https://github.com/codeharik/database
https://github.com/codeharik/database
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/codeharik/database
- Owner: CodeHariK
- Created: 2025-01-24T09:39:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-25T09:25:26.000Z (about 1 year ago)
- Last Synced: 2025-03-25T10:30:43.224Z (about 1 year ago)
- Language: Go
- Size: 966 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
README
[](https://www.youtube.com/watch?v=difrBNjGwLo)
#
* [#08 - Tree Indexes: B+Trees (CMU Intro to Database Systems)](https://www.youtube.com/watch?v=scUtG_6M_lU)
* [SQLite: How it works](https://www.youtube.com/watch?v=ZSKLA81tBis)
* [Write a database from scratch](https://www.youtube.com/playlist?list=PLWRwj01AnyEtjaw-ZnnAQWnVYPZF5WayV)
* [Understanding B-Trees: The Data Structure Behind Modern Databases](https://www.youtube.com/watch?v=K1a2Bk8NrYQ)
* [Build a NoSQL Database From Scratch in 1000 Lines of Code](https://medium.com/better-programming/build-a-nosql-database-from-the-scratch-in-1000-lines-of-code-8ed1c15ed924)
* [Writing a SQL database from scratch in Go: 1. SELECT, INSERT, CREATE and a REPL](https://notes.eatonphil.com/database-basics.html)
* [Database Engine Development](https://www.youtube.com/playlist?list=PLm7R-cUo29CXVu9a9TzBEwSQ9JPVGmISg)
* https://github.com/cmu-db/bustub
##
```
1. Persistence. How not to lose or corrupt your data. Recovering from a crash.
2. Indexing. Efficiently querying and manipulating your data. (B-tree).
3. Concurrency. How to handle multiple (large number of ) clients. And transactions.
```
## Vector Db
* https://github.com/skyzh/write-you-a-vector-db
## Search
* [The Art of Searching](https://www.youtube.com/watch?v=yst6VQ7Lwpo)
* [Algorithms & data-structures that power Lucene & ElasticSearch](https://www.youtube.com/watch?v=eQ-rXP-D80U)
* [How do Spell Checkers work? Levenshtein Edit Distance](https://www.youtube.com/watch?v=Cu7Tl7FGigQ)
* [The Algorithm Behind Spell Checkers](https://www.youtube.com/watch?v=d-Eq6x1yssU)
## Data Structure
* [Heaps, heapsort, and priority queues - Inside code](https://www.youtube.com/watch?v=pLIajuc31qk)
* [Trie data structure - Inside code](https://www.youtube.com/watch?v=qA8l8TAMyig)
* [Compressed trie](https://www.youtube.com/watch?v=qakGXuOW1S8)
## Probablistic Data structures
* [Hello Interview : Bloom Filters, Count-Min Sketch, HyperLogLog](https://www.youtube.com/watch?v=IgyU0iFIoqM)
* [Probablistic data structure lectures](https://www.youtube.com/playlist?list=PL2mpR0RYFQsAR5RyB54FyEE9vUiGtCSZM)
### Bloom filter
* [Wikipedia](https://en.wikipedia.org/wiki/Bloom_filter)
* [mCoding : Bloom Filters](https://www.youtube.com/watch?v=qZNJTh2NEiU)
* [Number0 : Bloom Filters](https://www.youtube.com/watch?v=eCUm4U3WDpM)
* [ByteByteGo : Bloom Filters](https://www.youtube.com/watch?v=V3pzxngeLqw)
* [Spanning Tree : What Are Bloom Filters?](https://www.youtube.com/watch?v=kfFacplFY4Y)
* [ByteMonk : Bloom Filters](https://www.youtube.com/watch?v=GT0En1dGntY)
Bloom filter is a space-efficient probabilistic data structure, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not - in other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed.
A Bloom filter is a representation of a set of _n_ items, where the main requirement is to make membership queries; _i.e._, whether an item is a member of a set.
#### Uses
##### Cache filtering
Content delivery networks deploy web caches around the world to cache and serve web content to users with greater performance and reliability. A key application of Bloom filters is their use in efficiently determining which web objects to store in these web caches. To prevent caching one-hit-wonders, a Bloom filter is used to keep track of all URLs that are accessed by users.
##### Web Crawler
### HyperLogLog
* [Wikipedia](https://en.wikipedia.org/wiki/HyperLogLog)
* [PapersWeLove : HyperLogLog](https://www.youtube.com/watch?v=y3fTaxA8PkU)
* [The Algorithm with the Best Name - HyperLogLog Explained](https://www.youtube.com/watch?v=2PlrMCiUN_s)
* [A problem so hard even Google relies on Random Chance](https://www.youtube.com/watch?v=lJYufx0bfpw)
* [Counting BILLIONS with Just Kilobytes](https://www.youtube.com/watch?v=f69hh3KgFEk)
* https://github.com/tylertreat/BoomFilters/blob/master/hyperloglog.go
HyperLogLog is an algorithm for the count-distinct problem, Probabilistic cardinality estimators.
### Count–min sketch
* [Wikepedia](https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch)
* https://github.com/tylertreat/BoomFilters/blob/master/countmin.go
* [Count-min Sketch](https://www.youtube.com/watch?v=Okdjn7o4q8E)
The goal of the basic version of the count–min sketch is to consume a stream of events, one at a time, and count the frequency of the different types of events in the stream.
### HeavyKeeper TopK
* [Understanding Probabilistic Data Structures](https://www.youtube.com/watch?v=2Dzc7fxA0us)
### TDigest
* [Sketching Data with T Digest](https://www.youtube.com/watch?v=ETUYhEZRtWE)
## Cache
* [TinyLFU: A Highly Efficient Cache Admission Policy](https://dgraph.io/blog/refs/TinyLFU%20-%20A%20Highly%20Efficient%20Cache%20Admission%20Policy.pdf)
* [Caffeine Design of a Modern Cache](https://docs.google.com/presentation/d/1NlDxyXsUG1qlVHMl4vsUUBQfAJ2c2NsFPNPr2qymIBs/edit#slide=id.p)
* [Design of a Modern Cache](https://highscalability.com/design-of-a-modern-cache/)
* [The State of Caching in Go](https://dgraph.io/blog/post/caching-in-go/)
* [Introducing Ristretto: A High-Performance Go Cache](https://dgraph.io/blog/post/introducing-ristretto-high-perf-go-cache/)
* [On Window TinyLFU](https://9vx.org/post/on-window-tinylfu/)
* https://en.wikipedia.org/wiki/Cache_replacement_policies#LRU
* https://github.com/hypermodeinc/ristretto
* https://github.com/dgryski/go-tinylfu
## LSM-Tree
[#04 - Database Storage: Log-Structured Merge Trees & Tuples (CMU Intro to Database Systems)](https://www.youtube.com/watch?v=IHtVWGhG0Xg&t=1372s)
https://github.com/facebook/rocksdb/wiki
https://github.com/krasun/lsmtree
https://github.com/skyzh/mini-lsm
## OLAP
* https://github.com/risinglightdb/risinglight
## Concurrency
* [Golang concurrency - Locks, Lock Free and everything in between](https://www.youtube.com/watch?v=gNQ6j2Y2HFs)
* [Optimistic Locking clearly explained](https://www.youtube.com/watch?v=d41JuPT_Wls)
* [Understanding the Disruptor](https://www.youtube.com/watch?v=DCdGlxBbKU4)
* https://en.wikipedia.org/wiki/Actor_model
* The Art of Multiprocessor Programming
* Seven Concurrency Models in Seven Weeks
* https://proto.actor/docs/
* [The Actor Model](https://www.youtube.com/watch?v=7erJ1DV_Tlo)
* [A brief introduction to the actor model & distributed actors](https://www.youtube.com/watch?v=YTQeJegJnbo)
* [Introduction to the Actor Model for Concurrent Computation](https://www.youtube.com/watch?v=lPTqcecwkJg)
* https://github.com/vladopajic/go-actor
* https://www.brianstorti.com/the-actor-model/
* [Code : Ring Buffer](https://www.youtube.com/watch?v=KyreJSKEagg)
* https://kmdreko.github.io/posts/20191003/a-simple-lock-free-ring-buffer/
* [Producer/Consumer, The RingBuffer and The Log](https://www.youtube.com/watch?v=uqSeuGQhnf0)
* [Disruptor](https://lmax-exchange.github.io/disruptor/files/Disruptor-1.0.pdf)
* https://github.com/LMAX-Exchange/disruptor/wiki/Blogs-And-Articles
* https://github.com/smarty-prototypes/go-disruptor
## Compress
* [Data Encodings used by Columnar and Time series databases](https://www.youtube.com/watch?v=wUO2snhiosk)