e6b466084c
## What changes were proposed in this pull request?
The PR adds a logic for easy concatenation of multiple array columns and covers:
- Concat expression has been extended to support array columns
- A Python wrapper
## How was this patch tested?
New tests added into:
- CollectionExpressionsSuite
- DataFrameFunctionsSuite
- typeCoercion/native/concat.sql
## Codegen examples
### Primitive-type elements
```
val df = Seq(
(Seq(1 ,2), Seq(3, 4)),
(Seq(1, 2, 3), null)
).toDF("a", "b")
df.filter('a.isNotNull).select(concat('a, 'b)).debugCodegen()
```
Result:
```
/* 033 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 034 */ ArrayData inputadapter_value = inputadapter_isNull ?
/* 035 */ null : (inputadapter_row.getArray(0));
/* 036 */
/* 037 */ if (!(!inputadapter_isNull)) continue;
/* 038 */
/* 039 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 040 */
/* 041 */ ArrayData[] project_args = new ArrayData[2];
/* 042 */
/* 043 */ if (!false) {
/* 044 */ project_args[0] = inputadapter_value;
/* 045 */ }
/* 046 */
/* 047 */ boolean inputadapter_isNull1 = inputadapter_row.isNullAt(1);
/* 048 */ ArrayData inputadapter_value1 = inputadapter_isNull1 ?
/* 049 */ null : (inputadapter_row.getArray(1));
/* 050 */ if (!inputadapter_isNull1) {
/* 051 */ project_args[1] = inputadapter_value1;
/* 052 */ }
/* 053 */
/* 054 */ ArrayData project_value = new Object() {
/* 055 */ public ArrayData concat(ArrayData[] args) {
/* 056 */ for (int z = 0; z < 2; z++) {
/* 057 */ if (args[z] == null) return null;
/* 058 */ }
/* 059 */
/* 060 */ long project_numElements = 0L;
/* 061 */ for (int z = 0; z < 2; z++) {
/* 062 */ project_numElements += args[z].numElements();
/* 063 */ }
/* 064 */ if (project_numElements > 2147483632) {
/* 065 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_numElements +
/* 066 */ " elements due to exceeding the array size limit 2147483632.");
/* 067 */ }
/* 068 */
/* 069 */ long project_size = UnsafeArrayData.calculateSizeOfUnderlyingByteArray(
/* 070 */ project_numElements,
/* 071 */ 4);
/* 072 */ if (project_size > 2147483632) {
/* 073 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_size +
/* 074 */ " bytes of data due to exceeding the limit 2147483632 bytes" +
/* 075 */ " for UnsafeArrayData.");
/* 076 */ }
/* 077 */
/* 078 */ byte[] project_array = new byte[(int)project_size];
/* 079 */ UnsafeArrayData project_arrayData = new UnsafeArrayData();
/* 080 */ Platform.putLong(project_array, 16, project_numElements);
/* 081 */ project_arrayData.pointTo(project_array, 16, (int)project_size);
/* 082 */ int project_counter = 0;
/* 083 */ for (int y = 0; y < 2; y++) {
/* 084 */ for (int z = 0; z < args[y].numElements(); z++) {
/* 085 */ if (args[y].isNullAt(z)) {
/* 086 */ project_arrayData.setNullAt(project_counter);
/* 087 */ } else {
/* 088 */ project_arrayData.setInt(
/* 089 */ project_counter,
/* 090 */ args[y].getInt(z)
/* 091 */ );
/* 092 */ }
/* 093 */ project_counter++;
/* 094 */ }
/* 095 */ }
/* 096 */ return project_arrayData;
/* 097 */ }
/* 098 */ }.concat(project_args);
/* 099 */ boolean project_isNull = project_value == null;
```
### Non-primitive-type elements
```
val df = Seq(
(Seq("aa" ,"bb"), Seq("ccc", "ddd")),
(Seq("x", "y"), null)
).toDF("a", "b")
df.filter('a.isNotNull).select(concat('a, 'b)).debugCodegen()
```
Result:
```
/* 033 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 034 */ ArrayData inputadapter_value = inputadapter_isNull ?
/* 035 */ null : (inputadapter_row.getArray(0));
/* 036 */
/* 037 */ if (!(!inputadapter_isNull)) continue;
/* 038 */
/* 039 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 040 */
/* 041 */ ArrayData[] project_args = new ArrayData[2];
/* 042 */
/* 043 */ if (!false) {
/* 044 */ project_args[0] = inputadapter_value;
/* 045 */ }
/* 046 */
/* 047 */ boolean inputadapter_isNull1 = inputadapter_row.isNullAt(1);
/* 048 */ ArrayData inputadapter_value1 = inputadapter_isNull1 ?
/* 049 */ null : (inputadapter_row.getArray(1));
/* 050 */ if (!inputadapter_isNull1) {
/* 051 */ project_args[1] = inputadapter_value1;
/* 052 */ }
/* 053 */
/* 054 */ ArrayData project_value = new Object() {
/* 055 */ public ArrayData concat(ArrayData[] args) {
/* 056 */ for (int z = 0; z < 2; z++) {
/* 057 */ if (args[z] == null) return null;
/* 058 */ }
/* 059 */
/* 060 */ long project_numElements = 0L;
/* 061 */ for (int z = 0; z < 2; z++) {
/* 062 */ project_numElements += args[z].numElements();
/* 063 */ }
/* 064 */ if (project_numElements > 2147483632) {
/* 065 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_numElements +
/* 066 */ " elements due to exceeding the array size limit 2147483632.");
/* 067 */ }
/* 068 */
/* 069 */ Object[] project_arrayObjects = new Object[(int)project_numElements];
/* 070 */ int project_counter = 0;
/* 071 */ for (int y = 0; y < 2; y++) {
/* 072 */ for (int z = 0; z < args[y].numElements(); z++) {
/* 073 */ project_arrayObjects[project_counter] = args[y].getUTF8String(z);
/* 074 */ project_counter++;
/* 075 */ }
/* 076 */ }
/* 077 */ return new org.apache.spark.sql.catalyst.util.GenericArrayData(project_arrayObjects);
/* 078 */ }
/* 079 */ }.concat(project_args);
/* 080 */ boolean project_isNull = project_value == null;
```
Author: mn-mikke <mrkAha12346github>
Closes #20858 from mn-mikke/feature/array-api-concat_arrays-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 0.10.6), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).