Documentation

Core Concepts

Understanding Webdriver's Solana optimization technology.

API Reference

Comprehensive API documentation and examples.

Advanced Usage

Fine-tuning and advanced optimization features.

Installation

Python

01pip install Webdriver-solana

JavaScript/TypeScript

01npm install @Webdriver/sdk

Quick Start

Get started with Webdriver's Solana optimization layer in minutes. Follow these examples to deploy and monitor your optimization layer.

Python Implementation

solana_optimizer.py

01from Webdriver.solana import NetworkOptimizer
02import asyncio
03
04async def optimize_network():
05    # Initialize network optimizer
06    optimizer = NetworkOptimizer(
07        network="mainnet-beta",
08        config={
09            "sharding": {
10                "enabled": True,
11                "shard_count": 16,
12                "dynamic_scaling": True
13            },
14            "memory_pool": {
15                "optimization_level": "aggressive",
16                "gc_interval": "50ms"
17            }
18        }
19    )
20
21    # Deploy optimization layer
22    deployment = await optimizer.deploy()
23    
24    # Monitor performance
25    async for stats in deployment.stream_stats():
26        print(f"Current TPS: {stats.current_tps}")
27        print(f"Latency: {stats.latency_ms}ms")

TypeScript Implementation

solana-optimizer.ts

01import { NetworkOptimizer } from '@Webdriver/sdk';
02
03const optimizer = new NetworkOptimizer({
04  network: 'mainnet-beta',
05  config: {
06    sharding: {
07      enabled: true,
08      shardCount: 16,
09      dynamicScaling: true
10    },
11    memoryPool: {
12      optimizationLevel: 'aggressive',
13      gcInterval: '50ms'
14    }
15  }
16});
17
18// Deploy and monitor
19const deployment = await optimizer.deploy();
20deployment.on('stats', (stats) => {
21  console.log(`Current TPS: ${stats.currentTps}`);
22  console.log(`Latency: ${stats.latencyMs}ms`);
23});

Core Concepts

Parallel Processing

Webdriver's parallel processing engine divides transactions into optimal shards, enabling concurrent processing while maintaining Solana's security guarantees. The system automatically adjusts shard sizes and batch parameters based on network conditions.

Memory Pool Optimization

Our advanced memory pool management system optimizes transaction queuing and prioritization, reducing memory bottlenecks and improving network throughput. The system uses AI to predict and prevent memory congestion before it occurs.

Dynamic Routing

Webdriver's dynamic routing system continuously analyzes network conditions and adjusts transaction paths to minimize latency and maximize throughput. The system includes automatic failover and load balancing capabilities.

Advanced Usage

Custom Optimization

Fine-tune the optimization layer for your specific needs with custom configuration options.

01optimizer.configure({
02  sharding: {
03    algorithm: "adaptive",
04    min_shard_size: "256MB",
05    max_shards: 32
06  },
07  routing: {
08    prioritization: "latency",
09    max_paths: 8,
10    rebalance_interval: "50ms"
11  }
12})

Performance Tuning

Advanced settings for optimizing performance in high-load scenarios.

01await deployment.tune({
02  target_tps: 100_000,
03  max_latency_ms: 5,
04  memory_limit: "16GB",
05  gc_aggressive: true
06})

Monitoring

Real-time monitoring and alerting capabilities.

01deployment.monitor({
02  metrics: ["tps", "latency", "memory"],
03  alert_thresholds: {
04    tps_drop: 0.3,
05    latency_spike: 100,
06    memory_usage: 0.9
07  },
08  callback: (alert) => {
09    console.log(`Alert: ${alert.type} - ${alert.message}`)
10  }
11})