The time of changes that were made to a code that then resulted in incidents, rollbacks, or any type of production failure.
This metric is crucial in evaluating the resilience and efficiency of a team‘s deployment process. A shorter recovery time indicates a team’s ability to swiftly detect issues, troubleshoot them, and restore the system to a functional state, minimizing downtime and impact on end-users.
DORA dashboard. See live demo.
The time from deployment to the incident corresponding to deployment is resolved. For example, if a deployment finishes at 10:00 AM and causes an incident at 10:20. Then, the incident gets resolved at 11:00 AM. The failed deployment recovery time is one hour.
Below are the 2023 DORA benchmarks for different development teams from Google‘s report. However, it’s difficult to tell which group a team falls into when the team's failed deployment recovery time is between one week and six months
. Therefore, DevLake provides its own benchmarks to address this problem:
Groups | Benchmarks | DevLake Benchmarks |
---|---|---|
Elite performers | Less than one hour | Less than one hour |
High performers | Less than one day | Less than one day |
Medium performers | Between one day and one week | Between one day and one week |
Low performers | More than six months | More than one week |
Data Sources Required
Deployments
from Jenkins, GitLab CI, GitHub Action, BitBucket Pipelines, Webhook, etc.Incidents
from Jira issues, GitHub issues, TAPD issues, PagerDuty Incidents, Webhook, etc.Transformation Rules Required
Define deployment
and incident
in data transformations while configuring the blueprint of a project to let DevLake know what CI/issue records can be regarded as deployments or incidents.
SQL Queries
If you want to measure the monthly trend of the Failed Deployment Recovery Time as the picture shown below, run the following SQL in Grafana.
-- ***** 2023 report ***** -- -- Metric 4: Failed deployment recovery time with _deployments as ( SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in ($project) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _incidents_for_deployments as ( SELECT i.id as incident_id, i.created_date as incident_create_date, i.resolution_date as incident_resolution_date, fd.deployment_id as caused_by_deployment, fd.deployment_finished_date, date_format(fd.deployment_finished_date,'%y/%m') as deployment_finished_month FROM issues i left join project_issue_metrics pim on i.id = pim.id join _deployments fd on pim.deployment_id = fd.deployment_id WHERE i.type = 'INCIDENT' and $__timeFilter(i.resolution_date) ), _recovery_time_ranks as ( SELECT *, percent_rank() over(PARTITION BY deployment_finished_month order by TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as ranks FROM _incidents_for_deployments ), _median_recovery_time as ( SELECT deployment_finished_month, max(TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as median_recovery_time FROM _recovery_time_ranks WHERE ranks <= 0.5 GROUP BY deployment_finished_month ), _metric_recovery_time_2023_report as ( SELECT cm.month, case when m.median_recovery_time is null then 0 else m.median_recovery_time/60 end as median_recovery_time_in_hour FROM calendar_months cm LEFT JOIN _median_recovery_time m on cm.month = m.deployment_finished_month WHERE $__timeFilter(cm.month_timestamp) ) SELECT cm.month, CASE WHEN '${dora_report}' = '2023' THEN mrt.median_recovery_time_in_hour END AS '${title_value} In Hours' FROM calendar_months cm LEFT JOIN _metric_recovery_time_2023_report mrt ON cm.month = mrt.month WHERE $__timeFilter(cm.month_timestamp)
If you want to measure in which category your team falls into as in the picture shown below, run the following SQL in Grafana.
-- ***** 2023 report ***** -- -- Metric 4: Failed deployment recovery time with _deployments as ( SELECT cdc.cicd_deployment_id as deployment_id, max(cdc.finished_date) as deployment_finished_date FROM cicd_deployment_commits cdc JOIN project_mapping pm on cdc.cicd_scope_id = pm.row_id and pm.`table` = 'cicd_scopes' WHERE pm.project_name in ($project) and cdc.result = 'SUCCESS' and cdc.environment = 'PRODUCTION' GROUP BY 1 HAVING $__timeFilter(max(cdc.finished_date)) ), _incidents_for_deployments as ( SELECT i.id as incident_id, i.created_date as incident_create_date, i.resolution_date as incident_resolution_date, fd.deployment_id as caused_by_deployment, fd.deployment_finished_date, date_format(fd.deployment_finished_date,'%y/%m') as deployment_finished_month FROM issues i left join project_issue_metrics pim on i.id = pim.id join _deployments fd on pim.deployment_id = fd.deployment_id WHERE i.type = 'INCIDENT' and $__timeFilter(i.resolution_date) ), _recovery_time_ranks as ( SELECT *, percent_rank() over(order by TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as ranks FROM _incidents_for_deployments ), _median_recovery_time as ( SELECT max(TIMESTAMPDIFF(MINUTE, deployment_finished_date, incident_resolution_date)) as median_recovery_time FROM _recovery_time_ranks WHERE ranks <= 0.5 ), _metric_recovery_time_2023_report as( SELECT CASE WHEN ('$dora_report') = '2023' THEN CASE WHEN median_recovery_time < 60 THEN CONCAT(round(median_recovery_time/60,1), "(elite)") WHEN median_recovery_time < 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), "(high)") WHEN median_recovery_time < 7 * 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), "(medium)") WHEN median_recovery_time >= 7 * 24 * 60 THEN CONCAT(round(median_recovery_time/60,1), "(low)") ELSE "N/A. Please check if you have collected incidents." END END AS median_recovery_time FROM _median_recovery_time ) SELECT median_recovery_time AS median_time_in_hour FROM _metric_recovery_time_2023_report WHERE ('$dora_report') = '2023'