指标

Prisma Client 指标为您提供关于 Prisma Client 如何与您的数据库交互的详细洞察。您可以使用此洞察来帮助诊断应用程序的性能问题。

信息

如果您想要更详细地了解 Prisma Client 的性能，例如在单个操作级别，请参阅追踪。

关于指标

您可以导出 JSON 或 Prometheus 格式的指标，并在控制台日志中查看它们，或者将它们集成到外部指标系统，例如 StatsD 或 Prometheus。如果您将它们集成到外部指标系统中，那么您可以随时间查看指标数据。例如，您可以使用指标来帮助诊断应用程序的空闲和活动连接数如何变化。

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：当前活跃的 Prisma Client 查询数。
  • prisma_client_queries_wait：当前等待连接的 Prisma Client 查询数，因为所有连接都在使用中。
  • prisma_pool_connections_busy：当前繁忙的连接池连接数。这些连接池连接当前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。这些连接池连接正在等待下一个数据源查询运行。
  • prisma_pool_connections_open：打开的连接池连接数。

• 直方图（指标数据被分成一系列值；我们将集合中的每个容器称为“bucket”）

  • prisma_client_queries_wait_histogram_ms：所有 Prisma Client 查询等待连接池连接的时间，单位为毫秒。
  • prisma_client_queries_duration_histogram_ms：所有已执行 Prisma Client 查询的执行时间，单位为毫秒。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：所有已执行数据源查询的执行时间，单位为毫秒。

您可以向您的指标数据添加全局标签，以帮助您对指标进行分组和分隔，例如按基础设施区域或服务器。

先决条件

要使用 Prisma Client 指标，您必须执行以下操作

1. 安装适当的依赖项.
2. 在您的 Prisma schema 文件中启用 metrics 功能标志.

1. 安装最新的 Prisma ORM 依赖项

使用版本 3.15.0 或更高版本的 prisma 和 @prisma/client npm 包。

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

2. 在 Prisma schema 文件中启用功能标志

在您的 schema.prisma 文件的 generator 代码块中，启用 metrics 功能标志

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

以 JSON 格式检索指标

当您以 JSON 格式检索指标时，您可以使用返回的格式，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，请将以下行添加到您的应用程序代码中

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

这将返回如下指标

{
  "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“bucket”都有两个值。第一个是 bucket 的上限，第二个是计数（落入该 bucket 的数据值的数量）。在以下示例中，有 2 个值在 11 到 20 之间，以及 5 个值在 21 到 30 之间

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

将 Prisma Client 指标与 StatsD 一起使用

您可以将 JSON 格式的指标发送到 StatsD 以可视化您的指标数据随时间的变化。

信息

注意：您必须将计数器指标作为一系列值提供给 StatsD，这些值是从先前检索的指标中递增或递减的。但是，Prisma Client 的计数器指标返回绝对值。因此，您必须将计数器指标转换为一系列递增和递减的值，并将它们作为仪表数据发送到 StatsD。在下面的代码示例中，我们将计数器指标转换为 diffHistograms 中的递增和递减仪表数据。

在以下示例中，我们每 10 秒将指标发送到 StatsD。此时间安排与 StatsD 的默认 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

当您以 Prometheus 格式检索 Prisma Client 指标时，您可以使用返回的格式，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，请将以下行添加到您的应用程序代码中

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

这将返回如下指标

# 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

histogram 类型的指标在 Prometheus 格式中公开三种不同类型的值

1. 多个用于观察 bucket 的累积计数器。这些计数器带有后缀 _bucket{le="<upper inclusive bound>"}。例如，prisma_datasource_queries_duration_histogram_ms 的计数器公开为 prisma_datasource_queries_duration_histogram_ms_bucket{le="1"}

   当观察到的值小于或等于 bucket 的上限（包含上限）时，Prisma Client 指标会将该 bucket 递增 1。假设您的 bucket 的上限（包含上限）分别为 0、1、5、10 和 50。如果观察到的值为 5，则 Prisma Client 指标会递增第三个 bucket 及以后的 bucket，因为该值大于 0 和大于 1，但小于或等于 5、10 和 50。

2. 所有观察到的值的单个总和。此计数器带有后缀 _sum。例如，prisma_datasource_queries_duration_histogram_ms 的总和公开为 prisma_datasource_queries_duration_histogram_ms_sum。

3. 已观察到的事件数的计数。此计数器带有后缀 _count。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 指标系统一起使用

在大多数情况下，Prometheus 必须抓取端点才能检索指标。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 指标与其他 Prometheus 客户端库组合使用，这些库也与 Express.js 结合使用 REST API 端点提供服务

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

您可以向指标添加全局标签，以帮助您对指标进行分组和分隔。Prisma Client 的每个实例都会将这些标签添加到它生成的指标中。例如，您可以使用类似 { 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，要向 JSON 格式的指标添加全局标签，请将以下代码添加到您的应用程序中

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

这将返回以下格式的信息

{
  "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": [
    ...
  ]
}