Algolia聽vs聽Amazon Elasticsearch Service

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Algolia vs Amazon Elasticsearch Service: What are the differences?

Algolia: Developer-friendly API and complete set of tools for building search. Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard; Amazon Elasticsearch Service: Real-time, distributed search and analytics engine that fits nicely into a cloud environment. .

Algolia and Amazon Elasticsearch Service belong to "Search as a Service" category of the tech stack.

"Ultra fast" is the primary reason why developers consider Algolia over the competitors, whereas "Easy setup, monitoring and scaling" was stated as the key factor in picking Amazon Elasticsearch Service.

Medium, StackShare, and Product Hunt are some of the popular companies that use Algolia, whereas Amazon Elasticsearch Service is used by esa, Zola, and Firecracker. Algolia has a broader approval, being mentioned in 258 company stacks & 54 developers stacks; compared to Amazon Elasticsearch Service, which is listed in 79 company stacks and 13 developer stacks.

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What is Algolia?

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

What is Amazon Elasticsearch Service?

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.
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Why do developers choose Algolia?
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    What are some alternatives to Algolia and Amazon Elasticsearch Service?
    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).
    Solr
    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
    Swiftype
    Swiftype is the easiest way to add great search to your website or mobile application.
    Azure Search
    Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.
    Klevu
    It is an intelligent site search solution designed to help eCommerce businesses increase onsite sales and improve the customer online shopping experience.
    See all alternatives
    Decisions about Algolia and Amazon Elasticsearch Service
    Tim Specht
    Tim Specht
    鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 53.4K views
    atDubsmashDubsmash
    Memcached
    Memcached
    Algolia
    Algolia
    Elasticsearch
    Elasticsearch
    #SearchAsAService

    Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

    Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

    Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

    #SearchAsAService

    See more
    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail | 16 upvotes 276K 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.

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    Chris McFadden
    Chris McFadden
    VP, Engineering at SparkPost | 8 upvotes 22.8K views
    atSparkPostSparkPost
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Node.js
    Node.js
    Amazon CloudSearch
    Amazon CloudSearch
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon DynamoDB
    Amazon DynamoDB

    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.

    See more
    Interest over time
    Reviews of Algolia and Amazon Elasticsearch Service
    Review ofAlgoliaAlgolia

    Maxime is a big supporter of Product Hunt, recognizing the continual request that I add search to the product from others in the community. Having seen many frustrating search implementations on other sites, I assumed it would be hard to integrate and provide something useful. Algolia proved me wrong (see the results here: http://producthunt.co/search).

    I'm impressed with the speed and amazing support from the Algolia team. The dashboard analytics and management are incredibly useful, providing insight into how people are using the product and ability to act on those learning without changing a line of code. I would highly recommend it.

    Review ofAlgoliaAlgolia

    Having a great search engine is extremely important for our app store. We find that users love to search, not only when they know what they are looking for but also to discover content around different themes.

    In a very rushed period with lots of things to do in parallel, we found that Algolia offered a high quality solution that perfectly solved our problem and we had a first version working great in less than a day.

    We also enjoyed getting their feedbacks and ideas to help us improve our search and we are now using Algolia in our internal tools as well. We strongly recommend them!

    Review ofAlgoliaAlgolia

    We were looking for a better search solution for GrowthHackers.com for a couple months. All the options we looked at were either too complicated to setup, didn't have the features we needed, or were too expensive. Algolia hit the right balance for us. It's super fast and easy to customize, and the documentation and examples for getting started are great. Most importantly though, their support rocks. It's always a pleasure talking with their team.

    Review ofAlgoliaAlgolia

    I'm Antonio, TVShow Time's CEO, a startup that has more than 100k+ active users.

    Before Algolia, we were using Elastic Search that was costing a lot (hosted on 2 big EC2 instances) and with results that weren't that relevant.

    Then we switched to Algolia, in 1 hour. We were blown away by how easy the integration was for such a good relevance in results and high performance.

    Review ofAlgoliaAlgolia

    We tried a lot of services at Socialcam to handle our massive user base. All of them couldn't handle that number of users.

    Algolia handles it without any problem but on top of that, it does it at full speed: we get results back in under a few milliseconds. Last but not least, it does it with error handling, which is great as typos are very frequent on mobile...

    How developers use Algolia and Amazon Elasticsearch Service
    Avatar of Packet
    Packet uses AlgoliaAlgolia

    Algolia helps us search across disparate pieces of information in our staff portal, and allows customers to easily jump around the portal between devices, support conversations, and documentation.

    Avatar of Charles LaPress
    Charles LaPress uses Amazon Elasticsearch ServiceAmazon Elasticsearch Service

    By streaming data from Dynamodb Elasticsearch provides the dynamic lookups for listings by activity, date, cost, ect. ect, providing a superior enduser experience.

    Avatar of kako.pk
    kako.pk uses AlgoliaAlgolia

    This is the bedrock of kako.pk - it not only serves the JSON data, it doubles as a (very fast) web-server if you connect to the client JS widget libraries

    Avatar of Shoes of Prey
    Shoes of Prey uses AlgoliaAlgolia

    We use algolia to power our product search / filtering (https://www.shoesofprey.com/shoes).

    Avatar of DevTube
    DevTube uses AlgoliaAlgolia

    We started with Algolia, but switched to our home-backed full-text search solution. It's serverless, based on Lunr.js

    Avatar of Pharmatolia
    Pharmatolia uses AlgoliaAlgolia

    For detailed searching listing and products posted by customers

    How much does Algolia cost?
    How much does Amazon Elasticsearch Service cost?
    Pricing unavailable