Nginx 日志分析可视化面板

songjxin2026-05-30 18:45

先看效果图

实现效果

📊 全局变量说明

在使用看板前，需了解以下两个核心控制变量：

变量名 描述 技术实现
网站 选择目标站点。 变量值直接映射为 ClickHouse 物理表名，实现多业务数据隔离。
时间粒度 设置指标查询的时间步长。 用于控制趋势图的计算精度，粒度越小曲线越平滑，但查询开销越大。

⚠️ 注意：本看板所有统计图表均严格遵循 Grafana 右上角选定的时间范围。

📈 一、流量概览与趋势

1. 请求趋势

展示站点的核心负载情况。

  • 指标：QPS (每秒查询率)、平均响应时间。
  • 逻辑：数据精度受"时间粒度"变量控制。
2. 状态码分布

统计全量 HTTP 状态码的分布情况，用于快速判断服务健康度（如 4xx 客户端错误或 5xx 服务端错误）。

⚙️ 二、上游服务监控

针对 Nginx 反向代理的后端服务进行专项分析：

1. 上游平均响应时长
  • 指标：后端服务器的平均响应耗时。
  • 说明 ：此处不统计 QPS。因为 Nginx 会根据负载均衡策略分发请求，单纯统计上游 QPS 参考意义有限。
2. 上游异常状态码 TOP10
  • 筛选 ：仅统计 >= 400 的错误状态码。
  • 维度：展示具体是哪台上游服务器的哪个 URI 报错最多。

🏆 三、TOP N 访问排行

统计项 说明
URL TOP10 按访问量排序。已排除静态资源 ，并对路径参数进行归一化 处理（如 /user/123 视为 /user/:id）。
来源 Referer TOP10 统计请求来源。若无 Referer，则归类为"直接访问"。
来源 IP TOP10 识别高频访问源 IP。
来源 C段 TOP10 统计 /24 网段（如 192.168.1.*）的访问密度，常用于识别机房爬虫。

💡 归一化逻辑 ：该处理在 Nginx 日志收集 Agent 阶段完成定义，避免 URL 发散导致的统计失效。

🌐 四、终端与地域分析

1. 终端设备分布

基于 User-Agent 进行设备指纹识别（逻辑集成在 Grafana SQL 查询中）：

  • 移动端 ：Mobile, Android, iPhone, iPad
  • 电脑端 ：Windows, Macintosh, Linux, X11
  • 其他：未匹配上述规则的流量。
2. 访问热力图 (Geo Map)
  • 展示：根据客户端 IP 的经纬度在地图上标记访问密度。
  • 精度 ：受限于当前 GeoIP 库，目前仅支持国家级别定位。

📍 GeoIP 库说明 ：

当前使用 GeoLite2-City.mmdb 库。由于数据较旧且未配置网络回源查询，暂不支持城市级统计 。如有需求，可直接替换为更新更全的 .mmdb 文件。

🔍 五、性能诊断与安全

1. 慢请求分析 (200请求平均耗时 TOP10)
  • 目的：排查具体哪个接口拖慢了整体服务。
  • 筛选 ：仅统计返回状态码为 200 的请求（排除错误请求干扰），并按平均耗时倒序排列。
  • 范围：不包含静态资源请求。
2. 疑似攻击请求统计
  • 逻辑：基于预定义的正则表达式规则集，匹配恶意扫描特征（如 SQL 注入、目录遍历、XSS 等）。
  • 实现 ：过滤规则在 日志收集阶段 定义，有效降低存储噪音并提前预警。

实现过程

1. Nginx 配置

nginx 格式化输出，这里直接用json输出，方便vector读取：

复制代码 
        log_format access_json escape=json
        '{'
        '"time":"$time_iso8601",'
        '"remote_addr":"$remote_addr",'
        '"x_forwarded_for":"$http_x_forwarded_for",'
        '"request_id":"$request_id",'
        '"host":"$host",'
        '"method":"$request_method",'
        '"uri":"$uri",'
        '"args":"$args",'
        '"protocol":"$server_protocol",'
        '"status":$status,'
        '"body_bytes_sent":$body_bytes_sent,'
        '"request_time":$request_time,'
        '"upstream_time":"$upstream_response_time",'
        '"upstream_status":"$upstream_status",'
        '"upstream_addr":"$upstream_addr",'
        '"referer":"$http_referer",'
        '"user_agent":"$http_user_agent",'
        '"accept":"$http_accept",'
        '"accept_language":"$http_accept_language",'
        '"accept_encoding":"$http_accept_encoding",'
        '"connection":"$http_connection",'
        '"scheme":"$scheme",'
        '"ssl_protocol":"$ssl_protocol",'
        '"ssl_cipher":"$ssl_cipher",'
        '"server_name":"$server_name",'
        '"pid":"$pid"'
        '}';

2. clickhouse 配置

clickhouse 建表SQL：

复制代码 
CREATE TABLE sitename_nginx_access_logs
(
ingest_time DateTime DEFAULT now(),
parse_error String DEFAULT '',
`time` Nullable(DateTime),
`remote_addr` Nullable(String),
`x_forwarded_for` Nullable(String),
`request_id` Nullable(String),
`host` Nullable(String),
`method` Nullable(String),
`uri` Nullable(String),
`args` Nullable(String),
`protocol` Nullable(String),
`status` Nullable(UInt16),
`body_bytes_sent` Nullable(UInt64),
`request_time` Nullable(Float32),
`upstream_time` Nullable(Float32),
`upstream_status` Nullable(UInt16),
`upstream_addr` Nullable(String),
`referer` Nullable(String),
`user_agent` Nullable(String),
`accept` Nullable(String),
`accept_language` Nullable(String),
`accept_encoding` Nullable(String),
`connection` Nullable(String),
`scheme` Nullable(String),
`ssl_protocol` Nullable(String),
`ssl_cipher` Nullable(String),
`server_name` Nullable(String),
`pid` Nullable(UInt32),
`country` Nullable(String),
`city` Nullable(String),
`is_static` Nullable(UInt8),
`ip_c` Nullable(String),
`attack_score` Nullable(UInt8),
`attack_type` Nullable(String),
`attack_level` Nullable(String),
`geo_lat` Nullable(String),
`geo_lon` Nullable(String),
`uri_normalized` Nullable(String),
INDEX idx_uri_normalized uri_normalized TYPE bloom_filter GRANULARITY 1,
INDEX idx_ip_c ip_c TYPE bloom_filter GRANULARITY 1,
INDEX idx_attack_level attack_level TYPE bloom_filter GRANULARITY 1
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(ingest_time)
ORDER BY (ingest_time)
TTL ingest_time + INTERVAL 180 DAY
SETTINGS index_granularity = 8192;

3. vector 配置

geoip 库下载地址：https://cdn.jsdelivr.net/npm/geolite2-city@1.0.67/

vector 配置文件

复制代码 
# ===============================
# GeoIP，提前下载GEOIP，放在这里
# ===============================
enrichment_tables:
  geoip_table:
    type: geoip
    path: /etc/vector/GeoLite2-City.mmdb
# =========================
# Source: Nginx JSON 日志
# =========================
sources:
  nginx_logs:
    type: file
    include:
      - /var/log/nginx/*.json
    ignore_older: 86400
    fingerprinting:
        strategy: "device_and_inode"




# =========================
# Transform: JSON 解码
# =========================
transforms:
  parse_json:
    type: remap
    inputs: [nginx_logs]
    source: |
      # ---------- JSON ----------
      if !exists(.message) {
        null
      }
      # ---------- JSON ----------
      ., err = parse_json(.message)
      if err != null {
        .parse_error = .
        abort
      }
      if !exists(.uri) {
        null
      }
      if .time == null {
        null
      }
      parsed_timestamp, err = parse_timestamp(.time, format: "%Y-%m-%dT%H:%M:%S%:z")
      # Check if the conversion was successful. Note here that all errors must be handled, more on that later.
      if err == null {
         # Note that the `to_unix_timestamp` expects a `timestamp` argument.
         # The following will compile because `parse_timestamp` returns a `timestamp`.
        .time = to_unix_timestamp(parsed_timestamp)
      } else {
        .parse_error,_ = join([string!(.parse_error), "time_parse_failed",string!(parsed_timestamp)], ",")
        # Conversion failed, in this case use the current time.
        .time = null
      }
      # ---------- status ----------
      val, err = to_int(.status)
      if err != null {
        .parse_error, _ = join([string!(.parse_error), "status_parse_failed",string!(.status)], ",")
        .status = null
      } else {
        .status = val
      }




      # ---------- body_bytes_sent ----------
      val, err = to_int(.body_bytes_sent)
      if err != null && .status == 200 {
        .parse_error, _ = join([string!(.parse_error), "body_bytes_sent_parse_failed",string!(.body_bytes_sent)], ",")
        .body_bytes_sent = null
      } else {
        .body_bytes_sent = val
      }




      # ---------- request_time ----------
      val, err = to_float(.request_time)
      if err != null && .status == 200 {
        .parse_error, _ = join([string!(.parse_error), "request_time_parse_failed",string!(.request_time)], ",")
        .request_time = null
      } else {
        .request_time = val
      }




      # ---------- pid ----------
      val, err = to_int(.pid)
      if err != null {
        .parse_error, _ = join([string!(.parse_error), "pid_parse_failed",string!(.pid)], ",")
        .pid = null
      } else {
        .pid = val
      }




      # ---------- upstream_time ----------
      val, err = to_float(.upstream_time)
      if err != null && .status == 200 {
        .parse_error, _ = join([string!(.parse_error), "upstream_time_parse_failed",string!(.upstream_time)], ",")
        .upstream_time = null
      } else {
        .upstream_time = val
      }




      # ---------- upstream_status ----------
      val, err = to_int(.upstream_status)
      if err != null && .status == 200 {
        .parse_error, _ = join([string!(.parse_error), "upstream_status_parse_failed",string!(.upstream_status)], ",")
        .upstream_status = null
      } else {
        .upstream_status = val
      }
      # =========================
      # GEOIP 
      # =========================
      geo = get_enrichment_table_record!("geoip_table", {
        "ip": .remote_addr
      })
      if geo != null {
        .country = geo.country_name
        .city = .geo.city_name
        .geo_lat = geo.latitude
        .geo_lon = geo.longitude
      } else {
        .geo_country = ""
        .geo_city = ""
      }
      # =========================
      # 识别攻击逻辑可以在这里配置
      # =========================
      # =========================
      # CRS 攻击检测
      # =========================
      payload,err = .uri + " " + .args + " " + .user_agent
      payload = downcase(string(payload))


      .attack_score = 0
      .attack_type = ""


      # =========================
      # SQLi
      # =========================
      if match_any(payload, [
        r'union select',
        r'or 1=1',
        r'sleep\(',
        r'benchmark\(',
        r'information_schema'
      ]) {
        .attack_score = .attack_score + 30
        .attack_type = "sqli"
      }


      if match_any(payload, [
        r'--',
        r'#',
        r'drop table',
        r'delete from',
        r'insert into'
      ]) {
        .attack_score = .attack_score + 20
      }


      # =========================
      # XSS
      # =========================
      if match_any(payload, [
        r'<script',
        r'onerror=',
        r'onload=',
        r'javascript:',
        r'document.cookie'
      ]) {
        .attack_score = .attack_score + 25
        .attack_type = "xss"
      }


      # =========================
      # RCE
      # =========================
      if match_any(payload, [
        r'curl',
        r'wget',
        r'bash',
        r'sh -c',
        r'nc -e',
        r'powershell'
      ]) {
        .attack_score = .attack_score + 40
        .attack_type = "rce"
      }


      # =========================
      # 扫描器识别
      # =========================
      if match_any(string!(.user_agent), [
        r'sqlmap',
        r'nmap',
        r'nikto',
        r'burp',
        r'acunetix'
      ]) {
        .attack_score = .attack_score + 50
        .attack_type = "scanner"
      }


      # =========================
      # LFI / Path Traversal
      # =========================
      if match_any(payload, [
        r'\.\./',
        r'/etc/passwd',
        r'windows\\system32',
        r'proc/self'
      ]) {
        .attack_score = .attack_score + 35
        .attack_type = "lfi"
      }


      # =========================
      # 风险等级
      # =========================
      if .attack_score >= 80 {
        .attack_level = "critical"
      } else if .attack_score >= 50 {
        .attack_level = "high"
      } else if .attack_score >= 20 {
        .attack_level = "medium"
      } else {
        .attack_level = "low"
      }


      # =========================
      # IP /24 归一化
      # =========================
      ip = .remote_addr


      if ip != null {
        parts = split(string!(ip), ".")


        if length(parts) == 4 {
          .ip_c, err = join([parts[0], parts[1], parts[2], "0/24"], ".")
          if err != null {
            .ip_c = "unknown"
          }
        } else {
          .ip_c = "unknown"
        }
      }


      # ===============================
      # 静态资源识别，非静态资源URI规范化
      # ===============================
      if match_any(string!(.uri), [
        r'\.css',
        r'\.js',
        r'\.png',
        r'\.jpg',
        r'\.jpeg',
        r'\.gif',
        r'\.ico',
        r'\.svg',
        r'\.woff',
        r'\.ttf'
      ]){
        .is_static = true
        .uri_normalized = .uri
      } else {
        .is_static = false
        # ========================================
        # 非静态资源URI规范化，其他规范化逻辑再这里写
        # ========================================
        # 先处理 /数字/
        tmp = replace!(.uri, r'/\d+/', "/:id/")
        
        # 再处理结尾 /数字
        tmp = replace(tmp, r'/\d+$', "/:id")
        # 统一 UUID
        .uri_normalized = replace(tmp, r'/[0-9a-fA-F-]{36}', "/:uuid")
      }


# =========================
# Sink: ClickHouse
# =========================
sinks:
  clickhouse:
    type: clickhouse
    inputs: [parse_json]
    endpoint: "https://IP:POART"
    database: default
    table: sitename_nginx_access_logs
    auth:
        strategy: basic
        user: <CK_USER>
        password: <CK_PASS>
    compression: gzip
# =============================================================
#  为了安全，配置了CA认证，如果clickhouse没有配置CA认证，可以不需要
# =============================================================
    tls:
      verify_certificate: true
      ca_file: /etc/vector/rootCA.crt
    batch:
      max_events: 500
      timeout_secs: 1
    encoding:
      timestamp_format: rfc3339

4. grafana 配置

grafana 上创建clickhouse的数据源，然后导入模板：

json 复制代码 
{
    "__inputs": [
    {
      "name": "DS_CK-LOGS",
      "label": "ck-logs",
      "description": "",
      "type": "datasource",
      "pluginId": "grafana-clickhouse-datasource",
      "pluginName": "ClickHouse"
    },
    {
      "name": "DS_EXPRESSION",
      "label": "Expression",
      "description": "",
      "type": "datasource",
      "pluginId": "__expr__"
    }
  ],
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "fiscalYearStartMonth": 0,
  "graphTooltip": 0,
  "id": 0,
  "links": [],
  "panels": [
    {
      "datasource": {
        "type": "grafana-clickhouse-datasource",
        "uid": "${DS_CK-LOGS}"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "custom": {
            "axisBorderShow": false,
            "axisCenteredZero": false,
            "axisColorMode": "text",
            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "barWidthFactor": 0.6,
            "drawStyle": "line",
            "fillOpacity": 0,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "smooth",
            "lineStyle": {
              "fill": "solid"
            },
            "lineWidth": 1,
            "pointSize": 5,
            "scaleDistribution": {
              "type": "linear"
            },
            "showPoints": "auto",
            "showValues": false,
            "spanNulls": false,
            "stacking": {
              "group": "A",
              "mode": "none"
            },
            "thresholdsStyle": {
              "mode": "off"
            }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": 0
              },
              {
                "color": "red",
                "value": 80
              }
            ]
          },
          "unit": "s"
        },
        "overrides": [
          {
            "matcher": {
              "id": "byName",
              "options": "qps"
            },
            "properties": [
              {
                "id": "unit",
                "value": "reqps"
              },
              {
                "id": "custom.axisPlacement",
                "value": "right"
              },
              {
                "id": "custom.barAlignment",
                "value": 0
              },
              {
                "id": "custom.drawStyle",
                "value": "line"
              },
              {
                "id": "custom.barWidthFactor",
                "value": 1
              },
              {
                "id": "custom.fillOpacity",
                "value": 26
              },
              {
                "id": "color",
                "value": {
                  "fixedColor": "dark-green",
                  "mode": "fixed"
                }
              }
            ]
          },
          {
            "matcher": {
              "id": "byName",
              "options": "avg_time_sec"
            },
            "properties": [
              {
                "id": "displayName",
                "value": "平均响应时间"
              },
              {
                "id": "custom.lineWidth",
                "value": 2
              },
              {
                "id": "color",
                "value": {
                  "fixedColor": "yellow",
                  "mode": "fixed",
                  "seriesBy": "last"
                }
              }
            ]
          }
        ]
      },
      "gridPos": {
        "h": 7,
        "w": 11,
        "x": 0,
        "y": 0
      },
      "id": 14,
      "options": {
        "legend": {
          "calcs": [],
          "displayMode": "list",
          "placement": "bottom",
          "showLegend": true
        },
        "tooltip": {
          "hideZeros": false,
          "mode": "single",
          "sort": "none"
        }
      },
      "pluginVersion": "12.3.2",
      "targets": [
        {
          "datasource": {
            "type": "grafana-clickhouse-datasource",
            "uid": "${DS_CK-LOGS}"
          },
          "editorType": "sql",
          "format": 1,
          "meta": {
            "builderOptions": {
              "columns": [],
              "database": "",
              "limit": 1000,
              "mode": "list",
              "queryType": "table",
              "table": ""
            }
          },
          "pluginVersion": "4.17.0",
          "queryType": "table",
          "rawSql": "SELECT\r\n    toDateTime(\r\n        intDiv(toUnixTimestamp(ingest_time), step) * step\r\n    ) AS time_window,\r\n    round(avg(request_time), 3) AS avg_time_sec,\r\n    round(count(*) / any(step), 2) AS qps\r\nFROM \"default\".\"$table_name\"\r\nCROSS JOIN (\r\n    SELECT\r\n        ($__toTime - $__fromTime) / ${step} AS step\r\n) AS _params\r\nWHERE ingest_time BETWEEN toDateTime($__fromTime)\r\n                     AND toDateTime($__toTime)\r\nGROUP BY time_window\r\nORDER BY time_window;",
          "refId": "A"
        }
      ],
      "title": "请求趋势",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "grafana-clickhouse-datasource",
        "uid": "${DS_CK-LOGS}"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "custom": {
            "axisBorderShow": false,
            "axisCenteredZero": false,
            "axisColorMode": "text",
            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "barWidthFactor": 0.6,
            "drawStyle": "line",
            "fillOpacity": 0,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 2,
            "pointSize": 5,
            "scaleDistribution": {
              "type": "linear"
            },
            "showPoints": "auto",
            "showValues": false,
            "spanNulls": false,
            "stacking": {
              "group": "A",
              "mode": "none"
            },
            "thresholdsStyle": {
              "mode": "off"
            }
          },
          "displayName": "${__field.labels.upstream}",
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green",
                "value": 0
              }
            ]
          },
          "unit": "s"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 7,
        "w": 10,
        "x": 11,
        "y": 0
      },
      "id": 13,
      "options": {
        "legend": {
          "calcs": [],
          "displayMode": "list",
          "placement": "bottom",
          "showLegend": true
        },
        "tooltip": {
          "hideZeros": false,
          "mode": "single",
          "sort": "none"
        }
      },
      "pluginVersion": "12.3.2",
      "targets": [
        {
          "datasource": {
            "type": "grafana-clickhouse-datasource",
            "uid": "${DS_CK-LOGS}"
          },
          "editorType": "sql",
          "format": 0,
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