Lucene vs Searchkick: What are the differences?
Developers describe Lucene as "A high-performance, full-featured text search engine library written entirely in Java". Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. On the other hand, Searchkick is detailed as "Intelligent search made easy". 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.
Lucene and Searchkick can be categorized as "Search Engines" tools.
Some of the features offered by Lucene are:
- over 150GB/hour on modern hardware
- small RAM requirements -- only 1MB heap
- incremental indexing as fast as batch indexing
On the other hand, Searchkick provides the following key features:
- stemming - tomatoes matches tomato
- special characters - jalapeno matches jalapeño
- extra whitespace - dishwasher matches dish washer
Searchkick is an open source tool with 4.96K GitHub stars and 582 GitHub forks. Here's a link to Searchkick's open source repository on GitHub.
What is Lucene?
What is Searchkick?
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Why do developers choose Lucene?
What are the cons of using Lucene?
What are the cons of using Searchkick?
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What tools integrate with Searchkick?
"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 document — how 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 user’s interaction history with channels and other users — their ‘work graph’."