Apache AGE VS hash-db

For more information and support, visit the Apache AGE website. or github.

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.

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!

Check Apache AGE graph database system here: Website: https://age.apache.org/ GitHub: https://github.com/apache/age

We are also open Source community at Apache https://github.com/apache/age

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.

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.

I wrote a toy dynamodb, SQL, Cypher graph and document storage database engine in Python for the learning.

https://github.com/samsquire/hash-db

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.