Sixth Data - Data storage and computing engine
- This is a subproject of Sixth
- This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
- Program author:
- Other software projects hosted at svjatoslav.eu
Provide versioned, clustered, flexible, distributed, multi-dimensional data storage engine for the Sixth computation engine.
- Speaking of traditional relational database and object oriented
- I hate object-relational impedance mismatch.
- I don't like to convert data between persistent database and runtime objects for every transaction. How about creating united database/computation engine instead to:
- Eliminate constant moving and converting of data between 2 systems and make computing happen close to where the data is stored.
- Abstract away difference between RAM VS persistent storage. Let the system decide at runtime which data to keep in what kind of memory.
- Relational databases:
- Indexable / Quickly searchable.
- Git (version control system)
- Branchable / mergeable.
- Transparent cansistency, checksumming and deduplication.
- (Git as a database:
- Brain appears to have more than 3D dimensional design: https://singularityhub.com/2017/06/21/is-there-a-multidimensional-mathematical-world-hidden-in-the-brains-computation/
- Brain appears to use geometry to map thoughts and even sounds:
- It directly inspires Geometrical computation idea and nicely fits with CM-1 Connection Machine design.
- see: Geometrical computation
- Computation unit has local CPU and RAM.
- Data is pre-distributed across computation units.
- Machine's internal 12-dimensional hypercube network allows to efficiently simulate arbitrary dimensional network topology between computational units. So that when we are solving/simulating for example 5 dimensional problem, we can arrange computational units into virtual 5D network. See: http://www.mission-base.com/tamiko/theory/cm_txts/di-ch2.html
Maybe every problem can be translated to geometry (use any shapes and as many dimensions as you need). Solution(s) to such problems would then appear as relatively simple search/comparison/lookup results. As a bonus, such geometrical *data storage* AND *computation* can be naturally made in *parallel* and *distributed*. That's what neurons in the brain appear to be doing ! :) . Learning means building/updating the model (the hard part). Question answering is making (relatively simple) lookups (geometrical queries) against the model.
Object oriented programming is inspired by the way human mind operates. It allows programmer to express ideas to computer in a more human-like terms.
It is possible to map object model to geometrical hyperspace:
- Object is a point in space (universe). Each object member variable translates to its own dimension. That is: if class declares 4 variables for an object, then corresponding object can be stored as a single point inside 4 dimensional space. Variable values translate to point coordinates in space. That is: Integer, floating point number and even boolean and string can be translated to linear value that can be used as a coordinate along particular dimension.
- Each class declares its own space (universe). All class instances (objects) are points inside that particular universe. References between objects of different types are hyperlinks (portals) between different universes.
Consider we want to create database of books and authors. Book can have multiple authors, and single person can be author for multiple books. It is possible to store how many hours of work each author has contributed to every book, using hyperspace as follows:
- Every dimension corresponds to one particular book author. (10
authors in the database, would require 10 dimensional space)
- Point in space corresponds to one particular book.
- Point location along particular (author) dimension corresponds to amount of work contributed by particular author for given book.
- Point in space corresponds to one particular book.
- Every dimension corresponds to one particular book.
- Point in space corresponds to one particular author in the entire
- Point location along particular (book) dimension corresponds to amount of work contributed for book by given author (point).
- Point in space corresponds to one particular author in the entire database.
4.2 Layered architecture
- layer 1
- disk / block storage / partition
- layer 2
- key/value storage. Keys are unique and are dictated by storage engine. Value is arbitrary but limited size byte array. This layer is responsible for handling disk defragmentation and consistency in case of crash recovery.
- layer 3
- key/value storage. Keys are content hashes. Values are arbitrary but limited size content byte arrays. This layer effectively implements content addressable storage. Content addressible storage enables GIT-like behavior (possibility for competing branches, retaining history, transparent deduplication)
- layer 4
- Implements arbitrary dimensional multiverse.
- layer 5
- Distributed computation engine.
5 Current status
6 See also
Interesting or competing projects with good ideas:
- CM-1 Connection Machine
- database in the form of a knowledge graph that uses machine reasoning to simplify data processing challenges for AI applications. https://grakn.ai/
- Multi-user object database for Squeak
- Completely distributed smalltalk based computing system.
- Completely distributed operating system/virtual machine:
- Assembler/C-Script/Lisp 64 bit, MIMD, multi CPU, multi threaded, multi core, multi user Parallel OS. With GUI, Terminal, OO Assembler, Class libraries, C-Script compiler, Lisp interpreter, Debugger, and more…