Alternatives to Amazon Elasticsearch Service logo

Alternatives to Amazon Elasticsearch Service

Elasticsearch, Amazon CloudSearch, Elastic Cloud, ELK, and Algolia are the most popular alternatives and competitors to Amazon Elasticsearch Service.
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What is Amazon Elasticsearch Service and what are its top alternatives?

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.

Top Alternatives to Amazon Elasticsearch Service

  • Elasticsearch

    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

    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

    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. ...

  • ELK

    ELK

    It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server鈥憇ide data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. ...

  • Algolia

    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

    Swiftype is the easiest way to add great search to your website or mobile application. ...

  • Azure Search

    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. ...

  • Amazon Kendra

    Amazon Kendra

    It is a highly accurate and easy to use enterprise search service that鈥檚 powered by machine learning. It delivers powerful natural language search capabilities to your websites and applications so your end users can more easily find the information they need within the vast amount of content spread across your company. ...

Amazon Elasticsearch Service alternatives & related posts

Elasticsearch logo

Elasticsearch

23.9K
17.9K
1.6K
Open Source, Distributed, RESTful Search Engine
23.9K
17.9K
+ 1
1.6K
PROS OF ELASTICSEARCH
  • 321
    Powerful api
  • 311
    Great search engine
  • 231
    Open source
  • 213
    Restful
  • 200
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Great docs
  • 3
    Awesome, great tool
  • 3
    Easy to scale
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Fast
  • 2
    Nosql DB
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Document Store
  • 2
    Great customer support
  • 1
    Reliable
  • 1
    Not stable
  • 1
    Potato
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Scalability
  • 0
    Easy to get hot data
  • 0
    Community
CONS OF ELASTICSEARCH
  • 6
    Diffecult to get started
  • 5
    Resource hungry
  • 4
    Expensive
  • 3
    Hard to keep stable at large scale

related Elasticsearch posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
Tymoteusz Paul
Devops guy at X20X Development LTD | 21 upvotes 路 4.3M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Amazon CloudSearch logo

Amazon CloudSearch

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Set up, manage, and scale a search solution for your website or application
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PROS OF AMAZON CLOUDSEARCH
  • 10
    Managed
  • 7
    Auto-Scaling
  • 5
    Compound Queries
  • 3
    Easy Setup
CONS OF AMAZON CLOUDSEARCH
    Be the first to leave a con

    related Amazon CloudSearch posts

    Chris McFadden
    VP, Engineering at SparkPost | 8 upvotes 路 127.7K views

    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
    Elastic Cloud logo

    Elastic Cloud

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    0
    Deploy, operate, and scale Elastic products and solutions in the cloud
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    PROS OF ELASTIC CLOUD
      Be the first to leave a pro
      CONS OF ELASTIC CLOUD
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        related Elastic Cloud posts

        Gunnar Sir茅us
        System Developer at Sir茅us Consulting AB | 2 upvotes 路 3.3K views
        Shared insights
        on
        Elastic Cloud
        Jelastic

        I wonder what the best option for hosting my home page https://www.sireus.se is? On Jelastic or Elastic Cloud? Which host is the most suitable and cost-effective?

        See more
        ELK logo

        ELK

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        The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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        PROS OF ELK
        • 11
          Open source
        • 1
          Good for startups with monetary limitations
        • 1
          External Network Goes Down You Aren't Without Logging
        • 1
          Can run locally
        • 0
          Json log supprt
        • 0
          Live logging
        • 0
          Easy to setup
        CONS OF ELK
        • 3
          Logstash configuration is a pain
        • 3
          Elastic Search is a resource hog
        • 1
          Bad for startups with personal limitations

        related ELK posts

        Wallace Alves
        Cyber Security Analyst | 1 upvote 路 548.9K views

        Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

        See more
        Algolia logo

        Algolia

        974
        900
        695
        Developer-friendly API and complete set of tools for building search
        974
        900
        + 1
        695
        PROS OF ALGOLIA
        • 125
          Ultra fast
        • 95
          Super easy to implement
        • 73
          Modern search engine
        • 71
          Excellent support
        • 70
          Easy setup, fast and relevant
        • 46
          Typos handling
        • 40
          Search analytics
        • 31
          Designed to search records, not pages
        • 30
          Multiple datacenters
        • 30
          Distributed Search Network
        • 10
          Smart Highlighting
        • 9
          Search as you type
        • 8
          Instantsearch.js
        • 8
          Multi-attributes
        • 6
          Super fast, easy to set up
        • 5
          Amazing uptime
        • 5
          Database search
        • 4
          Realtime
        • 4
          Great documentation
        • 4
          Highly customizable
        • 4
          Github-awesome-autocomple
        • 3
          Powerful Search
        • 3
          Beautiful UI
        • 3
          Places.js
        • 2
          Integrates with just about everything
        • 2
          Awesome aanltiycs and typos hnadling
        • 1
          Fast response time
        • 1
          Smooth platform
        • 1
          Github integration
        • 1
          Developer-friendly frontend libraries
        CONS OF ALGOLIA
        • 10
          Expensive

        related Algolia posts

        Julien DeFrance
        Principal Software Engineer at Tophatter | 16 upvotes 路 2.3M views

        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
        Tim Specht
        鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 路 305K views

        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
        Swiftype logo

        Swiftype

        191
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        11
        Powerful and scalable search for any application or website
        191
        77
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        PROS OF SWIFTYPE
        • 8
          Very easy setup and highly customizable for your search
        • 1
          Easy setup
        • 1
          Analytics
        • 1
          Role devision to develop, design, manage
        CONS OF SWIFTYPE
        • 1
          Expensive
        • 1
          API Calls Monitoring/Alerts
        • 1
          Cost Prediction
        • 1
          Customer Support

        related Swiftype posts

        Azure Search logo

        Azure Search

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        Search-as-a-service for web and mobile app development
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        PROS OF AZURE SEARCH
        • 4
          Easy to set up
        • 3
          Auto-Scaling
        • 3
          Managed
        • 2
          Easy Setup
        • 2
          More languages
        • 2
          Lucene based search criteria
        CONS OF AZURE SEARCH
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          related Azure Search posts

          Amazon Kendra logo

          Amazon Kendra

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          Enterprise search service powered by machine learning
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          PROS OF AMAZON KENDRA
            Be the first to leave a pro
            CONS OF AMAZON KENDRA
            • 3
              Expensive

            related Amazon Kendra posts