hash-db
kuzu
hash-db | kuzu | |
---|---|---|
5 | 11 | |
50 | 1,031 | |
- | 7.9% | |
0.0 | 9.9 | |
over 1 year ago | about 23 hours ago | |
Python | C++ | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hash-db
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CRDT-richtext: Rust implementation of Peritext and Fugue
https://github.com/samsquire/hash-db
I need to combine the ideas in each of these projects into a cohesive solution.
I did some work on trying to implement the YATA algorithm, poorly.
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Ask HN: How do you test SQL?
From an SQL database implementation perspective, in my toy Python barebones SQL database that barely supports inner joins (https://github.com/samsquire/hash-db) I tested by testing on postgresql and seeing if my query with two joins produces the same results.
I ought to produce unit tests that prove that tuples from each join operation produces the correct dataset.
For a user perspective, I guess you could write some tooling that loads example data into a database and does an incremental join with each part of the join statement added.
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Bullshit Graph Database Performance Benchmarks
I wrote a toy dynamodb, SQL, Cypher graph and document storage database engine in Python for the learning.
https://github.com/samsquire/hash-db
- Experimental distributed keyvalue database (it uses python dictionaries) imitating dynamodb querying with join only SQL support, distributed joins and simple Cypher graph support
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How necessary are the programming fundamentals?
I am interested in database internals. Btrees come up with regard to designing database systems that are efficient to query on disk. Postgres uses them for its indexes. Radix trees are memory efficient tries which are useful for answering prefix queries. They're also called prefix trees. I use them to get a list of prefixes of a string. Useful for simple intellisense style forms or dynamodb style querying. I've also been studying LSM trees which are used in Leveldb and RocksDB.
I experiment with database technology in my experimental project hash-db https://github.com/samsquire/hash-db The code should be readable.
I need to change my search tree to be self balancing currently it grows to the left or right without balancing. I think I need to use tree rotation depending on which branch has the highest height.
kuzu
- Unum: Vector Search engine in a single file
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Building a New Database Management System in Academia
These two posts[2,3] explain where we are from and where we're going, if anyone is interested.
[1]: https://github.com/kuzudb/kuzu
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Graph Database Community
Hi u/kyleireddit, I want to encourage you to try out KuzuDB: https://github.com/kuzudb/kuzu, which we are actively developing. One of our goals is to help educate developers more on where graph dbmss can offer value, so if you join our Slack channel and ask questions about graph dbmss and my students and I can answer some of your questions.
- Kùzu: an in-process property graph database management system (GDBMS)
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Best free graph database for order of 500 million nodes
Then you can try Kùzu: https://github.com/kuzudb/kuzu. It should do quite well. We are new but actively developing the system and would love to help you when you are prototyping your application.
- KùzuDB – In-Memory Graph Database
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PageRank Algorithm for Graph Databases
Not sqlite, but kuzu ( https://github.com/kuzudb/kuzu ) is an interesting project in this space. Fairly new, but already quite impressive IMHO.
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CIDR 2023 Database Conference from Memgraph’s Perspective
I already mentioned Kùzu folks. They are doing an outstanding job of explaining what they do. Just follow their web 😀 They presented KùzuDB paper which brings interesting concepts to the graph query executions called factorization, S-Join and ASP-Join.
- Bullshit Graph Database Performance Benchmarks
- What Every Competent Graph DBMS Should Do
What are some alternatives?
electric - Local-first sync layer for web and mobile apps. Build reactive, realtime, local-first apps directly on Postgres.
Memgraph - Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
dbt-unit-testing - This dbt package contains macros to support unit testing that can be (re)used across dbt projects.
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
pg_crdt - POC CRDT support in Postgres
NetworkX - Network Analysis in Python
data-diff - Compare tables within or across databases
mutable - A Database System for Research and Fast Prototyping
electric_dart - A Dart implementation for Electric (electric-sql.com).
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.