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 9.7K GitHub stars and 2.7K GitHub forks. Here’s a link to Memcached's open source repository on GitHub

Memcached alternatives & related posts

Redis logo

Redis

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An in-memory database that persists on disk
Redis logo
Redis
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Memcached

related Redis posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 22 upvotes · 396.9K views
atCircleCICircleCI
MongoDB
MongoDB
PostgreSQL
PostgreSQL
Redis
Redis
GitHub
GitHub
Amazon S3
Amazon S3

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.

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Thierry Schellenbach
Thierry Schellenbach
CEO at Stream · | 17 upvotes · 173.9K views
atStreamStream
Redis
Redis
Cassandra
Cassandra
RocksDB
RocksDB
#InMemoryDatabases
#DataStores
#Databases

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

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related Varnish posts

Tom Klein
Tom Klein
CEO at Gentlent · | 9 upvotes · 88.3K views
atGentlentGentlent
Git
Git
GitHub
GitHub
GitLab
GitLab
Docker
Docker
Kubernetes
Kubernetes
HAProxy
HAProxy
Varnish
Varnish
npm
npm
Visual Studio Code
Visual Studio Code

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.

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Node.js
Node.js
Python
Python
MySQL
MySQL
Memcached
Memcached
nginx
nginx
RabbitMQ
RabbitMQ
Redis
Redis
Django
Django
Tornado
Tornado
Varnish
Varnish
HAProxy
HAProxy

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.

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MongoDB logo

MongoDB

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The database for giant ideas
MongoDB logo
MongoDB
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Memcached

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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 24 upvotes · 547.8K views
atFundsCornerFundsCorner
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MongoDB Stitch
MongoDB Stitch
Node.js
Node.js
Amazon SQS
Amazon SQS
Python
Python
SQLAlchemy
SQLAlchemy
AWS Lambda
AWS Lambda
Zappa
Zappa

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!

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 22 upvotes · 396.9K views
atCircleCICircleCI
MongoDB
MongoDB
PostgreSQL
PostgreSQL
Redis
Redis
GitHub
GitHub
Amazon S3
Amazon S3

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

related Couchbase posts

Gabriel Pa
Gabriel Pa
CEO at NaoLogic Inc · | 5 upvotes · 38.4K views
atNaologicNaologic
Memcached
Memcached
Couchbase
Couchbase
CouchDB
CouchDB

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.

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Gabriel Pa
Gabriel Pa
CEO at NaoLogic Inc · | 1 upvotes · 34.5K views
atNaologicNaologic
CouchDB
CouchDB
Couchbase
Couchbase
Realm
Realm
RxDB
RxDB
Pouchdb
Pouchdb

If you want to use Pouchdb might as well use RxDB which is an observables wrapper for Pouch but much more comfortable to use. Realm is awesome but Pouchdb and RxDB give you more control. You can use Couchbase (recommended) or CouchDB to enable 2-way sync

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Memcached Cloud

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A fully-managed service for hosting and running your memcached in a reliable and fail-safe manner