Scalability
Apache Solr is highly scalable, capable of handling large amounts of data and numerous queries per second. It supports distributed search and indexing, which allows for horizontal scaling by adding more nodes.
Flexibility
Solr provides flexible schema management, allowing for dynamic field definitions and easy handling of various data types. It supports a variety of search query types and can be customized to meet specific search requirements.
Rich Feature Set
Solr comes with a wealth of features out-of-the-box, including faceted search, result highlighting, multi-index search, and advanced filtering capabilities. It also offers robust analytics and joins support.
Community and Documentation
Being an open-source project, Apache Solr has a strong community and comprehensive documentation, which ensures continuous improvements, updates, and extensive support resources for developers.
Integrations
Solr integrates well with a variety of databases and data sources, and it provides REST-like APIs for ease of integration with other applications. It also has strong support for popular programming languages like Java, Python, and Ruby.
Performance
Solr is built on top of Apache Lucene, which provides high performance for searching and indexing. It is optimized for speed and can handle rapid data ingestion and real-time indexing.
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Yes, Apache Solr is generally considered a good option for organizations seeking a reliable, scalable, and flexible search platform. It offers extensive features and is supported by a strong community, making it a solid choice for many use cases.
We have collected here some useful links to help you find out if Apache Solr is good.
Check the traffic stats of Apache Solr on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Apache Solr on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Apache Solr's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Apache Solr on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Apache Solr on Reddit. This can help you find out how popualr the product is and what people think about it.
SolrโโโOpen-source search platform built on Apache Lucene. - Source: dev.to / about 2 years ago
I want to spend the brunt of this article talking about how to do this in Postgres, partly because it's a little more difficult there. But let me start in Apache Solr, which is where I first worked on these issues. - Source: dev.to / about 2 years ago
Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / almost 3 years ago
Apache Solr can be used to index and search text-based documents. It supports a wide range of file formats including PDFs, Microsoft Office documents, and plain text files. https://solr.apache.org/. Source: about 3 years ago
If so, then https://solr.apache.org/ can be a solution, though there's a bit of setup involved. Oh yea, you get to write your own "search interface" too which would end up calling solr's api to find stuff. Source: over 3 years ago
Developers will use their SQL database when searching for specific things like client names, product names, or address search. Now when you want to level up from there and search all tables you better off using a separated server with a specific program like https://solr.apache.org/. Source: almost 4 years ago
Weโre using a self-managed OpenSearch node here, but you can use Lucene, SOLR, ElasticSearch or Atlas Search. Source: almost 4 years ago
Finally if you really want a full fledged solution the most popular are good old Sphinx http://sphinxsearch.com or Apache Solr https://solr.apache.org but as these take a bit of time to setup I'd make sure that's truly needed. Chances are unless your collection is the size of the Library of Congress you are probably fine with ripgrep on your own indexes as text or some R package or sqlite FTS5 extension... Source: about 4 years ago
As a fellow developer, I would have to recommend all of the above. However in terms of priorities, I'd suggest using a standard tag system first and then branching into text search which is a bit more complicated. If you've never made a search function before or simply never made one for a large scale media app, I'd recommend checking out Solr. Source: over 4 years ago
For fast searching, it usually requires indexing the files in question. There are a number of text-file indexing solutions, many of which use xapian, sphinx, or lucene/solr under the hood. Based on conditions (watching files/directories, cron jobs, new-mail triggers, etc), they'll add/remove files to the index, and you can then use a corresponding command to compose queries across that data. If it's indexed, it... Source: over 4 years ago
I use (and do consulting) Apache Solr (https://solr.apache.org/) for large scale search (into the billions of records) latest project is https://www.domaincodex.com 320 million records of domain intelligence data feel free to message me. Good luck! Source: over 4 years ago
Assuming that you have access to some anime data or you are willing to collect them, here is how you can get started: Most CS programs around the world offer a course to their students that is supposed to teach them exactly what you just described. That course is usually known as Information Retrieval. Just google this term, and you will find a lot of information about the problem you are facing. If you don't... Source: over 4 years ago
ListItem(name='Apache Solr', website='https://solr.apache.org/', category='Full-Text Search', short_description='Solr is an open-source enterprise-search platform, written in Java. Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features and rich document handling.'),. Source: over 4 years ago
The pre-production and production environments were composed of front-end and search (Solr) servers, also operated internally. This was another area for regular maintenance costs. - Source: dev.to / over 4 years ago
Thereโs Apache Solr which runs the same backend as Elasticsearch. Iโve heard itโs easier to manage than ES while still being similar in capabilities. Source: over 4 years ago
The underlying technology of all search engines is similar from a data structure perspective: an inverted index, but the datastore is very different. Industry-standard solutions for search such as Elastic Search, Apache Solr are built for horizontal scaling. - Source: dev.to / almost 5 years ago
PostgreSQL has a lot of advanced extensions and indexes to speed up the same. But one should keep in mind that these have their own limitations in terms of speed and might not work with languages other than English/multi-byte encoding, such as UTF-8. However, these fill the critical gap where the functionalities mentioned are necessary, but we cannot go for a full-fledged search solution such as Solr/Elastic Search. - Source: dev.to / almost 5 years ago
The bigger problem is the search capability. If you had a typical dynamic website with a database and some backend (be it PHP or any other language) you might get away by implementing the search capability through various SQL queries. However that often doesn't scale well and is complex. Personally I would recommend to just immediately use something like Apache Solr. Source: about 5 years ago
Apache Solr is the popular, blazing-fast, open-source enterprise search platform built on Apache Lucene. Solr is a standalone search server with a REST-like API. You can put documents in it (called "indexing") via JSON, XML, CSV, or binary over HTTP. You query it via HTTP GET and receive JSON, XML, CSV, or binary results. - Source: dev.to / about 5 years ago
Apache Solr continues to maintain a strong presence in the realm of open-source enterprise search solutions. As a robust search platform entrenched in the Apache Lucene project, Solr is recognized for its scalability and adaptability to high-volume traffic environments. It is often lauded for its near real-time indexing capabilities, ensuring the delivery of fresh and relevant search results, which is a crucial factor for many enterprises looking to maintain updated content accessibility.
A critical advantage of Solr is its extensibility through a plugin architecture, allowing organizations to tailor the search engine to specific requirements. This flexibility appeals particularly to businesses with unique needs that are not met by out-of-the-box solutions. The platform supports various file formats, including JSON, XML, CSV, and binary data, facilitating diverse indexing and querying processes through HTTP protocols.
Comparative analyses among its peersโparticularly ElasticSearch, a close competitorโhighlight Solr's strengths and limitations. While described as slightly slower than ElasticSearch, Solr is touted for handling static data effectively. The Solr system relies on global caching mechanisms, which introduce latency when data changes require cache refreshes. This contrasts with ElasticSearch's segment-based approach, offering more fluid updates and potentially swifter responses.
Despite these aspects, Solr is favorably reviewed as a self-hosted alternative to other managed services. Its open-source nature makes it an attractive choice for organizations that prefer to maintain control over their search infrastructure. In discussions comparing it to Algorithms like Algolia, Solr holds its ground with a balance of comprehensive features and cost efficiency as an open-source tool. Its customization capability extends beyond standard functionalities, allowing users to implement sophisticated search solutions tailored to specific business contexts.
Solr's application potential spans various use cases, from processing extensive text-based documents and handling diverse data types to supporting advanced data searches in complex environments. Many professionals highlight Solr's proficiency in managing large-scale search projects, operating efficiently with datasets extending into the billions of entries.
In conclusion, public opinion generally favors Apache Solr for its reliability, extensive customization options, and suitability for large-scale, robust search implementations. While it presents a learning curve and setup challengeโfactors that might discourage less experienced usersโits comprehensive feature set remains attractive to enterprises seeking a scalable and customizable search platform. As the industry continues to evolve, Solr stands as a steadfast choice for businesses keen on building sophisticated search capabilities internally.
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