評估

Prisma Accelerate 透過高階連線池和全球邊緣快取來最佳化資料庫互動。其連線池器在 16 個區域可用，並幫助應用程式根據需求進行負載均衡和擴充套件資料庫請求。

考慮到以上資訊，我們建議在高負載下評估 Accelerate 的效能表現。

Accelerate 的連線池如何在負載下最佳化效能

Prisma Accelerate 採用動態、無伺服器的連線池基礎設施。當發出請求時，會在配置 Prisma Accelerate 時分配的區域中，為專案快速配置一個連線池。此連線池保持活躍，在重用已建立的資料庫連線的同時，服務許多額外的請求。連線池會在一段時間不活動後斷開連線，因此使用持續的流量流來評估 Prisma Accelerate 非常重要。

主要優勢

• 最佳化的查詢效能：無伺服器連線池器會適應查詢負載，確保在高峰需求期間高效管理資料庫連線。

  Prisma Accelerate 的連線池器無法提升資料庫中查詢的效能。在查詢效能存在問題的場景下，我們建議最佳化 Prisma 查詢、應用索引或利用 Accelerate 的邊緣快取。

• 最大化連線複用：執行持續大量的查詢有助於保持 Accelerate 連線池器的活躍例項。這會增加連線複用，確保後續查詢的更快響應時間。

透過理解和利用此機制，您可以確保您的資料庫查詢在大規模下保持一致且高效的效能。

評估 Prisma Accelerate 連線池效能

您將在下方找到一個如何使用示例模型評估 Prisma Accelerate 的示例

model Notes {
  id        Int       @id @default(autoincrement())
  title     String
  createdAt DateTime  @default(now())
  updatedAt DateTime? @updatedAt
}

import { PrismaClient } from '@prisma/client'
import { withAccelerate } from '@prisma/extension-accelerate'

const prisma = new PrismaClient().$extends(withAccelerate())

function calculateStatistics(numbers: number[]): {
  average: number
  p50: number
  p75: number
  p99: number
} {
  if (numbers.length === 0) {
    throw new Error('The input array is empty.')
  }

  // Sort the array in ascending order
  numbers.sort((a, b) => a - b)

  const sum = numbers.reduce((acc, num) => acc + num, 0)
  const count = numbers.length

  const average = sum / count
  const p50 = getPercentile(numbers, 50)
  const p75 = getPercentile(numbers, 75)
  const p99 = getPercentile(numbers, 99)

  return { average, p50, p75, p99 }
}

function getPercentile(numbers: number[], percentile: number): number {
  if (percentile <= 0 || percentile >= 100) {
    throw new Error('Percentile must be between 0 and 100.')
  }

  const index = (percentile / 100) * (numbers.length - 1)
  if (Number.isInteger(index)) {
    // If the index is an integer, return the corresponding value
    return numbers[index]
  } else {
    // If the index is not an integer, interpolate between two adjacent values
    const lowerIndex = Math.floor(index)
    const upperIndex = Math.ceil(index)
    const lowerValue = numbers[lowerIndex]
    const upperValue = numbers[upperIndex]
    const interpolationFactor = index - lowerIndex
    return lowerValue + (upperValue - lowerValue) * interpolationFactor
  }
}

async function main() {
  const timings = []

  // fire a query before going to the loop
  await prisma.notes.findMany({
    take: 20,
  })

  // we recommend evaluationg Prisma Accelerate with a large loop
  const LOOP_LENGTH = 10000

  for (let i = 0; i < LOOP_LENGTH; i++) {
    const start = Date.now()
    await prisma.notes.findMany({
      take: 20,
    })

    timings.push(Date.now() - start)
  }

  const statistics = calculateStatistics(timings)
  console.log('Average:', statistics.average)
  console.log('P50:', statistics.p50)
  console.log('P75:', statistics.p75)
  console.log('P99:', statistics.p99)
}

main()
  .then(async () => {
    await prisma.$disconnect()
  })
  .catch((e) => {
    await prisma.$disconnect()
    process.exit(1)
  })

評估 Prisma Accelerate 快取效能

Prisma Accelerate 的邊緣快取也針對高查詢量進行了最佳化。快取會自動最佳化重複查詢。因此，快取命中率會隨著查詢頻率的增加而提高。將查詢結果新增到快取也是非阻塞的，因此短時間的查詢突增可能無法充分利用快取，而需要持續負載才能發揮作用。

要評估 Accelerate 的邊緣快取，您可以修改上述指令碼，如下所示

import { PrismaClient } from '@prisma/client'
import { withAccelerate } from '@prisma/extension-accelerate'

const prisma = new PrismaClient().$extends(withAccelerate())

function calculateStatistics(numbers: number[]): {
  average: number
  p50: number
  p75: number
  p99: number
} {
  if (numbers.length === 0) {
    throw new Error('The input array is empty.')
  }

  // Sort the array in ascending order
  numbers.sort((a, b) => a - b)

  const sum = numbers.reduce((acc, num) => acc + num, 0)
  const count = numbers.length

  const average = sum / count
  const p50 = getPercentile(numbers, 50)
  const p75 = getPercentile(numbers, 75)
  const p99 = getPercentile(numbers, 99)

  return { average, p50, p75, p99 }
}

function getPercentile(numbers: number[], percentile: number): number {
  if (percentile <= 0 || percentile >= 100) {
    throw new Error('Percentile must be between 0 and 100.')
  }

  const index = (percentile / 100) * (numbers.length - 1)
  if (Number.isInteger(index)) {
    // If the index is an integer, return the corresponding value
    return numbers[index]
  } else {
    // If the index is not an integer, interpolate between two adjacent values
    const lowerIndex = Math.floor(index)
    const upperIndex = Math.ceil(index)
    const lowerValue = numbers[lowerIndex]
    const upperValue = numbers[upperIndex]
    const interpolationFactor = index - lowerIndex
    return lowerValue + (upperValue - lowerValue) * interpolationFactor
  }
}

async function main() {
  const timings = []

  // fire a query before going to the loop
  await prisma.notes.findMany({
    take: 20,
    cacheStrategy: {
      ttl: 30,
    },
  })

  // we recommend evaluating Prisma Accelerate with a large loop
  const LOOP_LENGTH = 10000

  for (let i = 0; i < LOOP_LENGTH; i++) {
    const start = Date.now()
    await prisma.notes.findMany({
      take: 20,
      cacheStrategy: {
        ttl: 30,
      },
    })

    timings.push(Date.now() - start)
  }

  const statistics = calculateStatistics(timings)
  console.log('Average:', statistics.average)
  console.log('P50:', statistics.p50)
  console.log('P75:', statistics.p75)
  console.log('P99:', statistics.p99)
}

main()
  .then(async () => {
    await prisma.$disconnect()
  })
  .catch((e) => {
    await prisma.$disconnect()
    process.exit(1)
  })