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An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc.
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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.
Solr is a tool in the Search Engines category of a tech stack.

Who uses Solr?

187 companies reportedly use Solr in their tech stacks, including Slack, Coursera, and Accenture.

380 developers on StackShare have stated that they use Solr.

Solr Integrations

Datadog, Netdata, Lucene, Server Density, and ContainerShip are some of the popular tools that integrate with Solr. Here's a list of all 6 tools that integrate with Solr.
Public Decisions about Solr

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

Ganesa Vijayakumar
Full Stack Coder | Module Lead ยท | 18 upvotes ยท 1.8M 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

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Shared insights

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

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Shared insights

"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€™."

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Shared insights

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.... Solr

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Shared insights

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

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Solr's Features

  • Advanced Full-Text Search Capabilities
  • Optimized for High Volume Web Traffic
  • Standards Based Open Interfaces - XML, JSON and HTTP
  • Comprehensive HTML Administration Interfaces
  • Server statistics exposed over JMX for monitoring
  • Linearly scalable, auto index replication, auto failover and recovery
  • Near Real-time indexing
  • Flexible and Adaptable with XML configuration
  • Extensible Plugin Architecture

Solr Alternatives & Comparisons

What are some alternatives to Solr?
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
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 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).
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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.
See all alternatives

Solr's Followers
430 developers follow Solr to keep up with related blogs and decisions.