---
layout: global
title: Window Functions
displayTitle: Window Functions
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

Window functions operate on a group of rows, referred to as a window, and calculate a return value for each row based on the group of rows. Window functions are useful for processing tasks such as calculating a moving average, computing a cumulative statistic, or accessing the value of rows given the relative position of the current row.

### Syntax

{% highlight sql %}
window_function OVER
( [  { PARTITION | DISTRIBUTE } BY partition_col_name = partition_col_val ( [ , ... ] ) ]
  { ORDER | SORT } BY expression [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [ , ... ]
  [ window_frame ] )
{% endhighlight %}

### Parameters

<dl>
  <dt><code><em>window_function</em></code></dt>
  <dd>
    <ul>
      <li>Ranking Functions</li>
      <br>
      <b>Syntax:</b>
        <code>
          RANK | DENSE_RANK | PERCENT_RANK | NTILE | ROW_NUMBER
        </code>
    </ul>
    <ul>
      <li>Analytic Functions</li>
      <br>
      <b>Syntax:</b>
        <code>
          CUME_DIST | LAG | LEAD
        </code>
    </ul>
    <ul>
      <li>Aggregate Functions</li>
      <br>
      <b>Syntax:</b>
        <code>
          MAX | MIN | COUNT | SUM | AVG | ...
        </code>
        <br>
        Please refer to the <a href="api/sql/">Built-in Functions</a> document for a complete list of Spark aggregate functions.
    </ul>
  </dd>
</dl>
<dl>
  <dt><code><em>window_frame</em></code></dt>
  <dd>
    Specifies which row to start the window on and where to end it.<br>
    <b>Syntax:</b><br>
      <code>{ RANGE | ROWS } { frame_start | BETWEEN frame_start AND frame_end }</code><br>
      If frame_end is omitted it defaults to CURRENT ROW.<br><br>
      <ul>
      <code>frame_start</code> and <code>frame_end</code> have the following syntax<br>
      <b>Syntax:</b><br>
        <code>
          UNBOUNDED PRECEDING | offset PRECEDING | CURRENT ROW | offset FOLLOWING | UNBOUNDED FOLLOWING
        </code><br>
        <code>offset:</code>specifies the <code>offset</code> from the position of the current row.
      </ul>
  </dd>
</dl>

### Examples

{% highlight sql %}
CREATE TABLE employees (name STRING, dept STRING, salary INT, age INT);

INSERT INTO employees VALUES ("Lisa", "Sales", 10000, 35);
INSERT INTO employees VALUES ("Evan", "Sales", 32000, 38);
INSERT INTO employees VALUES ("Fred", "Engineering", 21000, 28);
INSERT INTO employees VALUES ("Alex", "Sales", 30000, 33);
INSERT INTO employees VALUES ("Tom", "Engineering", 23000, 33);
INSERT INTO employees VALUES ("Jane", "Marketing", 29000, 28);
INSERT INTO employees VALUES ("Jeff", "Marketing", 35000, 38);
INSERT INTO employees VALUES ("Paul", "Engineering", 29000, 23);
INSERT INTO employees VALUES ("Chloe", "Engineering", 23000, 25);

SELECT * FROM employees;
  +-----+-----------+------+-----+
  | name|       dept|salary|  age|
  +-----+-----------+------+-----+
  |Chloe|Engineering| 23000|   25|
  | Fred|Engineering| 21000|   28|
  | Paul|Engineering| 29000|   23|
  |Helen|  Marketing| 29000|   40|
  |  Tom|Engineering| 23000|   33|
  | Jane|  Marketing| 29000|   28|
  | Jeff|  Marketing| 35000|   38|
  | Evan|      Sales| 32000|   38|
  | Lisa|      Sales| 10000|   35|
  | Alex|      Sales| 30000|   33|
  +-----+-----------+------+-----+

SELECT name, dept, RANK() OVER (PARTITION BY dept ORDER BY salary) AS rank FROM employees;
  +-----+-----------+------+----+
  | name|       dept|salary|rank|
  +-----+-----------+------+----+
  | Lisa|      Sales| 10000|   1|
  | Alex|      Sales| 30000|   2|
  | Evan|      Sales| 32000|   3|
  | Fred|Engineering| 21000|   1|
  |  Tom|Engineering| 23000|   2|
  |Chloe|Engineering| 23000|   2|
  | Paul|Engineering| 29000|   4|
  |Helen|  Marketing| 29000|   1|
  | Jane|  Marketing| 29000|   1|
  | Jeff|  Marketing| 35000|   3|
  +-----+-----------+------+----+

SELECT name, dept, DENSE_RANK() OVER (PARTITION BY dept ORDER BY salary ROWS BETWEEN
    UNBOUNDED PRECEDING AND CURRENT ROW) AS dense_rank FROM employees;
  +-----+-----------+------+----------+
  | name|       dept|salary|dense_rank|
  +-----+-----------+------+----------+
  | Lisa|      Sales| 10000|         1|
  | Alex|      Sales| 30000|         2|
  | Evan|      Sales| 32000|         3|
  | Fred|Engineering| 21000|         1|
  |  Tom|Engineering| 23000|         2|
  |Chloe|Engineering| 23000|         2|
  | Paul|Engineering| 29000|         3|
  |Helen|  Marketing| 29000|         1|
  | Jane|  Marketing| 29000|         1|
  | Jeff|  Marketing| 35000|         2|
  +-----+-----------+------+----------+

SELECT name, dept, age, CUME_DIST() OVER (PARTITION BY dept ORDER BY age
    RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cume_dist FROM employees;
  +-----+-----------+------+------------------+
  | name|       dept|age   |         cume_dist|
  +-----+-----------+------+------------------+
  | Alex|      Sales|    33|0.3333333333333333|
  | Lisa|      Sales|    35|0.6666666666666666|
  | Evan|      Sales|    38|               1.0|
  | Paul|Engineering|    23|              0.25|
  |Chloe|Engineering|    25|              0.75|
  | Fred|Engineering|    28|              0.25|
  |  Tom|Engineering|    33|               1.0|
  | Jane|  Marketing|    28|0.3333333333333333|
  | Jeff|  Marketing|    38|0.6666666666666666|
  |Helen|  Marketing|    40|               1.0|
  +-----+-----------+------+------------------+

SELECT name, dept, salary, MIN(salary) OVER (PARTITION BY dept ORDER BY salary) AS min
    FROM employees;
  +-----+-----------+------+-----+
  | name|       dept|salary|  min|
  +-----+-----------+------+-----+
  | Lisa|      Sales| 10000|10000|
  | Alex|      Sales| 30000|10000|
  | Evan|      Sales| 32000|10000|
  |Helen|  Marketing| 29000|29000|
  | Jane|  Marketing| 29000|29000|
  | Jeff|  Marketing| 35000|29000|
  | Fred|Engineering| 21000|21000|
  |  Tom|Engineering| 23000|21000|
  |Chloe|Engineering| 23000|21000|
  | Paul|Engineering| 29000|21000|
  +-----+-----------+------+-----+

SELECT name, salary,
    LAG(salary) OVER (PARTITION BY dept ORDER BY salary) AS lag,
    LEAD(salary, 1, 0) OVER (PARTITION BY dept ORDER BY salary) AS lead
    FROM employees;
  +-----+-----------+------+-----+-----+
  | name|       dept|salary|  lag| lead|
  +-----+-----------+------+-----+-----+
  | Lisa|      Sales| 10000|NULL |30000|
  | Alex|      Sales| 30000|10000|32000|
  | Evan|      Sales| 32000|30000|    0|
  | Fred|Engineering| 21000| NULL|23000|
  |Chloe|Engineering| 23000|21000|23000|
  |  Tom|Engineering| 23000|23000|29000|
  | Paul|Engineering| 29000|23000|    0|
  |Helen|  Marketing| 29000| NULL|29000|
  | Jane|  Marketing| 29000|29000|35000|
  | Jeff|  Marketing| 35000|29000|    0|
  +-----+-----------+------+-----+-----+
{% endhighlight %}

### Related Statements

  * [SELECT](sql-ref-syntax-qry-select.html)