Support faster copy for a custom DataSource V2 which supplies Arrow d…

…ata (#1622)

* Add in data source v2, csv file and test for arrow copy
* remove commented out line

docs/configs.md

@@ -29,6 +29,7 @@ scala> spark.conf.set("spark.rapids.sql.incompatibleOps.enabled", true)
 
 Name | Description | Default Value
 -----|-------------|--------------
+<a name="alluxio.pathsToReplace"></a>spark.rapids.alluxio.pathsToReplace|List of paths to be replaced with corresponding alluxio scheme. Eg, when configureis set to "s3:/foo->alluxio://0.1.2.3:19998/foo,gcs:/bar->alluxio://0.1.2.3:19998/bar", which means:       s3:/foo/a.csv will be replaced to alluxio://0.1.2.3:19998/foo/a.csv and      gcs:/bar/b.csv will be replaced to alluxio://0.1.2.3:19998/bar/b.csv|None
 <a name="cloudSchemes"></a>spark.rapids.cloudSchemes|Comma separated list of additional URI schemes that are to be considered cloud based filesystems. Schemes already included: dbfs, s3, s3a, s3n, wasbs, gs. Cloud based stores generally would be total separate from the executors and likely have a higher I/O read cost. Many times the cloud filesystems also get better throughput when you have multiple readers in parallel. This is used with spark.rapids.sql.format.parquet.reader.type|None
 <a name="memory.gpu.allocFraction"></a>spark.rapids.memory.gpu.allocFraction|The fraction of total GPU memory that should be initially allocated for pooled memory. Extra memory will be allocated as needed, but it may result in more fragmentation. This must be less than or equal to the maximum limit configured via spark.rapids.memory.gpu.maxAllocFraction.|0.9
 <a name="memory.gpu.debug"></a>spark.rapids.memory.gpu.debug|Provides a log of GPU memory allocations and frees. If set to STDOUT or STDERR the logging will go there. Setting it to NONE disables logging. All other values are reserved for possible future expansion and in the mean time will disable logging.|NONE

integration_tests/src/main/python/datasourcev2_read.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2021, 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_are_equal_collect
+from marks import *
+from pyspark.sql.types import *
+from spark_session import with_cpu_session
+
+# This test requires a datasource v2 jar containing the class
+# org.apache.spark.sql.connector.InMemoryTableCatalog
+# which returns ArrowColumnVectors be specified in order for it to run.
+# If that class is not present it skips the tests.
+
+catalogName = "columnar"
+columnarClass = 'org.apache.spark.sql.connector.InMemoryTableCatalog'
+
+def createPeopleCSVDf(spark, peopleCSVLocation):
+    return spark.read.format("csv")\
+        .option("header", "false")\
+        .option("inferSchema", "true")\
+        .load(peopleCSVLocation)\
+        .withColumnRenamed("_c0", "name")\
+        .withColumnRenamed("_c1", "age")\
+        .withColumnRenamed("_c2", "job")
+
+def setupInMemoryTableWithPartitioning(spark, csv, tname, column_and_table):
+    peopleCSVDf = createPeopleCSVDf(spark, csv)
+    peopleCSVDf.createOrReplaceTempView(tname)
+    spark.table(tname).write.partitionBy("job").saveAsTable(column_and_table)
+
+def setupInMemoryTableNoPartitioning(spark, csv, tname, column_and_table):
+    peopleCSVDf = createPeopleCSVDf(spark, csv)
+    peopleCSVDf.createOrReplaceTempView(tname)
+    spark.table(tname).write.saveAsTable(column_and_table)
+
+def readTable(csvPath, tableToRead):
+    return lambda spark: spark.table(tableToRead)\
+        .orderBy("name", "age")
+
+def createDatabase(spark):
+    try:
+        spark.sql("create database IF NOT EXISTS " + catalogName)
+        spark.sql("use " + catalogName)
+    except Exception:
+        pytest.skip("Failed to load catalog for datasource v2 {}, jar is probably missing".format(columnarClass))
+
+def cleanupDatabase(spark):
+    spark.sql("drop database IF EXISTS " + catalogName)
+
+@pytest.fixture(autouse=True)
+def setupAndCleanUp():
+    with_cpu_session(lambda spark : createDatabase(spark),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+    yield
+    with_cpu_session(lambda spark : cleanupDatabase(spark),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+
+@allow_non_gpu('ShowTablesExec', 'DropTableExec')
+@pytest.mark.parametrize('csv', ['people.csv'])
+def test_read_round_trip_partitioned(std_input_path, csv, spark_tmp_table_factory):
+    csvPath = std_input_path + "/" + csv
+    tableName = spark_tmp_table_factory.get()
+    columnarTableName = catalogName + "." + tableName
+    with_cpu_session(lambda spark : setupInMemoryTableWithPartitioning(spark, csvPath, tableName, columnarTableName),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+    assert_gpu_and_cpu_are_equal_collect(readTable(csvPath, columnarTableName),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+
+@allow_non_gpu('ShowTablesExec', 'DropTableExec')
+@pytest.mark.parametrize('csv', ['people.csv'])
+def test_read_round_trip_no_partitioned(std_input_path, csv, spark_tmp_table_factory):
+    csvPath = std_input_path + "/" + csv
+    tableNameNoPart = spark_tmp_table_factory.get()
+    columnarTableNameNoPart = catalogName + "." + tableNameNoPart
+    with_cpu_session(lambda spark : setupInMemoryTableNoPartitioning(spark, csvPath, tableNameNoPart, columnarTableNameNoPart),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+    assert_gpu_and_cpu_are_equal_collect(readTable(csvPath, columnarTableNameNoPart),
+            conf={'spark.sql.catalog.columnar': columnarClass})
+
+@allow_non_gpu('ShowTablesExec', 'DropTableExec')
+@pytest.mark.parametrize('csv', ['people.csv'])
+def test_read_round_trip_no_partitioned_arrow_off(std_input_path, csv, spark_tmp_table_factory):
+    csvPath = std_input_path + "/" + csv
+    tableNameNoPart = spark_tmp_table_factory.get()
+    columnarTableNameNoPart = catalogName + "." + tableNameNoPart
+    with_cpu_session(lambda spark : setupInMemoryTableNoPartitioning(spark, csvPath, tableNameNoPart, columnarTableNameNoPart),
+            conf={'spark.sql.catalog.columnar': columnarClass,
+                  'spark.rapids.arrowCopyOptmizationEnabled': 'false'})
+    assert_gpu_and_cpu_are_equal_collect(readTable(csvPath, columnarTableNameNoPart),
+            conf={'spark.sql.catalog.columnar': columnarClass,
+                  'spark.rapids.arrowCopyOptmizationEnabled': 'false'})

integration_tests/src/test/resources/people.csv

@@ -0,0 +1,66 @@
+Jackelyn,23,Engineer
+Jeannie,40,Banker
+Mariella,20,Professor
+Ardith,33,Professor
+Elena,41,Banker
+Noma,39,Student
+Corliss,24,Student
+Denae,24,Banker
+Ned,44,Professor
+Karolyn,,Engineer
+Cornelia,45,Sales Manager
+Kiyoko,41,Banker
+Denisha,45,Engineer
+Hilton,21,Sales Manager
+Becky,32,Sales Manager
+Wendie,29,Banker
+Veronica,25,Sales Manager
+Carolina,26,Student
+Laquita,47,Banker
+Stephani,,Professor
+Emile,29,Professor
+Octavio,35,Banker
+Florencio,34,Banker
+Elna,38,Banker
+Cherri,23,Banker
+Raleigh,47,Banker
+Hollis,32,Professor
+Charlette,35,Professor
+Yetta,31,Student
+Alfreda,,Engineer
+Brigette,45,Banker
+Maryann,40,Sales Manager
+Miki,37,Banker
+Pearle,37,Sales Manager
+Damian,21,Sales Manager
+Theodore,34,Student
+Kristi,46,Engineer
+Izetta,45,Professor
+Tammi,43,Engineer
+Star,,Sales Manager
+Kaylee,27,Professor
+Lakeshia,48,Professor
+Elba,43,Sales Manager
+Valencia,20,Engineer
+Randa,35,Banker
+Lourie,36,Professor
+Tracie,31,Banker
+Antwan,40,Professor
+Gerry,23,Student
+Jason,,Banker
+Steve,51,CEO
+Faker,25,Gamer
+가나다라마바사,30,Banker
+가나다마바사,20,Banker
+가나다나다라마바,21,Professor
+가나다나라마바사,22,Engineer
+가나나나가나나,23,Sales Manager
+가나다나다나나다,24,Banker
+나다나다가나다,25,Student
+Elon,,CEO
+Yohwan,38,Gamer
+Kitaek,48,Jobless
+Kijung,22,Tutor
+KiWoo,23,Tutor
+Dongik,43,CEO
+Dasong,9,Student

shims/spark300/src/main/scala/com/nvidia/spark/rapids/shims/spark300/Spark300Shims.scala

@@ -16,10 +16,12 @@
 
 package com.nvidia.spark.rapids.shims.spark300
 
+import java.nio.ByteBuffer
 import java.time.ZoneId
 
 import com.nvidia.spark.rapids._
 import com.nvidia.spark.rapids.spark300.RapidsShuffleManager
+import org.apache.arrow.vector.ValueVector
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.SparkEnv
@@ -450,6 +452,19 @@ class Spark300Shims extends SparkShims {
     InMemoryFileIndex.shouldFilterOut(path)
   }
 
+  // Arrow version changed between Spark versions
+  override def getArrowDataBuf(vec: ValueVector): ByteBuffer = {
+    vec.getDataBuffer().nioBuffer()
+  }
+
+  override def getArrowValidityBuf(vec: ValueVector): ByteBuffer = {
+    vec.getValidityBuffer().nioBuffer()
+  }
+
+  override def getArrowOffsetsBuf(vec: ValueVector): ByteBuffer = {
+    vec.getOffsetBuffer().nioBuffer()
+  }
+
   override def replaceWithAlluxioPathIfNeeded(
       conf: RapidsConf,
       relation: HadoopFsRelation,

shims/spark311/src/main/scala/com/nvidia/spark/rapids/shims/spark311/Spark311Shims.scala

@@ -16,9 +16,12 @@
 
 package com.nvidia.spark.rapids.shims.spark311
 
+import java.nio.ByteBuffer
+
 import com.nvidia.spark.rapids._
 import com.nvidia.spark.rapids.shims.spark301.Spark301Shims
 import com.nvidia.spark.rapids.spark311.RapidsShuffleManager
+import org.apache.arrow.vector.ValueVector
 
 import org.apache.spark.SparkEnv
 import org.apache.spark.sql.SparkSession
@@ -387,4 +390,17 @@ class Spark311Shims extends Spark301Shims {
   override def shouldIgnorePath(path: String): Boolean = {
     HadoopFSUtilsShim.shouldIgnorePath(path)
   }
+
+  // Arrow version changed between Spark versions
+  override def getArrowDataBuf(vec: ValueVector): ByteBuffer = {
+    vec.getDataBuffer.nioBuffer()
+  }
+
+  override def getArrowValidityBuf(vec: ValueVector): ByteBuffer = {
+    vec.getValidityBuffer.nioBuffer()
+  }
+
+  override def getArrowOffsetsBuf(vec: ValueVector): ByteBuffer = {
+    vec.getOffsetBuffer.nioBuffer()
+  }
 }