Amazon Elasticsearch Service聽vs聽Qbox.io

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Amazon Elasticsearch Service
Amazon Elasticsearch Service

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Qbox.io
Qbox.io

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Amazon Elasticsearch Service vs Qbox.io: What are the differences?

Amazon Elasticsearch Service: Real-time, distributed search and analytics engine that fits nicely into a cloud environment. ; Qbox.io: Dedicated cloud hosting for Elasticsearch on Amazon EC2, Rackspace, and SoftLayer. Qbox is supported, dedicated, hosted Elasticsearch - the bleeding edge of full-text search and analytics. We provide an intuitive interface to provision, secure, and monitor ES clusters in Amazon EC2 and Rackspace datacenters everywhere.

Amazon Elasticsearch Service and Qbox.io can be categorized as "Search as a Service" tools.

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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.

What is Qbox.io?

Qbox is supported, dedicated, hosted Elasticsearch - the bleeding edge of full-text search and analytics. We provide an intuitive interface to provision, secure, and monitor ES clusters in Amazon EC2 and Rackspace datacenters everywhere.
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          What are some alternatives to Amazon Elasticsearch Service and Qbox.io?
          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).
          Amazon CloudSearch
          Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.
          Elastic Cloud
          A growing family of Elastic SaaS offerings that make it easy to deploy, operate, and scale Elastic products and solutions in the cloud. From an easy-to-use hosted and managed Elasticsearch experience to powerful, out-of-the-box search solutions.
          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.
          Swiftype
          Swiftype is the easiest way to add great search to your website or mobile application.
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          Decisions about Amazon Elasticsearch Service and Qbox.io
          Julien DeFrance
          Julien DeFrance
          Full Stack Engineering Manager at ValiMail | 16 upvotes 268.5K 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.

          See more
          Chris McFadden
          Chris McFadden
          VP, Engineering at SparkPost | 8 upvotes 22.4K 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.

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          Reviews of Amazon Elasticsearch Service and Qbox.io
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          How developers use Amazon Elasticsearch Service and Qbox.io
          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 Drillist.com
          Drillist.com uses Qbox.ioQbox.io

          Search as a Service

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