Revision be7f1e9979c38b1358b0af2b358bacb0bd523c80 authored by Dongjoon Hyun on 24 January 2024, 00:38:45 UTC, committed by Dongjoon Hyun on 24 January 2024, 00:38:53 UTC
### What changes were proposed in this pull request?

This PR aims to fix `spark-daemon.sh` usage by adding `decommission` command.

### Why are the changes needed?

This was missed when SPARK-20628 added `decommission` command at Apache Spark 3.1.0. The command has been used like the following.

https://github.com/apache/spark/blob/0356ac00947282b1a0885ad7eaae1e25e43671fe/sbin/decommission-worker.sh#L41

### Does this PR introduce _any_ user-facing change?

No, this is only a change on usage message.

### How was this patch tested?

Manual review.

### Was this patch authored or co-authored using generative AI tooling?

No.

Closes #44856 from dongjoon-hyun/SPARK-46817.

Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit 00a92d328576c39b04cfd0fdd8a30c5a9bc37e36)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
1 parent 05f7aa5
Raw File
sql-ref-functions-udf-hive.md
---
layout: global
title: Integration with Hive UDFs/UDAFs/UDTFs
displayTitle: Integration with Hive UDFs/UDAFs/UDTFs
license: |
  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.
---

### Description

Spark SQL supports integration of Hive UDFs, UDAFs and UDTFs. Similar to Spark UDFs and UDAFs, Hive UDFs work on a single row as input and generate a single row as output, while Hive UDAFs operate on multiple rows and return a single aggregated row as a result. In addition, Hive also supports UDTFs (User Defined Tabular Functions) that act on one row as input and return multiple rows as output. To use Hive UDFs/UDAFs/UTFs, the user should register them in Spark, and then use them in Spark SQL queries.

### Examples

Hive has two UDF interfaces: [UDF](https://github.com/apache/hive/blob/master/udf/src/java/org/apache/hadoop/hive/ql/exec/UDF.java) and [GenericUDF](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDF.java).
An example below uses [GenericUDFAbs](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFAbs.java) derived from `GenericUDF`.

```sql
-- Register `GenericUDFAbs` and use it in Spark SQL.
-- Note that, if you use your own programmed one, you need to add a JAR containing it
-- into a classpath,
-- e.g., ADD JAR yourHiveUDF.jar;
CREATE TEMPORARY FUNCTION testUDF AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs';

SELECT * FROM t;
+-----+
|value|
+-----+
| -1.0|
|  2.0|
| -3.0|
+-----+

SELECT testUDF(value) FROM t;
+--------------+
|testUDF(value)|
+--------------+
|           1.0|
|           2.0|
|           3.0|
+--------------+

-- Register `UDFSubstr` and use it in Spark SQL.
-- Note that, it can achieve better performance if the return types and method parameters use Java primitives.
-- e.g., UDFSubstr. The data processing method is UTF8String <-> Text <-> String. we can avoid UTF8String <-> Text. 
CREATE TEMPORARY FUNCTION hive_substr AS 'org.apache.hadoop.hive.ql.udf.UDFSubstr';

select hive_substr('Spark SQL', 1, 5) as value;
+-----+
|value|
+-----+
|Spark|
+-----+
```


An example below uses [GenericUDTFExplode](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDTFExplode.java) derived from [GenericUDTF](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDTF.java).

```sql
-- Register `GenericUDTFExplode` and use it in Spark SQL
CREATE TEMPORARY FUNCTION hiveUDTF
    AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDTFExplode';

SELECT * FROM t;
+------+
| value|
+------+
|[1, 2]|
|[3, 4]|
+------+

SELECT hiveUDTF(value) FROM t;
+---+
|col|
+---+
|  1|
|  2|
|  3|
|  4|
+---+
```

Hive has two UDAF interfaces: [UDAF](https://github.com/apache/hive/blob/master/udf/src/java/org/apache/hadoop/hive/ql/exec/UDAF.java) and [GenericUDAFResolver](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDAFResolver.java).
An example below uses [GenericUDAFSum](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDAFSum.java) derived from `GenericUDAFResolver`.

```sql
-- Register `GenericUDAFSum` and use it in Spark SQL
CREATE TEMPORARY FUNCTION hiveUDAF
    AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum';

SELECT * FROM t;
+---+-----+
|key|value|
+---+-----+
|  a|    1|
|  a|    2|
|  b|    3|
+---+-----+

SELECT key, hiveUDAF(value) FROM t GROUP BY key;
+---+---------------+
|key|hiveUDAF(value)|
+---+---------------+
|  b|              3|
|  a|              3|
+---+---------------+
```
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