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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. RocksDB vs Vitess

RocksDB vs Vitess

OverviewComparisonAlternatives

Overview

RocksDB
RocksDB
Stacks141
Followers290
Votes11
GitHub Stars30.9K
Forks6.6K
Vitess
Vitess
Stacks66
Followers166
Votes0

RocksDB vs Vitess: What are the differences?

Introduction

RocksDB and Vitess are both popular database management systems used in different scenarios. Despite both being data storage solutions, they have key differences that distinguish them from each other.

1. Storage Engine:

RocksDB is an embedded key-value storage engine that is designed for fast storage and retrieval operations, making it suitable for applications where high performance is critical. On the other hand, Vitess is a distributed SQL database that provides the scalability and reliability needed for handling large datasets in a cloud-native environment.

2. Data Model:

RocksDB operates at the key-value level, offering simple data storage and retrieval functionalities. In contrast, Vitess supports structured query language (SQL) for managing relational data, providing a more robust and familiar data model for developers.

3. Scalability:

While RocksDB can scale horizontally by replicating data across multiple instances, its scalability is limited when compared to Vitess. Vitess has sophisticated sharding and repartitioning mechanisms that allow for seamless scalability across thousands of nodes in a cluster.

4. Consistency:

RocksDB guarantees strong consistency in a single node setting, ensuring that data remains accurate and up-to-date. In the case of Vitess, distributed transactions are used to maintain consistency across multiple nodes, providing reliability in scenarios where data integrity is crucial.

5. Query Language Support:

Vitess supports SQL queries, enabling users to perform complex queries and analytics on their data. RocksDB, being a key-value store, lacks native support for SQL queries, requiring additional layers or tools to facilitate data manipulation and analysis.

6. Use Cases:

RocksDB is well-suited for applications that require high performance, such as caching layers or indexing services. In contrast, Vitess is preferred for handling large-scale transactional workloads typical in e-commerce platforms, social media applications, and other data-intensive environments.

In Summary, RocksDB and Vitess differ in terms of storage engine, data model, scalability, consistency, query language support, and use cases.

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Detailed Comparison

RocksDB
RocksDB
Vitess
Vitess

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

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.

Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM;Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory;Scales linearly with number of CPUs so that it works well on ARM processors
Scalability; Connection pooling; Manageability
Statistics
GitHub Stars
30.9K
GitHub Stars
-
GitHub Forks
6.6K
GitHub Forks
-
Stacks
141
Stacks
66
Followers
290
Followers
166
Votes
11
Votes
0
Pros & Cons
Pros
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed
No community feedback yet
Integrations
No integrations available
Amazon RDS
Amazon RDS
Kubernetes
Kubernetes
MySQL
MySQL

What are some alternatives to RocksDB, Vitess?

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.

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.

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