Question 1

How to install Ferret in a Ruby on Rails project?

Accepted Answer

Add the Ferret gem to your Gemfile and run bundle install. For detailed setup, check the ruby/README.md in the repository, which covers installation and basic configuration steps.

Question 2

Ferret vs Elasticsearch for Ruby applications?

Accepted Answer

Ferret is an embedded library similar to Lucene, ideal for single-server Ruby apps needing fast, local search. Elasticsearch is a distributed system better for scalable, multi-node deployments but requires separate infrastructure and management.

Question 3

Is Ferret still actively maintained?

Accepted Answer

Ferret has periodic updates and a Travis CI build status, but contributions are via email or tickets, suggesting lower activity. For production-critical systems, evaluate the maintenance pace against your needs.

Question 4

Does Ferret support fuzzy search and advanced text analysis?

Accepted Answer

Yes, Ferret supports fuzzy search, stemming, and other Lucene-like text analysis features, allowing for sophisticated querying as mentioned in its Lucene compatibility claim.

Question 5

What are the performance benchmarks for Ferret compared to other Ruby search libraries?

Accepted Answer

Ferret's C implementation generally offers higher performance than pure Ruby libraries like Sunspot or Textacular. However, benchmarks vary by use case; it's optimized for speed but may not match distributed engines in scale.

Question 6

How to index documents like PDFs with Ferret?

Accepted Answer

Ferret indexes text, so you need to extract text from PDFs using a library like PDF-reader first. Then, pass the extracted text to Ferret for indexing, as it handles the search logic but not file parsing.

Question 7

Can Ferret handle large-scale datasets efficiently?

Accepted Answer

Ferret is designed for single-node use and can handle moderate datasets with its optimized file format. For very large or distributed datasets, consider custom extensions or integrating with scalable systems like Solr.

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