Lucene vs AddSearch: What are the differences?
What is Lucene? 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.
What is AddSearch? The almighty search platform for all your web content. We help your website visitors find what they are looking for. AddSearch is a lightning fast, accurate and customizable site search engine with a Search API. AddSearch works on all devices and is easy to install, customize and tweak.
Lucene and AddSearch 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, AddSearch provides the following key features:
- Manage Search Results - Guide website visitors to the pages that matter most
- Analyze Search Intent - Your website is your main asset. Uncover what your visitors are searching for
- AddSearch for All Platforms - We support any platform from legacy systems to state of the art Single Page Applications
What is AddSearch?
What is Lucene?
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Why do developers choose AddSearch?
Why do developers choose Lucene?
What are the cons of using AddSearch?
What are the cons of using Lucene?
What companies use AddSearch?
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"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’."