LNX: Using Tantivy to Build One of the Fastest Search Engines Around | Harrison, Software Engineer

LNX aims to be one of the most performant and cost-effective search engine alternatives to Elasticsearch and Algolia for typo-tolerant search. It offers a wide range of features thanks to the ecosystem it stands on, including but not limited to: complex query parser, typo-tolerant fuzzy queries, typo-tolerant fast-fuzzy queries (pre-computed spell correction), more-like-this queries. It thrives to provide more for your hardware. In this talk, we take a look at how LNX uses Tantivy and other optimisations to create a lightning-fast search experience for you and your users.

⚙️Thank you Rust London for organizing the event! Check out more of their events on their Youtube channel:
   / @rustlondon  

🔎 LNX Repo: https://github.com/lnx-search/lnx

🏇 Tantivy Repo: https://github.com/quickwit-oss/tantivy
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🕰️ TIMECODE :

0:00 Intro & Tantivy Overview
1:25 Speaker Bio
2:00 What is LNX?
3:05 Why create a New Search Engine?
3:33 Problem with Elasticsearch
4:15 Pros & Cons of Competitors
5:34 Aim of LNX
8:14 Why Tantivy?
8:45 Implementing Tantivy in LNX
9:55 Crash Course on using Tantivy
13:40 Fuzzy Search & FuzzyTermQuery
19:49 Lnx Indexing
23:15 Transparency vs Ease of Use
24:38 The Query Builder
27:18 Fast Fuzzy vs Fuzzy
34:30 Roundoff
36:30 Q&A Section
49:50 Outro
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Topics: indexing, search, data, query, architecture, sre, devops, metastore, metadata, indexer, scaling indexes, cluster membership, search engine internals, devsecops, shards

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