Memcached vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Memcached

Stacks7.9K

Followers5.7K

Votes473

GitHub Stars14.0K

Forks3.3K

Memcached vs MongoDB: What are the differences?

Introduction:

In this article, we will discuss the key differences between Memcached and MongoDB, two popular data storage technologies used in web applications.

Scalability: Memcached is a distributed in-memory caching system designed to improve the performance of web applications by caching frequently accessed data in memory. It is highly scalable and can handle a large number of concurrent requests efficiently. On the other hand, MongoDB is a NoSQL database that stores data on disk, making it suitable for handling large volumes of structured and unstructured data. MongoDB's sharding and replication mechanisms allow it to scale horizontally across multiple servers.
Data Model: Memcached is a key-value store, where data is stored as a simple key-value pair. It does not support complex data types or querying capabilities, making it suitable for caching static or semi-static data. MongoDB, on the other hand, provides a flexible document-based data model, allowing the storage of more complex and structured data. It supports a wide range of data types, including nested objects and arrays, and provides powerful querying capabilities using the MongoDB Query Language (MQL).
Persistence: Memcached does not provide built-in persistence mechanisms, meaning that data is lost in case of server crashes or restarts. It is primarily used as a temporary cache for frequently accessed data. MongoDB, on the other hand, supports both in-memory and on-disk storage options. It can persist data on disk, ensuring durability, and can recover data in case of failures.
Querying and Indexing: Memcached does not support querying or indexing of stored data, as it is primarily designed for quick key-value lookups. It does not provide any advanced querying capabilities or indexing mechanisms. MongoDB, on the other hand, provides a powerful querying engine and supports various indexing strategies, making it suitable for complex data retrieval operations. It allows for efficient filtering, sorting, and aggregation of data using indexes.
Consistency and ACID Transactions: Memcached does not enforce strict consistency or support ACID (Atomicity, Consistency, Isolation, Durability) transactions. It is eventually consistent, meaning that changes to data may take some time to propagate across all the servers in a distributed environment. MongoDB, on the other hand, provides strong consistency guarantees and supports multi-document ACID transactions, ensuring data integrity and reliability.
Community and Ecosystem: Memcached has been around for a longer time and has a mature and extensive open-source community. It has many client libraries and integrations available for various programming languages and frameworks. MongoDB also has a vibrant community and provides official drivers for a wide range of programming languages. It has a rich ecosystem of tools and frameworks built around it, making it easier to work with and integrate into different application stacks.

In summary, Memcached is a distributed in-memory caching system designed for high-performance key-value lookups, while MongoDB is a document-based NoSQL database suitable for storing and querying complex and structured data.

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Advice on MongoDB, Memcached

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	Memcached
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars 14.0K
GitHub Forks 5.7K	GitHub Forks 3.3K
Stacks 96.6K	Stacks 7.9K
Followers 82.0K	Followers 5.7K
Votes 4.1K	Votes 473
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 139 Fast object cache 129 High-performance 91 Stable 65 Mature 33 Distributed caching system Cons 2 Only caches simple types

What are some alternatives to MongoDB, Memcached?

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.

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.

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.

Related Comparisons

Memcached vs MongoDB: What are the differences?

Introduction:

In this article, we will discuss the key differences between Memcached and MongoDB, two popular data storage technologies used in web applications.

Scalability: Memcached is a distributed in-memory caching system designed to improve the performance of web applications by caching frequently accessed data in memory. It is highly scalable and can handle a large number of concurrent requests efficiently. On the other hand, MongoDB is a NoSQL database that stores data on disk, making it suitable for handling large volumes of structured and unstructured data. MongoDB's sharding and replication mechanisms allow it to scale horizontally across multiple servers.
Data Model: Memcached is a key-value store, where data is stored as a simple key-value pair. It does not support complex data types or querying capabilities, making it suitable for caching static or semi-static data. MongoDB, on the other hand, provides a flexible document-based data model, allowing the storage of more complex and structured data. It supports a wide range of data types, including nested objects and arrays, and provides powerful querying capabilities using the MongoDB Query Language (MQL).
Persistence: Memcached does not provide built-in persistence mechanisms, meaning that data is lost in case of server crashes or restarts. It is primarily used as a temporary cache for frequently accessed data. MongoDB, on the other hand, supports both in-memory and on-disk storage options. It can persist data on disk, ensuring durability, and can recover data in case of failures.
Querying and Indexing: Memcached does not support querying or indexing of stored data, as it is primarily designed for quick key-value lookups. It does not provide any advanced querying capabilities or indexing mechanisms. MongoDB, on the other hand, provides a powerful querying engine and supports various indexing strategies, making it suitable for complex data retrieval operations. It allows for efficient filtering, sorting, and aggregation of data using indexes.
Consistency and ACID Transactions: Memcached does not enforce strict consistency or support ACID (Atomicity, Consistency, Isolation, Durability) transactions. It is eventually consistent, meaning that changes to data may take some time to propagate across all the servers in a distributed environment. MongoDB, on the other hand, provides strong consistency guarantees and supports multi-document ACID transactions, ensuring data integrity and reliability.
Community and Ecosystem: Memcached has been around for a longer time and has a mature and extensive open-source community. It has many client libraries and integrations available for various programming languages and frameworks. MongoDB also has a vibrant community and provides official drivers for a wide range of programming languages. It has a rich ecosystem of tools and frameworks built around it, making it easier to work with and integrate into different application stacks.

Memcached vs MongoDB

Overview