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

Memcached vs Vitess

OverviewComparisonAlternatives

Overview

Memcached
Memcached
Stacks7.9K
Followers5.7K
Votes473
GitHub Stars14.0K
Forks3.3K
Vitess
Vitess
Stacks66
Followers166
Votes0

Memcached vs Vitess: What are the differences?

Introduction

This Markdown code compares key differences between Memcached and Vitess.

  1. Deployment: Memcached is a simple, distributed, in-memory caching system widely used to speed up dynamic web applications. On the other hand, Vitess is a database clustering system for horizontal scaling of MySQL through generalized sharding. While Memcached is focused on caching, Vitess focuses on database scalability.

  2. Consistency: Memcached is eventually consistent, meaning it prioritizes performance over consistency by allowing data to be inconsistent for a short period. In contrast, Vitess offers strong consistency guarantees by providing features like distributed transactions and strict data consistency, ensuring that data integrity is maintained.

  3. Use Case: Memcached is primarily used for caching frequently accessed data to reduce database load and improve application performance. In contrast, Vitess is suitable for large-scale applications with high traffic and complex database requirements, providing a scalable and manageable solution for MySQL databases.

  4. Data Storage: Memcached stores data in-memory only, making it fast but limited in capacity based on system RAM. Vitess, on the other hand, stores data in a distributed storage layer such as MySQL or external storage systems like Google Cloud Storage, offering more flexibility in managing large datasets.

  5. Query Language: Memcached does not support complex queries or SQL operations as it is a key-value store with basic get, set, and delete operations. In contrast, Vitess supports SQL queries and operations for database management, allowing for more sophisticated data retrieval and manipulation capabilities.

  6. Scalability: Memcached lacks built-in support for automatic sharding and scaling, limiting its scalability options to manual partitioning and distributing data across multiple servers. In comparison, Vitess provides built-in support for sharding and scaling MySQL databases horizontally, enabling seamless scaling as application demands grow.

In Summary, Memcached and Vitess differ in deployment focus, consistency models, use cases, data storage mechanisms, query language support, and scalability options.

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Detailed Comparison

Memcached
Memcached
Vitess
Vitess

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.

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.

-
Scalability; Connection pooling; Manageability
Statistics
GitHub Stars
14.0K
GitHub Stars
-
GitHub Forks
3.3K
GitHub Forks
-
Stacks
7.9K
Stacks
66
Followers
5.7K
Followers
166
Votes
473
Votes
0
Pros & Cons
Pros
  • 139
    Fast object cache
  • 129
    High-performance
  • 91
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
Cons
  • 2
    Only caches simple types
No community feedback yet
Integrations
No integrations available
Amazon RDS
Amazon RDS
Kubernetes
Kubernetes
MySQL
MySQL

What are some alternatives to Memcached, 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.

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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