compressionType)
+      }
+    }
+  }
+}
+
+class GpuHiveParquetWriter(override val path: String, dataSchema: StructType,
+    context: TaskAttemptContext, compType: CompressionType)
+  extends ColumnarOutputWriter(context, dataSchema, "HiveParquet", true) {
 
-    Some(new GpuHiveTextFileFormat())
+  override protected val tableWriter: CudfTableWriter = {
+    val optionsBuilder = SchemaUtils
+      .writerOptionsFromSchema(ParquetWriterOptions.builder(), dataSchema,
+        writeInt96 = true,      // Hive 1.2 write timestamp as INT96
+        parquetFieldIdEnabled = false)
+      .withCompressionType(compType)
+    Table.writeParquetChunked(optionsBuilder.build(), this)
   }
+
 }
 
 class GpuHiveTextFileFormat extends ColumnarFileFormat with Logging {
 
-  override def supportDataType(dataType: DataType): Boolean = isSupportedType(dataType)
+  override def supportDataType(dataType: DataType): Boolean =
+    GpuHiveTextFileUtils.isSupportedType(dataType)
 
   override def prepareWrite(sparkSession: SparkSession,
                             job: Job,