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") then
action(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 name
set $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-ms
set $timestamp $1$2;
# 时间戳-s
set $times $1;
# 格式化之后的时间ms
set $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 timestamp
TTL 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
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"text": "ClickHouse_1.178",
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"label": "datasource",
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{
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"text": "atscontrol",
"value": "atscontrol"
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"type": "grafana-clickhouse-datasource",
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"definition": "select DISTINCT upstream_name from app_logs.nginx_gw_log",
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"label": "upstream_name",
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"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",
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"name": "nginx_host",
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最终呈现效果
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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