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DORA - Lead Time for Changes

What is this metric?

The median amount of time for a commit to be deployed into production.

Why is it important?

This metric measures the time it takes to commit code to the production environment and reflects the speed of software delivery. A lower average change preparation time means that your team is efficient at coding and deploying your project.

Which dashboard(s) does it exist in

DORA dashboard. See live demo.

How is it calculated?

This metric can be calculated in two ways:

  • If a deployment can be linked to PRs, then the lead time for changes of a deployment is the average cycle time of its associated PRs. For instance,
    • Compared to the previous deployment deploy-1, deploy-2 deployed three new commits commit-1, commit-2 and commit-3.
    • commit-1 is linked to pr-1, commit-2 is linked to pr-2 and pr-3, commit-3 is not linked to any PR. Then, deploy-2 is associated with pr-1, pr-2 and pr-3.
    • Deploy-2's lead time for changes = average cycle time of pr-1, pr-2 and pr-3.
  • If a deployment can't be linked to PRs, then the lead time for changes is computed based on its associated commits. For instance,
    • Compared to the previous deployment deploy-1, deploy-2 deployed three new commits commit-1, commit-2 and commit-3.
    • None of commit-1, commit-2 and commit-3 is linked to any PR.
    • Calculate each commit's lead time for changes, which equals to deploy-2's deployed_at - commit's authored_date
    • Deploy-2's Lead time for changes = average lead time for changes of commit-1, commit-2 and commit-3.

Below are the 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 median lead time for changes is between one week and one month. Therefore, DevLake provides its own benchmarks to address this problem:

GroupsBenchmarksDevLake Benchmarks
Elite performersLess than one hourLess than one hour
High performersBetween one day and one weekLess than one week
Medium performersBetween one month and six monthsBetween one week and six months
Low performersMore than six monthsMore than six months

Source: 2021 Accelerate State of DevOps, Google

Data Sources Required

This metric relies on deployments collected in multiple ways:

  • Open APIs of Jenkins, GitLab, GitHub, etc.
  • Webhook for general CI tools.
  • Releases and PR/MRs from GitHub, GitLab APIs, etc.
Transformation Rules Required

This metric relies on the deployment configuration in Jenkins, GitLab or GitHub transformation rules to let DevLake know what CI builds/jobs can be regarded as deployments.

SQL Queries

If you want to measure the monthly trend of median lead time for changes as the picture shown below, run the following SQL in Grafana.

with _deployment_change_lead_time as (
-- to get each deployment's change lead time
SELECT
ct.id as deployment_id,
ct.name as deployment_name,
date_format(ct.finished_date,'%y/%m') as month,
avg(pr.change_timespan) as change_lead_time_of_a_deployment
FROM
cicd_tasks ct
join cicd_pipeline_commits cpc on ct.pipeline_id = cpc.pipeline_id
join pull_requests pr on cpc.commit_sha = pr.merge_commit_sha
WHERE
ct.type = 'DEPLOYMENT'
and ct.result = 'success'
and $__timeFilter(ct.finished_date)
GROUP BY 1,2,3
),

_find_median_clt_each_month as (
SELECT
x.month, x.change_lead_time_of_a_deployment
from _deployment_change_lead_time x join _deployment_change_lead_time y on x.month = y.month
WHERE x.change_lead_time_of_a_deployment is not null and y.change_lead_time_of_a_deployment is not null
GROUP BY x.month, x.change_lead_time_of_a_deployment
HAVING SUM(SIGN(1-SIGN(y.change_lead_time_of_a_deployment-x.change_lead_time_of_a_deployment)))/COUNT(*) > 0.5
),

_find_clt_rank_each_month as (
SELECT
*,
rank() over(PARTITION BY month ORDER BY change_lead_time_of_a_deployment) as _rank
FROM
_find_median_clt_each_month
),

_clt as (
SELECT
month,
change_lead_time_of_a_deployment as med_change_lead_time
from _find_clt_rank_each_month
WHERE _rank = 1
),

_calendar_months as(
-- to deal with the month with no incidents
SELECT date_format(CAST((SYSDATE()-INTERVAL (month_index) MONTH) AS date), '%y/%m') as month
FROM ( SELECT 0 month_index
UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3
UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6
UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9
UNION ALL SELECT 10 UNION ALL SELECT 11
) month_index
WHERE (SYSDATE()-INTERVAL (month_index) MONTH) > SYSDATE()-INTERVAL 6 MONTH
)

SELECT
cm.month,
case
when _clt.med_change_lead_time is null then 0
else _clt.med_change_lead_time/60 end as med_change_lead_time_in_hour
FROM
_calendar_months cm
left join _clt on cm.month = _clt.month
ORDER BY 1

If you want to measure in which category your team falls into as the picture shown below, run the following SQL in Grafana.

with _deployment_change_lead_time as (
-- get one deployment's change lead time
SELECT
ct.id as deployment_id,
ct.name as deployment_name,
ct.finished_date as deployed_at,
avg(pr.change_timespan) as change_lead_time_of_a_deployment
FROM
cicd_tasks ct
join cicd_pipeline_commits cpc on ct.pipeline_id = cpc.pipeline_id
join pull_requests pr on cpc.commit_sha = pr.merge_commit_sha
WHERE
ct.type = 'DEPLOYMENT'
and ct.result = 'success'
and $__timeFilter(ct.finished_date)
GROUP BY 1,2,3
),

_median_change_lead_time as (
SELECT x.change_lead_time_of_a_deployment as median_change_lead_time from _deployment_change_lead_time x, _deployment_change_lead_time y
WHERE x.change_lead_time_of_a_deployment is not null and y.change_lead_time_of_a_deployment is not null
GROUP BY x.change_lead_time_of_a_deployment
HAVING SUM(SIGN(1-SIGN(y.change_lead_time_of_a_deployment-x.change_lead_time_of_a_deployment)))/COUNT(*) > 0.5
LIMIT 1
)

SELECT
CASE
WHEN median_change_lead_time < 60 then "Less than one hour"
WHEN median_change_lead_time < 7 * 24 * 60 then "Less than one week"
WHEN median_change_lead_time < 180 * 24 * 60 then "Between one week and six months"
ELSE "More than six months"
END as median_change_lead_time
FROM _median_change_lead_time

How to improve?

  • Break requirements into smaller, more manageable deliverables
  • Optimize the code review process
  • "Shift left", start QA early and introduce more automated tests
  • Integrate CI/CD tools to automate the deployment process