Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Vitess

Stacks66

Followers166

Votes0

MongoDB vs Vitess: What are the differences?

Introduction

MongoDB and Vitess are both popular database systems, but they have several key differences that set them apart. Understanding these differences can help you make an informed decision when choosing the right database system for your needs.

Data Model: MongoDB is a NoSQL database that uses a document data model, where data is stored in flexible, schema-less documents. It is designed to handle unstructured data and can easily adapt to changes in the data structure. On the other hand, Vitess is a SQL database that adheres to a relational data model. It enforces a strict schema and provides ACID (Atomicity, Consistency, Isolation, Durability) guarantees, making it suitable for structured data with complex relationships.
Scalability and Sharding: MongoDB is known for its horizontal scalability and built-in sharding capabilities. It allows you to distribute data across multiple servers or clusters, providing high availability and allowing for the processing of large volumes of data. Vitess, on the other hand, is built specifically for scaling relational databases. It provides sharding at the table level, allowing you to partition data across multiple instances of the database. This enables horizontal scalability while preserving the relational model.
Query Language: MongoDB uses its own query language, known as the MongoDB Query Language (MQL), which is based on JSON-like documents. This query language is powerful and flexible, allowing for complex queries and aggregations. Vitess, on the other hand, uses standard SQL as its query language. This makes it easy to integrate existing SQL-based applications and tools with Vitess, as developers are already familiar with SQL syntax.
Consistency Model: MongoDB offers different levels of consistency depending on the configuration. By default, it provides eventual consistency, where updates are not instantly propagated to all replicas. However, you can configure MongoDB to provide stronger consistency guarantees if required. Vitess, being a SQL database, provides strong consistency by default. This means that changes made to the database are immediately visible to all clients, ensuring that they always see the most up-to-date data.
Indexing: MongoDB supports various types of indexes, including single field, compound, multi-key, and geospatial indexes. These indexes improve query performance and allow for efficient data retrieval. Vitess, being a SQL database, also supports indexes, including primary keys, unique keys, and non-unique keys. Indexes in Vitess help speed up query execution by providing efficient lookup and sorting capabilities.
Data Replication: MongoDB offers built-in replication capabilities, allowing you to create replica sets for high availability and data durability. Replica sets provide automatic failover and recovery, ensuring that your data remains accessible even in the event of a server failure. Vitess, being designed for scaling relational databases, also supports data replication. It uses a master-slave replication model, where changes made to the master database are replicated to one or more slave databases, ensuring data durability and availability.

In summary, MongoDB and Vitess differ in their data models, scalability and sharding capabilities, query languages, consistency models, indexing options, and data replication mechanisms. Understanding these differences is crucial in choosing the right database system for your specific requirements.

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Advice on MongoDB, Vitess

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	Vitess
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 a database solution for deploying, scaling and managing large clusters of MySQL instances. It’s architected to run as effectively in a public or private cloud architecture as it does on dedicated hardware. It combines and extends many important MySQL features with the scalability of a NoSQL database.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Scalability; Connection pooling; Manageability
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 66
Followers 82.0K	Followers 166
Votes 4.1K	Votes 0
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	No community feedback yet
Integrations
No integrations available	Amazon RDS Kubernetes MySQL

What are some alternatives to MongoDB, Vitess?

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.

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.