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

Citus vs PostgreSQL

OverviewDecisionsComparisonAlternatives

Overview

PostgreSQL
PostgreSQL
Stacks103.0K
Followers83.9K
Votes3.6K
GitHub Stars19.0K
Forks5.2K
Citus
Citus
Stacks60
Followers124
Votes11
GitHub Stars12.0K
Forks736

Citus vs PostgreSQL: What are the differences?

Introduction

In this article, we will discuss the key differences between Citus and PostgreSQL. Citus is an extension to PostgreSQL that enables horizontal scaling of PostgreSQL databases by distributing data and queries across multiple nodes. PostgreSQL, on the other hand, is a powerful and feature-rich open-source relational database management system (RDBMS).

1. Architecture:

Citus utilizes a distributed architecture where data is distributed across multiple nodes, enabling horizontal scalability. PostgreSQL, on the other hand, follows a traditional single-node architecture where data is stored and processed on a single server.

2. Scalability:

Citus allows for easy scalability by leveraging a distributed architecture. It achieves this by horizontally scaling the workload across multiple nodes, allowing for increased processing power and storage capacity. PostgreSQL, on the other hand, is not designed for automatic scalability and requires manual configuration for scaling.

3. Performance:

Citus improves performance by leveraging parallel processing across multiple nodes and distributing the workload. This allows for faster query execution and improved overall performance. PostgreSQL, while also capable of handling large workloads, may experience performance limitations on a single node compared to Citus.

4. Data Distribution:

Citus distributes data across multiple nodes based on a sharding mechanism, where the data is divided into smaller chunks and stored on different nodes. This allows for better load balancing and improved read and write performance. In PostgreSQL, data is stored in a single node, without the distribution and sharding capabilities of Citus.

5. Query Optimization:

Citus optimizes queries by parallelizing the execution and distributing the workload across multiple nodes. This allows for faster query processing and improved response times. PostgreSQL, while also capable of query optimization, may not have the same level of parallelization and distributed processing capabilities as Citus.

6. Ease of Use:

Citus integrates seamlessly with PostgreSQL, providing a familiar interface and tools for managing the distributed database. It can be used as an extension to an existing PostgreSQL deployment, making it easier to adopt and incorporate horizontal scaling. While PostgreSQL itself is user-friendly, the additional complexity of managing a distributed database in Citus may require more expertise and effort.

In Summary, Citus and PostgreSQL differ in their architecture, scalability, performance, data distribution, query optimization, and ease of use. Citus enables horizontal scaling with its distributed architecture, improved performance, efficient data distribution, and query optimization, while PostgreSQL follows a traditional single-node architecture without these features.

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Advice on PostgreSQL, Citus

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
Citus
Citus

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.

It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.

-
Multi-Node Scalable PostgreSQL;Built-in Replication and High Availability;Real-time Reads/Writes On Multiple Nodes;Multi-core Parallel Processing of Queries;Tenant isolation
Statistics
GitHub Stars
19.0K
GitHub Stars
12.0K
GitHub Forks
5.2K
GitHub Forks
736
Stacks
103.0K
Stacks
60
Followers
83.9K
Followers
124
Votes
3.6K
Votes
11
Pros & Cons
Pros
  • 765
    Relational database
  • 511
    High availability
  • 439
    Enterprise class database
  • 383
    Sql
  • 304
    Sql + nosql
Cons
  • 10
    Table/index bloatings
Pros
  • 6
    Multi-core Parallel Processing
  • 3
    Drop-in PostgreSQL replacement
  • 2
    Distributed with Auto-Sharding
Integrations
No integrations available
.NET
.NET
Apache Spark
Apache Spark
Loggly
Loggly
Java
Java
Rails
Rails
Datadog
Datadog
Logentries
Logentries
Heroku
Heroku
Papertrail
Papertrail
Kafka
Kafka

What are some alternatives to PostgreSQL, Citus?

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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