Initial commit: Scaffold CoreState v2.0

This commit is contained in:
2025-07-22 23:52:39 +02:00
commit 16029af795
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# This file designates default owners for different parts of the codebase.
# See: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
* @YourGitHubUsername
/apps/android/ @android-team
/apps/web-dashboard/ @web-team
/services/ @backend-team
/module/ @kernel-team
/ml/ @ml-team
/infrastructure/ @devops-team

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name: Android App CI
on:
push:
branches: [ main, develop ]
paths:
- 'apps/android/**'
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Building Android app..."

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name: Microservices CI
on:
push:
branches: [ main, develop ]
paths:
- 'services/**'
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Building microservices..."

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name: ML Training CI
on:
push:
branches: [ main, develop ]
paths:
- 'ml/**'
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Running ML training..."

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name: Module Build CI
on:
push:
branches: [ main, develop ]
paths:
- 'module/**'
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Building module..."

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name: Performance Test
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Running performance test..."

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# .github/workflows/release-orchestration.yml
name: CoreState v2.0 Release Orchestration
on:
push:
tags:
- 'v2.*'
workflow_dispatch:
inputs:
release_type:
description: 'Release type'
required: true
default: 'stable'
type: choice
options:
- stable
- beta
- canary
env:
DOCKER_REGISTRY: ghcr.io
KUBERNETES_CLUSTER: corestate-prod
ML_TRAINING_CLUSTER: ml-cluster-prod
jobs:
# Security scanning
security-scan:
runs-on: ubuntu-latest
strategy:
matrix:
component: [android-app, microservices, module, web-dashboard]
steps:
- uses: actions/checkout@v4
- name: Run Trivy vulnerability scanner
uses: aquasecurity/trivy-action@master
with:
scan-type: 'fs'
scan-ref: '${{ matrix.component }}'
severity: 'CRITICAL,HIGH'
exit-code: '1'
- name: Run Semgrep
uses: returntocorp/semgrep-action@v1
with:
config: >-
p/security-audit
p/kotlin
p/rust
p/typescript
- name: SAST with CodeQL
uses: github/codeql-action/analyze@v2
with:
languages: kotlin,javascript,cpp,python
# Build all components
build-matrix:
needs: security-scan
strategy:
matrix:
include:
- component: android-app
build-command: ./gradlew assembleRelease bundleRelease
artifact-path: apps/android/androidApp/build/outputs
- component: ios-app
build-command: |
cd apps/android/iosApp
xcodebuild -scheme CoreState -configuration Release
artifact-path: apps/android/iosApp/build
- component: daemon
build-command: |
cd apps/daemon
cargo build --release --target x86_64-unknown-linux-musl
cargo build --release --target aarch64-unknown-linux-musl
artifact-path: apps/daemon/target
- component: web-dashboard
build-command: |
cd apps/web-dashboard
npm ci
npm run build:prod
artifact-path: apps/web-dashboard/dist
- component: microservices
build-command: |
./gradlew :services:build
docker buildx build --platform linux/amd64,linux/arm64 \
--tag ${{ env.DOCKER_REGISTRY }}/corestate/services:${{ github.ref_name }} \
--push services/
runs-on: ${{ matrix.component == 'ios-app' && 'macos-13' || 'ubuntu-latest' }}
steps:
- uses: actions/checkout@v4
with:
submodules: recursive
- name: Set up build environment
# uses: ./.github/actions/setup-build-env # This would be a custom action
run: echo "Setting up build environment for ${{ matrix.component }}"
- name: Build component
run: echo "Skipping build for now: ${{ matrix.build-command }}"
- name: Upload artifacts
run: |
mkdir -p ${{ matrix.artifact-path }}
touch ${{ matrix.artifact-path }}/placeholder.txt
uses: actions/upload-artifact@v4
with:
name: ${{ matrix.component }}-${{ github.sha }}
path: ${{ matrix.artifact-path }}
# Build KernelSU module with multiple Android versions
build-module:
needs: security-scan
strategy:
matrix:
android-version: [11, 12, 13, 14]
architecture: [arm64-v8a, x86_64]
runs-on: ubuntu-latest
container:
image: ubuntu:22.04 # Placeholder, would be a custom NDK image
steps:
- uses: actions/checkout@v4
- name: Build native components
run: echo "Building native components for Android ${{ matrix.android-version }} (${{ matrix.architecture }})"
- name: Package module
run: |
echo "Packaging module for ${{ matrix.architecture }}"
MODULE_NAME="CoreState-Module-v2.0.0-android${{ matrix.android-version }}-${{ matrix.architecture }}"
mkdir -p module_out
touch module_out/${MODULE_NAME}.zip
- name: Sign module
run: echo "Signing module"
- name: Upload module
uses: actions/upload-artifact@v4
with:
name: module-android${{ matrix.android-version }}-${{ matrix.architecture }}
path: 'module_out/*.zip'
# ML model training and validation
ml-pipeline:
needs: security-scan
runs-on: ubuntu-latest # Placeholder for [self-hosted, gpu]
steps:
- uses: actions/checkout@v4
- name: Set up Python environment
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: ML Pipeline Steps
run: |
echo "Running ML training, validation, and conversion..."
mkdir -p ml_artifacts
touch ml_artifacts/validation_report.json
touch ml_artifacts/backup_predictor_v2.tflite
- name: Upload ML artifacts
uses: actions/upload-artifact@v4
with:
name: ml-models-${{ github.sha }}
path: ml_artifacts/
# Integration testing
integration-tests:
needs: [build-matrix, build-module, ml-pipeline]
runs-on: ubuntu-latest # Placeholder for [self-hosted, android-farm]
steps:
- uses: actions/checkout@v4
- name: Download all artifacts
uses: actions/download-artifact@v4
with:
path: artifacts
- name: Run E2E and Performance Tests
run: echo "Running integration and performance tests..."
# Deploy to staging
deploy-staging:
needs: integration-tests
runs-on: ubuntu-latest
environment: staging
steps:
- uses: actions/checkout@v4
- name: Deploy to staging cluster
run: echo "Deploying to staging..."
- name: Run smoke tests
run: echo "Running smoke tests on staging..."
# Create release
create-release:
needs: deploy-staging
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Generate changelog
id: changelog
run: echo "changelog=### Changelog..." >> $GITHUB_OUTPUT
- name: Download all artifacts
uses: actions/download-artifact@v4
with:
path: artifacts
- name: Create release bundle
run: |
mkdir -p release_bundle
touch release_bundle/CoreState-v2.0.0-release.tar.gz
- name: Create GitHub Release
uses: softprops/action-gh-release@v1
with:
files: release_bundle/*
body: |
# CoreState ${{ github.ref_name }}
${{ steps.changelog.outputs.changelog }}
# Deploy to production
deploy-production:
needs: create-release
runs-on: ubuntu-latest
environment: production
if: github.event_name == 'push' && contains(github.ref, 'stable')
steps:
- name: Deploy to production clusters
run: echo "Deploying to production..."
- name: Notify stakeholders
run: echo "Notifying stakeholders of production release."

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name: Security Scan
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Build Placeholder
run: echo "Running security scan..."

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# CoreState v2.0 - Next-Generation Advanced Backup System
## 1. Executive Summary
CoreState v2.0 is a high-performance, distributed backup system designed for reliability, scalability, and advanced feature support. It leverages a microservices architecture to provide a robust platform for backing up and restoring data across various environments. CoreState v2.0 introduces a sophisticated backup engine, advanced ML-based optimizations, and a modular design to support future enhancements and integrations.
The system is built with a polyglot technology stack, including Rust for the high-performance daemon, Kotlin/Java for backend services, Python for machine learning, and a web-based dashboard for user interaction. It is designed to be cloud-native, with support for Kubernetes deployment and various storage backends.
## 2. Architecture Overview
CoreState v2.0 is composed of several key components that work together to provide a comprehensive backup solution.
![Architecture Diagram](docs/architecture/overview.md)
### Core Components:
* **Web Dashboard:** A React-based web interface for users to manage backups, monitor system status, and configure settings.
* **Daemon:** A lightweight, high-performance agent written in Rust that runs on client machines to perform backup and restore operations.
* **Backup Engine:** The core service, written in Kotlin, responsible for orchestrating the backup and restore workflows, including scheduling, data processing, and storage management.
* **ML Optimizer:** A Python-based service that uses machine learning models to optimize backup schedules, detect anomalies, and predict storage needs.
* **Sync Coordinator:** Manages data synchronization and consistency across distributed components.
* **Storage HAL (Hardware Abstraction Layer):** Provides a unified interface for interacting with different storage backends (e.g., S3, Azure Blob, GCP Cloud Storage, local filesystems).
### Supporting Services:
* **Analytics Engine:** Collects and processes system metrics for monitoring and reporting.
* **Compression Engine:** Provides data compression services to reduce storage footprint.
* **Deduplication Service:** Identifies and eliminates redundant data blocks to optimize storage.
* **Encryption Service:** Manages data encryption and key management to ensure data security.
* **Index Service:** Maintains an index of backed-up data for fast searching and retrieval.
## 3. Project Structure
The project is organized into the following directories:
```
CoreState-v2/
├── apps/ # Client applications (Web Dashboard, Daemon)
│ ├── android/
│ ├── daemon/
│ └── web-dashboard/
├── docs/ # Project documentation
│ ├── api/
│ └── architecture/
├── infrastructure/ # Infrastructure as Code (Kubernetes, Terraform)
│ ├── docker/
│ ├── kubernetes/
│ └── terraform/
├── ml/ # Machine Learning models and datasets
│ ├── datasets/
│ └── models/
├── module/ # Kernel module for advanced features
│ ├── kernel_patches/
│ └── native/
├── services/ # Backend microservices
│ ├── analytics-engine/
│ ├── backup-engine/
│ ├── compression-engine/
│ ├── deduplication-service/
│ ├── encryption-service/
│ ├── index-service/
│ ├── ml-optimizer/
│ ├── storage-hal/
│ └── sync-coordinator/
├── shared/ # Shared libraries, contracts, and protobuf definitions
│ ├── contracts/
│ ├── libs/
│ └── proto/
├── tests/ # E2E, integration, performance, and unit tests
│ ├── e2e/
│ ├── integration/
│ ├── performance/
│ └── unit/
└── tools/ # Developer and operational tools
├── benchmarking/
├── cli/
└── migration/
```
## 4. Feature Implementations
### 4.1. High-Performance Daemon
The CoreState Daemon is a native application written in Rust for maximum performance and minimal resource footprint on client systems. It is responsible for:
* File system monitoring for changes.
* Executing backup and restore tasks as directed by the Backup Engine.
* Client-side encryption and compression.
### 4.2. ML-Powered Optimization
The ML Optimizer service provides intelligent features:
* **Predictive Backups:** Analyzes data change patterns to predict optimal backup times.
* **Anomaly Detection:** Identifies unusual activity that might indicate a ransomware attack or data corruption.
* **Storage Optimization:** Recommends storage tiering strategies based on data access patterns.
### 4.3. Advanced Kernel-Level Features
For supported platforms, CoreState v2.0 can utilize a kernel module for advanced capabilities:
* **CoW Snapshots:** Near-instantaneous, low-overhead snapshots using Copy-on-Write.
* **Block-Level Tracking:** Efficiently tracks changed data blocks for incremental backups.
* **Hardware Acceleration:** Integrates with hardware security modules (HSMs) for enhanced encryption performance.
### 4.4. Cloud-Native and Distributed
The system is designed for the cloud:
* **Kubernetes-Native:** All services are containerized and can be deployed and managed with Kubernetes.
* **Scalable:** Services can be scaled independently to meet demand.
* **Resilient:** The distributed nature of the system ensures high availability.
## 5. Getting Started
### Prerequisites
* Docker
* Kubernetes (e.g., Minikube, Kind, or a cloud provider's EKS/AKS/GKE)
* `kubectl`
* `gradle` (for Backup Engine)
* `rustc` and `cargo` (for Daemon)
* `python` and `pip` (for ML Optimizer)
* `npm` (for Web Dashboard)
### Building and Running
1. **Build Services:** Each service in the `/services` directory contains instructions for building its Docker image. For example, for the Backup Engine:
```bash
cd services/backup-engine
./gradlew build
docker build -t corestate-backup-engine .
```
2. **Deploy to Kubernetes:**
```bash
kubectl apply -f infrastructure/kubernetes/
```
3. **Build and Run Web Dashboard:**
```bash
cd apps/web-dashboard
npm install
npm start
```
4. **Build and Run Daemon:**
```bash
cd apps/daemon
cargo build --release
```
## 6. API and Communication
Services communicate via gRPC. Protocol definitions are located in the `shared/proto` directory.
* [`backup.proto`](shared/proto/backup.proto): Defines messages and services for backup and restore operations.
* [`sync.proto`](shared/proto/sync.proto): Defines messages and services for data synchronization.
* [`analytics.proto`](shared/proto/analytics.proto): Defines messages and services for analytics and monitoring.
API documentation can be found in [`docs/api/grpc.md`](docs/api/grpc.md).
## 7. Contributing
Contributions are welcome! Please refer to the project's contribution guidelines and code of conduct.
## 8. License
This project is licensed under the [MIT License](LICENSE).

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[package]
name = "corestate-daemon"
version = "2.0.0"
edition = "2021"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[dependencies]
# Dependencies will be added later

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// CoreState Daemon Entry Point
fn main() {
println!("CoreState Daemon v2.0 starting...");
// Initialization logic will go here
}

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{
"name": "corestate-web-dashboard",
"version": "2.0.0",
"private": true,
"dependencies": {
"react": "^18.2.0",
"react-dom": "^18.2.0",
"react-scripts": "5.0.1"
},
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject"
},
"eslintConfig": {
"extends": [
"react-app",
"react-app/jest"
]
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
}
}

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import React from 'react';
import ReactDOM from 'react-dom/client';
const root = ReactDOM.createRoot(document.getElementById('root'));
root.render(
<React.StrictMode>
<h1>CoreState Web Dashboard v2.0</h1>
</React.StrictMode>
);

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# gRPC API Reference

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# CoreState v2.0 Architecture Overview

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# Kubernetes Deployment Guide

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apiVersion: apps/v1
kind: Deployment
metadata:
name: backup-engine
labels:
app: backup-engine
spec:
replicas: 3
selector:
matchLabels:
app: backup-engine
template:
metadata:
labels:
app: backup-engine
spec:
containers:
- name: backup-engine
image: ghcr.io/corestate/backup-engine:latest # Image will be updated by CI/CD
ports:
- containerPort: 8080

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apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: corestate-ingress
annotations:
nginx.ingress.kubernetes.io/rewrite-target: /
spec:
rules:
- http:
paths:
- path: /backup
pathType: Prefix
backend:
service:
name: backup-engine-svc
port:
number: 80

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apiVersion: v1
kind: Service
metadata:
name: backup-engine-svc
spec:
selector:
app: backup-engine
ports:
- protocol: TCP
port: 80
targetPort: 8080
type: ClusterIP

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terraform {
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
}
}
provider "aws" {
region = "us-east-1"
}
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
tags = {
Name = "corestate-vpc"
}
}
resource "aws_eks_cluster" "main" {
name = "corestate-eks-cluster"
role_arn = aws_iam_role.eks_cluster.arn
vpc_config {
subnet_ids = aws_subnet.private[*].id
}
depends_on = [
aws_iam_role_policy_attachment.eks_cluster_policy,
]
}
# NOTE: This is a simplified placeholder.
# A real configuration would require definitions for IAM roles,
# subnets, node groups, etc.
# The following are placeholders for required resources.
resource "aws_iam_role" "eks_cluster" {
name = "eks-cluster-role"
assume_role_policy = jsonencode({
Version = "2012-10-17"
Statement = [{
Action = "sts:AssumeRole"
Effect = "Allow"
Principal = {
Service = "eks.amazonaws.com"
}
}]
})
}
resource "aws_iam_role_policy_attachment" "eks_cluster_policy" {
policy_arn = "arn:aws:iam::aws:policy/AmazonEKSClusterPolicy"
role = aws_iam_role.eks_cluster.name
}
resource "aws_subnet" "private" {
count = 2
vpc_id = aws_vpc.main.id
cidr_block = "10.0.${count.index}.0/24"
}

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# ml-optimizer/models/anomaly_detector.py
# Note: Correcting path to ml/models as per new structure
import tensorflow as tf
# from tensorflow.keras import layers, models # Correct import path might vary
import numpy as np
class AnomalyDetector:
def __init__(self):
self.autoencoder = self._build_autoencoder()
self.threshold = 0.1 # Example threshold
def _build_autoencoder(self):
"""Autoencoder for detecting backup anomalies"""
input_dim = 512 # Feature vector size
encoding_dim = 32
# Encoder
input_layer = tf.keras.layers.Input(shape=(input_dim,))
encoded = tf.keras.layers.Dense(256, activation='relu')(input_layer)
encoded = tf.keras.layers.Dense(128, activation='relu')(encoded)
encoded = tf.keras.layers.Dense(encoding_dim, activation='relu')(encoded)
# Decoder
decoded = tf.keras.layers.Dense(128, activation='relu')(encoded)
decoded = tf.keras.layers.Dense(256, activation='relu')(decoded)
decoded = tf.keras.layers.Dense(input_dim, activation='sigmoid')(decoded)
autoencoder = tf.keras.models.Model(input_layer, decoded)
autoencoder.compile(optimizer='adam', loss='mse')
return autoencoder
def _extract_features(self, backup_metadata):
# Placeholder for feature extraction
return np.random.rand(1, 512)
def detect_corruption(self, backup_metadata):
"""Detect potential data corruption in backups"""
features = self._extract_features(backup_metadata)
reconstruction = self.autoencoder.predict(features)
mse = np.mean(np.power(features - reconstruction, 2), axis=1)
return mse > self.threshold

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# ml-optimizer/models/backup_predictor.py
# Note: Correcting path to ml/models as per new structure
import tensorflow as tf
# from tensorflow.keras import layers, models # Correct import path might vary
import numpy as np
# Placeholder for FeatureExtractor
class FeatureExtractor:
def extract(self, user_patterns, system_load):
# This should create a feature vector of shape (None, 168, 15)
return np.random.rand(1, 168, 15)
class BackupPredictor:
def __init__(self):
self.model = self._build_model()
self.feature_extractor = FeatureExtractor()
def _build_model(self):
"""LSTM-based model for predicting optimal backup times"""
# Using tf.keras submodule
model = tf.keras.models.Sequential([
tf.keras.layers.LSTM(128, return_sequences=True, input_shape=(168, 15)), # Week of hourly data
tf.keras.layers.Dropout(0.2),
tf.keras.layers.LSTM(64, return_sequences=True),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.LSTM(32),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(24, activation='sigmoid') # 24-hour prediction
])
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy', 'precision', 'recall']
)
return model
def _post_process_predictions(self, predictions):
# Placeholder for post-processing logic
return {"processed_predictions": predictions.tolist()}
def predict_optimal_backup_windows(self, user_patterns, system_load):
features = self.feature_extractor.extract(user_patterns, system_load)
predictions = self.model.predict(features)
return self._post_process_predictions(predictions)

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id=corestate_v2
name=CoreState Module v2
version=v2.0.0
versionCode=2
author=Wiktor/overspend1
description=Enhanced system-level operations for CoreState v2.0, including snapshotting and real-time monitoring.

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cmake_minimum_required(VERSION 3.10)
project(CoreStateModuleNative)
# Set standard to C++17
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# Add subdirectories for each native component
add_subdirectory(snapshot_manager)
add_subdirectory(fs_monitor)
add_subdirectory(hw_acceleration)
# Example of creating a shared library (will be expanded later)
# add_library(corestate_native SHARED ...)

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# Build stage
FROM gradle:8.4-jdk17-alpine AS build
WORKDIR /home/gradle/src
COPY --chown=gradle:gradle . .
RUN gradle build --no-daemon
# Package stage
FROM eclipse-temurin:17-jre-alpine
WORKDIR /app
COPY --from=build /home/gradle/src/build/libs/*.jar app.jar
ENTRYPOINT ["java", "-jar", "app.jar"]

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import org.jetbrains.kotlin.gradle.tasks.KotlinCompile
plugins {
id("org.springframework.boot") version "3.1.5"
id("io.spring.dependency-management") version "1.1.3"
kotlin("jvm") version "1.8.22"
kotlin("plugin.spring") version "1.8.22"
}
group = "com.corestate.services"
version = "2.0.0"
java {
sourceCompatibility = JavaVersion.VERSION_17
}
repositories {
mavenCentral()
}
dependencies {
implementation("org.springframework.boot:spring-boot-starter-web")
implementation("com.fasterxml.jackson.module:jackson-module-kotlin")
implementation("org.jetbrains.kotlin:kotlin-reflect")
testImplementation("org.springframework.boot:spring-boot-starter-test")
}
tasks.withType<KotlinCompile> {
kotlinOptions {
freeCompilerArgs += "-Xjsr305=strict"
jvmTarget = "17"
}
}
tasks.withType<Test> {
useJUnitPlatform()
}

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package com.corestate.backup
import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.boot.runApplication
@SpringBootApplication
class BackupEngineService
fun main(args: Array<String>) {
runApplication<BackupEngineService>(*args)
}

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from fastapi import FastAPI
app = FastAPI(
title="CoreState ML Optimizer Service",
version="2.0.0",
)
@app.get("/")
def read_root():
return {"message": "CoreState ML Optimizer Service is running."}
@app.post("/predict/backup-window")
def predict_backup_window(data: dict):
# Placeholder for prediction logic
return {"optimal_window_hours": [2, 3, 4, 22, 23]}
# Further endpoints for anomaly detection, etc., will be added here.

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ML models will be stored here.

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fastapi
uvicorn
tensorflow
scikit-learn
pandas
numpy

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ML training scripts will be stored here.

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syntax = "proto3";
package corestate.v2.analytics;
option java_package = "com.corestate.v2.proto.analytics";
option java_multiple_files = true;
// An event to be logged for analytics
message BackupEvent {
string event_id = 1;
int64 timestamp = 2;
string device_id = 3;
string tenant_id = 4;
string user_id = 5;
oneof event_payload {
BackupStarted started = 6;
BackupCompleted completed = 7;
BackupFailed failed = 8;
}
}
message BackupStarted {
string backup_id = 1;
string backup_type = 2;
}
message BackupCompleted {
string backup_id = 1;
int64 final_size_bytes = 2;
int64 duration_seconds = 3;
int32 file_count = 4;
double deduplication_ratio = 5;
double compression_ratio = 6;
}
message BackupFailed {
string backup_id = 1;
string error_code = 2;
string error_message = 3;
}
service AnalyticsService {
// Fire-and-forget event logging
rpc LogEvent(BackupEvent) returns (LogEventResponse);
}
message LogEventResponse {
bool acknowledged = 1;
}

64
shared/proto/backup.proto Normal file
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syntax = "proto3";
package corestate.v2.backup;
option java_package = "com.corestate.v2.proto.backup";
option java_multiple_files = true;
// Represents a single chunk of a file
message DataChunk {
string chunk_id = 1; // Blake3 hash of the content
bytes data = 2;
}
// Metadata for a single file in a backup
message FileMetadata {
string path = 1;
int64 size = 2;
int64 modified_time = 3;
string checksum = 4; // Blake3 checksum of the full file
repeated string chunk_ids = 5;
}
// A complete backup manifest
message BackupManifest {
string backup_id = 1;
int64 timestamp = 2;
enum BackupType {
FULL = 0;
INCREMENTAL = 1;
SYNTHETIC_FULL = 2;
}
BackupType type = 3;
repeated FileMetadata files = 4;
}
service BackupService {
rpc StartBackup(StartBackupRequest) returns (StartBackupResponse);
rpc UploadChunk(stream DataChunk) returns (UploadChunkResponse);
rpc FinishBackup(FinishBackupRequest) returns (FinishBackupResponse);
}
message StartBackupRequest {
BackupManifest.BackupType type = 1;
string device_id = 2;
}
message StartBackupResponse {
string backup_id = 1;
string upload_token = 2;
}
message UploadChunkResponse {
string chunk_id = 1;
bool success = 2;
}
message FinishBackupRequest {
string backup_id = 1;
BackupManifest manifest = 2;
}
message FinishBackupResponse {
bool success = 1;
}

27
shared/proto/sync.proto Normal file
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syntax = "proto3";
package corestate.v2.sync;
option java_package = "com.corestate.v2.proto.sync";
option java_multiple_files = true;
// CRDT-based state for a file
message FileVersion {
string path = 1;
int64 version = 2; // Lamport timestamp
int64 timestamp = 3; // Wall clock time
string checksum = 4;
string node_id = 5;
}
// Represents the sync state of a device
message SyncState {
string node_id = 1;
map<string, FileVersion> file_versions = 2; // LWW-Register Set
repeated string deleted_files = 3; // OR-Set
}
service SyncService {
// P2P sync between devices
rpc SyncWithPeer(stream SyncState) returns (stream SyncState);
}

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End-to-end tests go here.

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Integration tests go here.

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Performance benchmark tests go here.

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Unit tests go here.

29
tools/cli/main.py Normal file
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import click
@click.group()
def cli():
"""CoreState v2.0 Command-Line Interface."""
pass
@cli.command()
@click.option('--backup-id', required=True, help='The ID of the backup to restore.')
@click.option('--destination', default='/tmp/restore', help='The destination path for the restore.')
def restore(backup_id, destination):
"""
Restores a specific backup to a local destination.
"""
click.echo(f"Initiating restore for backup {backup_id} to {destination}...")
# Restore logic would be implemented here
click.echo("Restore placeholder complete.")
@cli.command()
def status():
"""
Checks the status of the CoreState services.
"""
click.echo("Checking service status...")
# Service status check logic would be implemented here
click.echo("All services are operational (placeholder).")
if __name__ == '__main__':
cli()

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click