hash-db
Apache AGE
hash-db | Apache AGE | |
---|---|---|
5 | 132 | |
50 | 2,695 | |
- | 3.6% | |
0.0 | 8.9 | |
over 1 year ago | 7 days ago | |
Python | C | |
- | Apache License 2.0 |
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.
Apache AGE
- Apache AGE: PostgreSQL Extension Graph Data Processing and Analytics
- Apache AGE: PostgreSQL Extension Graph Data Processing and Analytics for RDBMS
- Apache AGE supporting latest PostgreSQL (ver 16)
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Enhancing Fraud Detection with Apache AGE: A Graph Database Approach
For more information and support, visit the Apache AGE website. or github.
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Unlocking the Power of Apache Age: Advanced Techniques for SQL/Cypher Hybrid Queries
These are just a few examples of how you can use Cypher queries in SQL/Cypher Hybrid Queries. By using these advanced techniques, you can perform more complex and powerful queries on your graph data. Apache AGE offer a versatile and powerful platform for working with graph and relational data concurrentlyz. To learn more you can visit age website or github page.
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Mastering Graph Queries with Cypher in Apache Age
Cypher queries in Apache Age empower users to interact with graph data efficiently and intuitively. Whether you're creating nodes, establishing relationships, or performing complex traversals, Cypher provides a robust and expressive language for working with graph databases.As you explore Apache Age and Cypher further, you'll discover additional features and nuances that make graph database management a seamless experience. Embrace the power of graph queries, and unlock the full potential of your interconnected data with Apache Age. Happy graph querying!
- We built An Open-Source platform to process relational and Graph Query simultaneously
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Machine learning and graph databases
Check Apache AGE graph database system here: Website: https://age.apache.org/ GitHub: https://github.com/apache/age
- Is Open Sourcing Technologies Good for Society
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Open Source doesn't win by being cheaper
We are also open Source community at Apache https://github.com/apache/age
What are some alternatives?
electric - Local-first sync layer for web and mobile apps. Build reactive, realtime, local-first apps directly on Postgres.
surrealdb - A scalable, distributed, collaborative, document-graph database, for the realtime web
kuzu - Embeddable property graph database management system built for query speed and scalability. Implements Cypher.
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.
node-bindgen - Easy way to write Node.js module using Rust
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 🗄️
age-viewer - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
pg_crdt - POC CRDT support in Postgres
napi-rs - A framework for building compiled Node.js add-ons in Rust via Node-API
data-diff - Compare tables within or across databases
neon - Rust bindings for writing safe and fast native Node.js modules.