# APIG仪表盘模板
APIG（API Gateway）提供高性能、高可用、高安全的API托管服务，能快速将企业服务能力包装成标准API服务，帮助您轻松构建、管理和部署任意规模的API，并上架API云商店进行售卖。借助API网关，可以简单、快速、低成本、低风险地实现内部系统集成、业务能力开放及业务能力变现。API网关帮助您变现服务能力的同时，降低企业研发投入，让您专注于企业核心业务，提升运营效率。
APIG仪表盘模板支持[查看APIG访问中心]、[查看APIG监控中心]、[分析APIG秒级监控]。
#### 前提条件
- 已采集APIG日志，详情请参见[API网关APIG接入LTS](https://support.huaweicloud.com/usermanual-lts/lts_04_0506.html)。
- 日志配置结构化，详情请参见[设置云端结构化解析日志](https://support.huaweicloud.com/usermanual-lts/lts_07_0079.html)。
 
 #### 查看APIG访问中心
1. 登录[云日志服务控制台](https://console.huaweicloud.com/lts/?#/cts/manager/groups)，进入"日志管理"页面。
2. 在左侧导航栏中选择"仪表盘"。
3. 在仪表盘模板下方，选择"APIG仪表盘模板 \> APIG访问中心"，查看图表详情。 
   图1APIG访问中心   
   ![](https://support.huaweicloud.com/usermanual-lts/zh-cn_image_0000002498357530.png "点击放大")
   - 过滤请求域名，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(host)
     ```
     
   
   - 过滤app_id，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(app_id)
     ```
     
   
   - **PV对比昨日** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT diff[1] as "total", round((diff[1] - diff[2]) / diff[2] * 100, 2) as inc from(select compare( "pv" , 86400) as diff from (select count(1) as "pv" from log))
     ```
     
   
   - **PV对比上周** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT diff[1] as "total", round((diff[1] - diff[2]) / diff[2] * 100, 2) as inc from(select compare( "pv" , 604800) as diff from (select count(1) as "pv" from log))
     ```
     
   
   - **UV对比昨日** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT diff[1] as "total", round((diff[1] - diff[2]) / diff[2] * 100, 2) as inc from(select compare( "uv" , 86400) as diff from (select APPROX_COUNT_DISTINCT(my_remote_addr) as "uv" from log))
     ```
     
   
   - **UV对比上周** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT diff[1] as "total", round((diff[1] - diff[2]) / diff[2] * 100, 2) as inc from(select compare( "uv" , 604800) as diff from (select APPROX_COUNT_DISTINCT(my_remote_addr) as "uv" from log))
     ```
     
   
   - **访问量PV分布(中国)** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT ip_to_province(my_remote_addr) as province, sum(ori_pv) as pv from (select my_remote_addr, count(1) as ori_pv  group by my_remote_addr  ORDER BY ori_pv desc  LIMIT 10000)  where IP_TO_COUNTRY (my_remote_addr) = '中国'  group by province HAVING province not in ('','保留地址','*')
     ```
     
   
   - **访问量PV分布(世界)** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT ip_to_country(my_remote_addr) as country,sum(ori_pv) as PV from (select my_remote_addr, count(1) as ori_pv  group by my_remote_addr  ORDER BY ori_pv desc  LIMIT 10000) GROUP BY country HAVING country not in ('','保留地址','*')
     ```
     
   
   - **平均时延分布（中国）** 所关联的查询分析语句如下所示：
     ```
     * | SELECT province,round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)"FROM (SELECT ip_to_province(my_remote_addr) as province,sum(rt)/sum(ori_pv) * 1000 AS "平均延迟(ms)" from (select my_remote_addr, sum(request_time) as rt,count(1) as ori_pv group by my_remote_addr  ORDER BY ori_pv desc   LIMIT 10000) WHERE  IP_TO_COUNTRY (my_remote_addr) = '中国' GROUP BY province )  where province not in ('','保留地址','*')
     ```
     
   
   - **平均时延分布（世界）** 所关联的查询分析语句如下所示：
     ```
     * | SELECT country,round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 2 ) AS "平均延迟(ms)"FROM (SELECT ip_to_country(my_remote_addr) as country,sum(rt)/sum(ori_pv)  * 1000 AS "平均延迟(ms)" from (select my_remote_addr, sum(request_time) as rt,count(1) as ori_pv  group by my_remote_addr  ORDER BY ori_pv desc  LIMIT 10000) GROUP BY country )where  country not in ('','保留地址','*')
     ```
     
   
   - **今日PV/UV** 所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT600S') AS _time_ , count(1) as PV,  APPROX_COUNT_DISTINCT(my_remote_addr) as UV from log group by _time_ order by _time_
     ```
     
   
   - **区域访问TOP10(省份)** 所关联的查询分析语句如下所示：
     ```
     * | SELECT ip_to_province(my_remote_addr) as "province", sum(ori_pv) as "访问次数" from(select my_remote_addr, count(1) as ori_pv  group by my_remote_addr  ORDER BY ori_pv desc  LIMIT 10000)group by "province" HAVING "province" <> '-1' order by "访问次数" desc limit 10
     ```
     
   
   - **区域访问TOP10(城市)** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT ip_to_city(my_remote_addr) as "city", sum(ori_pv) as "访问次数" from(select my_remote_addr, count(1) as ori_pv  group by my_remote_addr  ORDER BY ori_pv desc  LIMIT 10000) group by "city" HAVING  "city" <> '-1' order by "访问次数" desc  limit 10
     ```
     
   
   - **Host访问TOP10** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT  host as "Host", count(1) as "PV" group by "Host" order by "PV" desc limit 10
     ```
     
   
   - **UserAgent访问TOP10** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT http_user_agent as "UserAgent", count(1) as "PV" group by "UserAgent" order by "PV" desc limit 10
     ```
     
   
   - **设备占比(终端)** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT case when regexp_like(lower(http_user_agent), 'iphone|ipod|android|ios') then '移动端' else 'PC端' end as type , count(1) as total group by  type
     ```
     
   
   - **设备占比(系统)** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT case when regexp_like(lower(http_user_agent), 'iphone|ipod|ios') then 'IOS' when regexp_like(lower(http_user_agent), 'android') then 'Android' else 'other' end as type , count(1) as total group by  type HAVING type != 'other'
     ```
     
   
   - **TOP URL** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT router_uri , count(1) as pv, APPROX_COUNT_DISTINCT(my_remote_addr) as UV, round(sum( case when status < 400 then 1 else 0 end   )  * 100.0 / count(1), 2) as "访问成功率" group by router_uri ORDER by pv desc
     ```
     
   
   - **TOP 访问IP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT my_remote_addr as "来源IP",ip_to_country(my_remote_addr) as "国家",ip_to_province(my_remote_addr) as "省份",ip_to_city(my_remote_addr) as "城市",ip_to_provider(my_remote_addr) as "运营商",count(1) as "PV" group by my_remote_addr ORDER by "PV" desc limit 100
     ```
     
   
   
   
   
 
 #### 查看APIG监控中心
1. 在仪表盘模板下方，选择"APIG仪表盘模板 \> APIG监控中心"，查看图表详情。 
   图2APIG监控中心   
   ![](https://support.huaweicloud.com/usermanual-lts/zh-cn_image_0000002498358262.png "点击放大")
   - 过滤请求域名，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(host)
     ```
     
   
   - 过滤app_id，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(app_id)
     ```
     
   
   - **访问量PV** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL ( __time, 'PT300S' ) AS _time_, count( 1 ) AS PV FROM log GROUP BY _time_ order by _time_
     ```
     
   
   - **请求成功率** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT ROUND(sum(case when status < 400 then 1 else 0 end) * 100.0 / count(1),2) as cnt
     ```
     
   
   - **平均延迟** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT round(avg(request_time) * 1000, 3) as cnt
     ```
     
   
   - **4XX请求数** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT COUNT(1) as cnt WHERE "status" >= 400 and "status" < 500
     ```
     
   
   - **404请求数** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT COUNT(1) as cnt WHERE "status" = 404
     ```
     
   
   - **429请求数** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT COUNT(1) as cnt WHERE "status" = 429
     ```
     
   
   - **504请求数** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT COUNT(1) as cnt WHERE "status" = 504
     ```
     
   
   - **5XX请求数** 图表所关联的查询分析语句如下所示：
     ```
     status" >= 500 | SELECT TIME_CEIL ( TIME_PARSE(time_local, 'dd/MMM/yyyy:HH:mm:ss ZZZ'), 'PT300S' ) AS _time_, count( 1 ) AS cnt FROM log GROUP BY _time_ order by _time_
     ```
     
   
   - **状态码分布** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT status, COUNT(1) AS rm GROUP BY status
     ```
     
   
   - **访问量UV** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT600S') AS _time_ , APPROX_COUNT_DISTINCT(my_remote_addr) as UV  from log group by _time_ order by _time_
     ```
     
   
   - **流量** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT600S') AS _time_,sum(request_length) / 1024.0 AS "入流量/KB",sum(bytes_sent) / 1024.0 AS "出流量/KB" group by  _time_ order by _time_
     ```
     
   
   - **访问失败率** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT600S') AS _time_,sum(case when status >= 400 then 1 else 0 end) * 100.0 / count(1) as "失败率" , sum(case when status >=500 THEN 1 ELSE 0 END)*100.0/COUNT(1) as "5XX比例" group by  _time_ order by _time_
     ```
     
   
   - **延迟** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT600S') as _time_,avg(request_time) * 1000 as "平均", APPROX_QUANTILE_DS("request_time", 0.50)*1000 as "P50", APPROX_QUANTILE_DS("request_time", 0.90)*1000 as "P90" ,APPROX_QUANTILE_DS("request_time", 0.99)*1000 as 'P99',APPROX_QUANTILE_DS("request_time", 0.9999)*1000 as 'P9999' group by  _time_ order by _time_
     ```
     
   
   - **Host请求TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT "host", pv, uv, round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "入流量(KB)" > 0 THEN "入流量(KB)" ELSE 0 END, 3 ) AS "入流量(KB)", round( CASE WHEN "出流量(KB)" > 0 THEN "出流量(KB)" ELSE 0 END, 3 ) AS "出流量(KB)"  FROM ( SELECT "host", count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( my_remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", sum( request_length ) / 1024.0 AS "入流量(KB)", sum( bytes_sent ) / 1024.0 AS "出流量(KB)"  WHERE "host" != ''  GROUP BY "host" ) ORDER BY pv DESC
     ```
     
   
   - **Host延迟TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT "host", pv, round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)", round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)" FROM ( SELECT "host", count( 1 ) AS pv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)",APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != ''  GROUP BY "host" ) ORDER BY "平均延迟(ms)" desc
     ```
     
   
   - **Host失败率TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT "host", pv,round( CASE WHEN "访问失败率(%)" > 0 THEN "访问失败率(%)" ELSE 0 END, 2 ) AS "访问失败率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)", round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)"  FROM ( SELECT "host", count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问失败率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != ''  GROUP BY "host"  ) ORDER BY "访问失败率(%)" desc
     ```
     
   
   - **URL请求TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT upstream_uri, pv,uv, round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "入流量(KB)" > 0 THEN "入流量(KB)" ELSE 0 END, 3 ) AS "入流量(KB)", round( CASE WHEN "出流量(KB)" > 0 THEN "出流量(KB)" ELSE 0 END, 3 ) AS "出流量(KB)"  FROM ( SELECT upstream_uri, count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( my_remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", sum( request_length ) / 1024.0 AS "入流量(KB)", sum( bytes_sent ) / 1024.0 AS "出流量(KB)"  WHERE "host" != ''  GROUP BY upstream_uri  ) ORDER BY pv desc
     ```
     
   
   - **URL失败率TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT upstream_uri, pv, round( CASE WHEN "访问失败率(%)" > 0 THEN "访问失败率(%)" ELSE 0 END, 2 ) AS "访问失败率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)", round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)" FROM( SELECT upstream_uri, count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问失败率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != '' GROUP BY upstream_uri  ) ORDER BY "访问失败率(%)" desc
     ```
     
   
   - **后端请求TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT addr, pv, uv, round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "入流量(KB)" > 0 THEN "入流量(KB)" ELSE 0 END, 3 ) AS "入流量(KB)", round( CASE WHEN "出流量(KB)" > 0 THEN "出流量(KB)" ELSE 0 END, 3 ) AS "出流量(KB)"  FROM ( SELECT my_remote_addr as addr, count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( my_remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", sum( request_length ) / 1024.0 AS "入流量(KB)", sum( bytes_sent ) / 1024.0 AS "出流量(KB)"  WHERE "host" != ''  GROUP BY addr  having length(my_remote_addr) > 2) ORDER BY "pv" desc
     ```
     
   
   - **后端延迟TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT addr,pv,round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)",round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)",round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)",round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)" FROM (SELECT my_remote_addr as addr,count( 1 ) AS pv,sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)",avg( request_time ) * 1000 AS "平均延迟(ms)",APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)",APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != '' and "my_remote_addr" != '-' GROUP BY addr ) ORDER BY "平均延迟(ms)" desc
     ```
     
   
   - **后端失败率TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT addr, pv, round( CASE WHEN "访问失败率(%)" > 0 THEN "访问失败率(%)" ELSE 0 END, 2 ) AS "访问失败率(%)", round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)", round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)", round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)"  FROM ( SELECT my_remote_addr as addr, count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问失败率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != '' and "my_remote_addr" != '-' GROUP BY addr) ORDER BY "访问失败率(%)" desc
     ```
     
   
   - **URL延迟TOP** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT upstream_uri, pv,round( CASE WHEN "访问成功率(%)" > 0 THEN "访问成功率(%)" ELSE 0 END, 2 ) AS "访问成功率(%)",round( CASE WHEN "平均延迟(ms)" > 0 THEN "平均延迟(ms)" ELSE 0 END, 3 ) AS "平均延迟(ms)",round( CASE WHEN "P90延迟(ms)" > 0 THEN "P90延迟(ms)" ELSE 0 END, 3 ) AS "P90延迟(ms)",round( CASE WHEN "P99延迟(ms)" > 0 THEN "P99延迟(ms)" ELSE 0 END, 3 ) AS "P99延迟(ms)" FROM (SELECT upstream_uri, count( 1 ) AS pv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "访问成功率(%)", avg( request_time ) * 1000 AS "平均延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90延迟(ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99延迟(ms)" WHERE "host" != ''  GROUP BY upstream_uri  ) ORDER BY "平均延迟(ms)" desc
     ```
     
   
   
   
   
 
 #### 查看APIG秒级监控
1. 在仪表盘模板下方，选择"APIG仪表盘模板 \> APIG秒级监控"，查看图表详情。 
   图3APIG秒级监控   
   ![](https://support.huaweicloud.com/usermanual-lts/zh-cn_image_0000002530278453.png "点击放大")
   - 过滤请求域名，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(host)
     ```
     
   
   - 过滤app_id，所关联的查询分析语句如下所示：
     ```
     * | SELECT distinct(app_id)
     ```
     
   
   - **QPS** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT1S') AS _time_ , COUNT(*) as QPS from log group by _time_ order by _time_ limit 100000
     ```
     
   
   - **成功率** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT5S') as "time", cast(sum(case when status < 400 then 1 else 0 end) * 100.0 / count(1) as double PRECISION) as '成功率' from log group by "time" order by "time" limit 10000
     ```
     
   
   - **延迟** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT5S') as "time", avg(request_time)* 1000.0 as "访问延迟",avg(upstream_response_time)* 1000.0 as "Upstream延迟" from log group by "time" order by time  limit 10000
     ```
     
   
   - **流量** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL(__time,'PT5S') as "time" ,  cast(sum("request_length") / 1024.0 as double PRECISION) as "请求流量",  cast(sum("body_bytes_sent") / 1024.0 as double PRECISION) as "返回body流量" group by "time" order by "time" limit 10000
     ```
     
   
   - **状态码** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL ( __time, 'PT5S' ) AS "time", SUM( CASE WHEN "status" >= 200 AND "status" < 300 THEN 1 ELSE 0 END ) AS "2XX", SUM( CASE WHEN "status" >= 300 AND "status" < 400 THEN 1 ELSE 0 END ) AS "3XX", SUM( CASE WHEN "status" >= 400 AND "status" < 500 THEN 1 ELSE 0 END ) AS "4XX", SUM( CASE WHEN "status" >= 500 AND "status" < 600 THEN 1 ELSE 0 END ) AS "5XX", SUM( CASE WHEN "status" < 200 OR "status" >= 600 THEN 1 ELSE 0 END ) AS "其他" FROM log  WHERE TIME_PARSE ( time_local, 'dd/MMM/yyyy:HH:mm:ss ZZ' ) IS NOT NULL GROUP BY "time"  ORDER BY "time" ASC LIMIT 100000
     ```
     
   
   - **后端响应码** 图表所关联的查询分析语句如下所示：
     ```
     * | SELECT TIME_CEIL ( __time, 'PT5S' ) AS "time", SUM( CASE WHEN "upstream_status" >= 200 AND "upstream_status" < 300 THEN 1 ELSE 0 END ) AS "2XX", SUM( CASE WHEN "upstream_status" >= 300 AND "upstream_status" < 400 THEN 1 ELSE 0 END ) AS "3XX", SUM( CASE WHEN "upstream_status" >= 400 AND "upstream_status" < 500 THEN 1 ELSE 0 END ) AS "4XX", SUM( CASE WHEN "upstream_status" >= 500 AND "upstream_status" < 600 THEN 1 ELSE 0 END ) AS "5XX", SUM( CASE WHEN "upstream_status" < 200 OR "upstream_status" >= 600 THEN 1 ELSE 0 END ) AS "其他" FROM log  WHERE TIME_PARSE ( time_local, 'dd/MMM/yyyy:HH:mm:ss ZZ' ) IS NOT NULL GROUP BY "time"  ORDER BY "time" ASC LIMIT 100000
     ```