Organizations that use traditional Data Warehouses face not only storage constraints, but also difficulties in processing the increasing volume of data. Amazon Redshift, on the other hand, provides a fast, dependable, and cloud-based Data Warehouse solution that eliminates scaling issues and enables analysts to gain key insights with business intelligence tools. Because Redshift uses SQL on the backend, it can also help in determing the ordial number of the current row using the Redshift ROW_NUMBER function to simplify data manipulation in various forms based on the requirements.
Upon a complete walkthrough of this article, you will gain a decent understanding of Amazon Redshift and the salient features that it offer. This article will also provide you key insights on Redshift ROW_NUMBER Function along with some examples.
Table of Contents
What is Amazon Redshift?
AWS Redshift is a cloud-based serverless Data Warehouse provided by Amazon as a part of Amazon Web Services. It is a fully managed and cost-effective Data Warehouse solution. AWS Redshift is designed to store Petabytes of data and perform real-time analysis to generate insights.
AWS Redshift is a column-oriented database and stores the data in a columnar format compared to traditional databases that store in a row format. Amazon Redshift has its own compute engine to perform computing and generate critical insights.
To know more about AWS Redshift, follow the official documentation here.
AWS Redshift Architecture
AWS Redshift has a straightforward architecture. It contains a Leader Node and cluster of Compute Nodes that perform analytics on data. The below snap depicts the schematics of AWS Redshift architecture:
Key Features of Amazon Redshift
Some of the key features of Amazon Redshift are as follows:
- Redshift allows users to write queries and export the data back to Data Lake.
- Redshift can seamlessly query the files like CSV, Avro, Parquet, JSON, ORC directly with the help of ANSI SQL.
- Redshift has exceptional support for Machine Learning, and developers can create, train and deploy Amazon Sagemaker models using SQL.
- Redshift has an Advanced Query Accelerator (AQUA) which performs the query 10x faster than other cloud Data Warehouses.
- Redshift’s Materialistic view allows you to achieve faster query performance for ETL, Batch Job Processing, and Dashboarding.
- Redshift has a Petabyte scalable architecture, and it scales quickly as per the requirements.
- Redshift enables secure sharing of the data across Redshift clusters.
- Amazon Redshift provides consistently fast performance, even with thousands of concurrent queries.
What are Window Functions?
Window Functions are analytical functions that enable users to create and execute analytical queries efficiently. The Window Function operates on a partition or window of defined limit and returns the result for each window. For example, if the user wants to add the salary of all the employees based on department, the Window Function will first all the rows for the same department and then perform the sum against the department.
The aggregations are calculated based on the rows present in that particular window, and it is based on three main concepts –
- Partition Clause that defines the partition value to create a window
- Over Clause to perform ordering of the rows within each window/partition.
- Window Frames are defined relative to each row to restrict the rows’ set further.
Amazon Redshift supports two types of Window Functions: Aggregate and Ranking.
These are the supported Aggregate functions:
- STDDEV_SAMP (synonym for STDDEV)
- VAR_SAMP (synonym for VARIANCE)
The supported Ranking Functions are –
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Understanding Redshift ROW_NUMBER Function
Redshift ROW_NUMBER Function is a ranking function that determines the ordinal number of the current row within a group of rows based on the order by clause.
The Redshift ROW-NUMBER Function has an optional partition that causes the row number to reset for each window. The counting for row numbers starts from 1 unless incremented by an external factor.
ROW_NUMBER () OVER
[ PARTITION BY expr_list ]
[ ORDER BY order_list ]
The Redshift ROW_NUMBER functions take no argument. However, they expect the optional partition by clause and order by clause to rank the rows based on the criteria:
- Redshift ROW_NUMBER: Rank function
- Partition by(Optional): One or more expressions define the Redshift ROW_NUMBER function.
- Order by(Optional): The expression defines the columns on which the tables are ordered. If no PARTITION BY is specified, ORDER BY uses the entire table. [If the OrderBy clause is not specified, then the row number is non-deterministic as rows can be processed in any order].
Impact of Redshift ROW_NUMBER Function
To get more understanding of how the Redshift ROW_NUMBER function work, let’s try to understand the following example –
Let us create a table and apply the Rank Function to see how it is working:
create table product(
Now let us insert a few duplicate values below and apply the ranking function ROW_NUMBER on it.
insert into product values ('Earphone', 10);
insert into product values ('Earphone', 20);
insert into product values ('Earphone', 30);
insert into product values ('Mobile', 10);
insert into product values ('Mobile', 10);
insert into product values ('Mobile', 20);
insert into product values ('Watch', 10);
insert into product values ('Watch', 20);
insert into product values ('Watch', 40);
Now that we have inserted the records into the table let’s apply the Redshift ROW_NUMBER function to the product table and see how it ranks the records and produces the output. The SQL goes like below –
SELECT Product_name, sale_quantity, ROW_NUMBER() OVER (PARTITION BY product_name ORDER BY sale_quantity desc) as RN
The output will look like this:
In the above output, you can see that the Redshift ROW_NUMBER function partitions the data by product_name and orders them by sales_quantity in descending manner, and then apply the ROW_NUMBER.
To understand more about the Redshift ROW_NUMBER functions, let us consider a real-time example that applies Redshift ROW_NUMBER functions to sort the data –
Create the table employee with the columns as emp_id, emp_name, emp_address, emp_mobile, and emp_salary as below –
create table employee(
emp_salary int );
Let us insert few rows in the above table as below and apply the Rank Function:
insert into employee values (1, 'Sam S', 'USA', '9987956479', 45110 );
insert into employee values (2, 'Fred F', 'UK', '9872367534', 95220 );
insert into employee values (3, 'Will W', 'Germany', '9679854678', 85330 );
insert into employee values (4, 'Ben B', 'London', '8879812345', 45098 );
insert into employee values (5, 'William W', 'rome', '7879809876', 95980 );
insert into employee values (6, 'Bentley B', 'Italy', '7877845678', 95090 );
insert into employee values (7, 'Sony S', 'USA', '8979800998', 75123 );
insert into employee values (8, 'Sian S', 'USA', '6579899887', 65345 );
insert into employee values (9, 'Shames S', 'London', '0979809890', 43768 );
insert into employee values (10, 'Harry H', 'USA', '9877890876', 56789 );
Now that we have inserted the data into the employee table, the Select statement will display the result as:
Now let’s implement the Redshift ROW_NUMBER function to the above table –
select *, ROW_NUMBER() OVER (PARTITION BY emp_address order by emp_salary desc) AS rn
Now let us apply the Redshift ROW_NUMBER function in the table “employee” without any partition by –
select * ,
ROW_NUMBER() OVER (order by emp_salary) AS rn
The output will be as follows:
As we have not specified any partition value, only the order by clause, the Redshift ROW_NUMBER function orders the entire table by salary and then assigns the row number.
In this article, we have discussed one of the ranking functions Redshift ROW_NUMBER that ranks the records from the table based on the partition and order by clause provided. With your Data Warehouse, Amazon Redshift live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.
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