Amazon Elasticsearch Service logo

Amazon Elasticsearch Service

Real-time, distributed search and analytics engine that fits nicely into a cloud environment
226
122
+ 1
19

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.
Amazon Elasticsearch Service is a tool in the Search as a Service category of a tech stack.

Who uses Amazon Elasticsearch Service?

Companies
124 companies reportedly use Amazon Elasticsearch Service in their tech stacks, including esa, LaunchDarkly, and Bagelcode.

Developers
94 developers on StackShare have stated that they use Amazon Elasticsearch Service.

Amazon Elasticsearch Service Integrations

Elasticsearch, Redash, LocalStack, AWS AppSync, and Amazon Transcribe are some of the popular tools that integrate with Amazon Elasticsearch Service. Here's a list of all 7 tools that integrate with Amazon Elasticsearch Service.

Why developers like Amazon Elasticsearch Service?

Here鈥檚 a list of reasons why companies and developers use Amazon Elasticsearch Service
Amazon Elasticsearch Service Reviews

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

Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 839.2K 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.

See more
Chris McFadden
Chris McFadden
VP, Engineering at SparkPost | 8 upvotes 69.8K 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.

See more
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

See more
Amit Bhatnagar
Amit Bhatnagar
Chief Architect at Qrvey | 3 upvotes 18.1K views
atQrveyQrvey
Amazon DynamoDB
Amazon DynamoDB
AWS Fargate
AWS Fargate
Amazon Elasticsearch Service
Amazon Elasticsearch Service
AWS CloudFormation
AWS CloudFormation
AWS CodePipeline
AWS CodePipeline

At Qrvey we moved from a SaaS application running in AWS to a deployed model where we would deploy the complete infrastructure and code to a customer's AWS account. This created a unique challenge as we were Cloud Native and hence were using a lot of AWS Services like Amazon DynamoDB, AWS Fargate , Amazon Elasticsearch Service, etc. We decided to first build AWS CloudFormation templates to convert all our infrastructure into code. Then created a AWS CloudFormation template that would first generate a AWS CodePipeline into a customer's AWS account. This pipeline would then deploy our Infrastructure AWS CloudFormation template and the code on that Infrastructure. This simplified and completely automated our upgrade process as well.

See more
Charles LaPress
Charles LaPress
Amazon Elasticsearch Service
Amazon 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. Amazon Elasticsearch Service

See more
TJ Holowaychuk
TJ Holowaychuk
Amazon Elasticsearch Service
Amazon Elasticsearch Service

Elasticsearch powers both internal logging and the storage for checks and events. Amazon Elasticsearch Service

See more

Amazon Elasticsearch Service Alternatives & Comparisons

What are some alternatives to 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).
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.
See all alternatives

Amazon Elasticsearch Service's Followers
122 developers follow Amazon Elasticsearch Service to keep up with related blogs and decisions.
Maxwell Everson
rnawhale
R OCarroll
guptaraul
Amani Anai
Brian Fidler
Min Koo Kang
J贸natan Einarsson
Nurullah 脰zdemir
Nicky G