LAG SQL function is an analytic function that lets you query more than one row in a table at a time without having to join the table itself, it returns values from a previous row in a table

1. LAG OLAP SQL Function
I like the Clear definition that google gave me:
“Is an analytic function that lets you query
more than one row in a table at a time
without having to join the table to itself. It
returns values from a previous row in a table.
To return a value from the next row, try using
the LEAD function”

2. This function saved my butt on a request. My
story started like this. I received a request to
produce a list of prices changes by items
where it should show last price and date
when it was established and previous
price and its date from a price changes
history file. I was lazy enough to create a
program for this request.

3. SQL came to my request like a 911 call. I
remember sql has two functions called LAG
and LEAD that could help me on this, so I
concentrated on the LAG function which
was the one that helped me on reaching my
goal and is the one I will explain on below
example. I will show through this example
the technique I used.Be aware that file, field
name and data are no real just served as an
illustration for my example. My history file
looks like this

4. ITEM# PRICE CHGDATE
116059 75.00 20170906
116059 74.00 20180124
116059 70.00 20180221
116059 65.00 20180321
116059 69.00 20180418
116059 70.00 20180613
116059 73.00 20180801
116059 81.00 20180919
116059 85.00 20181017
116059 75.00 20181114
104217 64.50 20170717
104217 67.50 20171016
104217 68.50 20180115
104227 69.50 20180416
104227 70.50 20180716
104227 75.50 20181015
File HSTPRCFL(Price changes history file)

5. So for Item 116059, 104217 And 104217 I
need to produce below row
So using below sql statement with LAG
function (Remember it produce current row
and previous row in one row) I was able to
generate below table
ITEM# Actual
Price
Date Price_Before Date
104217 68.50 20181015 67.50 20171016
104227 75.50 20181015 70.50 20180716
116059 75.00 20181114 85.00 20181017

6. Check for each row that it produce the current row plus data from previous row.
ITEM# Price1 Date1 Price2 Date2
104217 64.50 20170717 64.40 20170717
104217 67.50 20171016 64.50 20170717
104217 68.50 20181015 67.50 20171016
104217 69.50 20180416 68.50 20180115
104227 70.50 20180716 69.50 20180416
104227 75.50 20181015 70.50 20180716
116059 75.00 20170906 75.50 20181015
116059 75.00 20180124 75.00 20170906
116059 70.00 20180221 75.00 20180124
116059 65.00 20180321 70.00 20180221
116059 69.00 20180418 65.00 20180321
116059 70.00 20180613 69.00 20180418
116059 73.00 20180801 70.00 20180613
116059 81.00 20180919 73.00 20180801
116059 85.00 20181017 81.00 20180919
116059 75.00 20181114 85.00 20181017

7. Note on below sql statement that we use IFNULL because first
row doesn't have a previous row and then it will show null on
PRICE2 and DATE2 column and we do not want this, so with this
info I produce a temporary table where I generate a row number by
ITEM and DATE1 using ROW_NUMBER() function, this row
number will help me to capture the last date price was changed for
each item and from here produce what I need.
Declare Global Temporary Table ITEMPRICETBL As
With TEMPTABLE As(
Select ITEM, PRICE1, DATE1,
IFNULL (LAG(PRICE) Over(Order By
PRICE, DATE1) , PRICE) PRICE2,
IFNULL ((LAG(CHGDATE) Over(Order By
ITEM#, CHGDATE), CHGDATE) DATE2
From HSTPRICFL)
Select ITEM, PRICE1, DATE1, PRICE2, DATE2,
ROW_NUMBER() Over(PARTITION BY ITEM ORDER BY
DATE1 DESC) ROW From TEMPTABLE) With Data With
Replace.

8. That sql will produce below table
ITEM# Price1 Date1 Price2 Date2 ROW
104217 68.50 20180115 67.50 20171016 1
104217 67.50 20171016 64.50 20170717 2
104217 64.50 20170717 64.50 20170717 3
104217 75.50 20181015 70.50 20180716 1
104227 70.50 20180716 69.50 20180416 2
104227 69.50 20180416 68.50 20180115 3
116059 75.00 20181114 85.00 20181017 1
116059 85.00 20181017 81.00 20180919 2
116059 81.00 20180919 73.00 20180801 3
116059 73.00 20180801 70.00 20180613 4
116059 70.00 20180613 69.00 20180418 5
116059 69.00 20180418 65.00 20180321 6
116059 65.00 20180321 70.00 20180221 7
116059 70.00 20180221 75.00 20180124 8
116059 75.00 20180124 75.00 20170906 9
116059 75.00 20170906 75.50 20181015 10

9. Note that each record with row=1 is the last date
price was changed for each item and besides
on this row I got previous row data (Price and
Date) from here what left is just a piece of cake.
Select ITEM, PRICE1,DATE1,PRICE2,DATE2
From TEMPRICETBL where “ROW”=1
And voila! Here is my list
ITEM# Actual
Price
Date Price_Before Date
104217 68.50 20180115 67.50 20171016
104227 75.50 20181015 70.50 20180716
116059 75.00 20181114 85.00 20181017

10. Note that each record with row=1 is the last date
price was changed for each item and besides
on this row I got previous row data (Price and
Date) from here what left is just a piece of cake.
Select ITEM, PRICE1,DATE1,PRICE2,DATE2
From TEMPRICETBL where “ROW”=1
And voila! Here is my list
ITEM# Actual
Price
Date Price_Before Date
104217 68.50 20180115 67.50 20171016
104227 75.50 20181015 70.50 20180716
116059 75.00 20181114 85.00 20181017