Lucene vs Searchkick vs Solr
Overview
Detailed Comparison
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. | Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. | Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users. |
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 | over 150GB/hour on modern hardware;small RAM requirements -- only 1MB heap;incremental indexing as fast as batch indexing;index size roughly 20-30% the size of text indexed;ranked searching -- best results returned first;many powerful query types: phrase queries, wildcard queries, proximity queries, range queries;fielded searching (e.g. title, author, contents);sorting by any field;multiple-index searching with merged results;allows simultaneous update and searching;flexible faceting, highlighting, joins and result grouping;fast, memory-efficient and typo-tolerant suggesters;pluggable ranking models, including the Vector Space Model and Okapi BM25;configurable storage engine (codecs) | stemming - tomatoes matches tomato;special characters - jalapeno matches jalapeño;extra whitespace - dishwasher matches dish washer;misspellings - zuchini matches zucchini;custom synonyms - qtip matches cotton swab;query like SQL - no need to learn a new query language;reindex without downtime;easily personalize results for each user;autocomplete;“Did you mean” suggestions;works with ActiveRecord and Mongoid |
Statistics | ||
GitHub Stars - | GitHub Stars - | GitHub Stars 6.7K |
GitHub Forks - | GitHub Forks - | GitHub Forks 766 |
Stacks 780 | Stacks 173 | Stacks 17 |
Followers 644 | Followers 230 | Followers 34 |
Votes 126 | Votes 2 | Votes 1 |
Pros & Cons | ||
Pros
| Pros
| Pros
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Integrations | ||
| No integrations available | No integrations available | |
What are some alternatives to Solr, Lucene, Searchkick?
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.
Sphinx
It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.
MkDocs
It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.
Dejavu
dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).
Elassandra
Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.
Tantivy
It is a full-text search engine library inspired by Apache Lucene and written in Rust. It is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.
Jina
It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.
YugabyteDB
An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.
Mirage
The Elasticsearch query DSL supports 100+ query APIs ranging from full-text search, numeric range filters, geolocation queries to nested and span queries. Mirage is a modern, open-source web based query explorer for Elasticsearch.
