Memcached vs Riak: What are the differences?
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; Riak: A distributed, decentralized data storage system. Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your client can reach one Riak server, it should be able to write data. In most failure scenarios, the data you want to read should be available, although it may not be the most up-to-date version of that data.
Memcached and Riak belong to "Databases" category of the tech stack.
"Fast object cache" is the top reason why over 133 developers like Memcached, while over 9 developers mention "High Performance " as the leading cause for choosing Riak.
Memcached and Riak are both open source tools. Memcached with 8.99K GitHub stars and 2.6K forks on GitHub appears to be more popular than Riak with 3.24K GitHub stars and 530 GitHub forks.
According to the StackShare community, Memcached has a broader approval, being mentioned in 755 company stacks & 267 developers stacks; compared to Riak, which is listed in 15 company stacks and 10 developer stacks.
What is Memcached?
What is Riak?
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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.
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.
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.
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