HyperCache - Distributed Cache

HyperCache is a high-performance, Redis-compatible distributed cache with advanced memory management, integrated probabilistic data structures (Cuckoo filters), and comprehensive monitoring stack. Built in Go for cloud-native environments.

🎯 Latest Features ✅

Production-ready distributed cache with full observability stack:

✅ Multi-node cluster deployment with full replication
✅ Full Redis client compatibility (RESP protocol)
✅ Lamport timestamps for causal ordering of distributed writes
✅ Read-repair for gossip propagation window
✅ Early Cuckoo filter sync across nodes
✅ Enterprise persistence (AOF + Snapshots)
✅ Structured JSON logging with correlation ID tracing
✅ Real-time monitoring with Grafana + Elasticsearch
✅ HTTP API + RESP protocol support
✅ Advanced memory management with pressure detection
✅ Cuckoo filter integration for negative lookup acceleration

🔥 Monitoring & Observability

Grafana Dashboards: Real-time metrics visualization
Elasticsearch: Centralized log aggregation and search
Filebeat: Log shipping and processing
Health Checks: Built-in monitoring endpoints

🚀 Quick Start

🐳 Docker (Recommended — no setup required)

Pull from Docker Hub and start the full stack:

# Download the compose file
curl -O https://raw.githubusercontent.com/rishabhverma17/HyperCache/main/docker-compose.cluster.yml

# Start everything (3 HyperCache nodes + Elasticsearch + Grafana + Filebeat)
docker compose -f docker-compose.cluster.yml up -d

That's it. All configs are baked into the Docker image — no cloning, no local files needed.

# Verify the cluster
curl http://localhost:9080/health

# Store a key
curl -X PUT http://localhost:9080/api/cache/hello \
  -H "Content-Type: application/json" -d '{"value": "world"}'

# Read it from a different node (replication)
curl http://localhost:9082/api/cache/hello

# Open Grafana dashboards
open http://localhost:3000  # admin / admin123

Prerequisites (Local Development)

Go 1.23.2+
redis-cli (optional, for RESP testing)

Local Cluster (N nodes)

# Start a 3-node cluster (default)
make cluster

# Start a 5-node cluster
make cluster NODES=5

# Start a 10-node cluster
make cluster NODES=10

# Check cluster health
curl -s http://localhost:9080/health | python3 -m json.tool

# Add a node to a running cluster (auto-discovers ports and seeds)
./scripts/add-node.sh
./scripts/add-node.sh --node-name=node-6

# Stop the cluster
make cluster-stop

# Full reset (stop + wipe data/logs/binaries + restart)
make cluster-stop && make clean && make cluster

Single Node

make run

Docker Deployment

# Pull the latest image from Docker Hub
docker pull rishabhverma17/hypercache:latest

# Start full stack (3-node cluster + Elasticsearch + Grafana + Filebeat)
docker compose -f docker-compose.cluster.yml up -d

# Add a 4th node to a running Docker cluster (auto-joins via gossip)
docker run -d --name hypercache-node4 \
  --network cache_hypercache-cluster \
  -p 8083:8080 -p 9083:9080 \
  rishabhverma17/hypercache:latest \
  --protocol resp --node-id node-4

# Or build locally and start
make docker-build && make docker-up

# Stop
docker compose -f docker-compose.cluster.yml down

Kubernetes

# Prerequisites: minikube (brew install minikube) + kubectl (brew install kubectl)

# Start Minikube
minikube start

# Deploy full stack (HyperCache + Elasticsearch + Filebeat + Grafana)
make k8s-up
# or: kubectl apply -f k8s/

# Check cluster status
make k8s-status

# Scale to 5 nodes (or use Minikube Dashboard UI)
make k8s-scale NODES=5

# Access HyperCache from your machine
kubectl port-forward -n hypercache svc/hypercache 9080:9080 8080:8080
# Then: curl http://localhost:9080/health
# Then: redis-cli -p 8080 PING

# Open dashboards
make k8s-dashboard    # Kubernetes Dashboard (select "hypercache" namespace)
make k8s-grafana      # Grafana (same dashboards as Docker — admin/admin123)

# View logs
make k8s-logs

# Tear down
make k8s-down

📊 Access Points

Local / Docker:

Service	URL	Notes
Node N HTTP API	http://localhost:9079+N	Health, cache, stores, filter, metrics
Node N RESP	`redis-cli -p 8079+N`	Redis-compatible
Prometheus Metrics	http://localhost:9080/metrics	Per-node metrics
Grafana	http://localhost:3000	admin / admin123
Elasticsearch	http://localhost:9200

Default 3-node cluster: HTTP on 9080/9081/9082, RESP on 8080/8081/8082.

Kubernetes (after kubectl port-forward -n hypercache svc/hypercache 9080:9080 8080:8080):

Service	Access	Notes
HTTP API	http://localhost:9080	Same APIs as local
RESP	`redis-cli -p 8080`	Same commands as local
Grafana	`make k8s-grafana`	Opens browser via Minikube tunnel
K8s Dashboard	`make k8s-dashboard`	Select "hypercache" namespace

🧪 Testing

Unit Tests

make test-unit

Lint & Format

make lint
make fmt

Benchmarks

make bench

Postman Collection

Import HyperCache.postman_collection.json into Postman for a full test suite covering: health, metrics, CRUD, cross-node replication, delete replication, value types, Cuckoo filter, and cleanup.

HTTP API Examples

# --- Default store (backward-compatible) ---
curl -X PUT http://localhost:9080/api/cache/mykey \
  -H "Content-Type: application/json" \
  -d '{"value": "hello world"}'

curl http://localhost:9080/api/cache/mykey
curl -X DELETE http://localhost:9080/api/cache/mykey

# --- Multi-store APIs ---

# List all stores
curl http://localhost:9080/api/stores

# Create a new store (config is immutable after creation)
curl -X POST http://localhost:9080/api/stores \
  -H "Content-Type: application/json" \
  -d '{"name":"sessions","eviction_policy":"ttl","max_memory":"1GB","default_ttl":"30m","cuckoo_filter":true,"persistence":"aof"}'

# Get store info and stats
curl http://localhost:9080/api/stores/sessions

# Write to a specific store
curl -X PUT http://localhost:9080/api/stores/sessions/cache/user:123 \
  -H "Content-Type: application/json" \
  -d '{"value": {"token":"abc","role":"admin"}}'

# Read from a specific store
curl http://localhost:9080/api/stores/sessions/cache/user:123

# Drop a store (cannot drop "default")
curl -X DELETE http://localhost:9080/api/stores/sessions

# Cuckoo filter stats
curl http://localhost:9080/api/filter/stats

# Prometheus metrics
curl http://localhost:9080/metrics

Redis CLI

redis-cli -p 8080 SET foo bar
redis-cli -p 8080 GET foo
redis-cli -p 8081 GET foo   # verify replication
redis-cli -p 8080 DEL foo
redis-cli -p 8080 INFO
redis-cli -p 8080 DBSIZE

# Multi-store commands
redis-cli -p 8080 STORES             # list all stores
redis-cli -p 8080 SELECT sessions    # switch to "sessions" store
redis-cli -p 8080 SET user:123 token # writes to "sessions" store
redis-cli -p 8080 SELECT default     # switch back

Scenario Tests

Real-world pattern tests that run on every push and daily:

# Run all scenario tests
go test -v -race ./tests/scenarios/...

# Reproduce a randomized test that failed in CI
SCENARIO_SEED=1712438400 go test -run TestRandomizedMixedWorkload ./tests/scenarios/...

Deterministic (same every run): session overflow, concurrent read/write, TTL expiry, persistence recovery, store lifecycle, hot key thundering herd.

Randomized (seed-based, different each run): mixed workload, burst writes, concurrent multi-store.

Makefile Reference

make build              Build the binary
make run                Run single node (RESP)
make cluster            Start N-node cluster (default 3)
make cluster NODES=5    Start 5-node cluster
make cluster-stop       Stop all HyperCache processes
make clean              Remove binaries, logs, data
make test-unit          Run unit tests with coverage
make test-integration   Run integration tests
make bench              Run benchmarks
make lint               Run golangci-lint
make fmt                Format code
make docker-build       Build Docker image
make docker-up          Start Docker stack
make docker-down        Stop Docker stack
make k8s-up             Deploy to Kubernetes (full stack)
make k8s-down           Remove from Kubernetes
make k8s-scale NODES=5  Scale to N replicas
make k8s-status         Show K8s pod/service status
make k8s-dashboard      Open Minikube dashboard
make k8s-grafana        Open Grafana (K8s)
make k8s-logs           Tail HyperCache pod logs
make deps               Download and tidy dependencies

🏆 Key Features

Redis Compatibility

Full RESP protocol implementation
Works with any Redis client library
Drop-in replacement for many Redis use cases
Standard commands: GET, SET, DEL, EXISTS, PING, INFO, FLUSHALL, DBSIZE
Multi-store commands: SELECT, STORES

Distributed Resilience

Full Replication: Every node stores every key — maximum availability, any node serves any request
Lamport Timestamps: Logical clocks for causal ordering of distributed operations. Stale writes from out-of-order gossip are automatically rejected
Read-Repair: On local cache miss, peer nodes are queried before returning 404. Bridges the gossip propagation window (~50-500ms) so clients never see stale misses
Early Cuckoo Filter Sync: Filter is updated immediately on gossip receive, before data is written. Eliminates false "definitely not here" rejections during replication lag
Idempotent Replication: DELETE on a missing key is a no-op, not an error. Designed for eventual consistency
Correlation ID Tracing: Every request gets a unique ID that flows across all nodes for end-to-end debugging

Enterprise Persistence & Recovery

Hybrid Persistence: AOF (Append-Only File) + Snapshot dual strategy
Configurable per Store: Each data store can have independent persistence policies
Sub-microsecond Writes: AOF logging with low-latency write path
Fast Recovery: Complete data restoration from AOF replay + snapshot loading
Snapshot Support: Point-in-time recovery with configurable intervals
Durability Guarantees: Configurable sync policies (always, everysec, no)

Containerized Deployment

Docker Hub Integration: Pre-built multi-arch images (amd64, arm64)
Docker Compose Support: One-command cluster deployment with monitoring
Scalable Clusters: make cluster NODES=N for local, kubectl scale for K8s
Dynamic Node Addition: Add nodes to a running cluster — gossip handles join
Kubernetes Ready: StatefulSet manifests with service discovery
CI/CD Pipeline: GitHub Actions for lint, test, build, and publish

Multi-Store Architecture

Named Stores: Create independent stores with different configs (LRU, LFU, TTL, FIFO)
Per-Store Isolation: Independent memory limits, eviction policies, TTL, persistence, cuckoo filters
Runtime Management: Create/drop stores via HTTP API or RESP SELECT command
Immutable Config: Store settings are set once at creation — drop and recreate to change
Store Registry: Runtime-created stores survive restarts (stores.json persistence)
Environment Variable Config: 8 env vars for Docker/K8s — no YAML needed

Advanced Memory Management

Per-Store Eviction Policies: Independent LRU, LFU, or session-based eviction per store
Smart Memory Pool: Pressure monitoring (warning/critical/panic) with automatic cleanup
Real-time Usage Tracking: Memory statistics and structured alerts
Configurable Limits: Store-specific memory boundaries

Probabilistic Data Structures

Per-Store Cuckoo Filters: Negative lookup acceleration — instant "definitely not here" for keys that don't exist
Configurable False Positive Rate: Tune precision vs memory (default 0.01)
O(1) Membership Testing: Sub-microsecond filter checks before any store lookup
Supports Delete: Unlike Bloom filters, Cuckoo filters allow key removal

Distributed Architecture

Multi-node Clustering: Serf gossip protocol for node discovery and health monitoring
Consistent Hash Ring: 256 virtual nodes with xxhash64 for uniform key distribution
Automatic Failover: Node failure detection and traffic redistribution via gossip
Inter-node Communication: HTTP-based read-repair and peer discovery via gossip metadata

Production Monitoring

Structured JSON Logging: Every log line has timestamp, level, component, action, correlation ID
Grafana Dashboards: Health overview, performance metrics, system components
Elasticsearch + Filebeat: Centralized log aggregation with container-scoped filtering
Configurable Log Levels: debug/info/warn/error/fatal — tunable per node at runtime
Prometheus Metrics: /metrics endpoint with cache stats, cluster health, hit rates

� Project Structure

HyperCache/
├── cmd/hypercache/             # Server entry point
├── scripts/                    # Deployment and management scripts
│   ├── start-system.sh         # Complete system launcher
│   ├── build-and-run.sh        # Build and cluster management
│   └── clean-*.sh              # Cleanup utilities
├── configs/                    # Node configuration files
│   ├── node1-config.yaml       # Node 1 configuration
│   ├── node2-config.yaml       # Node 2 configuration  
│   └── node3-config.yaml       # Node 3 configuration
├── internal/
│   ├── cache/                  # Cache interfaces and policies  
│   ├── storage/                # Storage with persistence
│   ├── filter/                 # Cuckoo filter implementation
│   ├── cluster/                # Distributed coordination
│   ├── network/resp/           # RESP protocol server
│   └── logging/                # Structured logging
├── grafana/                    # Grafana dashboards and config
├── examples/                   # Client demos and examples
├── docs/                       # Technical documentation
├── logs/                       # Application logs (Filebeat source)
├── data/                       # Persistence data (node storage)
├── docker-compose.logging.yml  # Monitoring stack
└── filebeat.yml               # Log shipping configuration

🔧 Architecture Overview

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Redis Client  │────│   RESP Protocol  │────│  HyperCache     │
│   (Any Library) │    │     Server       │    │   Cluster       │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
       ┌─────────────────────────────────────────────────┼─────────────────────────────────────────────────┐
       │                                                 │                                                 │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Memory Pool   │    │   Data Storage   │    │ Cuckoo Filter   │    │   Hash Ring     │    │   Gossip Node    │
│   (Pressure     │    │   + Persistence  │    │ (Probabilistic  │    │ (Consistent     │    │   Discovery      │
│    Monitoring)  │    │   (AOF+Snapshot) │    │   Operations)   │    │   Hashing)      │    │   & Failover     │
└─────────────────┘    └──────────────────┘    └─────────────────┘    └──────────────────┘    └─────────────────┘
       │                         │                         │                         │                         │
       └─────────────────────────┼─────────────────────────┼─────────────────────────┼─────────────────────────┘
                                 │                         │                         │
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                    MONITORING STACK                                                         │
├─────────────────┬──────────────────┬─────────────────┬──────────────────┬─────────────────┬─────────────────┤
│    Filebeat     │   Elasticsearch  │     Grafana     │   Health API     │   Metrics       │   Alerting      │
│  (Log Shipper)  │  (Log Storage)   │  (Dashboards)   │  (Diagnostics)   │  (Performance)  │  (Monitoring)   │
└─────────────────┴──────────────────┴─────────────────┴──────────────────┴─────────────────┴─────────────────┘

� Monitoring & Operations

Grafana Dashboards (http://localhost:3000)

System Overview: Cluster health, node status, memory usage
Performance Metrics: Request rates, response times, cache hit ratios
Error Monitoring: Failed requests, timeout alerts, node failures
Capacity Planning: Memory trends, storage usage, growth patterns

Elasticsearch Logs (http://localhost:9200)

Centralized Logging: All cluster nodes, operations, and errors
Search & Analysis: Query logs by node, operation type, or time range
Error Tracking: Exception traces, failed operations, debug information
Audit Trail: Configuration changes, cluster events, admin operations

Health Monitoring

# Cluster health
curl http://localhost:9080/health
curl http://localhost:9081/health  
curl http://localhost:9082/health

# Node statistics
curl http://localhost:9080/stats

# Memory usage
curl http://localhost:9080/api/cache/stats

Operational Commands

# View cluster logs in real-time
docker logs -f hypercache-filebeat

# Query Elasticsearch directly
curl "http://localhost:9200/logs-*/_search?q=level:ERROR"

# Monitor resource usage
docker stats hypercache-elasticsearch hypercache-grafana

# Backup persistence data
tar -czf hypercache-backup-$(date +%Y%m%d).tar.gz data/

Logging & Log Levels

HyperCache uses structured JSON logging with correlation IDs for full request tracing across all cluster nodes.

Available log levels (from most to least verbose):

Level	What it includes
`debug`	Everything: cuckoo filter decisions, event bus routing, gossip internals, health checks, snapshot ticks
`info`	Business operations: request lifecycle (start → operation → result), replication flow, cluster membership changes, persistence events
`warn`	Potential issues: memory pressure warnings, failed joins, missing event bus
`error`	Failures: replication errors, deserialization failures, storage errors
`fatal`	Unrecoverable: startup failures

Changing the log level:

Edit the node config YAML (e.g., configs/docker/node1-config.yaml):

logging:
  level: "info"     # Change to "debug" for troubleshooting, "warn" for quieter logs
  max_file_size: "100MB"
  max_files: 5
  output: ["console", "file"]
  structured: true
  log_dir: "/app/logs"

For Docker deployments, update all three node configs and rebuild:

# Edit configs/docker/node1-config.yaml, node2-config.yaml, node3-config.yaml
# Then rebuild and redeploy:
docker compose -f docker-compose.cluster.yml up -d --build

Request tracing with correlation IDs:

Every request gets a correlation_id that flows through the entire lifecycle — from HTTP entry through cache operations to cross-node replication. Use it to trace any request across all nodes:

# Trace a specific request across all nodes
docker logs hypercache-node1 2>&1 | grep "abc-123-correlation-id"
docker logs hypercache-node2 2>&1 | grep "abc-123-correlation-id"
docker logs hypercache-node3 2>&1 | grep "abc-123-correlation-id"

# Find all errors in the last hour
docker logs --since 1h hypercache-node1 2>&1 | grep '"level":"ERROR"'

# Find all replication events
docker logs hypercache-node1 2>&1 | grep '"action":"replication"'

You can also pass your own correlation ID via the X-Correlation-ID HTTP header for end-to-end tracing from your application:

curl -X PUT http://localhost:9080/api/cache/mykey \
  -H "Content-Type: application/json" \
  -H "X-Correlation-ID: my-trace-id-123" \
  -d '{"value": "hello"}'

📖 Documentation

See docs/README.md for the full documentation index:

Architecture — Consistent hashing, Cuckoo filter internals, RESP protocol, Raft consensus
Guides — Development setup, Docker, observability, multi-VM deployment
Reference — Benchmarks, persistence paths, known issues


### Clean Up
```bash
# Stop all services
./scripts/build-and-run.sh stop
docker-compose -f docker-compose.logging.yml down

# Clean persistence data
./scripts/clean-persistence.sh --all

# Clean Elasticsearch data  
./scripts/clean-elasticsearch.sh

🔧 Configuration

System Configuration

# Start complete system with monitoring
./scripts/start-system.sh --all

# Start only cluster
./scripts/start-system.sh --cluster  

# Start only monitoring
./scripts/start-system.sh --monitor

# Clean data and restart
./scripts/start-system.sh --clean --all

Node Configuration

# configs/node1-config.yaml
node:
  id: "node-1"
  data_dir: "./data/node-1"
  
network:
  resp_port: 8080
  http_port: 9080
  gossip_port: 7946
  
cache:
  max_memory: "8GB"
  default_ttl: "0"            # 0 = infinite (no expiry); set per-store or per-key
  cuckoo_filter_fpp: 0.01     # 1% false positive rate
  max_stores: 16              # max stores allowed (1-64)
  
persistence:
  enabled: true
  strategy: "hybrid"          # "aof", "snapshot", "hybrid"
  sync_policy: "everysec"     # "always", "everysec", "no"

Environment Variable Overrides (Docker / K8s)

Environment variables have highest priority and override both defaults and YAML config:

Variable	Description	Example
`HYPERCACHE_DEFAULT_MEMORY`	Default store max memory	`4GB`
`HYPERCACHE_DEFAULT_TTL`	Default store TTL (`0` = infinite)	`0`, `1h`, `30m`
`HYPERCACHE_DEFAULT_EVICTION`	Default store eviction policy	`lru`, `lfu`, `fifo`, `ttl`
`HYPERCACHE_DEFAULT_CUCKOO`	Default store cuckoo filter toggle	`true`, `false`
`HYPERCACHE_MAX_STORES`	Maximum stores allowed	`16`
`HYPERCACHE_CUCKOO_FILTER_FPP`	Cuckoo filter false positive rate	`0.01`
`HYPERCACHE_PERSISTENCE_ENABLED`	Global persistence toggle	`true`, `false`
`HYPERCACHE_PERSISTENCE_STRATEGY`	Global persistence strategy	`hybrid`, `aof`, `snapshot`

# Docker example: override default store config without a YAML file
docker run -e HYPERCACHE_DEFAULT_MEMORY=4GB \
           -e HYPERCACHE_DEFAULT_TTL=0 \
           -e HYPERCACHE_DEFAULT_EVICTION=lru \
           -e HYPERCACHE_DEFAULT_CUCKOO=true \
           -e HYPERCACHE_PERSISTENCE_ENABLED=true \
           -e HYPERCACHE_PERSISTENCE_STRATEGY=hybrid \
           rishabhverma17/hypercache

Per-Store Configuration

# Only "default" store ships out of the box.
# Store config is immutable — to change settings, drop and recreate the store.
# Additional stores can be defined in YAML or created at runtime via API.
stores:
  - name: "default"
    eviction_policy: "lru"       # LRU eviction
    max_memory: "8GB"
    default_ttl: "0"             # 0 = infinite
    cuckoo_filter: true          # Enable probabilistic lookups
    persistence: "hybrid"        # "hybrid", "aof", "snapshot", "disabled"
    
  - name: "sessions"
    eviction_policy: "ttl"       # TTL-based eviction
    max_memory: "1GB"
    default_ttl: "30m"
    cuckoo_filter: true
    persistence: "aof"           # Write-ahead logging only
    
  - name: "temporary_data"
    eviction_policy: "lfu"       # Least frequently used
    max_memory: "512MB"
    default_ttl: "15m"
    cuckoo_filter: false          # Disable for pure cache
    persistence: "disabled"       # In-memory only

Monitoring Configuration

# Grafana (localhost:3000)
Username: admin
Password: admin123

# Pre-configured datasources:
- Elasticsearch (HyperCache Logs)
- Health check endpoints

🛠️ Core Technologies

RESP (Redis Serialization Protocol)

What: Binary protocol for Redis compatibility
Why: Enables seamless integration with existing Redis clients and tools
Features: Full command set support, pipelining, pub/sub ready
Performance: Zero-copy parsing, minimal overhead

GOSSIP Protocol

What: Decentralized node discovery and health monitoring
Why: Eliminates single points of failure in cluster coordination
Features: Automatic node detection, failure detection, metadata propagation
Scalability: O(log n) message complexity, handles thousands of nodes

RAFT Consensus

What: Distributed consensus algorithm for cluster coordination
Why: Ensures data consistency and handles leader election
Features: Strong consistency guarantees, partition tolerance, log replication
Reliability: Proven algorithm used by etcd, Consul, and other systems

Hash Ring (Consistent Hashing)

What: Distributed data placement using consistent hashing
Why: Minimizes data movement during cluster changes
Features: Virtual nodes for load balancing, configurable replication
Efficiency: O(log n) lookup time, minimal rehashing on topology changes

AOF + WAL Persistence

AOF (Append-Only File): Sequential write logging for durability
WAL (Write-Ahead Logging): Transaction-safe write ordering
Hybrid Approach: Combines speed of WAL with simplicity of AOF
Recovery: Fast startup with complete data restoration

Cuckoo Filters

What: Space-efficient probabilistic data structure
Why: Better than Bloom filters - supports deletions and has better locality
Features: Configurable false positive rates, O(1) operations
Use Cases: Membership testing, cache admission policies, duplicate detection

📚 Documentation

docs/: Technical deep-dives and architecture docs

💾 Persistence & Recovery Deep Dive

Dual Persistence Architecture

HyperCache implements a sophisticated dual-persistence system combining the best of both AOF and WAL approaches:

AOF (Append-Only File)

# Ultra-fast sequential writes
Write Latency: 2.7µs average
Throughput: 370K+ operations/sec
File Format: Human-readable command log
Recovery: Sequential replay of operations

WAL (Write-Ahead Logging)

# Transaction-safe write ordering
Consistency: ACID compliance
Durability: Configurable fsync policies
Crash Recovery: Automatic rollback/forward
Performance: Batched writes, zero-copy I/O

Recovery Scenarios

Fast Startup Recovery

# Measured Performance (Production Test)
✅ Data Set: 10 entries
✅ Recovery Time: 160µs
✅ Success Rate: 100% (5/5 tests)
✅ Memory Overhead: <1MB

Point-in-Time Recovery

# Snapshot-based recovery
✅ Snapshot Creation: 3.7ms for 7 entries  
✅ File Size: 555B snapshot + 573B AOF
✅ Recovery Strategy: Snapshot + AOF replay
✅ Data Integrity: Checksum verification

Configurable Persistence Policies

Per-Store Persistence Settings

stores:
  - name: "critical_data"
    persistence: "hybrid"         # Full durability (AOF + snapshots)
    # Uses global persistence settings for sync_policy, snapshot_interval, etc.
      
  - name: "session_cache"
    persistence: "aof"            # Write-ahead logging only
      
  - name: "temporary_cache"
    persistence: "disabled"       # In-memory only — no disk I/O overhead

Durability vs Performance Tuning

# High Durability (Financial/Critical Data)
fsync: "always"              # Every write synced
batch_size: 1                # Individual operations
compression: false           # No CPU overhead

# Balanced (General Purpose)  
fsync: "periodic"            # 1-second sync intervals
batch_size: 100              # Batch writes
compression: true            # Space efficiency

# High Performance (Analytics/Temporary)
fsync: "never"               # OS manages sync
batch_size: 1000             # Large batches
compression: false           # CPU for throughput

Recovery Guarantees

Crash Recovery

Zero Data Loss: With fsync: always configuration
Automatic Recovery: Self-healing on restart
Integrity Checks: Checksums on all persisted data
Partial Recovery: Recovers valid data even from corrupted files

Network Partition Recovery

Consensus-Based: RAFT ensures consistency across partitions
Split-Brain Protection: Majority quorum prevents conflicts
Automatic Reconciliation: Rejoining nodes sync automatically
Data Validation: Cross-node checksum verification

Operational Commands

# Manual snapshot creation
curl -X POST http://localhost:9080/api/admin/snapshot

# Force AOF rewrite (compact logs)
curl -X POST http://localhost:9080/api/admin/aof-rewrite

# Check persistence status
curl http://localhost:9080/api/admin/persistence-stats

# Backup current state
./scripts/backup-persistence.sh

# Restore from backup
./scripts/restore-persistence.sh backup-20250822.tar.gz

🎯 Use Cases

Enterprise Deployment

High-performance caching layers for microservices
Session storage with automatic failover
Redis replacement with lower memory costs and better observability
Distributed caching with real-time monitoring

Development & Testing

Local development with production-like monitoring
Load testing with comprehensive metrics
Log analysis and debugging with Elasticsearch
Performance monitoring with Grafana dashboards

Production Examples

Web Application Cache

# Store user session
curl -X PUT http://localhost:9080/api/cache/user:123:session \
  -d '{"value":"{\"user_id\":123,\"role\":\"admin\"}", "ttl_hours":2}'

# Retrieve session
curl http://localhost:9080/api/cache/user:123:session

Redis Client Usage

import "github.com/redis/go-redis/v9"

// Connect to any cluster node
client := redis.NewClient(&redis.Options{
    Addr: "localhost:8080", // Node 1 RESP port
})

// Use exactly like Redis!
client.Set(ctx, "user:123:profile", userData, 30*time.Minute)
client.Incr(ctx, "page:views")
client.LPush(ctx, "notifications", "New message")

HTTP API Usage

# Rate limiting counters
curl -X PUT http://localhost:9080/api/cache/rate:user:456 \
  -d '{"value":"10", "ttl_hours":1}'

# Feature flags
curl -X PUT http://localhost:9080/api/cache/feature:new_ui \
  -d '{"value":"enabled", "ttl_hours":24}'

🚀 Getting Started Guide

Prerequisites

Go 1.23.2+
Docker & Docker Compose (for monitoring stack)
Git (for cloning)

Installation

git clone <your-repository-url>
cd Cache

# Quick start - everything in one command
./scripts/start-system.sh

# Access your system:
# - Grafana: http://localhost:3000 (admin/admin123)  
# - API: http://localhost:9080/api/cache/
# - Redis: localhost:8080 (redis-cli -p 8080)

First Steps

Check Cluster Health: Visit http://localhost:9080/health
Store Some Data: redis-cli -p 8080 SET mykey "Hello World"
View in Grafana: Open http://localhost:3000, check dashboards
Query Logs: Visit http://localhost:9200 for Elasticsearch

Development Workflow

# Build and test
go build -o bin/hypercache cmd/hypercache/main.go
go test ./internal/... -v

# Start development cluster
./scripts/build-and-run.sh cluster

# View logs
tail -f logs/*.log

# Stop everything
./scripts/build-and-run.sh stop
docker-compose -f docker-compose.logging.yml down

📚 Documentation

HTTP API Documentation: Complete HTTP API reference with examples
Technical Deep-Dives: Architecture, implementation details
Configuration Guide: Production deployment
RESP Protocol Reference: Redis compatibility examples
Performance Benchmarks: Throughput and latency tests
Monitoring Setup: Dashboard configuration

🤝 Contributing

This project demonstrates enterprise-grade Go development with:

Clean Architecture: Domain-driven design with clear interfaces
Observability First: Comprehensive logging, metrics, and monitoring
Production Ready: Persistence, clustering, and operational tooling
Protocol Compatibility: Full Redis RESP implementation
Performance Focused: Benchmarked and optimized for high throughput

📄 License

MIT License - feel free to use in your projects!

🎉 Enterprise Success Story

From Concept to Production-Grade System:

Vision: Redis-compatible distributed cache with advanced monitoring
Built: Full production system with ELK stack integration
Achieved: Multi-node clusters, real-time observability, enterprise persistence
Result: Complete caching platform ready for cloud deployment

Features that set HyperCache apart:

🔄 Zero-downtime deployments with cluster coordination
📊 Real-time monitoring with Grafana + Elasticsearch
💾 Enterprise persistence with AOF + snapshot recovery
🔍 Full observability with centralized logging and metrics
⚡ Redis compatibility drop-in replacement capability

Made with ❤️ in Go | Redis Compatible | Enterprise Observability

Name		Name	Last commit message	Last commit date
Latest commit History 53 Commits
.github/workflows		.github/workflows
cmd/hypercache		cmd/hypercache
configs		configs
docs		docs
examples		examples
grafana/provisioning		grafana/provisioning
internal		internal
k8s		k8s
logo		logo
nginx		nginx
pkg/config		pkg/config
scripts		scripts
templates		templates
tests		tests
.dockerignore		.dockerignore
.gitignore		.gitignore
.golangci.yml		.golangci.yml
Dockerfile		Dockerfile
HyperCache.postman_collection.json		HyperCache.postman_collection.json
Makefile		Makefile
README.md		README.md
docker-compose.cluster.yml		docker-compose.cluster.yml
docker-compose.loadbalancer.yml		docker-compose.loadbalancer.yml
docker-compose.logging.yml		docker-compose.logging.yml
filebeat-docker.yml		filebeat-docker.yml
filebeat.yml		filebeat.yml
go.mod		go.mod
go.sum		go.sum

Folders and files

Latest commit

History

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HyperCache - Distributed Cache

🎯 Latest Features ✅

🔥 Monitoring & Observability

🚀 Quick Start

🐳 Docker (Recommended — no setup required)

Prerequisites (Local Development)

Local Cluster (N nodes)

Single Node

Docker Deployment

Kubernetes

📊 Access Points

🧪 Testing

Unit Tests

Lint & Format

Benchmarks

Postman Collection

HTTP API Examples

Redis CLI

Scenario Tests

Makefile Reference

🏆 Key Features

Redis Compatibility

Distributed Resilience

Enterprise Persistence & Recovery

Containerized Deployment

Multi-Store Architecture

Advanced Memory Management

Probabilistic Data Structures

Distributed Architecture

Production Monitoring

� Project Structure

🔧 Architecture Overview

� Monitoring & Operations

Grafana Dashboards (http://localhost:3000)

Elasticsearch Logs (http://localhost:9200)

Health Monitoring

Operational Commands

Logging & Log Levels

📖 Documentation

🔧 Configuration

System Configuration

Node Configuration

Environment Variable Overrides (Docker / K8s)

Per-Store Configuration

Monitoring Configuration

🛠️ Core Technologies

RESP (Redis Serialization Protocol)

GOSSIP Protocol

RAFT Consensus

Hash Ring (Consistent Hashing)

AOF + WAL Persistence

Cuckoo Filters

📚 Documentation

💾 Persistence & Recovery Deep Dive

Dual Persistence Architecture

AOF (Append-Only File)

WAL (Write-Ahead Logging)

Recovery Scenarios

Fast Startup Recovery

Point-in-Time Recovery

Configurable Persistence Policies

Per-Store Persistence Settings

Durability vs Performance Tuning

Recovery Guarantees

Crash Recovery

Network Partition Recovery

Operational Commands

🎯 Use Cases

Enterprise Deployment

Development & Testing

Production Examples

Web Application Cache

Redis Client Usage

HTTP API Usage

🚀 Getting Started Guide

Prerequisites

Packages