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

Citus vs MongoDB

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
Citus
Citus
Stacks60
Followers124
Votes11
GitHub Stars12.0K
Forks736

Citus vs MongoDB: What are the differences?

# Introduction
In this Markdown code, we will be discussing the key differences between Citus and MongoDB, focusing on specific aspects of these databases.

# 1. **Architecture**:
Citus is an extension to Postgres, enabling horizontal scaling by adding a distributed layer on top of Postgres, while MongoDB is a NoSQL document database designed for high availability, scalability, and flexibility.

# 2. **Data Model**:
MongoDB uses a flexible schemaless document-based model, allowing developers to store and query data without a fixed schema, whereas Citus maintains the relational data model of Postgres, supporting structured data with tables and relationships.

# 3. **Scalability**:
Citus excels in scaling out by distributing data across multiple nodes in a cluster, providing horizontal scalability for relational data. MongoDB, on the other hand, scales horizontally by sharding data across multiple servers, ideal for managing unstructured data at scale.

# 4. **Query Language**:
Citus supports SQL queries due to its integration with Postgres, allowing users to utilize the power of SQL for data manipulation and querying. MongoDB, in contrast, uses its query language, which is based on JSON-like documents for data retrieval and modification.

# 5. **Transactions**:
Citus supports distributed transactions across multiple nodes while maintaining ACID compliance, ensuring data consistency and reliability in distributed environments. MongoDB offers atomic operations within a single document, lacking true multi-document ACID transaction support.

# 6. **Tooling and Ecosystem**:
Citus benefits from the rich ecosystem of Postgres, leveraging tools like pgAdmin and Postman for database management and development. MongoDB has its suite of tools, including MongoDB Compass and MongoDB Atlas, designed specifically for managing MongoDB databases and deployments.

In Summary, the key differences between Citus and MongoDB lie in their architecture, data model, scalability approach, query language, transaction support, and tooling ecosystem.

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Advice on MongoDB, Citus

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
Ido
Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

MongoDB
MongoDB
Citus
Citus

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.

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.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
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
27.7K
GitHub Stars
12.0K
GitHub Forks
5.7K
GitHub Forks
736
Stacks
96.6K
Stacks
60
Followers
82.0K
Followers
124
Votes
4.1K
Votes
11
Pros & Cons
Pros
  • 829
    Document-oriented storage
  • 594
    No sql
  • 554
    Ease of use
  • 465
    Fast
  • 410
    High performance
Cons
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 2
    Proprietary query language
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
PostgreSQL
PostgreSQL

What are some alternatives to MongoDB, Citus?

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.

PostgreSQL

PostgreSQL

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.

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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