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Architecture

wind network's architecture is designed for scalability, reliability, and performance. The system is composed of several interconnected components that work together to provide a comprehensive indexing solution.

Components

wind network consists of several modular components:

  • Geyser Plugin: Connects directly to Solana validators to stream real-time data
  • Node Network: P2P network for data propagation and redundancy
  • Indexers: Specialized nodes that index and serve data via API
  • Client SDK: Libraries for interacting with wind network services

Data Flow

  1. Data Ingestion: Raw blockchain data is captured through the Geyser plugin interface from Solana validators
  2. Processing: Data is processed, filtered, and transformed according to indexing rules
  3. Storage: Processed data is stored in the selected storage backend
  4. Query: Users access indexed data through the HTTP/WebSocket API or SDK

Storage Options

wind network supports multiple storage backends to suit different use cases:

RocksDB (Default)

The default storage option, optimized for high-throughput write operations.

  • Pros: Fast writes, good for hot storage
  • Cons: Limited query capabilities
  • Best for: Real-time data ingestion, recent data access

Apache Parquet

Columnar storage format optimal for analytics workloads.

  • Pros: Excellent compression (3-4x better than JSON), high query performance
  • Cons: Append-only, not suited for frequent updates
  • Best for: Analytics, cold storage, historical data

PostgreSQL

Relational database with rich query capabilities.

  • Pros: SQL queries, excellent for complex analytics, point-in-time recovery
  • Cons: Higher resource usage, slower writes than specialized options
  • Best for: Complex queries, local development, transaction analysis

Hot/Cold Storage Architecture

wind network supports a hot/cold storage architecture:

  1. Hot Storage: Recent data stored in RocksDB for fast access
  2. Cold Storage: Historical data archived in Parquet or PostgreSQL

This approach provides the best of both worlds:

  • Fast write performance during data ingestion
  • Excellent query performance for analytics on historical data