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 QuestDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how PostgreSQL and QuestDB 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 vs QuestDB Breakdown
Time series database
PostgreSQL can be deployed on various platforms, such as on-premises, in virtual machines, or as a managed cloud service like Amazon RDS, Google Cloud SQL, or Azure Database for PostgreSQL.
QuestDB is designed for horizontal scaling, enabling you to distribute data and queries across multiple nodes for increased performance and availability. It can be deployed on-premises, in the cloud, or as a hybrid solution, depending on your infrastructure needs and preferences.
PostgreSQL license (similar to MIT or BSD)
Web applications, geospatial data, business intelligence, analytics, content management systems, financial applications, scientific applications
Monitoring, observability, IoT, Real-time analytics, Financial services, High-frequency trading
Supports vertical scaling, horizontal scaling through partitioning, sharding, and replication using available tools
High-performance with support for horizontal scaling and multi-threading
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.
QuestDB is an open-source relational column-oriented database designed specifically for time series and event data. It combines high-performance ingestion capabilities with SQL analytics, making it a powerful tool for managing and analyzing large volumes of time-based data. QuestDB addresses the challenges of handling high throughput and provides a simple way to analyze ingested data through SQL queries. It is well-suited for use cases such as financial market data and application metrics.
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.
QuestDB for Time Series Data
QuestDB excels in managing and analyzing time series data. With its high-performance ingestion capabilities, it can handle high data throughput, making it suitable for real-time data ingestion scenarios. QuestDB’s SQL extensions for time series enable users to perform real-time analytics and gain valuable insights from their time-stamped data. Whether it’s financial market data or application metrics, QuestDB simplifies the process of ingesting and analyzing time series data through its fast SQL queries and operational simplicity.
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.
QuestDB Key Concepts
- Time Series: QuestDB focuses on time series data, which represents data points indexed by time. It is optimized for storing and processing time-stamped data efficiently.
- Column-Oriented: QuestDB employs a column-oriented storage format, where data is organized and stored column by column rather than row by row. This format enables efficient compression and faster query performance.
- SQL Extensions: QuestDB extends the SQL language with functionalities specifically tailored for time series data. These extensions facilitate real-time analytics and allow users to leverage familiar SQL constructs for querying time-based data.
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.
QuestDB follows a hybrid architecture that combines features of columnar and row-based databases. It leverages a column-oriented storage format for efficient compression and query performance while retaining the ability to handle relational data with SQL capabilities. QuestDB supports both SQL and NoSQL-like functionalities, providing users with flexibility in their data modeling and querying approaches. The database consists of multiple components, including the ingestion engine, storage engine, and query engine, working together to ensure high-performance data ingestion and retrieval.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
PostgreSQL allows users to define custom data types, operators, and functions, making it highly adaptable to specific application requirements.
PostgreSQL has built-in support for full-text search, enabling users to perform complex text-based queries and analyses.
With the PostGIS extension, PostgreSQL can store and manipulate geospatial data, making it suitable for GIS applications.
QuestDB is optimized for high throughput ingestion, allowing users to efficiently ingest large volumes of time series data at high speeds.
Fast SQL Queries
QuestDB provides fast SQL queries for analyzing time series data. It extends the SQL language with time series-specific functionalities to assist with real-time analytics.
QuestDB aims to provide a user-friendly experience with operational simplicity. It supports schema-agnostic ingestion using popular protocols such as InfluxDB line protocol and PostgreSQL wire protocol. Additionally, a REST API is available for bulk imports and exports, simplifying data management tasks.
PostgreSQL Use Cases
PostgreSQL is a popular choice for large-scale enterprise applications due to its reliability, performance, and feature set.
With the PostGIS extension, PostgreSQL can be used for storing and analyzing geospatial data in applications like mapping, routing, and geocoding.
As a relational database, PostgreSQL is a good fit for pretty much any application that involves transactional workloads.
QuestDB Use Cases
Financial Market Data
QuestDB is well-suited for managing and analyzing financial market data. Its high-performance ingestion and fast SQL queries enable efficient processing and analysis of large volumes of market data in real time.
QuestDB can be used for collecting and analyzing application metrics. Its ability to handle high data throughput and provide real-time analytics capabilities makes it suitable for monitoring and analyzing performance metrics, logs, and other application-related data.
IoT Data Analysis
QuestDB’s high-performance ingestion and time series analytics capabilities make it a valuable tool for analyzing IoT sensor data.
PostgreSQL Pricing Model
PostgreSQL is open source software, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed PostgreSQL server. Several cloud-based managed PostgreSQL services, such as Amazon RDS, Google Cloud SQL, and Azure Database for PostgreSQL, offer different pricing models based on factors like storage, computing resources, and support.
QuestDB Pricing Model
QuestDB is an open-source project released under the Apache 2 License. It is freely available for usage and does not require any licensing fees. Users can access the source code on GitHub and deploy QuestDB on their own infrastructure without incurring direct costs. QuestDB also offers a managed cloud service.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.