bfbf2821f3
### What changes were proposed in this pull request?
There's a case where MapObjects has a lambda function which creates nested struct - unsafe data in safe data struct. In this case, MapObjects doesn't copy the row returned from lambda function (as outmost data type is safe data struct), which misses copying nested unsafe data.
The culprit is that `UnsafeProjection.toUnsafeExprs` converts `CreateNamedStruct` to `CreateNamedStructUnsafe` (this is the only place where `CreateNamedStructUnsafe` is used) which incurs safe and unsafe being mixed up temporarily, which may not be needed at all at least logically, as it will finally assembly these evaluations to `UnsafeRow`.
> Before the patch
```
/* 105 */ private ArrayData MapObjects_0(InternalRow i) {
/* 106 */ boolean isNull_1 = i.isNullAt(0);
/* 107 */ ArrayData value_1 = isNull_1 ?
/* 108 */ null : (i.getArray(0));
/* 109 */ ArrayData value_0 = null;
/* 110 */
/* 111 */ if (!isNull_1) {
/* 112 */
/* 113 */ int dataLength_0 = value_1.numElements();
/* 114 */
/* 115 */ ArrayData[] convertedArray_0 = null;
/* 116 */ convertedArray_0 = new ArrayData[dataLength_0];
/* 117 */
/* 118 */
/* 119 */ int loopIndex_0 = 0;
/* 120 */
/* 121 */ while (loopIndex_0 < dataLength_0) {
/* 122 */ value_MapObject_lambda_variable_1 = (int) (value_1.getInt(loopIndex_0));
/* 123 */ isNull_MapObject_lambda_variable_1 = value_1.isNullAt(loopIndex_0);
/* 124 */
/* 125 */ ArrayData arrayData_0 = ArrayData.allocateArrayData(
/* 126 */ -1, 1L, " createArray failed.");
/* 127 */
/* 128 */ mutableStateArray_0[0].reset();
/* 129 */
/* 130 */
/* 131 */ mutableStateArray_0[0].zeroOutNullBytes();
/* 132 */
/* 133 */
/* 134 */ if (isNull_MapObject_lambda_variable_1) {
/* 135 */ mutableStateArray_0[0].setNullAt(0);
/* 136 */ } else {
/* 137 */ mutableStateArray_0[0].write(0, value_MapObject_lambda_variable_1);
/* 138 */ }
/* 139 */ arrayData_0.update(0, (mutableStateArray_0[0].getRow()));
/* 140 */ if (false) {
/* 141 */ convertedArray_0[loopIndex_0] = null;
/* 142 */ } else {
/* 143 */ convertedArray_0[loopIndex_0] = arrayData_0 instanceof UnsafeArrayData? arrayData_0.copy() : arrayData_0;
/* 144 */ }
/* 145 */
/* 146 */ loopIndex_0 += 1;
/* 147 */ }
/* 148 */
/* 149 */ value_0 = new org.apache.spark.sql.catalyst.util.GenericArrayData(convertedArray_0);
/* 150 */ }
/* 151 */ globalIsNull_0 = isNull_1;
/* 152 */ return value_0;
/* 153 */ }
```
> After the patch
```
/* 104 */ private ArrayData MapObjects_0(InternalRow i) {
/* 105 */ boolean isNull_1 = i.isNullAt(0);
/* 106 */ ArrayData value_1 = isNull_1 ?
/* 107 */ null : (i.getArray(0));
/* 108 */ ArrayData value_0 = null;
/* 109 */
/* 110 */ if (!isNull_1) {
/* 111 */
/* 112 */ int dataLength_0 = value_1.numElements();
/* 113 */
/* 114 */ ArrayData[] convertedArray_0 = null;
/* 115 */ convertedArray_0 = new ArrayData[dataLength_0];
/* 116 */
/* 117 */
/* 118 */ int loopIndex_0 = 0;
/* 119 */
/* 120 */ while (loopIndex_0 < dataLength_0) {
/* 121 */ value_MapObject_lambda_variable_1 = (int) (value_1.getInt(loopIndex_0));
/* 122 */ isNull_MapObject_lambda_variable_1 = value_1.isNullAt(loopIndex_0);
/* 123 */
/* 124 */ ArrayData arrayData_0 = ArrayData.allocateArrayData(
/* 125 */ -1, 1L, " createArray failed.");
/* 126 */
/* 127 */ Object[] values_0 = new Object[1];
/* 128 */
/* 129 */
/* 130 */ if (isNull_MapObject_lambda_variable_1) {
/* 131 */ values_0[0] = null;
/* 132 */ } else {
/* 133 */ values_0[0] = value_MapObject_lambda_variable_1;
/* 134 */ }
/* 135 */
/* 136 */ final InternalRow value_3 = new org.apache.spark.sql.catalyst.expressions.GenericInternalRow(values_0);
/* 137 */ values_0 = null;
/* 138 */ arrayData_0.update(0, value_3);
/* 139 */ if (false) {
/* 140 */ convertedArray_0[loopIndex_0] = null;
/* 141 */ } else {
/* 142 */ convertedArray_0[loopIndex_0] = arrayData_0 instanceof UnsafeArrayData? arrayData_0.copy() : arrayData_0;
/* 143 */ }
/* 144 */
/* 145 */ loopIndex_0 += 1;
/* 146 */ }
/* 147 */
/* 148 */ value_0 = new org.apache.spark.sql.catalyst.util.GenericArrayData(convertedArray_0);
/* 149 */ }
/* 150 */ globalIsNull_0 = isNull_1;
/* 151 */ return value_0;
/* 152 */ }
```
### Why are the changes needed?
This patch fixes the bug described above.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT added which fails on master branch and passes on PR.
Closes #26173 from HeartSaVioR/SPARK-29503.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
|
||
|---|---|---|
| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-markdown.py | ||
| mkdocs.yml | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Running ./sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.