Updated April 4, 2023
Introduction to Redshift window functions
Windows provides different types of functions to the user, Redshift is one of the functions provided by windows. Basically, Redshift provides the scope for the traditional aggregate functions and provides a suitable way to execute complex aggregate functions fast and simple to write as per user requirements. A Windows function also provides different kinds of functionality to the user such as it allows the SQL developer to fetch all rows and perform the different calculations as per user requirement. In another way, we can say that the Redshift function performs the aggregate operation but it is based on the group of rows.
Redshift window functions
Now let’s see the different Redshift functions of windows as follows.
By utilizing window capacities, you can empower your clients to make insightful business inquiries all the more productively. Window capacities work on a segment or “window” of an outcome set and return an incentive for each column in that window. Conversely, no windowed capacities play out their computations as for each line in the outcome set. Dissimilar to bunch works that total outcome pushes, all columns in the table articulation are held.
The qualities returned are determined by utilizing values from the arrangements of lines in that window. For each column in the table, the window characterizes a bunch of lines that are utilized to figure extra credits. A window is characterized utilizing a window determination (the OVER provision), and depends on three primary ideas:
Window dividing, which structures gatherings of lines (PARTITION statement)
Window requesting, which characterizes a request or grouping of lines inside each parcel (ORDER BY proviso)
Window outlines, which are characterized comparative with each column to additionally limit the arrangement of lines (ROWS detail)
Window capacities are the last arrangement of tasks acted in an inquiry with the exception of the last ORDER BY provision. All joins and all WHERE, GROUP BY, and HAVING provisos are finished before the window capacities are handled. Consequently, window capacities can show up just in the select rundown or ORDER BY statement. You can utilize various window capacities inside a solitary question with various edge conditions. You can likewise utilize window capacities in other scalar articulations, like CASE.
The Redshift upholds two sorts of window capacities: aggregate and positioning.
Now first let’s see different types of aggregate function with examples as follows.
First, we need to create the table by using the following syntax as follows.
create table sstudent(roll_no int(30), stud_name varchar(30), city varchar(30), marks int(30));
Explanation:
By using the above statement we created a new table name as sstudent with different attributes such as roll_no, stud_name, city, and marks with different data types as shown. The final output of the above statement we illustrated by using the following screenshot as follows.
Now insert some records into the newly created table by using the following statement as follows.
insert into sstudent(roll_no, stud_name, city, marks) values(1, "Jenny", "Mumbai", 55);
insert into sstudent(roll_no, stud_name, city, marks) values(2, "John", "Hongkong", 68);
insert into sstudent(roll_no, stud_name, city, marks) values(3, "pooja", "Hongkong ", 78);
insert into sstudent(roll_no, stud_name, city, marks) values(4, "Sammer", "Pune", 85);
insert into sstudent(roll_no, stud_name, city, marks) values(5, "Sachine", "Hydrabad", 87);
select * from sstudent;
Explanation:
By using the above statement we inserted a few records into the sstudent. The final output of the above statement we illustrated by using the following screenshot as follows.
Now let’s see Redshift functions as follows.
Now let’s see how we can perform the aggregate function within a window as follows.
SUM function()
This is an aggregate function, it is used to make the sum of all marks from the specified table.
Example:
SELECT
roll_no, stud_name, marks
, sum(marks) OVER (PARTITION BY stud_name ORDER BY city) as "st_marks"
FROM sstudent;
Explanation:
In the above example, we try to implement the aggregate function of Redshift that is the sum as shown in the above example. In the above example, we use partition by clause to make the windows partition, after that we order by clause to define the order as well as rows as shown. The final output of the above statement we illustrated by using the following screenshot as follows.
Now let AVG () functions as follows.
Basically, the avg () function is used to make the avg as per user requirements. It comes under the aggregate function of Redshift.
Example:
SELECT
roll_no, stud_name, marks
, avg(marks) OVER (PARTITION BY stud_name ORDER BY city) as "st_ marks"
FROM sstudent;
In the above example, we try to implement the aggregate function of Redshift that is avg as shown in the above example. In the above example, we use partition by clause to make the windows partition, after that we order by clause to define the order as well as rows as shown. The final output of the above statement we illustrated by using the following screenshot as follows.
In the same way, we can implement the other aggregate functions such as count, cume_dist, first_value, lag, last_value, lead, max, median, min, nth_value, etc as per our requirements.
Now let’s see the Redshift ranking function as follows.
Rank() function
Example:
SELECT
roll_no, stud_name, marks
, rank() OVER (PARTITION BY city ORDER BY city DESC, city DESC) as "marks_rank"
FROM sstudent
LIMIT 4;
Explanation
In the above example, we try to implement the ranking function of Redshift as shown. In the above example. In the above example, we use partition by clause to make the windows partition, after that we order by clause to define the order as well as rows as shown. The final output of the above statement we illustrated by using the following screenshot as follows.
In this way, we can implement the other ranking Redshift functions such as dense_rank, ntile, percent_rank, and row_number as per user requirements.
Conclusion
We hope from this article you learn more about Redshift windows function. From the above article, we have learned the basic concept of Redshift functions and we also see the different functions of Redshift. From this article, we learned how and when we use the Redshift function.
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This is a guide to Redshift window functions. Here we discuss the basic concept of Redshift functions and we also see the different functions of Redshift. You may also have a look at the following articles to learn more –
