凌晨两点,验证码识别队列悄悄堆到 300+。直到第二天早上运营问”采集任务怎么全卡住了”,你才后知后觉。
把解决量、成功率、延迟和余额这几个信号接入 Datadog,管道真正断掉的那一刻就收到告警,而不是靠用户投诉倒推故障。
该监控哪些 CaptchaAI 指标
下面 7 个指标基本能覆盖一条验证码识别管道的健康状况:
| 指标 | 类型 | 作用 |
|---|---|---|
captcha.solve.count |
计数器 | 提交任务总数 |
captcha.solve.success |
计数器 | 成功解决的次数 |
captcha.solve.error |
计数器 | 失败次数(按错误类型细分) |
captcha.solve.latency |
直方图 | 从提交到出结果的耗时 |
captcha.queue.depth |
仪表值 | 队列中排队的任务数 |
captcha.balance |
仪表值 | 剩余 API 余额 |
captcha.worker.active |
仪表值 | 在线工作进程数 |
指标异常排查
常见坑:
- 指标不显示:DogStatsD agent 未运行。检查
DD_AGENT_HOST;用docker ps确认 agent 容器状态。 - 延迟直方图为空:没有解决成功的请求被记录。确认
statsd.histogram()在成功分支里被调用。 - 标签丢失:标签格式写错。使用
key:value格式,标签内不要有空格。 - 指标重复上报:多个上报进程同时在跑。每次部署只保留一个余额上报进程。
Python 集成:用 DogStatsD 自动埋点
下面用一个装饰器包住 solve 函数,自动上报提交次数、成功率和延迟:
import os
import time
import functools
import requests
from datadog import initialize, statsd
# Initialize Datadog
initialize(
statsd_host=os.environ.get("DD_AGENT_HOST", "localhost"),
statsd_port=int(os.environ.get("DD_DOGSTATSD_PORT", "8125"))
)
API_KEY = os.environ["CAPTCHAAI_API_KEY"]
session = requests.Session()
def track_captcha_metrics(captcha_type="recaptcha_v2"):
"""Decorator to track solve metrics."""
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
tags = [f"captcha_type:{captcha_type}"]
statsd.increment("captcha.solve.count", tags=tags)
start = time.time()
try:
result = func(*args, **kwargs)
elapsed = time.time() - start
if "solution" in result:
statsd.increment("captcha.solve.success", tags=tags)
statsd.histogram("captcha.solve.latency", elapsed, tags=tags)
else:
error = result.get("error", "unknown")
statsd.increment(
"captcha.solve.error",
tags=tags + [f"error:{error}"]
)
return result
except Exception as e:
statsd.increment(
"captcha.solve.error",
tags=tags + [f"error:{type(e).__name__}"]
)
raise
return wrapper
return decorator
@track_captcha_metrics(captcha_type="recaptcha_v2")
def solve_recaptcha(sitekey, pageurl):
resp = session.post("https://ocr.captchaai.com/in.php", data={
"key": API_KEY,
"method": "userrecaptcha",
"googlekey": sitekey,
"pageurl": pageurl,
"json": 1
})
data = resp.json()
if data.get("status") != 1:
return {"error": data.get("request")}
captcha_id = data["request"]
for _ in range(60):
time.sleep(5)
result = session.get("https://ocr.captchaai.com/res.php", params={
"key": API_KEY, "action": "get", "id": captcha_id, "json": 1
}).json()
if result.get("status") == 1:
return {"solution": result["request"]}
if result.get("request") != "CAPCHA_NOT_READY":
return {"error": result.get("request")}
return {"error": "TIMEOUT"}
def report_balance():
"""Send balance as a gauge metric."""
resp = session.get("https://ocr.captchaai.com/res.php", params={
"key": API_KEY, "action": "getbalance", "json": 1
})
data = resp.json()
if data.get("status") == 1:
balance = float(data["request"])
statsd.gauge("captcha.balance", balance)
return balance
return None
def report_queue_depth(depth):
"""Report current queue depth."""
statsd.gauge("captcha.queue.depth", depth)
def report_worker_count(active, total):
"""Report worker health."""
statsd.gauge("captcha.worker.active", active)
statsd.gauge("captcha.worker.total", total)
国内安装慢就换清华镜像:pip install -i https://pypi.tuna.tsinghua.edu.cn/simple datadog。
JavaScript 集成:Node.js 上报指标
Node.js 用 hot-shots 接入 DogStatsD,思路和 Python 版一致:
const { StatsD } = require("hot-shots");
const axios = require("axios");
const API_KEY = process.env.CAPTCHAAI_API_KEY;
const dogstatsd = new StatsD({
host: process.env.DD_AGENT_HOST || "localhost",
port: parseInt(process.env.DD_DOGSTATSD_PORT || "8125", 10),
prefix: "captcha.",
globalTags: [`env:${process.env.NODE_ENV || "development"}`],
});
async function solveCaptchaWithMetrics(sitekey, pageurl, captchaType = "recaptcha_v2") {
const tags = [`captcha_type:${captchaType}`];
dogstatsd.increment("solve.count", 1, tags);
const startTime = Date.now();
try {
const result = await solveCaptcha(sitekey, pageurl);
const elapsed = (Date.now() - startTime) / 1000;
if (result.solution) {
dogstatsd.increment("solve.success", 1, tags);
dogstatsd.histogram("solve.latency", elapsed, tags);
} else {
dogstatsd.increment("solve.error", 1, [...tags, `error:${result.error}`]);
}
return result;
} catch (err) {
dogstatsd.increment("solve.error", 1, [...tags, `error:${err.message}`]);
throw err;
}
}
async function solveCaptcha(sitekey, pageurl) {
const submitResp = await axios.post("https://ocr.captchaai.com/in.php", null, {
params: {
key: API_KEY,
method: "userrecaptcha",
googlekey: sitekey,
pageurl: pageurl,
json: 1,
},
});
if (submitResp.data.status !== 1) {
return { error: submitResp.data.request };
}
const captchaId = submitResp.data.request;
for (let i = 0; i < 60; i++) {
await new Promise((r) => setTimeout(r, 5000));
const pollResp = await axios.get("https://ocr.captchaai.com/res.php", {
params: { key: API_KEY, action: "get", id: captchaId, json: 1 },
});
if (pollResp.data.status === 1) return { solution: pollResp.data.request };
if (pollResp.data.request !== "CAPCHA_NOT_READY") {
return { error: pollResp.data.request };
}
}
return { error: "TIMEOUT" };
}
async function reportBalance() {
try {
const resp = await axios.get("https://ocr.captchaai.com/res.php", {
params: { key: API_KEY, action: "getbalance", json: 1 },
});
if (resp.data.status === 1) {
const balance = parseFloat(resp.data.request);
dogstatsd.gauge("balance", balance);
return balance;
}
} catch (err) {
console.error("Balance check failed:", err.message);
}
return null;
}
// Report balance every minute
setInterval(reportBalance, 60000);
module.exports = { solveCaptchaWithMetrics, reportBalance };
导入这份 JSON,快速搭出监控仪表板
把下面的 JSON 粘贴到 Datadog“导入 JSON”面板,直接拿到一个能用的监控看板:
{
"title": "CaptchaAI Pipeline",
"widgets": [
{
"definition": {
"type": "timeseries",
"title": "Solve Rate (Success vs Error)",
"requests": [
{"q": "sum:captcha.solve.success{*}.as_count()"},
{"q": "sum:captcha.solve.error{*}.as_count()"}
]
}
},
{
"definition": {
"type": "timeseries",
"title": "Solve Latency (p50, p95, p99)",
"requests": [
{"q": "avg:captcha.solve.latency{*}"},
{"q": "percentile:captcha.solve.latency{*},0.95"},
{"q": "percentile:captcha.solve.latency{*},0.99"}
]
}
},
{
"definition": {
"type": "query_value",
"title": "API Balance",
"requests": [{"q": "avg:captcha.balance{*}"}]
}
},
{
"definition": {
"type": "timeseries",
"title": "Queue Depth",
"requests": [{"q": "avg:captcha.queue.depth{*}"}]
}
}
]
}
告警规则怎么配
光看仪表板不够,得有人在管道断掉前被叫醒:
- 余额不足(警告):
captcha.balance < 10 - 余额告急(严重):
captcha.balance < 2 - 错误率过高(警告):5 分钟内错误率超过 10%
- 延迟突增(警告):p95 延迟连续 10 分钟超过 120 秒
- 队列积压(警告):队列深度连续 5 分钟增长且超过 100
- 工作进程离线(严重):
captcha.worker.active == 0
# Datadog monitor definition (API create)
- type: metric alert
name: "CaptchaAI Low Balance"
query: "avg(last_5m):avg:captcha.balance{*} < 10"
message: "CaptchaAI balance is low: {{value}}. Top up to avoid solve failures."
tags:
- team:scraping
- service:captcha
常见问题
一台主机上跑多个 worker,要装几个 Datadog agent?
一个就够。同一台主机上所有 worker 把指标发到本地这一个 agent,再统一转发给 Datadog。
从国内网络上报指标到 Datadog,会不会经常超时?
会有一定延迟,接收端点在海外,国内网络偶尔抖动很正常。agent 部署在服务器本地先聚合再批量转发,能明显减少超时影响。
要不要按 CaptchaAI 套餐的线程数设置容量告警?
值得加一条。比如用 STANDARD($30/月,15 线程),captcha.queue.depth 持续超过线程数数倍时,该升级 ADVANCE($90/月,50 线程),而不是先查错误率。
下一步
把可观测性接入识别管道——申请 CaptchaAI API Key,按上面的步骤接入 Datadog。