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feat: add read array support #1456

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2 changes: 1 addition & 1 deletion native/core/src/execution/serde.rs
Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,7 @@ pub fn to_arrow_datatype(dt_value: &DataType) -> ArrowDataType {
{
DatatypeStruct::List(info) => {
let field = Field::new(
"item",
"element",
to_arrow_datatype(info.element_type.as_ref().unwrap()),
info.contains_null,
);
Expand Down
31 changes: 18 additions & 13 deletions native/core/src/execution/shuffle/row.rs
Original file line number Diff line number Diff line change
Expand Up @@ -3196,58 +3196,63 @@ fn make_builders(
// Disable dictionary encoding for array element
let value_builder =
make_builders(field.data_type(), NESTED_TYPE_BUILDER_CAPACITY, 1.0)?;

let value_field = Arc::clone(field);

// List builders created using `with_field` to override the ListBuilder default field
// name which is `item`
match field.data_type() {
DataType::Boolean => {
let builder = downcast_builder!(BooleanBuilder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Int8 => {
let builder = downcast_builder!(Int8Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Int16 => {
let builder = downcast_builder!(Int16Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Int32 => {
let builder = downcast_builder!(Int32Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Int64 => {
let builder = downcast_builder!(Int64Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Float32 => {
let builder = downcast_builder!(Float32Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Float64 => {
let builder = downcast_builder!(Float64Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Decimal128(_, _) => {
let builder = downcast_builder!(Decimal128Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Timestamp(TimeUnit::Microsecond, _) => {
let builder = downcast_builder!(TimestampMicrosecondBuilder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Date32 => {
let builder = downcast_builder!(Date32Builder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Binary => {
let builder = downcast_builder!(BinaryBuilder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Utf8 => {
let builder = downcast_builder!(StringBuilder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
DataType::Struct(_) => {
let builder = downcast_builder!(StructBuilder, value_builder);
Box::new(ListBuilder::new(*builder))
Box::new(ListBuilder::new(*builder).with_field(value_field))
}
// TODO: nested list is not supported. Due to the design of `ListBuilder`, it has
// a `T: ArrayBuilder` as type parameter. It makes hard to construct an arbitrarily
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -182,7 +182,7 @@ object CometNativeScanExec extends DataTypeSupport {
case null => null
}

val newArgs = mapProductIterator(scanExec, transform(_))
val newArgs = mapProductIterator(scanExec, transform)
val wrapped = scanExec.makeCopy(newArgs).asInstanceOf[FileSourceScanExec]
val batchScanExec = CometNativeScanExec(
nativeOp,
Expand All @@ -202,9 +202,10 @@ object CometNativeScanExec extends DataTypeSupport {
}

override def isAdditionallySupported(dt: DataType): Boolean = {
// TODO add array and map
// TODO add map
dt match {
case s: StructType => s.fields.map(_.dataType).forall(isTypeSupported)
case a: ArrayType => isTypeSupported(a.elementType)
case _ => false
}
}
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,63 @@
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/

package org.apache.comet.exec

import org.scalactic.source.Position
import org.scalatest.Tag

import org.apache.spark.sql.CometTestBase
import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
import org.apache.spark.sql.internal.SQLConf

import org.apache.comet.CometConf

class CometNativeReaderSuite extends CometTestBase with AdaptiveSparkPlanHelper {
override protected def test(testName: String, testTags: Tag*)(testFun: => Any)(implicit
pos: Position): Unit = {
super.test(testName, testTags: _*) {
withSQLConf(
CometConf.COMET_EXEC_ENABLED.key -> "true",
SQLConf.USE_V1_SOURCE_LIST.key -> "parquet",
CometConf.COMET_ENABLED.key -> "true",
CometConf.COMET_EXPLAIN_FALLBACK_ENABLED.key -> "false",
CometConf.COMET_NATIVE_SCAN_IMPL.key -> CometConf.SCAN_NATIVE_DATAFUSION) {
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It would be good to also test with SCAN_NATIVE_ICEBERG_COMPAT

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@andygrove can we do this in follow up PR? I'm not sure if for iceberg its needed to create an iceberg table?

testFun
}
}
}

test("native reader - read simple struct fields") {
testSingleLineQuery(
"""
|select named_struct('firstName', 'John', 'lastName', 'Doe', 'age', 35) as personal_info union all
|select named_struct('firstName', 'Jane', 'lastName', 'Doe', 'age', 40) as personal_info
|""".stripMargin,
"select personal_info.* from tbl")
}

test("native reader - read simple array fields") {
testSingleLineQuery(
"""
|select array(1, 2, 3) as arr union all
|select array(2, 3, 4) as arr
|""".stripMargin,
"select arr from tbl")
}
}
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