Python Distributed Key-Value Store

After researching foundational distributed systems like Amazon's DynamoDB and Redis Cluster, I wanted to move to simulate it by implementing the core principles of resilience and scale myself. This project is the result: a fault-tolerant key-value store built from scratch to explore how systems can guarantee data safety in the face of network and server failures.

The system operates as a peer-to-peer cluster that partitions data using consistent hashing and replicates every write for high availability. It now includes write-through persistence to PostgreSQL and a read-through fallback to legacy APIs, simulating a real-world system migration scenario.

Key Features

• Legacy System Bridge (New): Implements a read-through caching strategy. If a key is not found in the cluster, the system automatically queries a simulated Legacy REST API via HTTP/JSON to fetch and hydrate the data, ensuring zero downtime for users during migrations.
• Durable Persistence (New): Uses a write-through strategy to persist all data to a PostgreSQL database using asyncpg. This guarantees data safety even if the entire cluster loses power.
• Distributed & Fault-Tolerant: Uses N-way replication (N=3) to ensure data safety. Resilience is verified by automated chaos tests that terminate live nodes during operation without data loss.
• High-Performance & Asynchronous: Built on Python's asyncio and gRPC to handle thousands of concurrent operations with low latency.
• Algorithmic Partitioning: Implements a consistent hashing ring from scratch to intelligently and dynamically distribute data across the cluster.

System Architecture

The system is a leaderless, peer-to-peer distributed system. Every node can serve any client request, acting as a coordinator for that specific request.

1. Consistent Hashing: Determines which node owns a key to minimize data reshuffling.
2. N-Way Replication: Replicates data across N nodes in parallel.
3. Migration Logic:
   • Write Path: Writes are committed to the PostgreSQL database immediately for durability.
   • Read Path: Cache misses trigger an asynchronous HTTP call to the Legacy API. If found, the data is returned to the client and populated into the cache.

Architecture Diagram

graph TD
    subgraph "External Systems"
        DB[(PostgreSQL)]
        Legacy[Legacy REST API]
    end

    subgraph "Distributed Cache Cluster"
        N1("Node 1")
        N2("Node 2 (Coordinator)")
        N3("Node 3")
    end
    
    Client -- "SET('key', 'val')" --> N2
    N2 -- "1. Persist to Disk" --> DB
    N2 -- "2. Replicate (gRPC)" --> N1
    N2 -- "2. Replicate (gRPC)" --> N3
    
    Client -- "GET('missing_key')" --> N2
    N2 -- "Cache Miss? Fetch via HTTP" --> Legacy
    Legacy -- "Return JSON" --> N2
    N2 -- "Return Data" --> Client

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Benchmark & Resilience Analysis

Benchmark Environment

All benchmarks were executed on the following developer-grade machine to ensure reproducibility:

• CPU: 12th Gen Intel(R) Core(TM) i7-12700H
• RAM: 16 GB
• Storage: NVMe SSD
• OS: Windows 11 Home (WSL2 Ubuntu 22.04)
• Network: Docker Compose bridge network

Performance & Resilience

Benchmarks were run against a 3-node cluster running in Docker containers, simulating a high-concurrency workload.

Metric	Result	Analysis
GET Throughput	~4,800 ops/sec	High throughput maintained even with the overhead of the new httpx fallback logic.
Fault Tolerance	Zero Data Loss	Verified by chaos_test.py which violently terminates live container nodes during active operations.
Persistence	100% Durability	Verified via direct SQL queries to the underlying PostgreSQL storage engine, ensuring data survives cluster restarts.

Getting Started

The project is fully containerized. You can spin up the entire architecture (Cache Cluster, Legacy API, PostgreSQL) using Docker Compose.

1. Setup & Launch

# Install dependencies for the test scripts
pip install -r requirements.txt

# Build and start the cluster
docker-compose up -d --build

2. Verify Key Features

Run the following scripts to validate the specific architectural claims made in the documentation.

Test A: Fault Tolerance (Chaos Engineering) Simulates a catastrophic node failure by killing a container while the system is running. Verifies that data remains accessible via replication.

python chaos_test.py
# Expected Output: "✅ CHAOS TEST PASSED ✅"

Test B: Legacy System Migration (Read-Through) Verifies the "Read-Through" capability. Queries a key that does not exist in the cache but does exist in the Legacy REST API.

python test_legacy.py
# Expected Output: "✅ SUCCESS: Fetched data from Legacy API!"

Test C: Database Persistence (Write-Through) Verifies that data is being persisted to the PostgreSQL database in real-time.

# 1. Write data to the cache
python client.py

# 2. Check the database directly
docker exec -it postgres_db psql -U admin -d cache_db -c "SELECT key, created_at FROM kv_store;"
# Expected Output: You should see 'my_special_key' listed in the SQL table.

License

This project is provided as-is for educational and demonstration purposes.