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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. PostgreSQL vs QuestDB

PostgreSQL vs QuestDB

OverviewDecisionsComparisonAlternatives

Overview

PostgreSQL
PostgreSQL
Stacks103.0K
Followers83.9K
Votes3.6K
GitHub Stars19.0K
Forks5.2K
QuestDB
QuestDB
Stacks19
Followers50
Votes17
GitHub Stars16.3K
Forks1.5K

PostgreSQL vs QuestDB: What are the differences?

PostgreSQL and QuestDB are both powerful relational database management systems (RDBMS) that offer a range of features for efficient data storage and retrieval. However, they differ in several key aspects that set them apart.
  1. Data Model: PostgreSQL is based on a traditional relational data model, where data is stored in tables with predefined schemas and relationships are established using foreign keys. On the other hand, QuestDB utilizes a time-series data model, which makes it particularly suited for storing and analyzing time-series data efficiently. This data model is optimized for continuous updates and queries on large volumes of time-stamped data.

  2. Performance: QuestDB leverages various performance optimizations, including columnar storage, parallel processing, and hardware acceleration, to provide ultra-fast query speeds, especially for time-series data. PostgreSQL, on the other hand, excels in handling complex queries involving multiple tables and offers a wide range of indexing techniques for efficient data retrieval.

  3. Scalability: PostgreSQL is known for its scalability and can handle large datasets and high transactional loads with ease. It supports a distributed architecture and provides features like sharding and replication for horizontal scalability. QuestDB, on the other hand, is optimized for high-throughput ingestion and query rates, making it suitable for applications that require extremely fast processing of time-series data.

  4. SQL Compatibility: PostgreSQL fully complies with the SQL standard, offering a wide range of SQL features and functionalities. It also provides support for advanced SQL concepts like window functions, common table expressions, and recursive queries. QuestDB, being a specific purpose-built time-series database, supports a subset of SQL functionality, focusing primarily on time-series data operations.

  5. Data Integration: PostgreSQL supports a variety of data formats, including JSON, XML, and geospatial data types, allowing for seamless integration with different types of applications and data sources. QuestDB, on the other hand, focuses primarily on time-series data and provides features specifically tailored for handling time-series datasets efficiently.

  6. Community and Ecosystem: PostgreSQL has a large and active community with a vast ecosystem of extensions, tools, and libraries built around it. It has been in development for several decades and is supported by a dedicated group of developers. QuestDB, being relatively new, has a smaller community but is rapidly growing with an active GitHub repository and community forum.

In Summary, PostgreSQL and QuestDB differ in their data models, performance characteristics, scalability options, SQL compatibility, data integration capabilities, and community support. These differences make each database suitable for specific use cases and environments.

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Advice on PostgreSQL, QuestDB

Kyle
Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

DecidedonPostGISPostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k
Comments
George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k
Comments
Navraj
Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceonMySQLMySQLPostgreSQLPostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k
Comments

Detailed Comparison

PostgreSQL
PostgreSQL
QuestDB
QuestDB

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

QuestDB is an open source database for time series, events, and analytical workloads with a primary focus on performance. It enhances ANSI SQL with time series extensions.

-
Relational model for time series; SIMD accelerated queries; Time partitioned; Heavy parallelization; Scalable ingestion; Immediate consistency; Time series and relational joins; Native InfluxDB line protocol; Grafana through Postgres wire support; Schema or schema-free; Aggregations and down sampling
Statistics
GitHub Stars
19.0K
GitHub Stars
16.3K
GitHub Forks
5.2K
GitHub Forks
1.5K
Stacks
103.0K
Stacks
19
Followers
83.9K
Followers
50
Votes
3.6K
Votes
17
Pros & Cons
Pros
  • 765
    Relational database
  • 511
    High availability
  • 439
    Enterprise class database
  • 383
    Sql
  • 304
    Sql + nosql
Cons
  • 10
    Table/index bloatings
Pros
  • 2
    Postgres wire protocol
  • 2
    No dependencies
  • 2
    SQL
  • 2
    Open source
  • 2
    Time-series data analysis
Integrations
No integrations available
InfluxDB
InfluxDB
Java
Java

What are some alternatives to PostgreSQL, QuestDB?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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