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Memcached
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Memcached vs TokuMX: What are the differences?

What is Memcached? High-performance, distributed memory object caching system. 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.

What is TokuMX? A high-performance, concurrent, compressing, drop-in replacement engine for MongoDB. TokuMX is a drop-in replacement for MongoDB, and offers 20X performance improvements, 90% reduction in database size, and support for ACID transactions with MVCC. TokuMX has the same binaries, supports the same drivers, data model, and features of MongoDB, because it shares much of its code with MongoDB.

Memcached and TokuMX can be primarily classified as "Databases" tools.

"Fast object cache" is the primary reason why developers consider Memcached over the competitors, whereas "When your two-week MongoDB love affair ends, try this" was stated as the key factor in picking TokuMX.

Memcached and TokuMX are both open source tools. It seems that Memcached with 8.99K GitHub stars and 2.6K forks on GitHub has more adoption than TokuMX with 679 GitHub stars and 90 GitHub forks.

What is 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.

What is TokuMX?

TokuMX is a drop-in replacement for MongoDB, and offers 20X performance improvements, 90% reduction in database size, and support for ACID transactions with MVCC. TokuMX has the same binaries, supports the same drivers, data model, and features of MongoDB, because it shares much of its code with MongoDB.
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      What are some alternatives to Memcached and TokuMX?
      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 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 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
      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 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.
      See all alternatives
      Decisions about Memcached and TokuMX
      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.

      See more
      Kir Shatrov
      Kir Shatrov
      Production Engineer at Shopify · | 12 upvotes · 78.3K views
      atShopifyShopify
      Rails
      Rails
      MySQL
      MySQL
      Memcached
      Memcached
      Redis
      Redis

      As is common in the Rails stack, since the very beginning, we've stayed with MySQL as a relational database, Memcached for key/value storage and Redis for queues and background jobs.

      In 2014, we could no longer store all our data in a single MySQL instance - even by buying better hardware. We decided to use sharding and split all of Shopify into dozens of database partitions.

      Sharding played nicely for us because Shopify merchants are isolated from each other and we were able to put a subset of merchants on a single shard. It would have been harder if our business assumed shared data between customers.

      The sharding project bought us some time regarding database capacity, but as we soon found out, there was a huge single point of failure in our infrastructure. All those shards were still using a single Redis. At one point, the outage of that Redis took down all of Shopify, causing a major disruption we later called “Redismageddon”. This taught us an important lesson to avoid any resources that are shared across all of Shopify.

      Over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

      See more
      Kir Shatrov
      Kir Shatrov
      Production Engineer at Shopify · | 13 upvotes · 146K views
      atShopifyShopify
      Docker
      Docker
      Kubernetes
      Kubernetes
      Google Kubernetes Engine
      Google Kubernetes Engine
      MySQL
      MySQL
      Redis
      Redis
      Memcached
      Memcached

      At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

      As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

      See more
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Heroku
      Heroku
      Ruby
      Ruby
      Rails
      Rails
      Amazon RDS for PostgreSQL
      Amazon RDS for PostgreSQL
      MariaDB
      MariaDB
      Microsoft SQL Server
      Microsoft SQL Server
      Amazon RDS
      Amazon RDS
      AWS Lambda
      AWS Lambda
      Python
      Python
      Redis
      Redis
      Memcached
      Memcached
      AWS Elastic Load Balancing (ELB)
      AWS Elastic Load Balancing (ELB)
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      Amazon ElastiCache
      Amazon ElastiCache

      We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

      We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

      In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

      Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

      See more
      StackShare Editors
      StackShare Editors
      Prometheus
      Prometheus
      Chef
      Chef
      Consul
      Consul
      Memcached
      Memcached
      Hack
      Hack
      Swift
      Swift
      Hadoop
      Hadoop
      Terraform
      Terraform
      Airflow
      Airflow
      Apache Spark
      Apache Spark
      Kubernetes
      Kubernetes
      gRPC
      gRPC
      HHVM (HipHop Virtual Machine)
      HHVM (HipHop Virtual Machine)
      Presto
      Presto
      Kotlin
      Kotlin
      Apache Thrift
      Apache Thrift

      Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

      Apps
      • Web: a mix of JavaScript/ES6 and React.
      • Desktop: And Electron to ship it as a desktop application.
      • Android: a mix of Java and Kotlin.
      • iOS: written in a mix of Objective C and Swift.
      Backend
      • The core application and the API written in PHP/Hack that runs on HHVM.
      • The data is stored in MySQL using Vitess.
      • Caching is done using Memcached and MCRouter.
      • The search service takes help from SolrCloud, with various Java services.
      • The messaging system uses WebSockets with many services in Java and Go.
      • Load balancing is done using HAproxy with Consul for configuration.
      • Most services talk to each other over gRPC,
      • Some Thrift and JSON-over-HTTP
      • Voice and video calling service was built in Elixir.
      Data warehouse
      • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
      Etc
      See more
      Julien DeFrance
      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 501.1K views
      atSmartZipSmartZip
      Rails
      Rails
      Rails API
      Rails API
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Capistrano
      Capistrano
      Docker
      Docker
      Amazon S3
      Amazon S3
      Amazon RDS
      Amazon RDS
      MySQL
      MySQL
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      Amazon ElastiCache
      Amazon ElastiCache
      Memcached
      Memcached
      Amazon CloudFront
      Amazon CloudFront
      Segment
      Segment
      Zapier
      Zapier
      Amazon Redshift
      Amazon Redshift
      Amazon Quicksight
      Amazon Quicksight
      Superset
      Superset
      Elasticsearch
      Elasticsearch
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      New Relic
      New Relic
      AWS Lambda
      AWS Lambda
      Node.js
      Node.js
      Ruby
      Ruby
      Amazon DynamoDB
      Amazon DynamoDB
      Algolia
      Algolia

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Yonas Beshawred
      Yonas Beshawred
      CEO at StackShare · | 9 upvotes · 29.1K views
      atStackShareStackShare
      MemCachier
      MemCachier
      PostgreSQL
      PostgreSQL
      Rails
      Rails
      Amazon ElastiCache
      Amazon ElastiCache
      Heroku
      Heroku
      Memcached
      Memcached
      #Caching
      #RailsCaching

      We decided to use MemCachier as our Memcached provider because we were seeing some serious PostgreSQL performance issues with query-heavy pages on the site. We use MemCachier for all Rails caching and pretty aggressively too for the logged out experience (fully cached pages for the most part). We really need to move to Amazon ElastiCache as soon as possible so we can stop paying so much. The only reason we're not moving is because there are some restrictions on the network side due to our main app being hosted on Heroku.

      #Caching #RailsCaching

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      Interest over time
      Reviews of Memcached and TokuMX
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      How developers use Memcached and TokuMX
      Avatar of Reactor Digital
      Reactor Digital uses MemcachedMemcached

      As part of the cacheing system within Drupal.

      Memcached mainly took care of creating and rebuilding the REST API cache once changes had been made within Drupal.

      Avatar of Casey Smith
      Casey Smith uses MemcachedMemcached

      Distributed cache exposed through Google App Engine APIs; use to stage fresh data (incoming and recently processed) for faster access in data processing pipeline.

      Avatar of The Independent
      The Independent uses MemcachedMemcached

      Memcache caches database results and articles, reducing overall DB load and allowing seamless DB maintenance during quiet periods.

      Avatar of eXon Technologies
      eXon Technologies uses MemcachedMemcached

      Used to cache most used files for our clients. Connected with CloudFlare Railgun Optimizer.

      Avatar of ScholaNoctis
      ScholaNoctis uses MemcachedMemcached

      Memcached is used as a simple page cache across the whole application.

      How much does Memcached cost?
      How much does TokuMX cost?
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