PostgreSQL vs Redis

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of PostgreSQL and Redis so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how PostgreSQL and Redis perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

PostgreSQL Overview

PostgreSQL, also known as Postgres, is an open-source relational database management system that was first released in 1996. It has a long history of being a robust, reliable, and feature-rich database system, widely used in various industries and applications. PostgreSQL is known for its adherence to the SQL standard and extensibility, which allows users to define their own data types, operators, and functions. It is developed and maintained by a dedicated community of contributors and is available on multiple platforms, including Windows, Linux, and macOS.

Redis Overview

Redis, which stands for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It was created by Salvatore Sanfilippo in 2009 and has since gained significant popularity due to its high performance and flexibility. Redis supports various data structures, such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, and geospatial indexes with radius queries.

PostgreSQL for Time Series Data

PostgreSQL can be used for time series data storage and analysis, although it was not specifically designed for this use case. With its rich set of data types, indexing options, and window function support, PostgreSQL can handle time series data. However, Postgres will not be as optimized for time series data as specialized time series databases when it comes to things like data compression, write throughput, and query speed. PostgreSQL also lacks a number of features that are useful for working with time series data like downsampling, retention policies, and custom SQL functions for time series data analysis.

Redis for Time Series Data

Redis has a dedicated module for working with time series data called RedisTimeSeries. RedisTimeSeries offers functionality like downsampling, data retention policies, and specialized queries for time series data in Redis. Being an in-memory database, Redis will be very fast for reading and writing time series data, but due to the cost of RAM compared to disk using Redis could become expensive depending on the size of your dataset. If your use case doesn’t require extremely fast response times, you could save money by going with a more traditional time series database.

PostgreSQL Key Concepts

• MVCC: Multi-Version Concurrency Control is a technique used by PostgreSQL to allow multiple transactions to be executed concurrently without conflicts or locking.
• WAL: Write-Ahead Logging is a method used to ensure data durability by logging changes to a journal before they are written to the main data files.
• TOAST: The Oversized-Attribute Storage Technique is a mechanism for storing large data values in a separate table to reduce the main table’s disk space consumption.

Redis Key Concepts

• In-memory store: Redis stores data in memory, which allows for faster data access and manipulation compared to disk-based databases .
• Data structures: Redis supports a wide range of data structures, including strings, hashes, lists, sets, and more, which provide flexibility in how data is modeled and stored.
• Persistence: Redis offers optional data persistence, allowing data to be periodically saved to disk or written to a log for durability.
• Pub/Sub: Redis provides a publish/subscribe messaging system, enabling real-time communication between clients without the need for a centralized message broker.

PostgreSQL Architecture

PostgreSQL is a client-server relational database system that uses the SQL language for querying and manipulation. It employs a process-based architecture, with each connection to the database being handled by a separate server process. This architecture provides isolation between different users and sessions. PostgreSQL supports ACID transactions and uses a combination of MVCC, WAL, and other techniques to ensure data consistency, durability, and performance. It also supports various extensions and external modules to enhance its functionality.

Redis Architecture

Redis is a NoSQL database that uses a key-value data model, where each key is associated with a value stored as one of Redis’ supported data structures. The database is single-threaded, which simplifies its internal architecture and reduces contention. Redis can be deployed as a standalone server, a cluster, or a master-replica setup for scalability and high availability. The Redis Cluster mode automatically shards data across multiple nodes, providing data partitioning and fault tolerance.