What is Ehcache and what are its top alternatives?
Top Alternatives to Ehcache
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. ...
- Apache Ignite
It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale ...
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. ...
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...
The Guava project contains several of Google's core libraries that we rely on in our Java-based projects: collections, caching, primitives support, concurrency libraries, common annotations, string processing, I/O, and so forth. ...
- GraphQL Cache
A custom middleware for graphql-ruby that handles key construction and cache reads/writes transparently. ...
Ehcache alternatives & related posts
- Fast object cache139
- Distributed caching system33
- Improved response time and throughput11
- Great for caching HTML3
- Only caches simple types2
related Memcached posts
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.
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.
- Multiple client language support4
- Written in java. runs on jvm4
- High Avaliability4
- Load balancing3
- Sql query support in cluster wide3
- Rest interface3
- Easy to use2
- Distributed compute2
- Better Documentation2
- Distributed Locking1
related Apache Ignite posts
- High Availibility11
- Distributed Locking6
- Distributed compute6
- Load balancing4
- Map-reduce functionality3
- Written in java. runs on jvm3
- Sql query support in cluster wide3
- Optimis locking for map2
- Multiple client language support2
- Rest interface2
- Admin Interface (Management Center)1
- Better Documentation1
- Easy to use1
- Super Fast1
- License needed for SSL4
related Hazelcast posts
- Super fast541
- Ease of use512
- In-memory cache443
- Advanced key-value cache323
- Open source193
- Easy to deploy182
- High Availability40
- Data Structures34
- Very Scalable32
- Great community22
- "NoSQL" key-value data store19
- Sorted Sets11
- BSD licensed9
- Integrates super easy with Sidekiq for Rails background8
- Async replication8
- Open Source7
- Keys with a limited time-to-live7
- Lua scripting6
- Awesomeness for Free5
- Written in ANSI C4
- LRU eviction of keys4
- Outstanding performance4
- Runs server side LUA4
- Feature Rich4
- Performance & ease of use3
- Data structure server3
- Object [key/value] size each 500 MB2
- Temporarily kept on disk2
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Existing Laravel Integration2
- Channels concept2
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1
related Redis posts
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.
We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.
Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis for cache and other time sensitive operations.
We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.
- Interface Driven API5
- Easy to setup1