Alternatives to Amazon CloudSearch logo

Alternatives to Amazon CloudSearch

Algolia, Elasticsearch, Solr, Azure Search, and Lucene are the most popular alternatives and competitors to Amazon CloudSearch.
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What is Amazon CloudSearch and what are its top alternatives?

Amazon CloudSearch is a fully managed search service that allows you to set up, manage, and scale a search solution for your website or application in the cloud. It automatically provisions the resources needed, indexes your data, and provides a fully managed search experience for your users. Some of its key features include automatic scaling, full-text search capabilities, faceted search, and multi-language support. However, Amazon CloudSearch has limitations such as complex configuration settings, potentially high costs for large datasets, and limited customization options.

  1. Elasticsearch: Elasticsearch is a highly scalable open-source search engine that provides real-time search and analytics capabilities. Key features include distributed search and analytics, support for complex queries, and integration with various data sources. Pros include high performance, rich query capabilities, and a thriving open-source community. Cons compared to Amazon CloudSearch include the need for self-management and potentially higher setup complexity.
  2. Solr: Apache Solr is a highly reliable and scalable enterprise search platform built on Apache Lucene. It offers features such as full-text search, faceted search, and near real-time indexing. Pros include extensive customization options, strong community support, and enterprise-grade reliability. However, compared to Amazon CloudSearch, Solr may require more resources for management and maintenance.
  3. Algolia: Algolia is a hosted search API that provides instant, relevant search results for websites and applications. It offers features like typo tolerance, faceted search, and geo-search. Pros include easy implementation, high performance, and built-in analytics. Compared to Amazon CloudSearch, Algolia may have limitations in terms of data storage and scaling options.
  4. Azure Search: Azure Search is a cloud-based search-as-a-service solution from Microsoft Azure. It offers features like full-text search, filtering, and custom scoring profiles. Pros include integration with other Azure services, AI-powered capabilities, and support for different data sources. Compared to Amazon CloudSearch, Azure Search may have a steeper learning curve for beginners.
  5. MeiliSearch: MeiliSearch is an open-source, fast, and relevant search engine designed for developers. It offers features such as typo-tolerance, filtering, and fast indexing. Pros include simplicity of use, high-speed search capabilities, and open-source nature. Compared to Amazon CloudSearch, MeiliSearch may have limitations in terms of managing large datasets and advanced customization options.
  6. Swiftype: Swiftype is a cloud-based search platform that provides powerful search and personalized insights for websites and applications. It offers features like automatic re-ranking, real-time indexing, and customizable search UI. Pros include ease of implementation, machine-learning capabilities, and extensive customer support. However, compared to Amazon CloudSearch, Swiftype may have higher costs for large datasets.
  7. Searchify: Searchify is a hosted search service that offers features like full-text search, faceted search, and geo-location search. Pros include easy setup, scalable infrastructure, and customizable search relevance settings. Compared to Amazon CloudSearch, Searchify may have limitations in terms of advanced analytics and data visualization options.
  8. Bonsai: Bonsai is a hosted Elasticsearch service that simplifies the deployment and management of Elasticsearch clusters. It offers features like automated scaling, data backups, and alert notifications. Pros include ease of use, high availability, and dedicated support. Compared to Amazon CloudSearch, Bonsai may be more focused on Elasticsearch-specific features and management.
  9. Meilisearch: Meilisearch is an open-source search engine that features typo tolerance, filtering, and real-time search capabilities. Pros include its simplicity, high-speed search functionality, and extensive documentation. Compared to Amazon CloudSearch, Meilisearch may have limitations concerning complex indexing configurations and large-scale data storage.
  10. Sphinx: Sphinx is an open-source full-text search engine designed for high performance and scalability. It offers features like full-text indexing, advanced querying, and real-time indexing. Pros include fast search speeds, scalability, and support for multiple languages. However, compared to Amazon CloudSearch, Sphinx may require more manual configuration and maintenance efforts.

Top Alternatives to Amazon CloudSearch

  • 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. ...

  • 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). ...

  • Solr
    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. ...

  • 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. ...

  • Lucene
    Lucene

    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. ...

  • Amazon Elasticsearch Service
    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. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Amazon CloudSearch alternatives & related posts

Algolia logo

Algolia

1.3K
1.1K
697
Developer-friendly API and complete set of tools for building search
1.3K
1.1K
+ 1
697
PROS OF ALGOLIA
  • 126
    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
    Distributed Search Network
  • 30
    Multiple datacenters
  • 10
    Smart Highlighting
  • 9
    Search as you type
  • 8
    Multi-attributes
  • 8
    Instantsearch.js
  • 6
    Super fast, easy to set up
  • 5
    Database search
  • 5
    Amazing uptime
  • 4
    Realtime
  • 4
    Github-awesome-autocomple
  • 4
    Great documentation
  • 4
    Highly customizable
  • 3
    Beautiful UI
  • 3
    Powerful Search
  • 3
    Places.js
  • 2
    Awesome aanltiycs and typos hnadling
  • 2
    Integrates with just about everything
  • 1
    Developer-friendly frontend libraries
  • 1
    Ok to use
  • 1
    Fast response time
  • 1
    Github integration
  • 1
    Smooth platform
  • 0
    Fuck
  • 0
    Giitera
  • 0
    Is it fool
  • 0
    Nooo
CONS OF ALGOLIA
  • 11
    Expensive

related Algolia posts

Josh Dzielak
Co-Founder & CTO at Orbit · | 19 upvotes · 428.9K views

Shortly after I joined Algolia as a developer advocate, I knew I wanted to establish a place for the community to congregate and share their projects, questions and advice. There are a ton of platforms out there that can be used to host communities, and they tend to fall into two categories - real-time sync (like chat) and async (like forums). Because the community was already large, I felt that a chat platform like Discord or Gitter might be overwhelming and opted for a forum-like solution instead (which would also create content that's searchable from Google).

I looked at paid, closed-source options like AnswerHub and ForumBee and old-school solutions like phpBB and vBulletin, but none seemed to offer the power, flexibility and developer-friendliness of Discourse. Discourse is open source, written in Rails with Ember.js on the front-end. That made me confident I could modify it to meet our exact needs. Discourse's own forum is very active which made me confident I could get help if I needed it.

It took about a month to get Discourse up-and-running and make authentication tied to algolia.com via the SSO plugin. Adding additional plugins for moderation or look-and-feel customization was fairly straightforward, and I even created a plugin to make the forum content searchable with Algolia. To stay on top of answering questions and moderation, we used the Discourse API to publish new messages into our Slack. All-in-all I would say we were happy with Discourse - the only caveat would be that it's very helpful to have technical knowledge as well as Rails knowledge in order to get the most out of it.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M 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
Elasticsearch logo

Elasticsearch

34K
26.6K
1.6K
Open Source, Distributed, RESTful Search Engine
34K
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PROS OF ELASTICSEARCH
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
CONS OF ELASTICSEARCH
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    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 · | 23 upvotes · 8.3M 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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Solr logo

Solr

774
643
126
A blazing-fast, open source enterprise search platform
774
643
+ 1
126
PROS OF SOLR
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Restful
  • 5
    Apache Software Foundation
  • 4
    Great Search engine
  • 2
    Security built-in
  • 1
    Easy Operating
CONS OF SOLR
    Be the first to leave a con

    related Solr posts

    Ganesa Vijayakumar
    Full Stack Coder | Technical Lead · | 19 upvotes · 4.6M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

    See more
    Shared insights
    on
    SolrSolrPHPPHPJavaJavaMySQLMySQL
    at

    One of the earliest public references to Slack’s stack comes from a Twitter conversation. The Slack account states that “the messaging server is java, the app is php, db is mysql and solr for search,” and that uploaded files are “Stored on S3, but private files require authentication so requests go through the app.”

    See more
    Azure Search logo

    Azure Search

    79
    220
    16
    Search-as-a-service for web and mobile app development
    79
    220
    + 1
    16
    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
      Be the first to leave a con

      related Azure Search posts

      Lucene logo

      Lucene

      169
      228
      2
      A high-performance, full-featured text search engine library written entirely in Java
      169
      228
      + 1
      2
      PROS OF LUCENE
      • 1
        Fast
      • 1
        Small
      CONS OF LUCENE
        Be the first to leave a con

        related Lucene posts

        Shared insights
        on
        SolrSolrLuceneLucene
        at

        "Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.

        Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."

        See more
        Amazon Elasticsearch Service logo

        Amazon Elasticsearch Service

        377
        286
        24
        Real-time, distributed search and analytics engine that fits nicely into a cloud environment
        377
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        24
        PROS OF AMAZON ELASTICSEARCH SERVICE
        • 10
          Easy setup, monitoring and scaling
        • 7
          Kibana
        • 7
          Document-oriented
        CONS OF AMAZON ELASTICSEARCH SERVICE
          Be the first to leave a con

          related Amazon Elasticsearch Service posts

          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M 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

          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
          JavaScript logo

          JavaScript

          350.8K
          267.1K
          8.1K
          Lightweight, interpreted, object-oriented language with first-class functions
          350.8K
          267.1K
          + 1
          8.1K
          PROS OF JAVASCRIPT
          • 1.7K
            Can be used on frontend/backend
          • 1.5K
            It's everywhere
          • 1.2K
            Lots of great frameworks
          • 896
            Fast
          • 745
            Light weight
          • 425
            Flexible
          • 392
            You can't get a device today that doesn't run js
          • 286
            Non-blocking i/o
          • 236
            Ubiquitousness
          • 191
            Expressive
          • 55
            Extended functionality to web pages
          • 49
            Relatively easy language
          • 46
            Executed on the client side
          • 30
            Relatively fast to the end user
          • 25
            Pure Javascript
          • 21
            Functional programming
          • 15
            Async
          • 13
            Full-stack
          • 12
            Setup is easy
          • 12
            Its everywhere
          • 12
            Future Language of The Web
          • 11
            JavaScript is the New PHP
          • 11
            Because I love functions
          • 10
            Like it or not, JS is part of the web standard
          • 9
            Expansive community
          • 9
            Everyone use it
          • 9
            Can be used in backend, frontend and DB
          • 9
            Easy
          • 8
            Easy to hire developers
          • 8
            No need to use PHP
          • 8
            For the good parts
          • 8
            Can be used both as frontend and backend as well
          • 8
            Powerful
          • 8
            Most Popular Language in the World
          • 7
            Popularized Class-Less Architecture & Lambdas
          • 7
            It's fun
          • 7
            Nice
          • 7
            Versitile
          • 7
            Hard not to use
          • 7
            Its fun and fast
          • 7
            Agile, packages simple to use
          • 7
            Supports lambdas and closures
          • 7
            Love-hate relationship
          • 7
            Photoshop has 3 JS runtimes built in
          • 7
            Evolution of C
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            1.6K Can be used on frontend/backend
          • 6
            Client side JS uses the visitors CPU to save Server Res
          • 6
            It let's me use Babel & Typescript
          • 6
            Easy to make something
          • 6
            Can be used on frontend/backend/Mobile/create PRO Ui
          • 5
            Promise relationship
          • 5
            Stockholm Syndrome
          • 5
            Function expressions are useful for callbacks
          • 5
            Scope manipulation
          • 5
            Everywhere
          • 5
            Client processing
          • 5
            Clojurescript
          • 5
            What to add
          • 4
            Because it is so simple and lightweight
          • 4
            Only Programming language on browser
          • 1
            Test2
          • 1
            Easy to learn
          • 1
            Easy to understand
          • 1
            Not the best
          • 1
            Hard to learn
          • 1
            Subskill #4
          • 1
            Test
          • 0
            Hard 彤
          CONS OF JAVASCRIPT
          • 22
            A constant moving target, too much churn
          • 20
            Horribly inconsistent
          • 15
            Javascript is the New PHP
          • 9
            No ability to monitor memory utilitization
          • 8
            Shows Zero output in case of ANY error
          • 7
            Thinks strange results are better than errors
          • 6
            Can be ugly
          • 3
            No GitHub
          • 2
            Slow

          related JavaScript posts

          Zach Holman

          Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

          But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

          But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

          Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

          How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

          Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

          Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

          https://eng.uber.com/distributed-tracing/

          (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

          Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

          See more
          Git logo

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          related Git posts

          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.2M views

          Our whole DevOps stack consists of the following tools:

          • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
          • Respectively Git as revision control system
          • SourceTree as Git GUI
          • Visual Studio Code as IDE
          • CircleCI for continuous integration (automatize development process)
          • Prettier / TSLint / ESLint as code linter
          • SonarQube as quality gate
          • Docker as container management (incl. Docker Compose for multi-container application management)
          • VirtualBox for operating system simulation tests
          • Kubernetes as cluster management for docker containers
          • Heroku for deploying in test environments
          • nginx as web server (preferably used as facade server in production environment)
          • SSLMate (using OpenSSL) for certificate management
          • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
          • PostgreSQL as preferred database system
          • Redis as preferred in-memory database/store (great for caching)

          The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

          • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
          • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
          • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
          • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
          • Scalability: All-in-one framework for distributed systems.
          • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 8.3M 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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