Amazon Redshift DENSE_RANK Function 101: Syntax, Usage & Examples Simplified

As an organization gains a grip on the market, the volume of the stored data expands, and so does the requirement for monitoring and analysis tools. With a traditional Database Warehouse solution, the process to monitor the information will take more time. As a result, it gets difficult to manage the data. With the rise of Cloud Computing, the need for better Warehousing solutions that can handle large-scale data sets is apparent. Thus, resulting in the demand for an alternate solution to old Warehousing solutions. Well! In that case, Amazon Redshift suits perfectly with the demand.

We’ll go through the Amazon Redshift DENSE_RANK Function in-depth in this article. You’ll discover how to utilize it to refine your findings and make data-driven decisions!

Table of Contents

• What is Amazon Redshift?
  • Key Features of Amazon Redshift
• What is an Analytics Function in Amazon Redshift?
  • COUNT Analytic Function
  • SUM Analytic Function
  • MIN and MAX Analytic Function
  • LEAD and LAG Analytic Function
  • FIRST_VALUE and LAST_VALUE Analytic Function
  • ROW_NUMBER, RANK, and DENSE_RANK Analytical Functions
• How to use the Amazon Redshift DENSE_RANK Function?
• What is the Difference between the Redshift DENSE_RANK & RANK Function?
• Conclusion

What is Amazon Redshift?

AWS Redshift is a popular Data Warehousing solution used for managing datasets and database migrations on a large scale. It provides a Cloud-based suite of Data Management that allows quick & scalable Data Processing solutions. It also includes compliance features and access to various Data Analytics tools.

AWS Redshift is a Column-oriented Database that stores data in a Columnar Format rather than the Row Format used by standard databases. To do computation and generate vital insights, Amazon Redshift uses its own compute engine. Amazon Redshift data is constantly encrypted, which adds an added layer of protection for users.

Businesses are now no longer required to massively invest their time, money, and expertise to optimize operations and grow revenue. Redshift covers it all. The Data Warehouse solution provides a complete infrastructure to run quick results and maintain efficiency.

Key Features of Amazon Redshift

• Massively Parallel Processing (MPP): As per this feature, the Data Warehouse solution follows a “divide and conquer” approach to manage and analyze large datasets. This feature converts large datasets into smaller tasks and distributes them among various processors or computer nodes. The healthy nodes perform computations simultaneously. As a result, it helps in fast query performance with the help of Columnar Storage and Data Compression.
• End-to-end Data Encryption: Amazon Redshift provides robust and highly customizable end-to-end Data Encryption options to protect Data Privacy. Users can configure and employ an AWS-managed or a customer-managed key as per their requirements. Also, one can choose between single or double encryption.
• Fault Tolerance: The feature makes sure if any component fails or clusters go offline, the system continues to operate and function smoothly. It automatically re-replicates data to other nodes for continuous functioning. Also, Amazon monitors its Clusters at all times.   

What is an Analytics Function in Amazon Redshift?

The role of the Amazon Redshift Analytics System is to provide useful functions to aggregate and save administrators’ time. These Data Warehouse Analytics functions go through a group of table rows to compute an aggregate value which further aids in improving query performance. Also, in the functions, you can explicitly specify to ignore the NULL in the data.

What are the Analytics Functions Supported by Amazon Redshift?

Have a look at some of Amazon Redshift’s Analytical Functions that perform day-to-day aggregations to improve query performance.

• COUNT Analytic Function
• SUM Analytic Function
• MIN and MAX Analytic Function
• LEAD and LAG Analytic Function
• FIRST_VALUE and LAST_VALUE Analytic Function
• ROW_NUMBER, RANK, and DENSE_RANK Analytical Functions

1. COUNT Analytic Function

The function counts all the rows in the query or table defined by the expression.

Syntax:

COUNT(column reference | value expression | *) over(window_spec);

2. SUM Analytic Function

The role of the Sum Analytic function in Amazon Redshift is to determine or calculate the sum of all columns defined with expressions. It also calculates the total sum of all rows in the table or group.

Syntax:

SUM(column | expression) OVER( window_spec);

3. MIN and MAX Analytic Function

The Amazon Redshift Analytic MIN and MAX functions help calculate the minimum and maximum of the column rows or input expression values or a group of rows. It performs similar to the SQL MIN and MAX functions.

Syntax:

MIN(column | expression) OVER( window_spec);

MAX(column | expression) OVER( window_spec);

4. LEAD and LAG Analytic Function

Lead and Lag Redshift Analytic Functions are used by administrators to monitor change and variation. They compare rows of a table at a given offset from the current row. Generally, the default value of the offset is 1. 

Syntax:

LEAD(column, offset, default) OVER( window_spec);

LAG(column, offset, default) OVER( window_spec);

Remember, the LEAD/LAG function returns NULL if there is no row before or after to access the function. The only solution to change the NULL value is to add “default” values.

5. FIRST_VALUE and LAST_VALUE Analytic Function

The Redshift First_Value and Last_value Analytic Functions provide the first and last value of a column or specified expression or rows within a group. Make sure to clearly state the criteria to determine the values.

Syntax:

FIRST_VALUE(column | expression) OVER( window_spec);

LAST_VALUE(column | expression) OVER( window_spec);

6. ROW_NUMBER, RANK, and DENSE_RANK Analytical Functions

These functions come into practice when there is a need to assign unique values to rows of a table or group of rows.

Syntax:

ROW_NUMBER() OVER( window_spec);
RANK() OVER( window_spec);
DENSE_RANK() OVER( window_spec);

How to use the Amazon Redshift DENSE_RANK Function?

The Redshift DENSE_RANK Function uses the ORDER BY expression in the OVER clause to compute the rank of a value in the value groups. Rows with equal values obtain similar rankings. Remember, you may have to reset the ranking for each row if the PARTITION BY clause is in practice.

Syntax for Redshift DENSE_RANK Function:

DENSE_RANK () OVER
(
[ PARTITION BY expr_list]
[ ORDER BY order_list ]
)

A) Arguments

1) ( )

• It involves no arguments, but there is a requirement for empty parentheses.

2) OVER

• It is the Amazon Redshift DENSE_RANK function’s window clause.

3) PARTITION BY expr_list

• It is an optional argument for the Amazon Redshift DENSE_RANK Function that defines the window using one or more expressions. 

4) ORDER BY order_list

• It is an optional argument. These are the expression that determines the ranking value. When the PARTITION BY is not added to the syntax, the entire table is used by the ORDER BY argument. Also, remember the return value will be 1 for each row if ORDER BY is omitted.
• If there is no unique ordering produced by the argument, the ranking value for rows will be non-deterministic.

B) Example Query

In this example, we have arranged the sold quantity in descending order. Each row is assigned an Amazon Redshift Dense_Rank and Rank function. Follow the results after the window function is sorted and applied.

select salesid, qty,
dense_rank() over(order by qty desc) as d_rnk,
rank() over(order by qty desc) as rnk
from winsales
order by 2,1;

salesid | qty | d_rnk | rnk
---------+-----+-------+-----
10001 |  10 |     5 |   8
10006 |  10 |     5 |   8
30001 |  10 |     5 |   8
40005 |  10 |     5 |   8
30003 |  15 |     4 |   7
20001 |  20 |     3 |   4
20002 |  20 |     3 |   4
30004 |  20 |     3 |   4
10005 |  30 |     2 |   2
30007 |  30 |     2 |   2
40001 |  40 |     1 |   1
(11 rows)

Have a look at the other example in which we have partitioned the table using SELLERID and ordered them in descending orders of their quantity. Further, each row is assigned a Redshift Dense_Rank function. Follow the results after the window function is sorted and applied:

select salesid, sellerid, qty,
dense_rank() over(partition by sellerid order by qty desc) as d_rnk
from winsales
order by 2,3,1;

salesid | sellerid | qty | d_rnk
---------+----------+-----+-------
10001 |        1 |  10 |     2
10006 |        1 |  10 |     2
10005 |        1 |  30 |     1
20001 |        2 |  20 |     1
20002 |        2 |  20 |     1
30001 |        3 |  10 |     4
30003 |        3 |  15 |     3
30004 |        3 |  20 |     2
30007 |        3 |  30 |     1
40005 |        4 |  10 |     2
40001 |        4 |  40 |     1
(11 rows)

What is the Difference between the Redshift DENSE_RANK & RANK Function?

The RANK Function returns ranked rows value depending on the ORDER BY Clause condition. For example, if you want to list car names with the third-highest power.

SELECT name, company, power, 
RANK() OVER(ORDER BY power DESC) AS PowerRank 
FROM Cars

The Redshift DENSE_RANK Function works similarly. But, it covers all ranks even if there is a tie in the rankings. 

SELECT name,company, power,
RANK() OVER(PARTITION BY company ORDER BY power DESC) AS PowerRank
FROM Cars

You can differentiate the function when duplicate values are present in the column using the ORDER BY Clause. The Rank Functions return the same rank value with the next rank value skipped.

For example, if there is a tie between previous records, the RANK function will skip the next N-1 ranks. However, the Amazon Redshift DENSE_RANK function will not skip the same.

This is how you can use the Amazon Redshift DENSE_Rank Function for your analytics use cases!

Conclusion

The change in the role of storage and computation demanded the need for a better Data Warehouse Solution. To perform, manage, monitor, and scale large datasets, Amazon Redshift came into practice. It is one of the Cost-effective, easy-to-install, and deploy Data Warehouse solutions. It also supports End-to-end Data Encryption features to maintain data privacy.

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Hevo Data supports 100+ Data Sources and helps you transfer your data from these sources to Data Warehouses like Amazon Redshift, etc.

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