Alternatives to Memcached logo

Alternatives to Memcached

Redis, Ehcache, Varnish, Hazelcast, and MongoDB are the most popular alternatives and competitors to Memcached.
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What is Memcached and what are its top alternatives?

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.
Memcached is a tool in the Databases category of a tech stack.
Memcached is an open source tool with 11.5K GitHub stars and 3K GitHub forks. Here’s a link to Memcached's open source repository on GitHub

Top Alternatives to Memcached

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • Ehcache

    Ehcache

    Ehcache is an open source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches. ...

  • Varnish

    Varnish

    Varnish Cache is a web application accelerator also known as a caching HTTP reverse proxy. You install it in front of any server that speaks HTTP and configure it to cache the contents. Varnish Cache is really, really fast. It typically speeds up delivery with a factor of 300 - 1000x, depending on your architecture. ...

  • Hazelcast

    Hazelcast

    With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution. ...

  • 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. ...

  • Couchbase

    Couchbase

    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands. ...

  • Memcached Cloud

    Memcached Cloud

    Memcached Cloud is a fully-managed service for running your Memcached in a reliable and fail-safe manner. Your dataset is constantly replicated, so if a node fails, an auto-switchover mechanism guarantees data is served without interruption. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. ...

  • etcd

    etcd

    etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines. It’s open-source and available on GitHub. etcd gracefully handles master elections during network partitions and will tolerate machine failure, including the master. ...

Memcached alternatives & related posts

Redis logo

Redis

44.2K
33.6K
3.9K
An in-memory database that persists on disk
44.2K
33.6K
+ 1
3.9K
PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 152
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

See more
Ehcache logo

Ehcache

92
126
4
Java's Most Widely-Used Cache
92
126
+ 1
4
PROS OF EHCACHE
  • 1
    Way Faster than Redis and Elasticache Redis
  • 1
    Easy setup
  • 1
    Simpler to run in testing environment
  • 1
    Container doesn't have to be running for local tests
CONS OF EHCACHE
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    related Ehcache posts

    Varnish logo

    Varnish

    11.4K
    1.9K
    359
    High-performance HTTP accelerator
    11.4K
    1.9K
    + 1
    359
    PROS OF VARNISH
    • 103
      High-performance
    • 66
      Very Fast
    • 56
      Very Stable
    • 43
      Very Robust
    • 36
      HTTP reverse proxy
    • 20
      Open Source
    • 17
      Web application accelerator
    • 10
      Easy to config
    • 4
      Widely Used
    • 3
      Great community
    • 1
      Essential software for HTTP
    CONS OF VARNISH
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      related Varnish posts

      Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

      See more
      Tom Klein

      We're using Git through GitHub for public repositories and GitLab for our private repositories due to its easy to use features. Docker and Kubernetes are a must have for our highly scalable infrastructure complimented by HAProxy with Varnish in front of it. We are using a lot of npm and Visual Studio Code in our development sessions.

      See more
      Hazelcast logo

      Hazelcast

      249
      375
      56
      Clustering and highly scalable data distribution platform for Java
      249
      375
      + 1
      56
      PROS OF HAZELCAST
      • 10
        High Availibility
      • 6
        Distributed Locking
      • 5
        Distributed compute
      • 5
        Sharding
      • 4
        Load balancing
      • 3
        Sql query support in cluster wide
      • 3
        Map-reduce functionality
      • 3
        Written in java. runs on jvm
      • 3
        Publish-subscribe
      • 2
        Performance
      • 2
        Simple-to-use
      • 2
        Multiple client language support
      • 2
        Rest interface
      • 2
        Optimis locking for map
      • 1
        Super Fast
      • 1
        Admin Interface (Management Center)
      • 1
        Better Documentation
      • 1
        Easy to use
      CONS OF HAZELCAST
      • 3
        License needed for SSL

      related Hazelcast posts

      MongoDB logo

      MongoDB

      66.8K
      56.2K
      4.1K
      The database for giant ideas
      66.8K
      56.2K
      + 1
      4.1K
      PROS OF MONGODB
      • 826
        Document-oriented storage
      • 591
        No sql
      • 548
        Ease of use
      • 465
        Fast
      • 407
        High performance
      • 256
        Free
      • 215
        Open source
      • 180
        Flexible
      • 143
        Replication & high availability
      • 110
        Easy to maintain
      • 42
        Querying
      • 38
        Easy scalability
      • 37
        Auto-sharding
      • 36
        High availability
      • 31
        Map/reduce
      • 27
        Document database
      • 25
        Full index support
      • 25
        Easy setup
      • 16
        Reliable
      • 15
        Fast in-place updates
      • 14
        Agile programming, flexible, fast
      • 12
        No database migrations
      • 8
        Enterprise
      • 8
        Easy integration with Node.Js
      • 6
        Enterprise Support
      • 5
        Great NoSQL DB
      • 3
        Drivers support is good
      • 3
        Aggregation Framework
      • 3
        Support for many languages through different drivers
      • 2
        Awesome
      • 2
        Schemaless
      • 2
        Managed service
      • 2
        Fast
      • 2
        Easy to Scale
      • 1
        Consistent
      • 1
        Acid Compliant
      CONS OF MONGODB
      • 5
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

      related MongoDB posts

      Jeyabalaji Subramanian

      Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

      We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

      Based on the above criteria, we selected the following tools to perform the end to end data replication:

      We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

      We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

      In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

      Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

      In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

      See more
      Robert Zuber

      We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

      As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

      When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

      See more
      Couchbase logo

      Couchbase

      396
      504
      111
      Document-Oriented NoSQL Database
      396
      504
      + 1
      111
      PROS OF COUCHBASE
      • 19
        High performance
      • 18
        Flexible data model, easy scalability, extremely fast
      • 9
        Mobile app support
      • 7
        You can query it with Ansi-92 SQL
      • 6
        All nodes can be read/write
      • 5
        Equal nodes in cluster, allowing fast, flexible changes
      • 5
        Both a key-value store and document (JSON) db
      • 5
        Open source, community and enterprise editions
      • 4
        Automatic configuration of sharding
      • 4
        Local cache capability
      • 3
        Cross data center replication
      • 3
        Elasticsearch connector
      • 3
        Easy setup
      • 3
        Web based management, query and monitoring panel
      • 3
        Linearly scalable, useful to large number of tps
      • 3
        Easy cluster administration
      • 3
        SDKs in popular programming languages
      • 2
        Map reduce views
      • 2
        DBaaS available
      • 2
        NoSQL
      • 1
        FTS + SQL together
      • 1
        Buckets, Scopes, Collections & Documents
      CONS OF COUCHBASE
      • 3
        Terrible query language

      related Couchbase posts

      Ilias Mentzelos
      Software Engineer at Plum Fintech · | 9 upvotes · 49.8K views
      Shared insights
      on
      MongoDBMongoDBCouchbaseCouchbase

      Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

      See more
      Gabriel Pa

      We implemented our first large scale EPR application from naologic.com using CouchDB .

      Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

      It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

      See more
      Memcached Cloud logo

      Memcached Cloud

      13
      15
      24
      A fully-managed service for hosting and running your memcached in a reliable and fail-safe manner
      13
      15
      + 1
      24
      PROS OF MEMCACHED CLOUD
      • 6
        High-availability
      • 6
        Heroku add-on
      • 3
        Fast
      • 2
        Email alerts
      • 2
        Fail-safe
      • 1
        24/7 monitoring & support
      • 1
        Backups and import
      • 1
        Offered by Redis Labs
      • 1
        Auto-switchover
      • 1
        Seamless scalability
      CONS OF MEMCACHED CLOUD
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        related Memcached Cloud posts

        etcd logo

        etcd

        238
        326
        23
        A distributed consistent key-value store for shared configuration and service discovery
        238
        326
        + 1
        23
        PROS OF ETCD
        • 11
          Service discovery
        • 6
          Fault tolerant key value store
        • 2
          Bundled with coreos
        • 1
          Open Source
        • 1
          Privilege Access Management
        • 1
          Consol integration
        • 1
          Secure
        CONS OF ETCD
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          related etcd posts