I want to dive into how to make search engines

1

CommonCrawl

As someone who works in this space, ^ this. I would say don't overthink any component, use common crawl (https://commoncrawl.org/) to build your initial index, use a pagerank implementation that's been thoroughly researched and published, and use off the shelf components from the apache foundation when you can.

#Search Engine #Web Scraping #Data Extraction 91 social mentions

Https://youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU SQL and NoSQL:.

3

Apache Solr

#Custom Search Engine #Custom Search #Search Engine 17 social mentions

4

PostgreSQL

#Databases #Relational Databases #NoSQL Databases 15 social mentions

5

ElasticSearch

#Search Engine #Custom Search Engine #Custom Search 17 social mentions

6

Yippy Search

Missing: https://yippy.com https://unbubble.eu https://symbolhound.com https://www.exalead.com https://www.searchencrypt.com https://suggest.hulbee.com IME, analysing alternative "search engines" will reveal most are a waste of time.

#Search Engine #Web Search #Internet Search 3 social mentions

7

Unbubble

Missing: https://yippy.com https://unbubble.eu https://symbolhound.com https://www.exalead.com https://www.searchencrypt.com https://suggest.hulbee.com IME, analysing alternative "search engines" will reveal most are a waste of time.

#Search Engine #Internet Search #Web Search 1 social mentions

8

Tantivy

I've never worked on a project that encompasses as many computer science algorithms as a search engine. There are a lot of topics you can lookup in "Information Storage and Retrieval": - Tries (patricia, radix, etc...) - Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..) - Consensus (raft, paxos, etc..) - Block storage (disk block size optimizations, mmap files, delta storage, etc..) - Probabilistic filters (hyperloloog, bloom filters, etc...) - Binary Search (sstables, sorted inverted indexes, roaring bitmaps) - Ranking (pagerank, tf/idf, bm25, etc...) - NLP (stemming, POS tagging, subject identification, sentiment analysis etc...) - HTML (document parsing/lexing) - Images (exif extraction, removal, resizing / proxying, etc...) - Queues (SQS, NATS, Apollo, etc...) - Clustering (k-means, density, hierarchical, gaussian distributions, etc...) - Rate limiting (leaky bucket, windowed, etc...) - Compression - Applied linear algebra - Text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting) - etc... I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech. - https://github.com/quickwit-oss/tantivy - https://github.com/valeriansaliou/sonic - https://github.com/mosuka/phalanx - https://github.com/meilisearch/MeiliSearch - https://github.com/blevesearch/bleve - https://github.com/thomasjungblut/go-sstables A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. The problem is that search with good rankings often requires custom storage so calculations can be sharded among multiple nodes and you can do layered ranking without passing huge blobs of results between systems.

#Open Source #Search Engine #Tech 26 social mentions

9

YaCy

Have you seen YaCy Search Engine? https://yacy.net/ This might be something to build on or explore.

#Search Engine #Internet Search #Web Search 71 social mentions