Memcached vs ToroDB: 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; ToroDB: Open source, document-oriented, JSON database that runs on top of PostgreSQL. ToroDB is an open source, document-oriented, JSON database that runs on top of PostgreSQL, providing storage and I/O savings and ACID semantics. ToroDB is MongoDB-compatible, so you can use Mongo clients to connect to it.
Memcached and ToroDB can be primarily classified as "Databases" tools.
Memcached and ToroDB are both open source tools. It seems that Memcached with 8.99K GitHub stars and 2.6K forks on GitHub has more adoption than ToroDB with 10 GitHub stars and 2 GitHub forks.
What is Memcached?
What is ToroDB?
Need advice about which tool to choose?Ask the StackShare community!
Why do developers choose ToroDB?
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Memcached?
What are the cons of using ToroDB?
What companies use ToroDB?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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
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
- 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.
- 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.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
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.
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.
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.
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.
Distributed cache exposed through Google App Engine APIs; use to stage fresh data (incoming and recently processed) for faster access in data processing pipeline.
Memcache caches database results and articles, reducing overall DB load and allowing seamless DB maintenance during quiet periods.
Used to cache most used files for our clients. Connected with CloudFlare Railgun Optimizer.