LAG [analytic]
Returns the value of the input expression at the given offset before the current row within a window. This function lets you access more than one row in a table at the same time. This is useful for comparing values when the relative positions of rows can be reliably known. It also lets you avoid the more costly self join, which enhances query processing speed.
For information on getting the rows that follow, see LEAD.
Behavior type
ImmutableSyntax
LAG ( expression[, offset ] [, default ] ) OVER (
[ window-partition-clause ]
window-order-clause )
Parameters
expression
- The expression to evaluate—for example, a constant, column, non-analytic function, function expression, or expressions involving any of these.
offset
- Indicates how great is the lag. The default value is 1 (the previous row). This parameter must evaluate to a constant positive integer.
default
- The value returned if
offset
falls outside the bounds of the table or partition. This value must be a constant value or an expression that can be evaluated to a constant; its data type is coercible to that of the first argument.
Examples
This example sums the current balance by date in a table and also sums the previous balance from the last day. Given the inputs that follow, the data satisfies the following conditions:
-
For each
some_id
, there is exactly 1 row for each date represented bymonth_date
. -
For each
some_id
, the set of dates is consecutive; that is, if there is a row for February 24 and a row for February 26, there would also be a row for February 25. -
Each
some_id
has the same set of dates.=> CREATE TABLE balances ( month_date DATE, current_bal INT, some_id INT); => INSERT INTO balances values ('2009-02-24', 10, 1); => INSERT INTO balances values ('2009-02-25', 10, 1); => INSERT INTO balances values ('2009-02-26', 10, 1); => INSERT INTO balances values ('2009-02-24', 20, 2); => INSERT INTO balances values ('2009-02-25', 20, 2); => INSERT INTO balances values ('2009-02-26', 20, 2); => INSERT INTO balances values ('2009-02-24', 30, 3); => INSERT INTO balances values ('2009-02-25', 20, 3); => INSERT INTO balances values ('2009-02-26', 30, 3);
Now run LAG to sum the current balance for each date and sum the previous balance from the last day:
=> SELECT month_date,
SUM(current_bal) as current_bal_sum,
SUM(previous_bal) as previous_bal_sum FROM
(SELECT month_date, current_bal,
LAG(current_bal, 1, 0) OVER
(PARTITION BY some_id ORDER BY month_date)
AS previous_bal FROM balances) AS subQ
GROUP BY month_date ORDER BY month_date;
month_date | current_bal_sum | previous_bal_sum
------------+-----------------+------------------
2009-02-24 | 60 | 0
2009-02-25 | 50 | 60
2009-02-26 | 60 | 50
(3 rows)
Using the same example data, the following query would not be allowed because LAG is nested inside an aggregate function:
=> SELECT month_date,
SUM(current_bal) as current_bal_sum,
SUM(LAG(current_bal, 1, 0) OVER
(PARTITION BY some_id ORDER BY month_date)) AS previous_bal_sum
FROM some_table GROUP BY month_date ORDER BY month_date;
The following example uses the VMart database. LAG first returns the annual income from the previous row, and then it calculates the difference between the income in the current row from the income in the previous row:
=> SELECT occupation, customer_key, customer_name, annual_income,
LAG(annual_income, 1, 0) OVER (PARTITION BY occupation
ORDER BY annual_income) AS prev_income, annual_income -
LAG(annual_income, 1, 0) OVER (PARTITION BY occupation
ORDER BY annual_income) AS difference
FROM customer_dimension ORDER BY occupation, customer_key LIMIT 20;
occupation | customer_key | customer_name | annual_income | prev_income | difference
------------+--------------+----------------------+---------------+-------------+------------
Accountant | 15 | Midori V. Peterson | 692610 | 692535 | 75
Accountant | 43 | Midori S. Rodriguez | 282359 | 280976 | 1383
Accountant | 93 | Robert P. Campbell | 471722 | 471355 | 367
Accountant | 102 | Sam T. McNulty | 901636 | 901561 | 75
Accountant | 134 | Martha B. Overstreet | 705146 | 704335 | 811
Accountant | 165 | James C. Kramer | 376841 | 376474 | 367
Accountant | 225 | Ben W. Farmer | 70574 | 70449 | 125
Accountant | 270 | Jessica S. Lang | 684204 | 682274 | 1930
Accountant | 273 | Mark X. Lampert | 723294 | 722737 | 557
Accountant | 295 | Sharon K. Gauthier | 29033 | 28412 | 621
Accountant | 338 | Anna S. Jackson | 816858 | 815557 | 1301
Accountant | 377 | William I. Jones | 915149 | 914872 | 277
Accountant | 438 | Joanna A. McCabe | 147396 | 144482 | 2914
Accountant | 452 | Kim P. Brown | 126023 | 124797 | 1226
Accountant | 467 | Meghan K. Carcetti | 810528 | 810284 | 244
Accountant | 478 | Tanya E. Greenwood | 639649 | 639029 | 620
Accountant | 511 | Midori P. Vogel | 187246 | 185539 | 1707
Accountant | 525 | Alexander K. Moore | 677433 | 677050 | 383
Accountant | 550 | Sam P. Reyes | 735691 | 735355 | 336
Accountant | 577 | Robert U. Vu | 616101 | 615439 | 662
(20 rows)
The next example uses LEAD and LAG to return the third row after the salary in the current row and fifth salary before the salary in the current row:
=> SELECT hire_date, employee_key, employee_last_name,
LEAD(hire_date, 1) OVER (ORDER BY hire_date) AS "next_hired" ,
LAG(hire_date, 1) OVER (ORDER BY hire_date) AS "last_hired"
FROM employee_dimension ORDER BY hire_date, employee_key;
hire_date | employee_key | employee_last_name | next_hired | last_hired
------------+--------------+--------------------+------------+------------
1956-04-11 | 2694 | Farmer | 1956-05-12 |
1956-05-12 | 5486 | Winkler | 1956-09-18 | 1956-04-11
1956-09-18 | 5525 | McCabe | 1957-01-15 | 1956-05-12
1957-01-15 | 560 | Greenwood | 1957-02-06 | 1956-09-18
1957-02-06 | 9781 | Bauer | 1957-05-25 | 1957-01-15
1957-05-25 | 9506 | Webber | 1957-07-04 | 1957-02-06
1957-07-04 | 6723 | Kramer | 1957-07-07 | 1957-05-25
1957-07-07 | 5827 | Garnett | 1957-11-11 | 1957-07-04
1957-11-11 | 373 | Reyes | 1957-11-21 | 1957-07-07
1957-11-21 | 3874 | Martin | 1958-02-06 | 1957-11-11
(10 rows)
See also
-
[LEAD](/en/sql-reference/functions/analytic-functions/lead-analytic/)
- SQL analytics