Help Center/ Log Tank Service/ User Guide/ Log Visualization/ Visualizing Logs in Dashboards/ Dashboard Templates/ Nginx Dashboard Templates

Updated on 2024-11-18 GMT+08:00

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Nginx Dashboard Templates

LTS can collect Nginx logs and analyze them from multiple dimensions. LTS provides a one-stop wizard to collect Nginx logs. It also enables structuring and offers dashboards for Nginx logs. Nginx is a high-performance HTTP and reverse proxy web server that also provides IMAP, POP3, and SMTP services.

Nginx dashboard templates support Viewing Nginx Monitoring by the Second, Viewing Nginx Access Center, and Viewing Nginx Monitoring Center.

Prerequisites

Logs have been structured. For details, see Setting Cloud Structuring Parsing.

Viewing Nginx Monitoring by the Second

Choose NGINX dashboard templates under Dashboard Templates and click Nginx monitoring by the second to view the chart details.

QPS. The associated query and analysis statement is:

SELECT TIME_FORMAT(TIME_CEIL(__time,'PT1S'),'yyyy-MM-dd HH:mm:ss','+08:00') AS _time_ , COUNT(*) as QPS from log group by _time_

Success Rate. The associated query and analysis statement is:

select __time,round(CASE WHEN "Success Rate" > 0 THEN "Success Rate" else 0 end,2) as "Success Rate" from (select TIME_FORMAT(TIME_CEIL(__time,'PT5S'),'yyyy-MM-dd HH:mm:ss','+08:00') as __time, sum(case when status < 400 then 1 else 0 end) * 100.0 / count(1) as 'Success Rate' from log group by __time)

Latency. The associated query and analysis statement is:

select __time,round(CASE WHEN "Access latency" > 0 THEN "Access latency" else 0 end,2) as "Access latency",round(CASE WHEN "Upstream latency" > 0 THEN "Upstream latency" else 0 end,2) as "Upstream latency" from (select TIME_FORMAT(TIME_CEIL(__time,'PT5S'),'yyyy-MM-dd HH:mm:ss','+08:00') as __time, avg(request_time)* 1000 as 'Access latency',avg(upstream_response_time)* 1000 as 'Upstream latency' from log group by __time)

Traffic. The associated query and analysis statement is:

select TIME_FORMAT(TIME_CEIL(__time,'PT5S'),'yyyy-MM-dd HH:mm:ss','+08:00') as __time , sum("request_length") as "Incoming", sum("body_bytes_sent") as "Outgoing body" group by __time

Status Codes. The associated query and analysis statement is:

select t.t as "time",
      CASE WHEN a."2XX" IS NOT NULL THEN  CAST(a."2XX" AS BIGINT) ELSE 0 END as "2XX",
      CASE WHEN b."3XX" IS NOT NULL THEN  CAST(b."3XX" AS BIGINT) ELSE 0 END as "3XX",
      CASE WHEN c."4XX" IS NOT NULL THEN  CAST(c."4XX" AS BIGINT) ELSE 0 END as "4XX",
      CASE WHEN d."5XX" IS NOT NULL THEN  CAST(d."5XX" AS BIGINT) ELSE 0 END as "5XX",
      CASE WHEN e."Other" IS NOT NULL THEN  CAST(e."Other" AS BIGINT)  ELSE 0 END as "Other" 
      from (select TIME_CEIL(__time,'PT5S') as t from log group by t order by t asc  ) t left join (select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "2XX" from log WHERE "status" >= 200 and "status" < 300 group by t order by t asc ) a on t.t =a.t left join (select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "3XX" from log WHERE "status" >= 300 and "status" < 400 group by t order by t asc) b on t.t =b.t left join (select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "4XX" from log WHERE "status" >= 400 and "status" < 500 group by t order by t asc) c on t.t =c.t left join (select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "5XX" from log WHERE "status" >= 500 and "status" < 600 group by t order by t asc) d on t.t =d.t left join (select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "Other" from log WHERE "status" < 200 or "status" >= 600 group by t order by t asc) e on t.t =e.t

Backend Response Codes. The associated query and analysis statement is:

select t.t as "time",
      CASE WHEN a."2XX" IS NOT NULL THEN  CAST(a."2XX" AS BIGINT) ELSE 0 END as "2XX",
      CASE WHEN b."3XX" IS NOT NULL THEN  CAST(b."3XX" AS BIGINT) ELSE 0 END as "3XX",
      CASE WHEN c."4XX" IS NOT NULL THEN  CAST(c."4XX" AS BIGINT) ELSE 0 END as "4XX",
      CASE WHEN d."5XX" IS NOT NULL THEN  CAST(d."5XX" AS BIGINT) ELSE 0 END as "5XX",
      CASE WHEN e."Other" IS NOT NULL THEN  CAST(e."Other" AS BIGINT)  ELSE 0 END as "Other"
      from (
      select TIME_CEIL(__time,'PT5S') as t from log group by t order by t asc  
      ) t
      left join(
      select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "2XX" from log WHERE "upstream_status" >= 200 and "upstream_status" < 300 group by t order by t asc) a 
      on t.t = a.t
      left join (
      select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "3XX" from log WHERE "upstream_status" >= 300 and "upstream_status" < 400 group by t order by t asc) b 
      on t.t =b.t
      left join (
      select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "4XX" from log WHERE "upstream_status" >= 400 and "upstream_status" < 500 group by t order by t asc) c 
      on t.t =c.t
      left join (
      select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "5XX" from log WHERE "upstream_status" >= 500 and "upstream_status" < 600 group by t order by t asc) d
      on t.t =d.t
      left join (
      select TIME_CEIL(__time,'PT5S') as t , CAST(COUNT(1) as VARCHAR) as "Other" from log WHERE "upstream_status" < 200 or "upstream_status" >= 600 group by t order by t asc) e 
      on t.t =e.t

Viewing Nginx Access Center

Choose NGINX dashboard templates under Dashboard Templates and click Nginx access center to view the chart details.

Day-over-day PV Change. The associated query and analysis statement is:

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))

Week-on-week PV Change. The associated query and analysis statement is:

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))

Day-over-day UV Change. The associated query and analysis statement is:

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))

Week-on-week UV Change. The associated query and analysis statement is:

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))

PV Distribution (China). The associated query and analysis statement is:

select ip_to_province(remote_addr) as province, count(1) as pv where IP_TO_COUNTRY (remote_addr) = 'China'  group by province HAVING province not in ('','Reserved address','*')

PV Distribution (Global). The associated query and analysis statement is:

SELECT ip_to_country(remote_addr) as country,COUNT(1) as PV GROUP BY country HAVING country not in ('','Reserved address','*')

UV Distribution (China). The associated query and analysis statement is:

select ip_to_province(remote_addr) as province, APPROX_COUNT_DISTINCT(remote_addr) as UV where IP_TO_COUNTRY (remote_addr) = 'China'   group by province HAVING province not in ('','Reserved address','*')

UV Distribution (Global). The associated query and analysis statement is:

select ip_to_country(remote_addr) as country, APPROX_COUNT_DISTINCT(remote_addr) as uv group by country HAVING country not in ('','Reserved address','*')

Average Latency Distribution (China). The associated query and analysis statement is:

SELECT province,round( CASE WHEN "Average latency (ms)" > 0 THEN "Average latency (ms)" ELSE 0 END, 3 ) AS "Average latency (ms)"FROM (SELECT ip_to_province(remote_addr) as province,avg(request_time) * 1000 AS "Average latency (ms)"WHERE  IP_TO_COUNTRY (remote_addr) = 'China'GROUP BY province HAVING province not in ('','Reserved address','*'))

Average Latency Distribution (Global). The associated query and analysis statement is:

SELECT country,round( CASE WHEN "Average latency (ms)" > 0 THEN "Average latency (ms)" ELSE 0 END, 2 ) AS "Average latency (ms)"FROM (SELECT ip_to_country(remote_addr) as country,avg(request_time) * 1000 AS "Average latency (ms)" GROUP BY country HAVING country not in ('','Reserved address','*'))

PV/UV Today. The associated query and analysis statement is:

SELECT TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,PV,UV FROM (select TIME_CEIL(__time,'PT600S') AS _time_ , count(1) as PV,  APPROX_COUNT_DISTINCT(my_remote_addr) as UV from log WHERE __time <= CURRENT_TIMESTAMP  and __time >= DATE_TRUNC( 'DAY',(CURRENT_TIMESTAMP + INTERVAL '8' HOUR)) - INTERVAL '8' HOUR group by _time_ order by _time_) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

PV/UV in 7 Days. The associated query and analysis statement is:

SELECT TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,PV,UV FROM (select  TIME_CEIL(__time,'PT600S') AS _time_ , count(1) as PV,  APPROX_COUNT_DISTINCT(remote_addr) as UV from log WHERE __time <= CURRENT_TIMESTAMP  and __time >= DATE_TRUNC( 'DAY',(CURRENT_TIMESTAMP + INTERVAL '8' HOUR)) - INTERVAL '8' HOUR - INTERVAL '7' DAY group by _time_ order by _time_ ) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

Top 10 Provinces by Visits. The associated query and analysis statement is:

select ip_to_province(remote_addr) as "province", count(1) as "Visits" group by "province" HAVING "province" <> '-1' order by "Visits" asc limit 10

Top 10 Cities by Visits. The associated query and analysis statement is:

select ip_to_city(remote_addr) as "city", count(1) as "Visits" group by "city" HAVING  "city" <> '-1' order by "Visits" asc  limit 10

Top 10 Hosts by Visits. The associated query and analysis statement is:

select  host as "Host", count(1) as "PV" group by "Host" order by "PV" asc limit 10

Top 10 UserAgents by Visits. The associated query and analysis statement is:

select http_user_agent as "UserAgent", count(1) as "PV" group by "UserAgent" order by "PV" asc limit 10

Device Distribution by Type. The associated query and analysis statement is:

select case when regexp_like(lower(http_user_agent), 'iphone|ipod|android|ios') then 'Mobile' else 'PC' end as type , count(1) as total group by  type

Device Distribution by System. The associated query and analysis statement is:

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. The associated query and analysis statement is:

select request_uri , count(1) as PV, APPROX_COUNT_DISTINCT(remote_addr) as UV, round(sum( case when status < 400 then 1 else 0 end ) * 100.0 / count(1), 2) as "Access Success Rate" group by request_uri ORDER by PV desc

Top IP Addresses by Visits. The associated query and analysis statement is:

select remote_addr as "Source IP Address",ip_to_country(remote_addr) as "Country/Region",ip_to_province(remote_addr) as "Province",ip_to_city(remote_addr) as "City",ip_to_provider(remote_addr) as "Carrier",count(1) as "PV",http_user_agent as "UserAgent Sampling",request_uri as "URL Sampling" group by remote_addr,http_user_agent,request_uri ORDER by "PV" desc

Viewing Nginx Monitoring Center

Choose NGINX dashboard templates under Dashboard Templates and click Nginx monitoring center to view the chart details.

PV. The associated query and analysis statement is:

SELECT TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,PV FROM ( SELECT TIME_CEIL ( __time, 'PT300S' ) AS _time_, count( 1 ) AS PV FROM log GROUP BY _time_ )  WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100 OFFSET 1

Request Success Rate. The associated query and analysis statement is:

select ROUND(sum(case when status < 400 then 1 else 0 end) * 100.0 / count(1),2) as cnt

Average Latency. The associated query and analysis statement is:
```
select round(avg(request_time) * 1000, 3) as cnt
```

4xx Requests. The associated query and analysis statement is:

SELECT COUNT(1) as cnt WHERE "status" >= 400 and "status" < 500

404 Requests. The associated query and analysis statement is:
```
SELECT COUNT(1) as cnt WHERE "status" = 404
```
429 Requests. The associated query and analysis statement is:
```
SELECT COUNT(1) as cnt WHERE "status" = 429
```
504 Requests. The associated query and analysis statement is:
```
SELECT COUNT(1) as cnt WHERE "status" = 504
```

5xx Requests. The associated query and analysis statement is:

select TIME_FORMAT(TIME_CEIL(__time,'PT300S'),'yyyy-MM-dd HH:mm:ss','+08:00') AS _time_ , count(1) as cnt where "status" >= 500 group by _time_

Status Code Distribution. The associated query and analysis statement is:
```
SELECT status, COUNT(1) AS rm GROUP BY status
```

UV. The associated query and analysis statement is:

SELECT TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,UV FROM (select TIME_CEIL(__time,'PT600S') AS _time_ , APPROX_COUNT_DISTINCT(remote_addr) as UV  from log group by _time_) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

Traffic. The associated query and analysis statement is:

select TIME_FORMAT(_time_,'yyyy-MM-dd HH:mm:ss','+08:00') AS _time_,round( CASE WHEN "Inbound" > 0 THEN "Inbound" ELSE 0 END, 2 ) AS "Inbound",round( CASE WHEN "Outbound" > 0 THEN "Outbound" ELSE 0 END, 2 ) AS "Outbound" FROM (SELECT TIME_CEIL(__time,'PT600S') AS _time_,sum(request_length) / 1024.0 AS "Inbound",sum(bytes_sent) / 1024.0 AS "Outbound" group by  _time_) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

Access Failure Rate. The associated query and analysis statement is:

SELECT TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,round( CASE WHEN "Failure rate" > 0 THEN "Failure rate" ELSE 0 END, 2 ) AS "Failure rate",round( CASE WHEN "5XX" > 0 THEN "5XX" ELSE 0 END, 2 ) AS "5XX" from (select TIME_CEIL(__time,'PT600S') AS _time_,sum(case when status >= 400 then 1 else 0 end) * 100.0 / count(1) as 'Failure rate' , sum(case when status >=500 THEN 1 ELSE 0 END)*100.0/COUNT(1) as '5XX' group by  _time_) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

Latency. The associated query and analysis statement is:

select TIME_FORMAT( _time_, 'yyyy-MM-dd HH:mm:ss', '+08:00' ) as _time_,round( CASE WHEN "Avg." > 0 THEN "Avg." ELSE 0 END, 2 ) AS "Avg.",round( CASE WHEN "P50" > 0 THEN "P50" ELSE 0 END, 2 ) AS "P50",round( CASE WHEN "P90" > 0 THEN "P90" ELSE 0 END, 2 ) AS "P90",round( CASE WHEN "P99" > 0 THEN "P99" ELSE 0 END, 2 ) AS "P99",round( CASE WHEN "P9999" > 0 THEN "P9999" ELSE 0 END, 2 ) AS "P9999" from (select TIME_CEIL(__time,'PT600S') as _time_,avg(request_time) * 1000 as "Avg.", 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_) WHERE _time_ <= CURRENT_TIMESTAMP LIMIT 100000 OFFSET 1

Top Host Requests. The associated query and analysis statement is:

SELECT "host", pv, uv, round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "Inbound (KB)" > 0 THEN "Inbound (KB)" ELSE 0 END, 3 ) AS "Inbound (KB)", round( CASE WHEN "Outbound (KB)" > 0 THEN "Outbound (KB)" ELSE 0 END, 3 ) AS "Outbound (KB)"  FROM ( SELECT "host", count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", sum( request_length ) / 1024.0 AS "Inbound (KB)", sum( bytes_sent ) / 1024.0 AS "Outbound (KB)"  WHERE "host" != ''  GROUP BY "host" ) ORDER BY pv DESC

Top Host Latencies. The associated query and analysis statement is:

SELECT "host", pv, round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)", round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)" FROM ( SELECT "host", count( 1 ) AS pv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)",APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != ''  GROUP BY "host" ) ORDER BY "Average Latency (ms)" desc

Top Host Failure Rates. The associated query and analysis statement is:

SELECT "host", pv,round( CASE WHEN "Access Failure Rate (%)" > 0 THEN "Access Failure Rate (%)" ELSE 0 END, 2 ) AS "Access Failure Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)", round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)"  FROM ( SELECT "host", count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Failure Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != ''  GROUP BY "host"  ) ORDER BY "Access Failure Rate (%)" desc

Top URL Requests. The associated query and analysis statement is:

SELECT request_uri, pv,uv, round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "Inbound (KB)" > 0 THEN "Inbound (KB)" ELSE 0 END, 3 ) AS "Inbound (KB)", round( CASE WHEN "Outbound (KB)" > 0 THEN "Outbound (KB)" ELSE 0 END, 3 ) AS "Outbound (KB)"  FROM ( SELECT request_uri, count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", sum( request_length ) / 1024.0 AS "Inbound (KB)", sum( bytes_sent ) / 1024.0 AS "Outbound (KB)"  WHERE "host" != ''  GROUP BY request_uri  ) ORDER BY pv desc

Top URL Latencies. The associated query and analysis statement is:

SELECT request_uri, pv,round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)",round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)",round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)",round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)" FROM (SELECT request_uri, count( 1 ) AS pv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != ''  GROUP BY request_uri  ) ORDER BY "Average Latency (ms)" desc

Top URL Failure Rates. The associated query and analysis statement is:

SELECT request_uri, pv, round( CASE WHEN "Access Failure Rate (%)" > 0 THEN "Access Failure Rate (%)" ELSE 0 END, 2 ) AS "Access Failure Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)",round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)", round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)" FROM( SELECT request_uri, count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Failure Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != '' GROUP BY request_uri  )ORDER BY "Access Failure Rate (%)" desc

Top Backend Requests. The associated query and analysis statement is:

SELECT addr, pv, uv, round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "Inbound (KB)" > 0 THEN "Inbound (KB)" ELSE 0 END, 3 ) AS "Inbound (KB)", round( CASE WHEN "Outbound (KB)" > 0 THEN "Outbound (KB)" ELSE 0 END, 3 ) AS "Outbound (KB)"  FROM ( SELECT upstream_addr as addr, count( 1 ) AS pv, APPROX_COUNT_DISTINCT ( remote_addr ) AS uv, sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", sum( request_length ) / 1024.0 AS "Inbound (KB)", sum( bytes_sent ) / 1024.0 AS "Outbound (KB)"  WHERE "host" != ''  GROUP BY addr  having length(upstream_addr) > 2) ORDER BY "pv" desc

Top Backend Latencies. The associated query and analysis statement is:

SELECT addr,pv,round( CASE WHEN "Access Success Rate (%)" > 0 THEN "Access Success Rate (%)" ELSE 0 END, 2 ) AS "Access Success Rate (%)",round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)",round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)",round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)" FROM (SELECT upstream_addr as addr,count( 1 ) AS pv,sum( CASE WHEN "status" < 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Success Rate (%)",avg( request_time ) * 1000 AS "Average Latency (ms)",APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)",APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != '' GROUP BY addr having length(upstream_addr) > 2) ORDER BY "Average Latency (ms)" desc

Top Backend Failure Rates. The associated query and analysis statement is:

SELECT addr, pv, round( CASE WHEN "Access Failure Rate (%)" > 0 THEN "Access Failure Rate (%)" ELSE 0 END, 2 ) AS "Access Failure Rate (%)", round( CASE WHEN "Average Latency (ms)" > 0 THEN "Average Latency (ms)" ELSE 0 END, 3 ) AS "Average Latency (ms)", round( CASE WHEN "P90 Latency (ms)" > 0 THEN "P90 Latency (ms)" ELSE 0 END, 3 ) AS "P90 Latency (ms)", round( CASE WHEN "P99 Latency (ms)" > 0 THEN "P99 Latency (ms)" ELSE 0 END, 3 ) AS "P99 Latency (ms)"  FROM ( SELECT upstream_addr as addr, count( 1 ) AS pv, sum( CASE WHEN "status" >= 400 THEN 1 ELSE 0 END ) * 100.0 / count( 1 ) AS "Access Failure Rate (%)", avg( request_time ) * 1000 AS "Average Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.9) * 1000 AS "P90 Latency (ms)", APPROX_QUANTILE_DS(request_time, 0.99) * 1000 AS "P99 Latency (ms)" WHERE "host" != ''  GROUP BY addr having length(upstream_addr) > 2)ORDER BY "Access Failure Rate (%)" desc

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