Redshift Delete Table and Drop Command 101: Syntax, Usage, and Example Queries Simplified

• January 18th, 2022

Redshift Delete Table and Drop Table Commands

The solution you choose to warehouse your data is just as important as the way you collect Business Intelligence data. If you want to maximize the potential of your data, it must be easily accessed, well-organized, and simple to store and manipulate. With Amazon’s Redshift Data Warehouse tools, you can deliver lightning-fast performance and scalable data processing solutions without having to invest in massive infrastructure. Additionally, Redshift provides access to Machine Learning and Artificial Intelligence tools, as well as Data Analytics tools.

Amazon’s Redshift emphasizes accessibility and ease of use by creating features that mimic MySQL, such as the Redshift DELETE Table and Redshift DROP TABLE commands.

In this article, we will describe how Amazon Redshift works, as well as its key features and benefits. Next, you will learn about Redshift Delete Table and Drop Table Command, its Syntax, Usage, and some Examples. 

Table of Contents

What is Amazon Redshift?

Redshift Logo
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The Amazon Redshift Data Warehouse is a fully-managed Petabyte-scale cloud-based service intended for large-scale Data Analysis and Storage. Large-scale data migrations can also be performed with it.

In addition to being designed to connect with SQL-based clients and business intelligence tools, Redshift’s Column-oriented database provides real-time data access to users. Redshift allows teams to make sound business decisions and analyses by delivering fast performance and efficient querying built on PostgreSQL 8.

Key Features of Amazon Redshift

Key Features of Redshift
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While Redshift is known for its continuous innovation, it’s the architecture of the platform that makes it one of the most powerful Cloud Data Warehouses. Here are key features that distinguish Redshift from other Data Warehouses.

1. Column-Oriented Databases

There are two ways to organize data: rows or columns. The type of method to use is determined by the type of workload.

By far, the most common method of organizing data is by row. The reason is that row-oriented systems are built to handle a large number of small operations rapidly. Most operational databases use this process, which is known as Online Transaction Processing, or OLTP.

Conversely, Column-Oriented databases allow access to large amounts of data to be performed faster. As an example, users of Online Analytical Processing (OLAP) environments generally apply fewer queries to much larger datasets. Due to its column-oriented design, Redshift is capable of completing Massive Data Processing jobs quickly.

2. Massively parallel processing (MPP)

Massively Parallel Processing (MPP) is a distributed design approach that divides the work evenly among several processors. Typically, large jobs are divided into smaller ones, which are each distributed among a cluster of processors (Nodes). 

Rather than completing their computations one at a time, these processors operate simultaneously. As a result, Redshift has significantly reduced the time it takes to complete a single, massive job.

3. End-to-End Data Encryption

Data Privacy and Security Regulations apply to every business or organization, and Encryption is one of the pillars of Data Protection. In this regard, it is particularly important to comply with laws such as GDPR, HIPAA, the Sarbanes-Oxley Act, and the California Privacy Act.

Redshift offers robust encryption options that can be customized to any degree. With this flexibility, users can choose a method that is best suited to their needs. Among Redshift’s security features are:

  • An AWS-managed Key or a Customer-managed Key can be used.
  • Migration of data between Encrypted Clusters and Unencrypted Clusters.
  • You can choose between AWS Key Management Service and HSM (Hardware Security Module).

4. Network Isolation

Admins can choose to isolate a network within Redshift if they want additional security for their networks. By enabling Amazon Virtual Private Cloud (VPC), network access to an organization’s clusters is restricted. The user’s data warehouse keeps its connection to their existing IT infrastructure via IPsec VPN.

5. Fault Tolerance

When some components fail, Fault Tolerance allows the system to continue to function. In Data Warehouses, Fault Tolerance determines the ability to continue running a job even if some processors or clusters are unavailable.

The accessibility and reliability of Data Warehouses are crucial for any user. Amazon Web Services keeps an eye on its clusters at all times. During a Drive, Node, or Cluster Failure, Redshift automatically re-replicates data and moves data to healthy nodes.

6. Concurrency Limits

During a given period of time, users can provision a maximum number of Nodes or Clusters. These limits make it possible for all users to have access to adequate computing resources. As a result, concurrency limits democratize Data Warehouses.

While Redshift maintains similar concurrency limits as other data warehouses, it offers some flexibility as well. For instance, the number of available Nodes Per Cluster is determined by its Node type. In addition, Redshift applies limits based on regions, rather than applying a single limit to everyone. Users may also request an increase in limits in certain circumstances.

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Understanding Redshift Delete Table Command

Amazon Redshift Delete Table Command helps to delete rows from tables. The syntax, parameters explanation and examples are discussed below.

Syntax of Redshift Delete Table Command

[ WITH [RECURSIVE] common_table_expression [, common_table_expression , ...] ]
DELETE [ FROM ] table_name
    [ {USING } table_name, ... ]
    [ WHERE condition ]

Parameters Involved:

  • WITH clause: With the WITH clause, you can specify one or more common-table-expressions. 
  • FROM: It is optional to specify the FROM keyword, except for when the USING clause is specified. All rows in the EVENT table will be deleted by using the statements delete from the event.
  • Table_name: This is a temporary or persistent table. Delete rows from a table can only be performed by the owner or a user with the DELETE privilege.
  • TRUNCATE can be used for fast unqualified deletion of large tables.
  • USING table_name : With the USING keyword, an additional table list is created when another table is referenced in the WHERE clause. 
  • Here is an example of a statement that deletes all rows from the EVENT table matching the join condition over the EVENT and SALES tables. It is important to name the SALES table explicitly in the FROM list.
  • The DELETE operation performs a self-join if you repeat the target table name in the USING clause. In an alternative way to write the same query, you can use a subquery instead of the USING syntax.
  • WHERE condition: By specifying this clause, only those rows matching the condition will be deleted. You can use a condition to restrict a column, to conditionally join columns, or to conditionally restrict your query results. 
  • It is possible for the query to refer to tables other than the target of the DELETE command. An example is:
  • delete from t1 where col1 in(select col2 from t2): Unless a condition is specified, all rows in the table will be deleted.

Example Queries of Redshift Delete Table Command

To delete all rows from the CATEGORY table, follow this step:

delete from category;

To delete rows with CATID values between 0 and 9, follow these steps:

delete from category
where catid between 0 and 9;

If you want to delete rows from the LISTING table whose SELLERID values are not in the SALES table:

delete from listing
where listing.sellerid not in(select sales.sellerid from sales);

Using a join between CATEGORY and EVENT and a restriction on the CATID column, the following Redshift Delete Table queries delete one row each from the CATEGORY table:

delete from category
using event
where event.catid=category.catid and category.catid=9;
delete from category
where catid in
(select category.catid from category, event
where category.catid=event.catid and category.catid=9);

Understanding Redshift Drop Table Command

The DROP TABLE command removes all tables from a database. You can drop a table only if you are the owner of the table, the schema owner, a superuser, or you have been granted the DROP privilege.

If you want to empty the rows of a table without removing the table, use the DELETE or TRUNCATE command.

The DROP TABLE command removes all constraints from the target table. It is possible to drop multiple tables with a single DROP TABLE command.

A DROP TABLE with an external table cannot be run inside a transaction (BEGIN … END). 

Syntax of Redshift Drop Table Command

DROP TABLE [ IF EXISTS ] name [, ...] [ CASCADE | RESTRICT ]

Parameters Involved:

  • IF EXISTS: This clause specifies that if the specified table does not exist, the command should make no changes and return an error message rather than terminating with an error.
  • When scripting, this clause ensures that the script does not fail if DROP TABLE is called against a nonexistent table.
  • name: This is the name of the table to drop.
  • CASCADE: A CASCADE clause indicates that all objects connected to the view, such as other views, should be automatically dropped.
  • In order to define a view that isn’t dependent on other objects, such as tables and views, including the clause WITH NO SCHEMA BINDING in the definition. 
  • RESTRICT: This clause states not to drop a table if any objects depend on it. It is the default action.

Example Queries of Redshift Drop Table Command

1) To drop a table without any dependencies

In the following example, we create and drop a table called FEEDBACK that does not have any dependencies:

create table feedback(a int);
drop table feedback;

Amazon Redshift displays a message such as the following if the table contains columns that are referenced by views or other tables.

Invalid operation: cannot drop table feedback because other objects depend on it

2) To drop two tables concurrently

As an example, the following command set creates the FEEDBACK table and the BUYERS table, then drops both tables with a single command:

create table feedback(a int);

create table buyers(a int);
drop table feedback, buyers;

3) Dropping a table that has a dependency

Following are the steps you need to take to drop a table called FEEDBACK using the CASCADE switch.

The first thing you need to do is create a simple table called FEEDBACK using the CREATE TABLE command:

create table feedback(a int);

As a next step, create a view called FEEDBACK_VIEW that relies on the table FEEDBACK using the CREATE VIEW command:

create view feedback_view as select * from feedback;

In the following example, we drop the table FEEDBACK and also drop the view FEEDBACK_VIEW, because FEEDBACK_VIEW relies on FEEDBACK:

drop table feedback cascade;

4) Viewing the dependencies for a table

A view that contains information about the relationships between all of the tables in a database can be created. You should query this view to determine whether a given table has dependencies before dropping it.

To create a view that combines dependencies and object references, type the following command:

create view find_depend as
select distinct c_p.oid as tbloid,
n_p.nspname as schemaname, c_p.relname as name,
n_c.nspname as refbyschemaname, c_c.relname as refbyname,
c_c.oid as viewoid
from pg_catalog.pg_class c_p
join pg_catalog.pg_depend d_p
on c_p.relfilenode = d_p.refobjid
join pg_catalog.pg_depend d_c
on d_p.objid = d_c.objid
join pg_catalog.pg_class c_c
on d_c.refobjid = c_c.relfilenode
left outer join pg_namespace n_p
on c_p.relnamespace = n_p.oid
left outer join pg_namespace n_c
on c_c.relnamespace = n_c.oid
where d_c.deptype = 'i'::"char"
and c_c.relkind = 'v'::"char";

From the SALES table, create a SALES_VIEW:
create view sales_view as select * from sales;

View dependencies in the database by querying the FIND_DEPEND view. You should limit your query to the PUBLIC schema, as shown in the following code:

select * from find_depend
where refbyschemaname='public'
order by name;

Based on this query, we can see that the SALES_VIEW view is dropped along with the SALES table when we are dropping the CASCADE option:

Redshift Delete Table and Drop Table Command
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Conclusion

This blog introduced you to Amazon Redshift along with the salient features that it offers. It also discussed the Redshift Delete Table and Drop Table Commands in detail. 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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