---
layout: global
title: TRANSFORM
displayTitle: TRANSFORM
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
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---

### Description

The `TRANSFORM` clause is used to specify a Hive-style transform query specification 
to transform the inputs by running a user-specified command or script.

Spark's script transform supports two modes:

  1. Hive support disabled: Spark script transform can run with `spark.sql.catalogImplementation=in-memory`
  or without `SparkSession.builder.enableHiveSupport()`. In this case, now Spark only uses the script transform with
  `ROW FORMAT DELIMITED` and treats all values passed to the script as strings. 
  2. Hive support enabled: When Spark is run with `spark.sql.catalogImplementation=hive` or Spark SQL is started
  with `SparkSession.builder.enableHiveSupport()`, Spark can use the script transform with both Hive SerDe and 
  `ROW FORMAT DELIMITED`.

### Syntax

```sql
SELECT TRANSFORM ( expression [ , ... ] )
    [ ROW FORMAT row_format ]
    [ RECORDWRITER record_writer_class ]
    USING command_or_script [ AS ( [ col_name [ col_type ] ] [ , ... ] ) ]
    [ ROW FORMAT row_format ]
    [ RECORDREADER record_reader_class ]
```

### Parameters

* **expression**
    
    Specifies a combination of one or more values, operators and SQL functions that results in a value.

* **row_format** 

    Specifies the row format for input and output. See [HIVE FORMAT](sql-ref-syntax-hive-format.html) for more syntax details.

* **RECORDWRITER**

    Specifies a fully-qualified class name of a custom RecordWriter. The default value is `org.apache.hadoop.hive.ql.exec.TextRecordWriter`.

* **RECORDREADER**

    Specifies a fully-qualified class name of a custom RecordReader. The default value is `org.apache.hadoop.hive.ql.exec.TextRecordReader`.

* **command_or_script**

    Specifies a command or a path to script to process data.

### ROW FORMAT DELIMITED BEHAVIOR

When Spark uses `ROW FORMAT DELIMITED` format:
 - Spark uses the character `\u0001` as the default field delimiter and this delimiter can be overridden by `FIELDS TERMINATED BY`.
 - Spark uses the character `\n` as the default line delimiter and this delimiter can be overridden by `LINES TERMINATED BY`.
 - Spark uses a string `\N` as the default `NULL` value in order to differentiate `NULL` values 
 from the literal string `NULL`. This delimiter can be overridden by `NULL DEFINED AS`.
 - Spark casts all columns to `STRING` and combines columns by tabs before feeding to the user script.
 For complex types such as `ARRAY`/`MAP`/`STRUCT`, Spark uses `to_json` casts it to an input `JSON` string and uses 
 `from_json` to convert the result output `JSON` string to `ARRAY`/`MAP`/`STRUCT` data.
 - `COLLECTION ITEMS TERMINATED BY` and `MAP KEYS TERMINATED BY` are delimiters to split complex data such as 
 `ARRAY`/`MAP`/`STRUCT`, Spark uses `to_json` and `from_json` to handle complex data types with `JSON` format. So 
 `COLLECTION ITEMS TERMINATED BY` and `MAP KEYS TERMINATED BY` won't work in default row format.
 - The standard output of the user script is treated as tab-separated `STRING` columns. Any cell containing only a string `\N`
 is re-interpreted as a literal `NULL` value, and then the resulting `STRING` column will be cast to the data types specified in `col_type`.
 - If the actual number of output columns is less than the number of specified output columns,
  additional output columns will be filled with `NULL`. For example:
     ```
     output tabs: 1, 2
     output columns: A: INT, B INT, C: INT
     result: 
       +---+---+------+
       |  a|  b|     c|
       +---+---+------+
       |  1|  2|  NULL|
       +---+---+------+
     ```
 - If the actual number of output columns is more than the number of specified output columns, 
 the output columns only select the corresponding columns, and the remaining part will be discarded.
 For example, if the output has three tabs and there are only two output columns:
     ```
     output tabs: 1, 2, 3
     output columns: A: INT, B INT
     result: 
       +---+---+
       |  a|  b|
       +---+---+
       |  1|  2|
       +---+---+
     ```
 - If there is no `AS` clause after `USING my_script`, the output schema is `key: STRING, value: STRING`.
 The `key` column contains all the characters before the first tab and the `value` column contains the remaining characters after the first tab.
 If there are no tabs, Spark returns the `NULL` value. For example:
      ```
      output tabs: 1, 2, 3
      output columns: 
      result: 
        +-----+-------+
        |  key|  value|
        +-----+-------+
        |    1|      2|
        +-----+-------+
   
      output tabs: 1, 2
      output columns: 
      result: 
        +-----+-------+
        |  key|  value|
        +-----+-------+
        |    1|   NULL|
        +-----+-------+
      ```

### Hive SerDe behavior

When Hive support is enabled and Hive SerDe mode is used:
 - Spark uses the Hive SerDe `org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe` by default, so columns are cast
 to `STRING` and combined by tabs before feeding to the user script.
 - All literal `NULL` values are converted to a string `\N` in order to differentiate literal `NULL` values from the literal string `NULL`.
 - The standard output of the user script is treated as tab-separated `STRING` columns, any cell containing only a string `\N` is re-interpreted
 as a `NULL` value, and then the resulting STRING column will be cast to the data type specified in `col_type`.
 - If the actual number of output columns is less than the number of specified output columns,
  additional output columns will be filled with `NULL`.
 - If the actual number of output columns is more than the number of specified output columns,
 the output columns only select the corresponding columns, and the remaining part will be discarded.
 - If there is no `AS` clause after `USING my_script`, the output schema is `key: STRING, value: STRING`.
 The `key` column contains all the characters before the first tab and the `value` column contains the remaining characters after the first tab.
 If there is no tab, Spark returns the `NULL` value.
 - These defaults can be overridden with `ROW FORMAT SERDE` or `ROW FORMAT DELIMITED`. 

### Examples

```sql
CREATE TABLE person (zip_code INT, name STRING, age INT);
INSERT INTO person VALUES
    (94588, 'Zen Hui', 50),
    (94588, 'Dan Li', 18),
    (94588, 'Anil K', 27),
    (94588, 'John V', NULL),
    (94511, 'David K', 42),
    (94511, 'Aryan B.', 18),
    (94511, 'Lalit B.', NULL);

-- With specified output without data type
SELECT TRANSFORM(zip_code, name, age)
   USING 'cat' AS (a, b, c)
FROM person
WHERE zip_code > 94511;
+-------+---------+-----+
|    a  |        b|    c|
+-------+---------+-----+
|  94588|   Anil K|   27|
|  94588|   John V| NULL|
|  94588|  Zen Hui|   50|
|  94588|   Dan Li|   18|
+-------+---------+-----+

-- With specified output with data type
SELECT TRANSFORM(zip_code, name, age)
   USING 'cat' AS (a STRING, b STRING, c STRING)
FROM person
WHERE zip_code > 94511;
+-------+---------+-----+
|    a  |        b|    c|
+-------+---------+-----+
|  94588|   Anil K|   27|
|  94588|   John V| NULL|
|  94588|  Zen Hui|   50|
|  94588|   Dan Li|   18|
+-------+---------+-----+

-- Using ROW FORMAT DELIMITED
SELECT TRANSFORM(name, age)
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    LINES TERMINATED BY '\n'
    NULL DEFINED AS 'NULL'
    USING 'cat' AS (name_age string)
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY '@'
    LINES TERMINATED BY '\n'
    NULL DEFINED AS 'NULL'
FROM person;
+---------------+
|       name_age|
+---------------+
|      Anil K,27|
|    John V,null|
|     ryan B.,18|
|     David K,42|
|     Zen Hui,50|
|      Dan Li,18|
|  Lalit B.,null|
+---------------+

-- Using Hive Serde
SELECT TRANSFORM(zip_code, name, age)
    ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
    WITH SERDEPROPERTIES (
      'field.delim' = '\t'
    )
    USING 'cat' AS (a STRING, b STRING, c STRING)
    ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
    WITH SERDEPROPERTIES (
      'field.delim' = '\t'
    )
FROM person
WHERE zip_code > 94511;
+-------+---------+-----+
|    a  |        b|    c|
+-------+---------+-----+
|  94588|   Anil K|   27|
|  94588|   John V| NULL|
|  94588|  Zen Hui|   50|
|  94588|   Dan Li|   18|
+-------+---------+-----+

-- Schema-less mode
SELECT TRANSFORM(zip_code, name, age)
    USING 'cat'
FROM person
WHERE zip_code > 94500;
+-------+---------------------+
|    key|                value|
+-------+---------------------+
|  94588|	  Anil K    27|
|  94588|	  John V    \N|
|  94511|	Aryan B.    18|
|  94511|	 David K    42|
|  94588|	 Zen Hui    50|
|  94588|	  Dan Li    18|
|  94511|	Lalit B.    \N|
+-------+---------------------+
```

### Related Statements

* [SELECT Main](sql-ref-syntax-qry-select.html)
* [WHERE Clause](sql-ref-syntax-qry-select-where.html)
* [GROUP BY Clause](sql-ref-syntax-qry-select-groupby.html)
* [HAVING Clause](sql-ref-syntax-qry-select-having.html)
* [ORDER BY Clause](sql-ref-syntax-qry-select-orderby.html)
* [SORT BY Clause](sql-ref-syntax-qry-select-sortby.html)
* [DISTRIBUTE BY Clause](sql-ref-syntax-qry-select-distribute-by.html)
* [LIMIT Clause](sql-ref-syntax-qry-select-limit.html)
* [CASE Clause](sql-ref-syntax-qry-select-case.html)
* [PIVOT Clause](sql-ref-syntax-qry-select-pivot.html)
* [LATERAL VIEW Clause](sql-ref-syntax-qry-select-lateral-view.html)