Memcached vs PerconaXtraDBCluster

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Memcached
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Memcached vs PerconaXtraDBCluster: What are the differences?

Developers describe Memcached as "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. On the other hand, PerconaXtraDBCluster is detailed as "Percona XtraDB Cluster is an active/active high availability and high scalability open source solution for MySQL® clustering". Percona XtraDB Cluster is an active/active high availability and high scalability open source solution for MySQL® clustering. It integrates Percona Server and Percona XtraBackup with the Codership Galera library of MySQL high availability solutions in a single package that enables you to create a cost-effective MySQL high availability cluster.

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

Memcached is an open source tool with 9K GitHub stars and 2.6K GitHub forks. Here's a link to Memcached's open source repository on GitHub.

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 PerconaXtraDBCluster?

Percona XtraDB Cluster is an active/active high availability and high scalability open source solution for MySQL® clustering. It integrates Percona Server and Percona XtraBackup with the Codership Galera library of MySQL high availability solutions in a single package that enables you to create a cost-effective MySQL high availability cluster.
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          What are some alternatives to Memcached and PerconaXtraDBCluster?
          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 PerconaXtraDBCluster
          HAProxy
          HAProxy
          Varnish
          Varnish
          Tornado
          Tornado
          Django
          Django
          Redis
          Redis
          RabbitMQ
          RabbitMQ
          nginx
          nginx
          Memcached
          Memcached
          MySQL
          MySQL
          Python
          Python
          Node.js
          Node.js

          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 · 50.5K views
          atShopifyShopify
          Redis
          Redis
          Memcached
          Memcached
          MySQL
          MySQL
          Rails
          Rails

          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.

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          Kir Shatrov
          Kir Shatrov
          Production Engineer at Shopify · | 13 upvotes · 87.6K views
          atShopifyShopify
          Memcached
          Memcached
          Redis
          Redis
          MySQL
          MySQL
          Google Kubernetes Engine
          Google Kubernetes Engine
          Kubernetes
          Kubernetes
          Docker
          Docker

          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.

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

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

          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
          Full Stack Engineering Manager at ValiMail · | 16 upvotes · 283.7K views
          atSmartZipSmartZip
          Amazon DynamoDB
          Amazon DynamoDB
          Ruby
          Ruby
          Node.js
          Node.js
          AWS Lambda
          AWS Lambda
          New Relic
          New Relic
          Amazon Elasticsearch Service
          Amazon Elasticsearch Service
          Elasticsearch
          Elasticsearch
          Superset
          Superset
          Amazon Quicksight
          Amazon Quicksight
          Amazon Redshift
          Amazon Redshift
          Zapier
          Zapier
          Segment
          Segment
          Amazon CloudFront
          Amazon CloudFront
          Memcached
          Memcached
          Amazon ElastiCache
          Amazon ElastiCache
          Amazon RDS for Aurora
          Amazon RDS for Aurora
          MySQL
          MySQL
          Amazon RDS
          Amazon RDS
          Amazon S3
          Amazon S3
          Docker
          Docker
          Capistrano
          Capistrano
          AWS Elastic Beanstalk
          AWS Elastic Beanstalk
          Rails API
          Rails API
          Rails
          Rails
          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 · 23.5K views
          atStackShareStackShare
          Memcached
          Memcached
          Heroku
          Heroku
          Amazon ElastiCache
          Amazon ElastiCache
          Rails
          Rails
          PostgreSQL
          PostgreSQL
          MemCachier
          MemCachier
          #RailsCaching
          #Caching

          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 PerconaXtraDBCluster
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          How developers use Memcached and PerconaXtraDBCluster
          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.

          Avatar of POROWNEO.PL
          POROWNEO.PL uses PerconaXtraDBClusterPerconaXtraDBCluster

          How we store our data across a quorum of 3 nodes.

          How much does Memcached cost?
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