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Architecture

Overview​

Clustron DKV is a distributed, .NET-native key-value data engine designed for high-throughput, low-latency workloads in modern microservice environments.

The system is built around:

  • Horizontal scalability
  • Clear separation of control and data responsibilities
  • Extensible internal engine components
  • Predictable performance under load
  • Instance-level isolation

Clustron DKV is evolving into a distributed data foundation rather than a simple cache.

Clustron DKV Architecture​

Clustron Architecture

Each node operates as an independent process and participates in cluster coordination.

Control Plane vs Data Plane​

Clustron DKV separates responsibilities into:

Data Plane​

Responsible for:

  • Key-value operations (Get, Put, Delete)
  • Prefix queries
  • Watch subscriptions
  • TTL enforcement
  • Index lookups
  • Query execution
  • Lease and locking primitives
  • Counter operations

Data Plane is optimized for:

  • Throughput
  • Minimal GC pressure
  • Efficient async networking
  • Low overhead serialization

Control Plane​

Responsible for:

  • Store creation
  • Instance lifecycle management
  • Cluster configuration
  • Port management
  • Instance isolation
  • Node startup and shutdown
  • Configuration templates

Each DKV instance:

  • Runs independently
  • Has its own configuration
  • Has isolated logging
  • Can be deployed multiple times per host

This separation ensures operational clarity and horizontal scalability.

Node Architecture​

Each Clustron DKV node contains the following core components:

1. Request Pipeline​

Handles incoming client requests and routes them to appropriate engine components.

Designed for:

  • Async I/O efficiency
  • Minimal context switching
  • Backpressure handling
  • Structured error propagation

2. Segment Store​

The core in-memory storage engine.

Responsibilities:

  • Key partitioning
  • Concurrent access management
  • Data layout optimization
  • Efficient memory usage

Segment-based partitioning allows scalable concurrency without global locking.

3. Index Manager​

Provides:

  • Equality indexing
  • Range indexing
  • Query acceleration
  • Efficient lookup paths

Indexes are maintained alongside primary storage to avoid full scans for query operations.

4. Expiration Engine (TimeWheel-Based)​

Clustron DKV uses a TimeWheel-inspired expiration model for TTL handling.

Benefits:

  • O(1) expiration scheduling
  • Predictable cleanup cost
  • Reduced per-key timer overhead
  • Avoids expensive scanning

This design improves performance under large TTL workloads.

5. Watch Engine​

Supports prefix-based watch subscriptions.

Capabilities:

  • Subscription to key prefixes
  • Event delivery on mutations
  • Snapshot + live event support
  • Ordered event propagation

Designed for building reactive distributed systems.

6. Lease and Lock Engine​

Provides distributed coordination primitives:

  • Leases
  • Lock acquisition
  • Expiry-based coordination
  • Safe release mechanisms

These primitives enable higher-level distributed workflows.

7. Counter Engine​

Supports atomic distributed counters with:

  • Increment
  • Decrement
  • Read
  • TTL support

Designed for metrics, throttling, and coordination scenarios.

Cluster Model​

Clustron DKV nodes form a logical cluster.

Key characteristics:

  • Peer-to-peer coordination
  • Node-aware routing
  • Failure detection
  • Rebalancing support (planned)
  • Configurable priority-based reconnect behavior

Clients maintain:

  • Priority server list
  • Reconnect delay strategy
  • Failover handling

Cluster design emphasizes consistency of operations within node ownership boundaries while maintaining horizontal scalability.

Multi-Instance Deployment​

Clustron DKV supports multiple instances per machine.

Each instance:

  • Runs on its own port
  • Has isolated configuration
  • Maintains independent logs
  • Can be independently started/stopped

This enables:

  • Resource partitioning
  • Environment isolation
  • Controlled multi-tenant deployments

Networking Model​

The networking layer is designed for:

  • High-throughput async operations
  • Efficient socket usage
  • Controlled connection lifecycles
  • Graceful failover handling

The architecture avoids unnecessary abstraction layers to reduce overhead.

Scalability Philosophy​

Clustron DKV is built around:

  • Horizontal scale-out
  • Stateless client design
  • Predictable memory behavior
  • Controlled coordination overhead
  • Modular extensibility

The system is intentionally structured to allow future expansion into:

  • Advanced query capabilities
  • Observability integration
  • Vector and similarity search
  • Distributed job scheduling
  • Additional distributed primitives

Observability (Planned Enhancements)​

Future improvements include:

  • Built-in metrics export
  • Prometheus integration
  • Per-instance performance tracking
  • Cluster health reporting
  • Operation tracing

Design Principles​

Clustron DKV follows these principles:

  • Performance-first
  • Predictability over abstraction
  • Extensibility without rewriting core engine
  • Clear separation of responsibilities
  • Operational clarity
  • Practical distributed systems engineering

Current Status​

Clustron DKV is in active development.

Core engine stabilization and documentation publication are ongoing.

Public open-source release of the core engine is planned for 2026 under a Business Source License.

What’s Next​

👉 Continue to Store to understand how clusters are structured.