Bonsai聽vs聽Elasticsearch

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Bonsai
Bonsai

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Elasticsearch
Elasticsearch

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Bonsai vs Elasticsearch: What are the differences?

What is Bonsai? Fast, reliable full-text search, managed for you by experts. Your customers expect fast, near-magical results from your search. Help them find what they鈥檙e looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

What is Elasticsearch? Open Source, Distributed, RESTful Search Engine. 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).

Bonsai and Elasticsearch can be categorized as "Search as a Service" tools.

Some of the features offered by Bonsai are:

  • The Open Source Advantage- Our service is proudly powered by Elasticsearch and Apache Lucene, the open source industry standard for fast full-text search.
  • Results in Real-Time- Search your site's activity as it happens, with Elasticsearch's cutting-edge real-time updates
  • High Availability by default

On the other hand, Elasticsearch provides the following key features:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

Uber Technologies, Instacart, and Slack are some of the popular companies that use Elasticsearch, whereas Bonsai is used by Listium, Growstuff, and reclamador.es. Elasticsearch has a broader approval, being mentioned in 2000 company stacks & 976 developers stacks; compared to Bonsai, which is listed in 3 company stacks and 4 developer stacks.

- No public GitHub repository available -

What is Bonsai?

Your customers expect fast, near-magical results from your search. Help them find what they鈥檙e looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

What is 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).
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      What are some alternatives to Bonsai and Elasticsearch?
      QuickBooks
      It is an accounting software package. You can access and manage your books from your computer, laptop, tablet, or smartphone anytime you choose. Create access privileges so that your colleague or accountant can login and work.
      Freshbooks
      It is simple and intuitive. It makes running your small business easy, fast and secure. Easily send invoices, track time, manage expenses, and get paid online.
      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.
      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.
      Swiftype
      Swiftype is the easiest way to add great search to your website or mobile application.
      See all alternatives
      Decisions about Bonsai and Elasticsearch
      Tim Specht
      Tim Specht
      鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 127.2K views
      atDubsmashDubsmash
      Elasticsearch
      Elasticsearch
      Algolia
      Algolia
      Memcached
      Memcached
      #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
      Principal Software Engineer at Tophatter | 16 upvotes 897K 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
      Interest over time
      Reviews of Bonsai and Elasticsearch
      Review ofBonsaiBonsai

      We used Bonsai happily for 2 years, great service, but then they forced us to upgrade or we'd have to pay 5x the price! VERY unhappy about that. caused us days of unnecessary work.

      How developers use Bonsai and Elasticsearch
      Avatar of imgur
      imgur uses ElasticsearchElasticsearch

      Elasticsearch is the engine that powers search on the site. From a high level perspective, it鈥檚 a Lucene wrapper that exposes Lucene鈥檚 features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

      Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

      Avatar of Instacart
      Instacart uses ElasticsearchElasticsearch

      The very first version of the search was just a Postgres database query. It wasn鈥檛 terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn鈥檛 come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

      Avatar of AngeloR
      AngeloR uses ElasticsearchElasticsearch

      We use ElasticSearch for

      • Session Logs
      • Analytics
      • Leaderboards

      We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

      The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

      Avatar of Brandon Adams
      Brandon Adams uses ElasticsearchElasticsearch

      Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

      Avatar of Ana Phi Sancho
      Ana Phi Sancho uses ElasticsearchElasticsearch

      Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

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