High-Performance Data Layering Architecture

To build a truly usable Web3 social infrastructure, ITRIX has innovatively designed a four-layer collaborative architecture that achieves Web2-comparable interactive experiences and AI enhancement capabilities while ensuring core decentralization principles. This architecture constructs a technical foundation supporting social scenarios for hundreds of millions of users through vertical integration of blockchain, distributed storage, high-performance indexing, and elastic computing resources.

On-Chain Trust Layer

As the trust anchor of the entire architecture, it implements:

• Core data storage:
  • User DID identity credentials (W3C standard compliant)
  • Content fingerprint CID (IPFS compatible format)
  • Social relationship graph (dynamic PDA account derivation)
• Performance breakthrough:

  # State synchronization optimization algorithm
  def state_sync():
      use LightSpeed consensus optimization # Block time 400ms
      apply Sealevel parallel execution # TPS exceeding 5000
      implement QUIC network protocol # Global node latency <300ms

• Security features:
  • Zero-knowledge proof verification for private transactions
  • Hardware-level key management (HSM integration)

Distributed Storage Layer

A hybrid solution breaking through traditional blockchain storage limitations:

Indexing and Caching Layer

Self-developed Solana RPC optimization solution and Jito MEV protection mechanism, with million-level data query responses under 500 milliseconds. Key designs solving Web3 data retrieval pain points:

1. Query optimization stack:

• Self-developed Solana RPC gateway:

  • Query cache hit rate >95%
  • Support for GraphQL syntax parsing

• Jito-Style MEV protection:

  • Transaction pre-confirmation <0.5s
  • Slippage control <0.3%

2. Real-time analysis engine:

graph TB
  A[On-chain data] --> B[Stream processing cluster]
  B --> C[Real-time indexing]
  C --> D[Multi-level caching]
  D --> E[API gateway]

AI Computation Layer

Revolutionary distributed AI training architecture:

• Computational network characteristics:

  • Utilizing idle GPUs of Firedancer validation nodes

  • Dynamic task scheduling algorithm:

def schedule(task):
    priority = task.urgency * 0.6 + node.reputation * 0.4
    return nearest_available_node(priority)

• Performance comparison:

Architectural Synergy

The four-layer architecture achieves organic linkage through bidirectional data buses:

• Trust propagation: On-chain hash values verify the integrity of lower-layer data

• Performance transfer: Each layer provides more than 10x acceleration for the layer above

• Economic cycle: Services at each layer consume ITX tokens and generate value backflow