getRecentPerformanceSamples

Documentation Index

Fetch the complete documentation index at: https://docs.orbitflare.com/llms.txt

Use this file to discover all available pages before exploring further.

​

参数

​

响应

​

代码示例

​

基本请求

curl https://fra.rpc.orbitflare.com?api_key=YOUR-API-KEY -X POST -H "Content-Type: application/json" -d '{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "getRecentPerformanceSamples",
  "params": []
}'

​

带限制的请求

curl https://fra.rpc.orbitflare.com?api_key=YOUR-API-KEY -X POST -H "Content-Type: application/json" -d '{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "getRecentPerformanceSamples",
  "params": [
    100
  ]
}'

​

使用 web3.js

import { Connection } from '@solana/web3.js';

const connection = new Connection('https://fra.rpc.orbitflare.com?api_key=YOUR-API-KEY');

// Get recent performance samples
const samples = await connection.getRecentPerformanceSamples();
console.log('Performance samples:', samples);

// Calculate average TPS
function calculateAverageTPS(samples: any[]): number {
  const totalTransactions = samples.reduce(
    (sum, sample) => sum + sample.numTransactions,
    0
  );
  const totalSeconds = samples.reduce(
    (sum, sample) => sum + sample.samplePeriodSecs,
    0
  );
  return totalTransactions / totalSeconds;
}

​

注意事项

1. 返回网络的近期性能样本
2. 每个样本覆盖一段时间（通常为 60 秒）
3. 样本包括交易计数和 slot 信息
4. 响应是即时的，因为它从当前状态读取
5. 默认限制为 720 个样本（约 12 小时）

​

最佳实践

1. 根据分析需求使用适当的限制数量
2. 在适当时缓存结果以减少 RPC 负载
3. 监控性能的显著变化
4. 考虑使用 WebSocket 订阅获取实时更新
5. 适当处理网络错误并重试

​

常见错误

​

用例

1. 性能分析
   interface PerformanceMetrics {
     averageTPS: number;
     peakTPS: number;
     totalTransactions: number;
     totalSlots: number;
     metadata: {
       timestamp: number;
       sampleCount: number;
     };
   }
   
   async function analyzePerformance(
     limit: number = 720
   ): Promise<PerformanceMetrics> {
     const samples = await connection.getRecentPerformanceSamples(limit);
     
     const totalTransactions = samples.reduce(
       (sum, sample) => sum + sample.numTransactions,
       0
     );
     const totalSeconds = samples.reduce(
       (sum, sample) => sum + sample.samplePeriodSecs,
       0
     );
     const peakTPS = Math.max(
       ...samples.map(sample => sample.numTransactions / sample.samplePeriodSecs)
     );
     
     return {
       averageTPS: totalTransactions / totalSeconds,
       peakTPS,
       totalTransactions,
       totalSlots: samples.reduce((sum, sample) => sum + sample.numSlots, 0),
       metadata: {
         timestamp: Date.now(),
         sampleCount: samples.length
       }
     };
   }
   
2. 性能监控
   interface PerformanceAlert {
     type: 'high_tps' | 'low_tps' | 'anomaly';
     currentTPS: number;
     threshold: number;
     metadata: {
       timestamp: number;
       slot: number;
     };
   }
   
   class PerformanceMonitor {
     private previousTPS: number | null = null;
     private readonly threshold = 0.2; // 20% change threshold
     
     async monitorPerformance(
       interval: number = 60000
     ): Promise<PerformanceAlert | null> {
       const samples = await connection.getRecentPerformanceSamples(1);
       const currentSample = samples[0];
       const currentTPS = currentSample.numTransactions / currentSample.samplePeriodSecs;
       
       if (this.previousTPS === null) {
         this.previousTPS = currentTPS;
         return null;
       }
       
       const change = Math.abs(currentTPS - this.previousTPS) / this.previousTPS;
       
       if (change > this.threshold) {
         const alert: PerformanceAlert = {
           type: change > 0 ? 'high_tps' : 'low_tps',
           currentTPS,
           threshold: this.threshold,
           metadata: {
             timestamp: Date.now(),
             slot: currentSample.slot
           }
         };
         
         this.previousTPS = currentTPS;
         return alert;
       }
       
       return null;
     }
   }
   
3. 性能历史
   interface PerformanceHistory {
     samples: Array<{
       slot: number;
       tps: number;
       nonVoteTPS: number;
       timestamp: number;
     }>;
     summary: {
       averageTPS: number;
       peakTPS: number;
       totalTransactions: number;
     };
     metadata: {
       startTime: number;
       endTime: number;
       sampleCount: number;
     };
   }
   
   async function getPerformanceHistory(
     limit: number = 720
   ): Promise<PerformanceHistory> {
     const samples = await connection.getRecentPerformanceSamples(limit);
     
     const processedSamples = samples.map(sample => ({
       slot: sample.slot,
       tps: sample.numTransactions / sample.samplePeriodSecs,
       nonVoteTPS: sample.numNonVoteTransaction / sample.samplePeriodSecs,
       timestamp: Date.now() - (samples.length - samples.indexOf(sample)) * sample.samplePeriodSecs * 1000
     }));
     
     const totalTransactions = samples.reduce(
       (sum, sample) => sum + sample.numTransactions,
       0
     );
     const totalSeconds = samples.reduce(
       (sum, sample) => sum + sample.samplePeriodSecs,
       0
     );
     
     return {
       samples: processedSamples,
       summary: {
         averageTPS: totalTransactions / totalSeconds,
         peakTPS: Math.max(...processedSamples.map(sample => sample.tps)),
         totalTransactions
       },
       metadata: {
         startTime: processedSamples[0].timestamp,
         endTime: processedSamples[processedSamples.length - 1].timestamp,
         sampleCount: samples.length
       }
     };
   }