Understanding the Amazon Redshift Boolean Data Type Made Easy 101

Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools.

The data types in Amazon Redshift are very similar to those in standard Relational Databases. Boolean is one of the most popular and often used data types. The Redshift Boolean data type is used to store logical Boolean values that can be either True or False. Though the Redshift Boolean data type appears simple to implement in your database, it can be extremely confusing if not approached the right way.

In this article, you will gain information about Amazon Redshift Boolean Datatype. You will also gain a holistic understanding of Amazon Redshift, its key features, prerequisites before working with Amazon Redshift Boolean Data type, using Amazon Redshift Boolean Data type along with examples.. Read along to find out in-depth information about Amazon Redshift Boolean Data type.

Table of Contents

• What is Amazon Redshift?
  • Key Features of Amazon Redshift
    • Massively Parallel Processing
    • Fault Tolerance
    • Redshift ML
    • Column-oriented Design
• Prerequisites for Using the Redshift Boolean Data Type
• Getting Started with Redshift Boolean Data Type
• Using the Redshift Boolean data type
• Redshift Boolean Data Type Examples
• Conclusion

What is Amazon Redshift?

Amazon Web Services (AWS) is a subsidiary of Amazon saddled with the responsibility of providing a cloud computing platform and APIs to individuals, corporations, and enterprises. AWS offers high computing power, efficient content delivery, database storage with increased flexibility, scalability, reliability, and relatively inexpensive cloud computing services.

Amazon Redshift, a part of AWS, is a Cloud-based Data Warehouse service designed by Amazon to handle large data and make it easy to discover new insights from them. Its operations enable you to query and combine exabytes of structured and semi-structured data across various Data Warehouses, Operational Databases, and Data Lakes.

Amazon Redshift is built on industry-standard SQL with functionalities to manage large datasets, support high-performance analysis, provide reports, and perform large-scaled database migrations. Amazon Redshift also lets you save queried results to your S3 Data Lake using open formats like Apache Parquet from which additional analysis can be done on your data from other Amazon Web Services such as EMR, Athena, and SageMaker.

For further information on Amazon Redshift, you can follow the Official Documentation.

Key Features of Amazon Redshift

The key features of Amazon Redshift are as follows:

• Massively Parallel Processing
• Fault Tolerance
• Redshift ML
• Column-oriented Design

1) Massively Parallel Processing (MPP)

Massively Parallel Processing (MPP) is a distributed design approach in which the divide and conquer strategy is applied by several processors to large data jobs. A large processing job is broken down into smaller jobs which are then distributed among a cluster of Compute Nodes. These Nodes perform their computations parallelly rather than sequentially. As a result, there is a considerable reduction in the amount of time Redshift requires to complete a single, massive job.

2) Fault Tolerance

Data Accessibility and Reliability are of paramount importance for any user of a database or a Data Warehouse. Amazon Redshift monitors its Clusters and Nodes around the clock. When any Node or Cluster fails, Amazon Redshift automatically replicates all data to healthy Nodes or Clusters.

3) Redshift ML

Amazon Redshift houses a functionality called Redshift ML that gives data analysts and database developers the ability to create, train and deploy Amazon SageMaker models using SQL seamlessly.

4) Column-Oriented Design

Amazon Redshift is a Column-oriented Data Warehouse. This makes it a simple and cost-effective solution for businesses to analyze all their data using their existing Business Intelligence tools. Amazon Redshift achieves optimum query performance and efficient storage by leveraging Massively Parallel Processing (MPP), Columnar Data Storage, along with efficient and targeted Data Compression Encoding schemes.

Prerequisites for the Using Redshift Boolean Data Type

To get started on the Redshift boolean data type, below are the basic things you need to have set up:

• An Amazon Redshift account
• Install a SQL client
• Launch a Redshift cluster
• Connect your SQL client to a database in your Redshift cluster

Getting Started with Redshift Boolean Data Type

You must first set up your database, user, and table before you can use the Redshift Boolean data type.

1) Creating your Database 

Create your database when you’ve verified that your cluster is up and functioning. This is the database where you create tables, load your data, and execute queries. An important aspect of Redshift is that you can host multiple databases on one cluster. 

For example: You can run the following command in your SQL tool to create a database named “CUSTOMERDB”.

CREATE DATABASE CUSTOMERDB;

2) Creating a User 

You can create a user for your database using the command, “CREATE USER“. You will provide the new user a name and a password when you create a new user. It is necessary that you give the user a password. The password must be between 8 and 64 characters long, and it must contain at least one lowercase, one uppercase, and one numeric.

For Example: To create a user with the name “VISITOR” and password “VICy0987”, run the following command.

CREATE USER VISITOR PASSWORD ' VICy0987';

You can the same password you used to create the VISITOR user to connect to the CUSTOMERDB database.

3) Create Tables

After you have created your new database, you need to create tables to hold your data. You can use the Redshift Create Table command to add new tables to your Redshift instance. Note that the table and columns must be given a name, as well as their data types.

Creating a table in Redshift can be created in various ways. Here are the three main variations:

I) Create a Table (Default Method)

This command creates a new table. You have the freedom to select the table name, column names, and data types for each column. 

Run the following command:

CREATE TABLE tables_name (
columns_name1 data_type1,
columns_name2 data_type2
columns_name3 data_type3
)

II) Create a Table Using AS

This table variation allows you to create a new table using the SELECT command to select data from another table. The old table’s data, column names, and data types are copied to the new one.

Using the AS clause/parameter, you can inherit the table structure and data from the SQL query. However, this command doesn’t enable inheriting constraints, default values, and other parameters.

Run the following command:

CREATE TABLE AS tables_name SELECT * from old_table
or
CREATE TABLE tables_name AS Select * from old_table;

The AS clause/parameter can be used with the Redshift Create Table command in the former way.

II) Create a Table Using LIKE

The LIKE command allows you to designate a separate table whose structure can be copied without the data being copied.

Run the following command:

CREATE TABLE tables_name LIKE previous_tables_name

If you want to use this function, you must include the LIKE clause and the parent table name in your command. The INCLUDING DEFAULTS clause can also be used to inherit default values.

Run the following command to use the INCLUDING DEFAULTS command:

CREATE TABLE tables_name (LIKE previous_tables_name INCLUDING DEFAULTS);

After successfully creating your table using any of the methods mentioned above, now you can implement the Redshift Boolean data type into the table.

Using the Redshift Boolean Data Type 

A Redshift Boolean data type is a byte column that stores “True” or “False” values. To indicate a True value in your input, you can use the characters ‘1’, ‘t’,’ y’, ‘yes’, ‘true’, or ‘TRUE‘. False values can be represented in the input as ‘0’, ‘f’, ‘n’, ‘no’, ‘false’ or ‘FALSE‘.

The table below lists the three possible states for a Boolean value, as well as the literal values that correspond to each state.

State	Values	Storage
False	‘f’ ‘false’ ‘n’ ‘no’ ‘0’ FALSE	1 byte
True	‘t’ ‘true’ ‘y’ ‘yes’ ‘1’ TRUE	I byte
Unknown	NULL	1 byte

Redshift Boolean Data Type Examples

The following are some samples of how you can use the Redshift Boolean data type in various ways. A users table has been created for the following queries.

1) Determining User’s Choice

You can use the Redshift Boolean data type to select users from the USERS table who like running but do not like jumping:

select fname, lname, likerunning, likejumping
from users
where likerunning is true and likejummping is false 
order by userid limit 5;

Output:

fname	Lname	likerunning	Likejumping
Lars	Ratliff	t	F
Mufutau	Waltkins	t	f
Charlie	Scott	t	F
Winifred	Mayer	t	f
(5 rows)

The query in this example chooses users from the USERS table who enjoy running but not jumping.

2) Using the IS Comparison 

You can only use the IS comparison as a base in the WHERE clause to check a Boolean value.

In the following example, you can select users from the USERS table whose preference for RnB music is unknown.

select ftname, lname, likernd
from users
where likernb is unknown
order by userid limit 10;

Output:

fname	lname	likernb
Mufutau	Waltkins
Charlie	Scott
Winifred	Mayer
James	Corbin
Thomas	Anthony
Samantha	Rice
Grace	Washington
Vanessa	Clinton

Here is the result:

select ftname, lname, likernb is true as "check"
from users
order by userid limit 9;
[Amazon] (500453) Invalid operation: Not implemented

The following command generated an error because an IS comparison was used in the SELECT list.

You can implement it this way

select ftname, tname, likernb = true as "check"
from users
order by userid limit 9;

Output:

fname	lname	likernb
Lars	Ratliff	true
Mufutau	Waltkins
Winifred	Mayer
James	Corbin	true
Thomas	Anthony
Richard	Cypher	true
Samantha	Rice	false
Grace	Washington
Vanessa	Clinton	true

The query was successful because an equal comparison (=) was used in the SELECT list instead of an IS comparison.

NOTE: You must ensure that you always check the Boolean values clearly to avoid mistakes or errors.

For further information on Amazon Redshift Boolean Data type, you can visit here.

Conclusion

In this article, you have learned about Amazon Redshift Boolean Datatype. This article also provided information on Amazon Redshift, its key features, prerequisites before working with Amazon Redshift Boolean Data type, using Amazon Redshift Boolean Data type along with examples.

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