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Amazon ElastiCache

Deploy, operate, and scale an in-memory cache in the cloud
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What is Amazon ElastiCache?

ElastiCache improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports Memcached and Redis.
Amazon ElastiCache is a tool in the Managed Memcache category of a tech stack.

Who uses Amazon ElastiCache?

Companies
447 companies reportedly use Amazon ElastiCache in their tech stacks, including Airbnb, Instacart, and Asana.

Developers
317 developers on StackShare have stated that they use Amazon ElastiCache.

Amazon ElastiCache Integrations

Why developers like Amazon ElastiCache?

Here鈥檚 a list of reasons why companies and developers use Amazon ElastiCache
Top Reasons
Amazon ElastiCache Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon ElastiCache in their tech stack.

Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 485.9K 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.

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John Kodumal
John Kodumal
CTO at LaunchDarkly | 15 upvotes 177K views
atLaunchDarklyLaunchDarkly
Amazon RDS
Amazon RDS
PostgreSQL
PostgreSQL
TimescaleDB
TimescaleDB
Patroni
Patroni
Consul
Consul
Amazon ElastiCache
Amazon ElastiCache
Amazon EC2
Amazon EC2
Redis
Redis
Amazon Kinesis
Amazon Kinesis
Kafka
Kafka

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data鈥攖his is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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Yonas Beshawred
Yonas Beshawred
CEO at StackShare | 9 upvotes 28.7K 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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Chris McFadden
Chris McFadden
VP, Engineering at SparkPost | 8 upvotes 43K views
atSparkPostSparkPost
Amazon DynamoDB
Amazon DynamoDB
Amazon ElastiCache
Amazon ElastiCache
Amazon CloudSearch
Amazon CloudSearch
Node.js
Node.js
Amazon Elasticsearch Service
Amazon Elasticsearch Service

We send over 20 billion emails a month on behalf of our customers. As a result, we manage hundreds of millions of "suppression" records that track when an email address is invalid as well as when a user unsubscribes or flags an email as spam. This way we can help ensure our customers are only sending email that their recipients want, which boosts overall delivery rates and engagement. We need to support two primary use cases: (1) fast and reliable real-time lookup against the list when sending email and (2) allow customers to search, edit, and bulk upload/download their list via API and in the UI. A single enterprise customer's list can be well over 100 million. Over the years as the size of this data started small and has grown increasingly we have tried multiple things that didn't scale very well. In the recent past we used Amazon DynamoDB for the system of record as well as a cache in Amazon ElastiCache (Redis) for the fast lookups and Amazon CloudSearch for the search function. This architecture was overly complicated and expensive. We were able to eliminate the use of Redis, replacing it with direct lookups against DynamoDB, fronted with a stripped down Node.js API that performs consistently around 10ms. The new dynamic bursting of DynamoDB has helped ensure reliable and consistent performance for real-time lookups. We also moved off the clunky and expensive CloudSearch to Amazon Elasticsearch Service for the search functionality. Beyond the high price tag for CloudSearch it also had severe limits streaming updates from DynamoDB, which forced us to batch them - adding extra complexity and CX challenges. We love the fact that DynamoDB can stream directly to ElasticSearch and believe using these two technologies together will handle our scaling needs in an economical way for the foreseeable future.

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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

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Pedro Arnal Puente
Pedro Arnal Puente
CTO at La Cupula Music SL | 7 upvotes 67K views
atLa Cupula Music SLLa Cupula Music SL
Debian
Debian
Amazon EC2
Amazon EC2
Amazon S3
Amazon S3
Amazon RDS for Aurora
Amazon RDS for Aurora
Redis
Redis
Amazon ElastiCache
Amazon ElastiCache
Terraform
Terraform
Packer
Packer
Ansible
Ansible

Our base infrastructure is composed of Debian based servers running in Amazon EC2 , asset storage with Amazon S3 , and Amazon RDS for Aurora and Redis under Amazon ElastiCache for data storage.

We are starting to work in automated provisioning and management with Terraform , Packer , and Ansible .

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Amazon ElastiCache's Features

  • Support for two engines: Memcached and Redis
  • Ease of management via the AWS Management Console. With a few clicks you can configure and launch instances for the engine you wish to use.
  • Compatibility with the specific engine protocol. This means most of the client libraries will work with the respective engines they were built for - no additional changes or tweaking required.
  • Detailed monitoring statistics for the engine nodes at no extra cost via Amazon CloudWatch
  • Pay only for the resources you consume based on node hours used

Amazon ElastiCache Alternatives & Comparisons

What are some alternatives to Amazon ElastiCache?
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.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
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.
Azure Redis Cache
It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.
MemCachier
MemCachier provides an easy and powerful managed caching solution for all your performance and scalability needs. It works with the ubiquitous memcache protocol so your favourite language and framework already supports it.
See all alternatives

Amazon ElastiCache's Followers
342 developers follow Amazon ElastiCache to keep up with related blogs and decisions.
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Scott Brinkmeyer
Ratan Jena
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Nurullah 脰zdemir
praveen kumar
Hirofumi Kubo
lukeon kim
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Sajjad vafaie