Amazon Redshift vs PostgreSQL Comparison: Choosing the Right Data Warehouse

on Data Warehouse, Amazon Redshift, PostgreSQL • December 30th, 2021 • Write for Hevo

Amazon Redshift Vs PostgreSQL - Featured Image

In this post, you will explore the similarities and the differences between the two Data Warehouse choices – Redshift Vs Postgres, specifically in terms of factors that are critical to the life of a Data Engineer. 

Table of Contents

Understanding Amazon Redshift and PostgreSQL

With the advent of completely managed Data Warehouse services, the ETL space is undergoing a paradigm shift with many businesses choosing to migrate from their on-premise database installations. The biggest advantage of such services is the relief from all the housekeeping activities related to maintaining a reliable and always available database. Redshift is a very popular choice for a completely managed Data Warehouse service which can scale up to petabytes of data. 

For companies using on-premise databases, Postgres has remained as one of the most favourites because of its performance, reliability, and features. Postgres has been in development for over 30 years and has a strong querying layer which makes it a great choice for OLTP workloads. 

Redshift vs PostgreSQL – Features

In this section, you will learn about Redshift vs PostgreSQL features:

  1. Amazon Redshift Features
  2. PostgreSQL Features

Amazon Redshift Features

Redshift vs Postgres: Amazon Redshift
Amazon Redshift

Redshift is a completely managed data warehouse as a service offered by Amazon. Redshift has a query layer very similar to PostgreSQL query standard but lacks many features that standard PostgreSQL querying layer has. Redshift can store petabytes of data and is designed for running complex analytical queries spanning over millions of rows. 

Under the hood, Redshift has a cluster of nodes, of which one is designated as a leader node. The leader node is responsible for all client communications, query optimization, and planning, delegating tasks to member nodes, etc. Member nodes are the ones storing the data and execute the query plan provided by the leader node. A detailed account of how Redshift works under the hood can be found here.

Redshift allows the end-users to scale by increasing the number of nodes to upgrading the node configuration or both. Scaling usually happens in minutes for the newer generation nodes with elastic resizing support, but could take hours for certain older generation nodes. Redshift also allows the customers to choose from different types of nodes according to their performance requirements and budget. All the administrative tasks related to maintaining the cluster is automated and there is little intervention needed from the users for keeping the Redshift cluster running. 

Redshift is a paid offering from Amazon and the pricing is inclusive of the software, computing resources, and storage resources. Amazon allows the customers to pay only for their actual use and enables them to scale whenever required seamlessly. This closer control of the budget is possible in case of querying performance also by adjusting querying slots and AWS workload management queues accordingly. 

PostgreSQL Features

Redshift vs Postgres: PostgreSQL
PostgreSQL

PostgreSQL is a free and open-source database that is best suited for transactional workloads. It is a row-oriented database and is known for its stability with little need for maintenance activities.PostgreSQL offers all the typical features expected of an enterprise transactional database like unique primary keys, foreign key referential integrity, user-defined types, multi-version concurrency control, etc. 

The latest version of PostgreSQL makes it possible to have tables that can store 100s of TBs of data and in practice is only limited by disk space constraints. Internally PostgreSQL works based on a single server architecture with no concept of clusters. PostgreSQL runs as a single process and support for multi-core in case of single queries is possible from version 9.6 onwards. 

The biggest advantage when it comes to PostgreSQL is that it is completely free and the end-user only needs to pay for the infrastructure on which it is deployed. Upgrading the storage capacity in PostgreSQL can be a pain since the end-user will have to manage a lot of data backing up and copying using the tools provided. 

We now have a basic idea of the features of both Redshift PostgreSQL. It is now time to compare these two databases to see which one can be the best database for your use case.

Redshift vs PostgreSQL Comparison

In this section, you will go through Redshift vs Postgres comparison on the basis of the following features:

Redshift Vs PostgreSQL: Scaling

Redshift was designed with seamless scaling as a basic criterion. Redshift can be scaled in a matter of minutes for the new generation nodes with support for elastic resize. The accepted way of scaling Redshift is to add nodes, upgrade node configuration or through a combination of both. All this is managed by the AWS console and the end-user does not have to intervene other than initiating the scaling. Read more about Amazon Redshift scaling here.

Since PostgreSQL is an on-premise database based on a single server, scaling is not one of its strong points. If storage capacity needs to be upgraded, the customer will have to first copy the data to new disk drives for a seamless transition. That said if PostgreSQL is now available as a completely managed service from Amazon through their RDS offering. In such cases, scaling is relatively easy since it is managed by AWS.

Redshift Vs PostgreSQL: Performance

Redshift is optimized for complex analytical workloads. It exhibits better performance for queries with a large scan range. In the case of simpler queries with short data scan ranges, Redshift does poorly when compared to traditional databases. This is because Redshift has a sophisticated query optimizer and planner which at times takes more time than the actual execution time. This means, if there is nothing much to optimize, this query planner will actually pull Redshift’s performance. Redshift also has a mechanism called concurrency scaling which allows it to support practically unlimited concurrent users and concurrent queries. Read more about Redshift performance here.

PostgreSQL generally exhibits good performance when it comes to queries that do not test its limits. PostgreSQL is optimized for online transactional workloads and does very well until the queries have to scan millions of rows. In such cases, the time to execution can go up drastically leading to never-ending query processes. Read more about PostgreSQL performance and performance tips here.

Redshift Vs PostgreSQL: Data Replication

With PostgreSQL, loading data can be accomplished using the COPY FROM command which loads the rows from a file to table. This command is optimized for loading a large number of rows parallel. 

Redshift also has a COPY command which can be used to load data from S3 to Redshift tables. As a best practice, it is better to use this command with several evenly sized files to take advantage of Redshift’s massive parallelism. 

Things to note when moving data to Redshift or PostgreSQL:

Given you are trying to move data for into one of these systems for key business use cases – it is important to have reliable, accurate and consistent data. While building custom scripts seem like a straightforward option to move data, there are many limitations, caveats and challenges that you will uncover in your journey. Read more about the challenges here. 

Hevo Data: A Reliable Method to Move Data to Redshift or PostgreSQL

A much reliable and error-free way of moving data from any source to Amazon Redshift or Postgres would be to use a Data Pipeline Platform like Hevo Data. Hevo’s fault-free architecture ensures that your data is moved to the destination in a secure and reliable fashion in real-time. Hevo also supports pre-built integration from 100+ data sources. You can achieve all this, without writing any code.

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Some benefits of Hevo are listed below:

  1. Simple: Hevo offers a simple and intuitive user interface to the users. It has a minimal learning curve. 
  2. Secure: Hevo provides end-to-end encryption and two-factor authentication and makes sure your data is secure.
  3. Scalability: Hevo is built to handle millions of records per minute without any latency. 
  4. Real-Time: Hevo provides real-time data migration. So, your data is always ready for analysis.
  5. Fully Automated: Hevo can be set up in a few minutes and requires zero maintenance and management. 
  6. Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

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Redshift Vs PostgreSQL: Data Structure

PostgreSQL is a row-oriented database while Redshift is a column-oriented database. This difference has an effect on the query processing ability of the databases. Redshift does well in cases where fast retrieval of columns is needed.

PostgreSQL offers great support for unique key constraints and ensures foreign key referential integrity. Redshift does not have this ability and it is the responsibility of end-user to ensure this.

When it comes to data types, Redshift only supports hand full of them. Given below is a list of data types supported by Redshift.  

Data TypeAliasesDescription
SMALLINTINT2Signed two-byte integer
INTEGERINT, INT4Signed four-byte integer
BIGINTINT8Signed eight-byte integer
DECIMALNUMERICExact numeric of selectable precision
REALFLOAT4Single precision floating-point number
DOUBLE PRECISIONFLOAT8, FLOATDouble precision floating-point number
BOOLEANBOOLLogical Boolean (true/false)
CHARCHARACTER, NCHAR, BPCHARFixed-length character string
VARCHARCHARACTER VARYING, NVARCHAR, TEXTVariable-length character string with a user-defined limit
DATE Calendar date (year, month, day)
TIMESTAMPTIMESTAMP WITHOUT TIME ZONEDate and time (without time zone)
TIMESTAMPTZTIMESTAMP WITH TIME ZONEDate and time (with time zone)

PostgreSQL, on the other hand, supports all the above data types and many other data types. Some of the notable PostgreSQL data types not supported by Redshift are the Datetime data types, arrays, and JSON. There are many more data types that are unsupported in Redshift, but to summarize, PostgreSQL has much more comprehensive data type support when compared to Redshift. 

Redshift Vs PostgreSQL: Maintenance

Both Redshift and PostgreSQL requires some administrative tasks to be executed by the end-user to keep the service running smoothly. Both of these databases use delete markers while executing UPDATE and DELETE statements and hence need to recover the disk space later through a manual command called VACUUM. Recent versions of both the databases have support for auto executing these commands based on a threshold number of rows changing, but still, it is recommended to manually do this in case the user has a definite knowledge of many updates or deletes in a table.

Another such command is the ANALYZE command which helps the query planner keep its statistics up to date. Since these statistics can directly influence the query processing performance, it is recommended manually do this once in a while.

Redshift Vs PostgreSQL: Pricing

Redshift is a paid service and the lowest current generation dense compute dc2.large starts at .25 $ per hour. There is also another type of instance called dense storage that comes with higher storage capacity using HDDs instead of SSDs. It costs more than dense compute nodes and starts at .85 $.  Read more about Redshift pricing here.

PostgreSQL is completely free and opensource. Customers do not need to pay anything for the software, but they will have to purchase the hardware themselves based on their budget. 

Since these two follow different kinds of pricing strategies, there is no point in comparing based on it. If you are looking for the cheapest solution, nothing can beat a PostgreSQL deployed on-premise in an old server.

Redshift Vs PostgreSQL: Data Security

Redshift comes with the complete suite of security by default. Customers can choose to isolate Redshift clusters using amazon’s virtual private clouds. Amazon’s IAM console allows fine-grained control of which user has access t what options. Data is encrypted at rest and in motion and SSL support is also present. Read more about Redshift security here.

PostgreSQL also provides encryption, SSL support, user access management, etc, but it is the user’s responsibility to manage everything and ensure sufficient security by making use of these features. Network isolation will need to be done for the actual hardware server separately. Read more about PostgreSQL security here.

With the comparison out of the way, it is now time to enumerate some specific use cases whether each of these databases will be a sensible choice. 

Redshift Vs PostgreSQL: SQL Statements

Let’s discuss some SQL command difference between Redshift and Postgres.

  1. CREATE TABLE:
    Amazon Redshift doesn’t support tablespace, inheritance, table partition, etc. Redshift enables you to define the sort and distribution algorithm for your table to optimise your parallel processing in it.
  2. ALTER TABLE:
    As Redshift doesn’t support ALTER COLUMN, ADD COLUMN supports adding only one column in each ALTER TABLE command.
  3. COPY:
    The COPY command of Redshift can load data from Amazon S3 buckets and Amazon DynamoDB tables and is capable of facilitating automatic compression.
  4. INSERT, UPDATE, DELETE:
    Redshift doesn’t support the with clause.
  5. VACUUM:
    The VACUUM operation in Redshift is VACUUM FULL that reclaims full disk space and resorts all rows whereas VACUUM operation in PostgreSQL reclaims space and make it available for use.

Redshift Vs PostgreSQL: Use cases

Let’s discuss some Redshift vs Postgres use cases in the section.

When to use Amazon Redshift?

  1. Your data volume can go up to petabytes
  2. You want the offload all of your maintenance and infrastructure activities and focus only on the core business logic.
  3. Your workload is mainly analytical and reporting type and you anticipate them interfering with OLTP workloads if handled using the same database.
  4. You are willing to take responsibility of ensuring unique key constraints and referential integrity.
  5. You do not mind having to design the data structure using SORT KEYS AND DIST KEYS to get the maximum performance out of your data warehouse
  6. Your workload needs a lot of column processing and the number of columns is in the range of hundreds or thousands.

When to use PostgreSQL?

  1. Your main use case is online transaction processing and does not anticipate high load reporting or analytical workload
  2. The data volume is in TBs and a large increase in data to be processed is not expected in the near future.
  3. You are particular that your data should stay inside your network completely under your control 
  4. You have enough resources to maintain a highly available and reliable database.
  5. Your queries are simpler and do not have very large data scan ranges.
  6. Your workload is mainly dealing with complete rows and the number of columns in your tables is not in hundreds or thousands.

Conclusions

The final call to go for Redhishift or Postgres completely depends on your unique set of business requirements. We hope this detailed blog was able to provide you with perspectives on important parameters that matter.

But if you looking for a much reliable and error-free way of moving data from any source to Amazon Redshift or Postgres, then Hevo is the right choice. Hevo Data is a No-code Data Pipeline. It supports pre-built integration with 100+ data sources at a reasonable price. With Hevo, loading data from any source to Redshift or PostgreSQL becomes a cakewalk.

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Let us know how you went about choosing the right Data Warehouse or Database for your use case in the comments. 

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