MongoDB vs PostgreSQL vs RethinkDB

Overview

RethinkDB

Stacks292

Followers406

Votes307

GitHub Stars27.0K

Forks1.9K

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

MongoDB vs PostgreSQL vs RethinkDB: What are the differences?

MongoDB, PostgreSQL, and RethinkDB are popular database management systems that offer different features and functionalities to cater to varying data storage and manipulation needs.

1. **Data Model**: MongoDB is a NoSQL database that stores data in a document-oriented format, while PostgreSQL is a traditional relational database that stores data in tables with predefined schemas. RethinkDB falls under the category of NoSQL databases like MongoDB but has a real-time query system that continuously pushes data changes to applications.

2. **Query Language**: MongoDB uses a query language that is similar to JSON called MongoDB Query Language (MQL), whereas PostgreSQL uses SQL, a standard language for relational databases. RethinkDB supports a query language called ReQL, which allows users to perform complex queries by chaining functions and operators.

3. **Scalability**: MongoDB is known for its horizontal scalability, allowing users to distribute data across multiple servers easily. PostgreSQL also supports scaling through techniques like sharding but is more complex to set up. RethinkDB offers automatic sharding and replication for easier scalability.

4. **Data Consistency**: MongoDB provides eventual consistency by default, which means that updates to the database may not be immediately reflected across all nodes. In contrast, PostgreSQL ensures strong consistency, where all transactions are ACID compliant. RethinkDB focuses on consistency and availability, making it capable of handling real-time applications effectively.

5. **Join Operations**: While PostgreSQL supports complex join operations between tables, MongoDB lacks native support for joins and promotes denormalized data models for better performance. RethinkDB, on the other hand, allows users to perform joins similar to SQL databases through its query language.

6. **Community Support**: MongoDB has a large and active community that continuously contributes to its development and provides extensive documentation and resources. PostgreSQL also has a strong community backing and is known for its stability and reliability. RethinkDB faced challenges in the past but has a dedicated community that keeps the project alive through community-driven efforts.

In Summary, MongoDB, PostgreSQL, and RethinkDB differ in terms of their data models, query languages, scalability options, data consistency, join operations, and community support.

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Advice on RethinkDB, PostgreSQL, MongoDB

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

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

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

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

RethinkDB	PostgreSQL	MongoDB
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.	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.	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.
JSON data model and immediate consistency.;Distributed joins, subqueries, aggregation, atomic updates.;Secondary, compound, and arbitrarily computed indexes.;Hadoop-style map/reduce.;Friendly web and command-line administration tools.;Takes care of machine failures and network interrupts.;Multi-datacenter replication and failover.;Sharding and replication to multiple nodes.;Queries are automatically parallelized and distributed.;Lock-free operation via MVCC concurrency.	-	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars 27.0K	GitHub Stars 19.0K	GitHub Stars 27.7K
GitHub Forks 1.9K	GitHub Forks 5.2K	GitHub Forks 5.7K
Stacks 292	Stacks 103.0K	Stacks 96.6K
Followers 406	Followers 83.9K	Followers 82.0K
Votes 307	Votes 3.6K	Votes 4.1K
Pros & Cons
Pros 48 Powerful query language 46 Excellent dashboard 42 JSON 41 Distributed database 38 Open source	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings	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
Integrations
Amazon EC2	No integrations available	No integrations available

What are some alternatives to RethinkDB, PostgreSQL, MongoDB?

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

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

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 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

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.

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 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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

Oracle

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Related Comparisons

MongoDB vs PostgreSQL vs RethinkDB: What are the differences?

MongoDB, PostgreSQL, and RethinkDB are popular database management systems that offer different features and functionalities to cater to varying data storage and manipulation needs.

1. **Data Model**: MongoDB is a NoSQL database that stores data in a document-oriented format, while PostgreSQL is a traditional relational database that stores data in tables with predefined schemas. RethinkDB falls under the category of NoSQL databases like MongoDB but has a real-time query system that continuously pushes data changes to applications.

2. **Query Language**: MongoDB uses a query language that is similar to JSON called MongoDB Query Language (MQL), whereas PostgreSQL uses SQL, a standard language for relational databases. RethinkDB supports a query language called ReQL, which allows users to perform complex queries by chaining functions and operators.

3. **Scalability**: MongoDB is known for its horizontal scalability, allowing users to distribute data across multiple servers easily. PostgreSQL also supports scaling through techniques like sharding but is more complex to set up. RethinkDB offers automatic sharding and replication for easier scalability.

4. **Data Consistency**: MongoDB provides eventual consistency by default, which means that updates to the database may not be immediately reflected across all nodes. In contrast, PostgreSQL ensures strong consistency, where all transactions are ACID compliant. RethinkDB focuses on consistency and availability, making it capable of handling real-time applications effectively.

5. **Join Operations**: While PostgreSQL supports complex join operations between tables, MongoDB lacks native support for joins and promotes denormalized data models for better performance. RethinkDB, on the other hand, allows users to perform joins similar to SQL databases through its query language.

6. **Community Support**: MongoDB has a large and active community that continuously contributes to its development and provides extensive documentation and resources. PostgreSQL also has a strong community backing and is known for its stability and reliability. RethinkDB faced challenges in the past but has a dedicated community that keeps the project alive through community-driven efforts.

In Summary, MongoDB, PostgreSQL, and RethinkDB differ in terms of their data models, query languages, scalability options, data consistency, join operations, and community support.

MongoDB vs PostgreSQL vs RethinkDB

Overview