92c2f00bd2
## What changes were proposed in this pull request?
The PR adds the `map_from_entries` function that returns a map created from the given array of entries.
## How was this patch tested?
New tests added into:
- `CollectionExpressionSuite`
- `DataFrameFunctionSuite`
## CodeGen Examples
### Primitive-type Keys and Values
```
val idf = Seq(
Seq((1, 10), (2, 20), (3, 10)),
Seq((1, 10), null, (2, 20))
).toDF("a")
idf.filter('a.isNotNull).select(map_from_entries('a)).debugCodegen
```
Result:
```
/* 042 */ boolean project_isNull_0 = false;
/* 043 */ MapData project_value_0 = null;
/* 044 */
/* 045 */ for (int project_idx_2 = 0; !project_isNull_0 && project_idx_2 < inputadapter_value_0.numElements(); project_idx_2++) {
/* 046 */ project_isNull_0 |= inputadapter_value_0.isNullAt(project_idx_2);
/* 047 */ }
/* 048 */ if (!project_isNull_0) {
/* 049 */ final int project_numEntries_0 = inputadapter_value_0.numElements();
/* 050 */
/* 051 */ final long project_keySectionSize_0 = UnsafeArrayData.calculateSizeOfUnderlyingByteArray(project_numEntries_0, 4);
/* 052 */ final long project_valueSectionSize_0 = UnsafeArrayData.calculateSizeOfUnderlyingByteArray(project_numEntries_0, 4);
/* 053 */ final long project_byteArraySize_0 = 8 + project_keySectionSize_0 + project_valueSectionSize_0;
/* 054 */ if (project_byteArraySize_0 > 2147483632) {
/* 055 */ final Object[] project_keys_0 = new Object[project_numEntries_0];
/* 056 */ final Object[] project_values_0 = new Object[project_numEntries_0];
/* 057 */
/* 058 */ for (int project_idx_1 = 0; project_idx_1 < project_numEntries_0; project_idx_1++) {
/* 059 */ InternalRow project_entry_1 = inputadapter_value_0.getStruct(project_idx_1, 2);
/* 060 */
/* 061 */ project_keys_0[project_idx_1] = project_entry_1.getInt(0);
/* 062 */ project_values_0[project_idx_1] = project_entry_1.getInt(1);
/* 063 */ }
/* 064 */
/* 065 */ project_value_0 = org.apache.spark.sql.catalyst.util.ArrayBasedMapData.apply(project_keys_0, project_values_0);
/* 066 */
/* 067 */ } else {
/* 068 */ final byte[] project_byteArray_0 = new byte[(int)project_byteArraySize_0];
/* 069 */ UnsafeMapData project_unsafeMapData_0 = new UnsafeMapData();
/* 070 */ Platform.putLong(project_byteArray_0, 16, project_keySectionSize_0);
/* 071 */ Platform.putLong(project_byteArray_0, 24, project_numEntries_0);
/* 072 */ Platform.putLong(project_byteArray_0, 24 + project_keySectionSize_0, project_numEntries_0);
/* 073 */ project_unsafeMapData_0.pointTo(project_byteArray_0, 16, (int)project_byteArraySize_0);
/* 074 */ ArrayData project_keyArrayData_0 = project_unsafeMapData_0.keyArray();
/* 075 */ ArrayData project_valueArrayData_0 = project_unsafeMapData_0.valueArray();
/* 076 */
/* 077 */ for (int project_idx_0 = 0; project_idx_0 < project_numEntries_0; project_idx_0++) {
/* 078 */ InternalRow project_entry_0 = inputadapter_value_0.getStruct(project_idx_0, 2);
/* 079 */
/* 080 */ project_keyArrayData_0.setInt(project_idx_0, project_entry_0.getInt(0));
/* 081 */ project_valueArrayData_0.setInt(project_idx_0, project_entry_0.getInt(1));
/* 082 */ }
/* 083 */
/* 084 */ project_value_0 = project_unsafeMapData_0;
/* 085 */ }
/* 086 */
/* 087 */ }
```
### Non-primitive-type Keys and Values
```
val sdf = Seq(
Seq(("a", null), ("b", "bb"), ("c", "aa")),
Seq(("a", "aa"), null, (null, "bb"))
).toDF("a")
sdf.filter('a.isNotNull).select(map_from_entries('a)).debugCodegen
```
Result:
```
/* 042 */ boolean project_isNull_0 = false;
/* 043 */ MapData project_value_0 = null;
/* 044 */
/* 045 */ for (int project_idx_1 = 0; !project_isNull_0 && project_idx_1 < inputadapter_value_0.numElements(); project_idx_1++) {
/* 046 */ project_isNull_0 |= inputadapter_value_0.isNullAt(project_idx_1);
/* 047 */ }
/* 048 */ if (!project_isNull_0) {
/* 049 */ final int project_numEntries_0 = inputadapter_value_0.numElements();
/* 050 */
/* 051 */ final Object[] project_keys_0 = new Object[project_numEntries_0];
/* 052 */ final Object[] project_values_0 = new Object[project_numEntries_0];
/* 053 */
/* 054 */ for (int project_idx_0 = 0; project_idx_0 < project_numEntries_0; project_idx_0++) {
/* 055 */ InternalRow project_entry_0 = inputadapter_value_0.getStruct(project_idx_0, 2);
/* 056 */
/* 057 */ if (project_entry_0.isNullAt(0)) {
/* 058 */ throw new RuntimeException("The first field from a struct (key) can't be null.");
/* 059 */ }
/* 060 */
/* 061 */ project_keys_0[project_idx_0] = project_entry_0.getUTF8String(0);
/* 062 */ project_values_0[project_idx_0] = project_entry_0.getUTF8String(1);
/* 063 */ }
/* 064 */
/* 065 */ project_value_0 = org.apache.spark.sql.catalyst.util.ArrayBasedMapData.apply(project_keys_0, project_values_0);
/* 066 */
/* 067 */ }
```
Author: Marek Novotny <mn.mikke@gmail.com>
Closes #21282 from mn-mikke/feature/array-api-map_from_entries-to-master.
|
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| setup.py | ||
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page
Python Packaging
This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".
The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.
NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.
Python Requirements
At its core PySpark depends on Py4J (currently version 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).