This query results in rules with successful executions in the last 24 hours but no actions performed. These rules require optimization to reduce their context so that they aren't triggered by the current context events anymore.
MSSQL
select
AI."OBJECT_ITEM_NAME" as Rule_Name
,sum(CASE WHEN AI."CATEGORY" = 'SUCCESS' THEN 1 ELSE 0 END) AS Successful_Executions
,sum(CASE WHEN AI."CATEGORY" = 'NO_ACTIONS_PERFORMED' THEN 1 ELSE 0 END) AS No_Actions_Performed
from jiraschema."AO_589059_AUDIT_ITEM" AI
join jiraschema."AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= DATEADD(day, -1, GETDATE())
group by AI."OBJECT_ITEM_NAME"
PostgreSQL
select
AI."OBJECT_ITEM_NAME" as Rule_Name
,sum(CASE WHEN AI."CATEGORY" = 'SUCCESS' THEN 1 ELSE 0 END) AS Successful_Executions
,sum(CASE WHEN AI."CATEGORY" = 'NO_ACTIONS_PERFORMED' THEN 1 ELSE 0 END) AS No_Actions_Performed
from "AO_589059_AUDIT_ITEM" AI
join "AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= NOW() - INTERVAL '30 day'
group by AI."OBJECT_ITEM_NAME"
Rule actors with the highest execution count
This query provides the actors running the most rules. You can use it to identify users that might be creating unoptimized automation rules.
MSSQL
select
cwu.user_name as Username
,count(AI."ID") as Num_Of_Executions
from jiraschema."AO_589059_AUDIT_ITEM" AI
join jiraschema."AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
join jiraschema.app_user au on AI."AUTHOR_KEY" = au.user_key
join jiraschema.cwd_user cwu on au.lower_user_name = cwu.lower_user_name
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= DATEADD(day, -1, GETDATE())
group by cwu.user_name
PostgreSQL
select
cwu.user_name as Username
,count(AI."ID") as Num_Of_Executions
from "AO_589059_AUDIT_ITEM" AI
join "AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
join app_user au on AI."AUTHOR_KEY" = au.user_key
join cwd_user cwu on au.lower_user_name = cwu.lower_user_name
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= NOW() - INTERVAL '30 day'
group by cwu.user_name
Rules with the longest execution time
This query provides the last 300 rule executions that had the longest execution or duration time. You can use it to identify rules that could be taking a long time to execute and hurting the environment performance.
MSSQL
select
top 300
AI."OBJECT_ITEM_NAME" as Rule_Name
,AI."DURATION"
from jiraschema."AO_589059_AUDIT_ITEM" AI
join jiraschema."AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= DATEADD(day, -1, GETDATE())
order by AI."DURATION" desc
PostgreSQL
select
AI."OBJECT_ITEM_NAME" as Rule_Name
,AI."DURATION"
from "AO_589059_AUDIT_ITEM" AI
join "AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= NOW() - INTERVAL '30 day'
order by AI."DURATION" desc
limit 300
Events triggering the most rules
This query provides the events that are triggering the most rules. You can use it to identify triggers like a commented event that could be causing hundreds of rule executions and change them.
MSSQL
select
AI."EVENT_SOURCE",count(*) as Times_Triggered
from jiraschema."AO_589059_AUDIT_ITEM" AI
join jiraschema."AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= DATEADD(day, -1, GETDATE())
group by Event_Source
having count(*) >= 1
PostgreSQL
select
AI."EVENT_SOURCE",count(*) as Times_Triggered
from "AO_589059_AUDIT_ITEM" AI
join "AO_589059_AUDIT_ITEM_ASC_ITEM" AIAI on AI."ID" = AIAI."AUDIT_ITEM_ID"
where 1=1
AND AI."CATEGORY" != 'CONFIG_CHANGE'
AND AI."CREATED" >= NOW() - INTERVAL '30 day'
group by AI."EVENT_SOURCE"
having count(*) >= 1
order by times_triggered desc