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指标

Prisma Client 指标为你提供了关于 Prisma Client 如何与你的数据库交互的详细信息。你可以利用这些信息来诊断应用程序的性能问题。

信息

如果你想更详细地了解 Prisma Client 的性能（甚至到单个操作的级别），请参阅追踪。

关于指标​

你可以以 JSON 或 Prometheus 格式导出指标，并在控制台日志中查看，或将其集成到外部指标系统，如 StatsD 或 Prometheus。如果将其集成到外部指标系统，则可以随着时间推移查看指标数据。例如，你可以使用指标来帮助诊断应用程序的空闲和活动连接数量如何变化。

Prisma Client 提供以下指标

  • 计数器（总是增加）

    • prisma_client_queries_total: 执行的 Prisma Client 查询总数。
    • prisma_datasource_queries_total: 执行的数据源查询总数（关系型数据库中的 SQL 查询，以及 MongoDB 中的命令）。
      • prisma_datasource_queries_total 返回的值可能大于 prisma_client_queries_total，因为某些 Prisma Client 操作会创建多个查询。
    • prisma_pool_connections_closed_total: 关闭的连接池连接总数。
    • prisma_pool_connections_opened_total: 当前打开的连接池连接数。

  • 仪表（可以增加或减少）

    • prisma_client_queries_active: 当前活动的 Prisma Client 查询数量。
    • prisma_client_queries_wait: 由于所有连接都在使用中，当前等待连接的 Prisma Client 查询数量。
    • prisma_pool_connections_busy: 当前正在忙碌的连接池连接数量。这些连接池连接正在执行数据源查询。
    • prisma_pool_connections_idle: 当前未被使用的连接池连接数量。这些连接池连接正在等待下一次数据源查询运行。
    • prisma_pool_connections_open: 打开的连接池连接数量。

  • 直方图（指标数据分为一组值；我们称集合中的每个容器为“桶”）

    • prisma_client_queries_wait_histogram_ms: 所有 Prisma Client 查询等待连接池连接的时间（毫秒）。
    • prisma_client_queries_duration_histogram_ms: 所有已执行的 Prisma Client 查询的执行时间（毫秒）。这包括执行所有数据库查询，以及执行所有数据库引擎活动（例如连接数据和将数据转换为正确格式）所花费的时间。
    • prisma_datasource_queries_duration_histogram_ms: 所有已执行的数据源查询的执行时间（毫秒）。

你可以为你的指标数据添加全局标签，以帮助你对指标进行分组和区分，例如按基础设施区域或服务器。

先决条件​

要使用 Prisma Client 指标，你必须执行以下操作

  1. 安装相应的依赖.
  2. 在你的 Prisma schema 文件中启用 metrics 功能标志.

1. 安装最新的 Prisma ORM 依赖​

使用 prisma 和 @prisma/client npm 包的 3.15.0 或更高版本。

npm install prisma@latest --save-dev
npm install @prisma/client@latest

2. 在 Prisma schema 文件中启用功能标志​

在你的 schema.prisma 文件的 generator 块中，启用 metrics 功能标志

generator client {
  provider        = "prisma-client-js"
  previewFeatures = ["metrics"]
}

以 JSON 格式检索指标​

当你以 JSON 格式检索指标时，你可以直接使用它们返回的格式，或者将它们发送到 StatSD 以可视化它们随时间的变化。

要以 JSON 格式检索指标，请将以下行添加到你的应用程序代码中

const metrics = await prisma.$metrics.json()
console.log(metrics)

这将按如下方式返回指标

{
  "counters": [
    {
      "key": "prisma_client_queries_total",
      "labels": {},
      "value": 0,
      "description": "Total number of Prisma Client queries executed"
    },
    {
      "key": "prisma_datasource_queries_total",
      "labels": {},
      "value": 0,
      "description": "Total number of Datasource Queries executed"
    },
    {
      "key": "prisma_pool_connections_closed_total",
      "labels": {},
      "value": 0,
      "description": "Total number of Pool Connections closed"
    },
    {
      "key": "prisma_pool_connections_opened_total",
      "labels": {},
      "value": 1,
      "description": "Total number of Pool Connections opened"
    }
    ...
  ],
  "gauges": [
    ...
  ],
  "histograms": [
    ...
  ]
}
展开以查看完整输出
{
  "counters": [
    {
      "key": "prisma_client_queries_total",
      "labels": {},
      "value": 2,
      "description": "Total number of Prisma Client queries executed"
    },
    {
      "key": "prisma_datasource_queries_total",
      "labels": {},
      "value": 5,
      "description": "Total number of Datasource Queries executed"
    },
    {
      "key": "prisma_pool_connections_open",
      "labels": {},
      "value": 1,
      "description": "Number of currently open Pool Connections"
    }
  ],
  "gauges": [
    {
      "key": "prisma_client_queries_active",
      "labels": {},
      "value": 0,
      "description": "Number of currently active Prisma Client queries"
    },
    {
      "key": "prisma_client_queries_wait",
      "labels": {},
      "value": 0,
      "description": "Number of Prisma Client queries currently waiting for a connection"
    },
    {
      "key": "prisma_pool_connections_busy",
      "labels": {},
      "value": 0,
      "description": "Number of currently busy Pool Connections (executing a datasource query)"
    },
    {
      "key": "prisma_pool_connections_idle",
      "labels": {},
      "value": 21,
      "description": "Number of currently unused Pool Connections (waiting for the next datasource query to run)"
    },
    {
      "key": "prisma_pool_connections_open",
      "labels": {},
      "value": 1,
      "description": "Number of currently open Pool Connections"
    }
  ],
  "histograms": [
    {
      "key": "prisma_client_queries_duration_histogram_ms",
      "labels": {},
      "value": {
        "buckets": [
          [0, 0],
          [1, 0],
          [5, 0],
          [10, 1],
          [50, 1],
          [100, 0],
          [500, 0],
          [1000, 0],
          [5000, 0],
          [50000, 0]
        ],
        "sum": 47.430541000000005,
        "count": 2
      },
      "description": "Histogram of the duration of all executed Prisma Client queries in ms"
    },
    {
      "key": "prisma_client_queries_wait_histogram_ms",
      "labels": {},
      "value": {
        "buckets": [
          [0, 0],
          [1, 3],
          [5, 0],
          [10, 0],
          [50, 0],
          [100, 0],
          [500, 0],
          [1000, 0],
          [5000, 0],
          [50000, 0]
        ],
        "sum": 0.0015830000000000002,
        "count": 3
      },
      "description": "Histogram of the wait time of all Prisma Client Queries in ms"
    },
    {
      "key": "prisma_datasource_queries_duration_histogram_ms",
      "labels": {},
      "value": {
        "buckets": [
          [0, 0],
          [1, 0],
          [5, 2],
          [10, 2],
          [50, 1],
          [100, 0],
          [500, 0],
          [1000, 0],
          [5000, 0],
          [50000, 0]
        ],
        "sum": 47.134498,
        "count": 5
      },
      "description": "Histogram of the duration of all executed Datasource Queries in ms"
    }
  ]
}

JSON 数据中的直方图​

每个直方图“桶”有两个值。第一个是桶的上限，第二个是计数（落入该桶的数据值的数量）。在以下示例中，11 到 20 之间的值有两个实例，21 到 30 之间的值有五个实例

...
[20, 2],
[30, 5],
...

将 Prisma Client 指标与 StatsD 结合使用​

你可以将 JSON 格式的指标发送到 StatsD，以随着时间推移可视化你的指标数据。

信息

注意：你必须将计数器指标作为一系列值提供给 StatsD，这些值从之前检索的指标中递增或递减。然而，Prisma Client 的计数器指标返回的是绝对值。因此，你必须将计数器指标转换为一系列递增和递减的值，并将其作为仪表数据发送给 StatsD。在下面的代码示例中，我们使用 diffHistograms 将计数器指标转换为递增和递减的仪表数据。

在以下示例中，我们每 10 秒向 StatsD 发送一次指标。这个时间与 StatsD 默认的 10 秒刷新率一致。

import StatsD from 'hot-shots'
let statsd = new StatsD({
  port: 8125,
})

const diffMetrics = (metrics: Metric<MetricHistogram>[]) => {
  return metrics.map((metric) => {
    let prev = 0;

    const diffBuckets = metric.value.buckets.map<MetricHistogramBucket>(
      (values) => {
        const [bucket, value] = values
        const diff = value - prev
        prev = value
        return [bucket, diff]
      }
    )

    metric.value.buckets = diffBuckets
    return metric
  })
}

let previousHistograms: Metric<MetricHistogram>[] = []


const statsdSender = async () => {
  const metrics = await prisma.$metrics.json()
  
  metrics.counters.forEach((counter: any) => {
    statsd.gauge('prisma.' + counter.key, counter.value, (...res) => {})
  });

  metrics.gauges.forEach((counter: any) => {
    statsd.gauge('prisma.' + counter.key, counter.value, (...res) => {})
  })

  if (!previousHistograms.length) {
    previousHistograms = diffMetrics(metrics.histograms)

    return
  }

  const diffHistograms = diffMetrics(metrics.histograms);

  diffHistograms.forEach((diffHistogram, histogramIndex) => {
    diffHistogram.value.buckets.forEach((values, bucketIndex) => {
      const [bucket, count] = values
      const [_, prev] =
        previousHistograms[histogramIndex].value.buckets[bucketIndex]
      const change = count - prev

      for (let sendTimes = 0; sendTimes < change; sendTimes++) {
        statsd.timing('prisma.' + diffHistograms.key, bucket)
      }
    })
  })

  previousHistograms = diffHistograms
}

setInterval(async () => await statsdSender(), 10000)

以 Prometheus 格式检索指标​

当你以 Prometheus 格式检索 Prisma Client 指标时，你可以直接使用它们返回的格式，或者将它们发送到 Prometheus 指标系统，以可视化它们随时间的变化。

要以 Prometheus 格式检索指标，请将以下行添加到你的应用程序代码中

const metrics = await prisma.$metrics.prometheus()
console.log(metrics)

这将按如下方式返回指标

# HELP prisma_client_queries_total Total number of Prisma Client queries executed
# TYPE prisma_client_queries_total counter
prisma_client_queries_total 14

...
# HELP prisma_pool_connections_busy The number of active connections in use.
# TYPE prisma_pool_connections_busy gauge
prisma_pool_connections_busy 0

...
# HELP prisma_client_queries_wait_histogram_ms The wait time for a worker to get a connection.
# TYPE prisma_client_queries_wait_histogram_ms histogram
prisma_client_queries_wait_histogram_ms_bucket{le="0"} 0
prisma_client_queries_wait_histogram_ms_bucket{le="1"} 3
展开以查看完整输出
# HELP query_total_operations
# TYPE query_total_operations counter
query_total_operations 2

# HELP prisma_datasource_queries_total
# TYPE prisma_datasource_queries_total counter
prisma_datasource_queries_total 28

# HELP prisma_pool_connections_closed_total Total number of Pool Connections closed
# TYPE prisma_pool_connections_closed_total counter
prisma_pool_connections_closed_total 0

# HELP prisma_pool_connections_opened_total Total number of Pool Connections opened
# TYPE prisma_pool_connections_opened_total counter
prisma_pool_connections_opened_total 0

# HELP prisma_client_queries_active Number of currently active Prisma Client queries
# TYPE prisma_client_queries_active gauge
prisma_client_queries_active 0

# HELP prisma_client_queries_wait Number of queries currently waiting for a connection
# TYPE prisma_client_queries_wait gauge
prisma_client_queries_wait 0

# HELP prisma_pool_connections_busy Number of currently busy Pool Connections (executing a datasource query)
# TYPE prisma_pool_connections_busy gauge
prisma_pool_connections_busy 0

# HELP prisma_pool_connections_idle Number of currently unused Pool Connections (waiting for the next pool query to run)
# TYPE prisma_pool_connections_idle gauge
prisma_pool_connections_idle 21

# HELP prisma_pool_connections_open Number of currently open Pool Connections
# TYPE prisma_pool_connections_open gauge
prisma_pool_connections_open 1

# HELP prisma_pool_connections_open Number of currently open Pool Connections (able to execute a datasource query)
# TYPE prisma_pool_connections_open gauge
prisma_pool_connections_open 0

# HELP prisma_client_queries_wait_histogram_ms The wait time for a worker to get a connection.
# TYPE prisma_client_queries_wait_histogram_ms histogram
prisma_client_queries_wait_histogram_ms_bucket{le="0"} 0
prisma_client_queries_wait_histogram_ms_bucket{le="1"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="5"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="10"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="50"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="100"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="500"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="1000"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="5000"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="50000"} 3
prisma_client_queries_wait_histogram_ms_bucket{le="+Inf"} 3
prisma_client_queries_wait_histogram_ms_sum 0.023208
prisma_client_queries_wait_histogram_ms_count 3

# HELP prisma_client_queries_duration_histogram_ms Histogram of the duration of all executed Prisma Client queries in ms
# TYPE prisma_client_queries_duration_histogram_ms histogram
prisma_client_queries_duration_histogram_ms_bucket{le="0"} 0
prisma_client_queries_duration_histogram_ms_bucket{le="1"} 1
prisma_client_queries_duration_histogram_ms_bucket{le="5"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="10"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="50"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="100"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="500"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="1000"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="5000"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="50000"} 2
prisma_client_queries_duration_histogram_ms_bucket{le="+Inf"} 2
prisma_client_queries_duration_histogram_ms_sum 3.197624
prisma_client_queries_duration_histogram_ms_count 2

# HELP prisma_datasource_queries_duration_histogram_ms Histogram of the duration of all executed Datasource Queries in ms
# TYPE prisma_datasource_queries_duration_histogram_ms histogram
prisma_datasource_queries_duration_histogram_ms_bucket{le="0"} 0
prisma_datasource_queries_duration_histogram_ms_bucket{le="1"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="5"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="10"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="50"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="100"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="500"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="1000"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="5000"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="50000"} 5
prisma_datasource_queries_duration_histogram_ms_bucket{le="+Inf"} 5
prisma_datasource_queries_duration_histogram_ms_sum 1.8407059999999997
prisma_datasource_queries_duration_histogram_ms_count 5

类型为 histogram 的指标以 Prometheus 格式公开三种不同类别的值

  1. 观察桶的多个累积计数器。这些计数器以后缀 _bucket{le="<上限（包含）>"} 结尾。例如，prisma_datasource_queries_duration_histogram_ms 有一个计数器公开为 prisma_datasource_queries_duration_histogram_ms_bucket{le="1"}

    当观察到的值小于或等于桶的上限（包含）时，Prisma Client 指标将该桶加 1。假设你有的桶的上限（包含）分别为 0、1、5、10 和 50。如果观察到的值为 5，则 Prisma Client 指标将从第三个桶开始递增，因为该值大于 0 和 1，但小于或等于 5、10 和 50。

  2. 所有观测值的单个总和。此计数器以后缀 _sum 结尾。例如，prisma_datasource_queries_duration_histogram_ms 的总和公开为 prisma_datasource_queries_duration_histogram_ms_sum。

  3. 已观察事件数量的计数。此计数器以后缀 _count 结尾。例如，prisma_datasource_queries_duration_histogram_ms 事件的总计数公开为 prisma_datasource_queries_duration_histogram_ms_count。

有关更多信息，请阅读 Prometheus 文档中关于指标类型的内容。

将 Prisma Client 指标与 Prometheus 指标系统结合使用​

在大多数情况下，Prometheus 必须抓取一个端点来检索指标。以下示例展示了如何使用 Express.js 发送数据

import { PrismaClient } from '@prisma/client'
import express, { Request, Response } from 'express'

const app = express()
const port = 4000
const prisma = new PrismaClient()

app.get('/metrics', async (_req, res: Response) => {
  const metrics = await prisma.$metrics.prometheus()
  res.end(metrics)
})

app.listen(port, () => {
  console.log(`Example app listening on port ${port}`)
})

以下示例展示了如何将 Prisma Client 指标与通过 REST API 端点（结合 Express.js）提供的其他 Prometheus 客户端库结合使用

import { PrismaClient } from '@prisma/client'
import express, { Request, Response } from 'express'
import prom from 'prom-client'

const app = express()
const port = 4000
const prisma = new PrismaClient()

const register = new prom.Registry()
prom.collectDefaultMetrics({ register })

app.get('/metrics', async (_req, res: Response) => {
  const prismaMetrics = await prisma.$metrics.prometheus()
  const appMetrics = await register.metrics()
  res.end(prismaMetrics + appMetrics)
})

app.listen(port, () => {
  console.log(`Example app listening on port ${port}`)
})

全局标签​

你可以为指标添加全局标签，以帮助你对指标进行分组和区分。每个 Prisma Client 实例都会将其生成的指标添加这些标签。例如，你可以使用类似 { server: 'us_server1', 'app_version': 'one' } 的标签按基础设施区域或服务器对指标进行分组。

全局标签适用于 JSON 和 Prometheus 格式的指标。

例如，要为 JSON 格式的指标添加全局标签，请将以下代码添加到你的应用程序中

const metrics = prisma.$metrics.json({
  globalLabels: { server: 'us_server1', app_version: 'one' },
})
console.log(metrics)

这将以以下格式返回信息

{
  "counters": [
    {
      "key": "query_total_operations",
      "labels": { "server": "us_server1", "app_version": "one" },
      "value": 0,
      "description": "The total number of operations executed"
    },
    {
      "key": "prisma_datasource_queries_total",
      "labels": { "server": "us_server1", "app_version": "one" },
      "value": 0,
      "description": "The total number of queries executed"
    },
    ...
  ],
  "gauges": [
    ...
  ],
  "histograms": [
    ...
  ]
}
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