Lucene vs Searchkick
Overview
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CLI (Node.js)
Manual
Detailed Comparison
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. |
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 6.7K |
GitHub Forks - | GitHub Forks 766 |
Stacks 175 | Stacks 18 |
Followers 230 | Followers 34 |
Votes 2 | Votes 1 |
Pros & Cons | |
Pros
| Pros
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Integrations | |
| No integrations available | |
What are some alternatives to Lucene, Searchkick?
Sphinx
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MkDocs
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YugabyteDB
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Apache Solr
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Qdrant
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Weaviate
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AddSearch
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ArangoSearch
It is a C++ based full-text search engine including similarity ranking capabilities natively integrated into ArangoDB. It allows users to combine two information retrieval techniques: boolean and generalized ranking retrieval. Search results “approved” by the boolean model can be ranked by relevance to the respective query using the Vector Space Model in conjunction with BM25 or TFIDF weighting schemes.
Carrot2
It organizes your search results into topics. With an instant overview of what's available, you will quickly find what you're looking for.
