Azure Search vs Lucene: What are the differences?
Developers describe Azure Search as "Search-as-a-service for web and mobile app development". Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios. On the other hand, Lucene is detailed 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.
Azure Search and Lucene are primarily classified as "Search as a Service" and "Search Engines" tools respectively.
Some of the features offered by Azure Search are:
- Powerful, reliable performance
- Easily tune search indices to meet business goals
- Scale out simply
On the other hand, Lucene provides the following key features:
- over 150GB/hour on modern hardware
- small RAM requirements -- only 1MB heap
- incremental indexing as fast as batch indexing
What is Azure Search?
What is Lucene?
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What are the cons of using Azure Search?
What are the cons of using Lucene?
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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’."