nginx+rsyslog+kafka+clickhouse+grafana 实现nginx 网关监控

需求

  • 我想做一个类似腾讯云网关日志最终以仪表方式呈现，比如说qps、p99、p95的请求响应时间等等

流程图

数据流转就像标题
nginx ----> rsyslog ----> kafka —> clickhouse —> grafana

部署

kafka

kafka 相关部署这里不做赘述，只要创建一个topic 就可以
这里kafka地址是 192.168.1.180,topic是``

rsyslog 设置

rsyslog 具体是啥东西这个我这里也不做介绍，本人也是一个后端开发不是做运维的，只知道这个东西性能不错，算是logstash 平替把

# 安装rsyslog-kafka 插件
yum install -y rsyslog-kafka
# 创建一个配置vim /etc/rsyslog.d/rsyslog_nginx_kafka_cluster.conf

conf 内容

module(load="imudp")
input(type="imudp" port="514")# nginx access log ==> rsyslog server(local) ==> kafka
module(load="omkafka")template(name="nginxLog" type="string" string="%msg%")if $inputname == "imudp" then {# 这里的名字和下面nginx 配置tag 相同if ($programname == "nginx_access_log") thenaction(type="omkafka"template="nginxLog"# kafka 地址broker=["192.168.1.180:9092"]# topic 名字topic="rsyslog_nginx"partitions.auto="on"confParam=["socket.keepalive.enable=true"])
}:rawmsg, contains, "nginx_access_log" ~

最后重启rsyslog 即可

nginx

http 节点中设置如下

log_format jsonlog '{''"host": "$host",''"server_addr": "$server_addr",''"remote_addr":"$remote_addr",''"time_format":"$time_ms",''"time_sec":$times,''"timestamp":$timestamp,''"method":"$request_method",''"request_url":"$request_uri",''"status":$status,''"upstream_name":"$upstream_name",''"http_user_agent":"$http_user_agent",''"upstream_addr":"$upstream_addr",''"trace_id":"$http_traceid",''"upstream_status":"$upstream_status",''"upstream_response_time":"$upstream_response_time",''"request_time":$request_time,''"nginx_host":"$nginx_host"''}';# 配置rsyslog，tag 和上文rsyslog 中相同！！！access_log syslog:server=192.168.1.179,facility=local7,tag=nginx_access_log,severity=info jsonlog;#access_log  /var/log/nginx/access.log  jsonlog;

配置实际转发upstream

upstream testcontrol{server 192.168.10.123:8081;
}server {listen       18081;server_name  _;# 设置up nameset $upstream_name 'testcontrol';include /etc/nginx/conf/time.conf;location / {proxy_set_header traceid $http_traceid;proxy_set_header n-t $msec;#proxy_set_header X-Real-IP $remote_addr;proxy_pass http://testcontrol;}location ~ /status {stub_status   on;}
}

/etc/nginx/conf/time.conf; 配置

set $nginx_host '192.168.8.64';
if ($time_iso8601 ~ "^(\d{4})-(\d{2})-(\d{2})T(\d{2}):(\d{2}):(\d{2})") {set $year $1;set $month $2;set $day $3;set $hour $4;set $minutes $5;set $seconds $6;set $time_sec "$1-$2-$3 $4:$5:$6";}# 获取毫秒时间戳，并拼接到$time_zh之后if ($msec ~ "(\d+)\.(\d+)") {# 时间戳-msset $timestamp $1$2;# 时间戳-sset $times $1;# 格式化之后的时间msset $time_ms $time_sec.$2;# traceId，方便查日志set $http_traceid $nginx_host#$timestamp#$request_id;
}

clickhouse

先创建一个 nginx_gw_log 表


create table app_logs.nginx_gw_log
(timestamp              DateTime64(3, 'Asia/Shanghai'),server_addr            String,remote_addr            String,time_sec               DateTime('Asia/Shanghai'),method                 String,request_url            String,status                 Int16,upstream_name          String,http_user_agent        String,upstream_addr          String,trace_id               String,upstream_status        String,upstream_response_time String,request_time           Int32,nginx_host              String,host                   String
) engine = MergeTree PARTITION BY toYYYYMMDD(timestamp)ORDER BY timestampTTL toDateTime(timestamp) + toIntervalDay(1);

上面设置了过期1天，可以自由修改
添加kafka 引擎

create table app_logs.nginx_gw_log_kafka
(timestamp              DateTime64(3, 'Asia/Shanghai'),server_addr            String,remote_addr            String,time_sec               DateTime('Asia/Shanghai'),method                 String,request_url            String,status                 Int32,upstream_name          String,http_user_agent        String,upstream_addr          String,trace_id               String,upstream_status        String,upstream_response_time String,request_time           Decimal32(3),nginx_host              String,host                   String
) engine = Kafka()SETTINGS kafka_broker_list = '192.168.1.180:9092',kafka_topic_list = 'rsyslog_nginx',kafka_group_name = 'nginx_ck',kafka_format = 'JSONEachRow',kafka_skip_broken_messages = 100000,kafka_num_consumers = 1;

最后设置mv

CREATE MATERIALIZED VIEW app_logs.nginx_gw_log_mv TO app_logs.nginx_gw_log AS
SELECT timestamp,server_addr,remote_addr,time_sec,method,request_url,status,upstream_name,http_user_agent,upstream_addr,trace_id,upstream_status,upstream_response_time,toInt32(multiply(request_time, 1000)) as request_time,host
FROM app_logs.nginx_gw_log_kafka;

这里做了一个类型转换，因为nginx，request-time 单位是s，我想最终呈现在grafana 中是ms，所以这里做了转换，当然grafana中也可以做

上面都配置完了之后可以先验证下，保证数据最终到ck，如果有问题，需要再每个节点调试，比如先调试nginx->rsyslog ，可以先不配置kafka 输出，配置为console或者文件输出都可以，具体这里就不写了

grafana

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"datasource","options": [],"query": "grafana-clickhouse-datasource","queryValue": "","refresh": 1,"regex": "","skipUrlSync": false,"type": "datasource"},{"current": {"selected": true,"text": "atscontrol","value": "atscontrol"},"datasource": {"type": "grafana-clickhouse-datasource","uid": "${datasource}"},"definition": "select DISTINCT upstream_name from app_logs.nginx_gw_log","hide": 0,"includeAll": false,"label": "upstream_name","multi": false,"name": "upstream_name","options": [],"query": "select DISTINCT upstream_name from app_logs.nginx_gw_log","refresh": 2,"regex": "","skipUrlSync": false,"sort": 0,"type": "query"},{"current": {"isNone": true,"selected": false,"text": "None","value": ""},"datasource": {"type": "grafana-clickhouse-datasource","uid": "${datasource}"},"definition": "select DISTINCT nginx_host from app_logs.nginx_gw_log where upstream_name = \"$upstream_name\"","hide": 0,"includeAll": false,"label": "nginx_host","multi": false,"name": "nginx_host","options": [],"query": "select DISTINCT nginx_host from app_logs.nginx_gw_log where upstream_name = \"$upstream_name\"","refresh": 1,"regex": "","skipUrlSync": false,"sort": 0,"type": "query"}]},"time": {"from": "now-5m","to": "now"},"timepicker": {},"timezone": "","title": "nginx_gw","uid": "lZrbSYOIkA","version": 2,"weekStart": ""
}

最终呈现效果

在这里插入图片描述

隐患点

  • clickhouse 本身查询qps 就不高，如果数据量很大可以考虑集群或者其他的存储，doris、es等等
  • 不知道nginx+rsyslog 对性能有多少影响，目前测试单机nginx大几千的qps 都没啥问题

有其他更好方案的小伙伴留言哦

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