MongoDB vs Tarantool

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Tarantool

Stacks32

Followers45

Votes9

GitHub Stars3.6K

Forks394

MongoDB vs Tarantool: What are the differences?

Introduction

MongoDB and Tarantool are both popular choices for databases, but they have several key differences that set them apart. In this comparison, we will explore these differences in detail.

Data Model: MongoDB is a document-oriented NoSQL database, while Tarantool is an in-memory data grid that supports key-value storage. MongoDB stores data in flexible, JSON-like documents, making it suitable for complex data structures. In contrast, Tarantool focuses on simple key-value storage that enables high-speed data retrieval.
Indexes: Both MongoDB and Tarantool support indexing to optimize query performance. However, MongoDB offers more indexing options, including single field indexes, composite indexes, geospatial indexes, and full-text indexes. Tarantool primarily relies on primary key indexing, which may limit the types of queries that can be efficiently executed.
Replication and Clustering: MongoDB provides built-in support for replication and sharding, allowing for scalability and high availability. It utilizes a primary-secondary replication model and automatic failover. Tarantool also supports replication but takes a different approach called "horizontal scaling," where data is distributed across multiple instances without a master-slave setup.
Querying: MongoDB employs a flexible and powerful query language that supports complex querying operations, including aggregation pipelines, geo-queries, and text search capabilities. Tarantool, on the other hand, uses a Lua-based query language with limited functionality. While Lua offers flexibility, it may require more effort for complex queries compared to MongoDB's query language.
Supported Languages: MongoDB has extensive language support, with official drivers available for multiple programming languages, including Java, Python, and Node.js. Tarantool provides libraries for several languages, such as C/C++, Lua, and Python, but its language support is more limited compared to MongoDB.
Storage Engine: MongoDB utilizes pluggable storage engines, allowing users to choose between options like WiredTiger and In-Memory. This provides flexibility in terms of performance and workload requirements. Tarantool, on the other hand, uses its own storage engine optimized for in-memory data storage and retrieval.

In summary, MongoDB and Tarantool differ in their data models, indexing capabilities, replication approaches, querying languages, language support, and storage engines. These differences make each database suitable for different use cases and priorities.

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Advice on MongoDB, Tarantool

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

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

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

MongoDB	Tarantool
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 is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Fast; Open source; Easy to use;Multiple index types: HASH, TREE, RTREE, BITSET;Asynchronous master-master replication;Authentication and access control;The database is just a C extension to the application server and can be turned off
Statistics
GitHub Stars 27.7K	GitHub Stars 3.6K
GitHub Forks 5.7K	GitHub Forks 394
Stacks 96.6K	Stacks 32
Followers 82.0K	Followers 45
Votes 4.1K	Votes 9
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 3 Performance 2 Super fast 2 Open source 1 Advanced key-value cache 1 In-memory cache
Integrations
No integrations available	Node.js Perl Java Python Golang NGINX C#

What are some alternatives to MongoDB, Tarantool?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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 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 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.

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

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.

Related Comparisons

MongoDB vs Tarantool: What are the differences?

Introduction

MongoDB and Tarantool are both popular choices for databases, but they have several key differences that set them apart. In this comparison, we will explore these differences in detail.

Data Model: MongoDB is a document-oriented NoSQL database, while Tarantool is an in-memory data grid that supports key-value storage. MongoDB stores data in flexible, JSON-like documents, making it suitable for complex data structures. In contrast, Tarantool focuses on simple key-value storage that enables high-speed data retrieval.
Indexes: Both MongoDB and Tarantool support indexing to optimize query performance. However, MongoDB offers more indexing options, including single field indexes, composite indexes, geospatial indexes, and full-text indexes. Tarantool primarily relies on primary key indexing, which may limit the types of queries that can be efficiently executed.
Replication and Clustering: MongoDB provides built-in support for replication and sharding, allowing for scalability and high availability. It utilizes a primary-secondary replication model and automatic failover. Tarantool also supports replication but takes a different approach called "horizontal scaling," where data is distributed across multiple instances without a master-slave setup.
Querying: MongoDB employs a flexible and powerful query language that supports complex querying operations, including aggregation pipelines, geo-queries, and text search capabilities. Tarantool, on the other hand, uses a Lua-based query language with limited functionality. While Lua offers flexibility, it may require more effort for complex queries compared to MongoDB's query language.
Supported Languages: MongoDB has extensive language support, with official drivers available for multiple programming languages, including Java, Python, and Node.js. Tarantool provides libraries for several languages, such as C/C++, Lua, and Python, but its language support is more limited compared to MongoDB.
Storage Engine: MongoDB utilizes pluggable storage engines, allowing users to choose between options like WiredTiger and In-Memory. This provides flexibility in terms of performance and workload requirements. Tarantool, on the other hand, uses its own storage engine optimized for in-memory data storage and retrieval.

MongoDB vs Tarantool

Overview