Elasticsearchย vsย Solr

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Elasticsearch vs Solr: What are the differences?

Developers describe Elasticsearch as "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). On the other hand, Solr is detailed as "An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc". 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.

Elasticsearch and Solr are primarily classified as "Search as a Service" and "Search Engines" tools respectively.

Some of the features offered by Elasticsearch are:

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

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

  • Advanced Full-Text Search Capabilities
  • Optimized for High Volume Web Traffic
  • Standards Based Open Interfaces - XML, JSON and HTTP

"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "Powerful" was stated as the key factor in picking Solr.

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 Solr is used by Slack, Coursera, and Zalando. Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to Solr, which is listed in 140 company stacks and 42 developer stacks.

- No public GitHub repository available -

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).

What is 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.
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    What are some alternatives to Elasticsearch and Solr?
    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.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    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.
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    Kibana
    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.
    See all alternatives
    Decisions about Elasticsearch and Solr
    StackShare Editors
    StackShare Editors
    Lucene
    Lucene
    Solr
    Solr

    "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 documentโ€Šโ€”โ€Šhow 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 userโ€™s interaction history with channels and other usersโ€Šโ€”โ€Štheir โ€˜work graphโ€™."

    See more
    Tim Specht
    Tim Specht
    โ€ŽCo-Founder and CTO at Dubsmash ยท | 16 upvotes ยท 79.5K views
    atDubsmashDubsmash
    Memcached
    Memcached
    Algolia
    Algolia
    Elasticsearch
    Elasticsearch
    #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 ยท | 16 upvotes ยท 411.2K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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    Ganesa Vijayakumar
    Ganesa Vijayakumar
    Full Stack Coder | Module Lead ยท | 15 upvotes ยท 414.3K views
    SonarQube
    SonarQube
    Codacy
    Codacy
    Docker
    Docker
    Git
    Git
    Apache Maven
    Apache Maven
    Amazon EC2 Container Service
    Amazon EC2 Container Service
    Microsoft Azure
    Microsoft Azure
    Amazon Route 53
    Amazon Route 53
    Elasticsearch
    Elasticsearch
    Solr
    Solr
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Heroku
    Heroku
    Hibernate
    Hibernate
    MySQL
    MySQL
    Node.js
    Node.js
    Java
    Java
    Bootstrap
    Bootstrap
    jQuery Mobile
    jQuery Mobile
    jQuery UI
    jQuery UI
    jQuery
    jQuery
    JavaScript
    JavaScript
    React Native
    React Native
    React Router
    React Router
    React
    React

    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

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    Interest over time
    Reviews of Elasticsearch and Solr
    No reviews found
    How developers use Elasticsearch and Solr
    Avatar of imgur
    imgur uses ElasticsearchElasticsearch

    Elasticsearch is the engine that powers search on the site. From a high level perspective, itโ€™s a Lucene wrapper that exposes Luceneโ€™s 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 wasnโ€™t 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 doesnโ€™t 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.

    Avatar of Kang Hyeon Ku
    Kang Hyeon Ku uses SolrSolr

    elastic search ์™€ ํ•จ๊ป˜ ์œ ๋ช…ํ•œ ๊ฒ€์ƒ‰ ์—”์ง„ ์˜คํ”ˆ ์†Œ์Šค ์ค‘ ํ•˜๋‚˜ ์ด๋‹ค. ์ฒ˜์Œ ์„ค์ •ํ•  ๊ฒƒ์ด ๋งŽ์€๋ฐ, ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์ดํ•ด๊ฐ€ ์—†๋‹ค๋ฉด ์žฆ์€ ์ˆ˜์ •์ด ํ•„์š”ํ•˜๋‹ค. Solr Client ๋กœ ์ œ์–ด ํ•  ์ˆ˜ ์—†๊ณ  Server ์—์„œ ์„ค์ •ํ•ด ์ค˜์•ผํ•˜๋Š” ๊ฒƒ๋“ค์ด ์žˆ์–ด ์„œ๋ฒ„ ์„ค์ •ํ•˜๋Š” ๋ถ€๋ถ„์ด ์ค‘์š”ํ•˜๋‹ค. ์„œ๋ฒ„ ์„ค์ •๋งŒ ์ž˜ ๋˜์žˆ๋‹ค๋ฉด, Client ์ชฝ ์†Œ์Šค๋Š” ๋ณ„๊ฒŒ ์—†๋‹ค.

    ์ค‘์š”ํ•œ ๊ฑด ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ....

    Avatar of The Independent
    The Independent uses SolrSolr

    Full text search is provided by a SOLR cluster. This is done on Master/Slave replication with Varnish as a cache.

    Avatar of Blue Kangaroo
    Blue Kangaroo uses SolrSolr

    Personalized search engine (for content-based filtering)

    Avatar of Satoru Ishikawa
    Satoru Ishikawa uses SolrSolr

    ๆŸใƒ—ใƒญใƒ—ใƒฉใ‚คใ‚จใ‚ฟใƒชใชWebใ‚ขใƒ—ใƒชใ‚’ๅ‹•ใ‹ใ™ใฎใซๅฟ…่ฆใ ใฃใŸ(ใ‚คใƒณใ‚นใƒˆใƒผใƒซใจๆง‹็ฏ‰ใฎใฟ)

    Avatar of SAP Hybris
    SAP Hybris uses SolrSolr

    standard hybris-commerce implementation

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