| KPI | Health | Status |
|---|---|---|
| GitLab.com Availability | Attention |
|
| Mean Time To Production (MTTP) | Okay |
|
| GitLab.com Hosting Cost per GitLab.com Unique Monthly Active Users | Confidential |
|
| Infrastructure Team Member Retention | Confidential |
|
| Infrastructure Average Age of Open Positions | Attention |
|
| Corrective Action SLO | Okay |
|
Percentage of time during which GitLab.com is fully operational and providing service to users within SLO parameters. Definition is available on the GitLab.com Service Level Availability page.
Target: equal or greater than 99.95%
Health: Attention
URL(s)
Measures the elapsed time (in hours) from merging a change in gitlab-org/gitlab projects master branch, to deploying that change to gitlab.com. It serves as an indicator of our speed capabilities to deploy application changes into production.
Target: less than 12 hours
Health: Okay
URL(s)
This metric reflects an estimate of the dollar cost necessary to support one user in GitLab.com. It is an important metric because it allows us to estimate infrastructure costs as our user base grows. GitLab.com Hosting Cost comes from Netsuite; it is a sum of actual amounts within GitLab.com:Marketing and GitLab.com departments, with account numbers of 5026 or 6026. This cost is divided by UMAU. In the same dashboard is the free & paid breakdown of this metric. Free Hosting Cost include costs from the GitLab.com:Marketing department, while Paid cost is from the GitLab.com department. Paid Cost is divided by total paid licensed users, rather than UMAU.
Target: less than .80
This KPI cannot be public.
Health: Confidential
URL(s)
This is a subset of an existing KPI. Please see the definition for the parent KPI.
We need to be able to retain talented team members. Retention measures our ability to keep them sticking around at GitLab. Team Member Retention = (1-(Number of Team Members leaving GitLab/Average of the 12 month Total Team Member Headcount)) x 100. GitLab measures team member retention over a rolling 12 month period.
Target: at or above 84%
This KPI cannot be public.
Health: Confidential
URL(s)
This is a subset of an existing KPI. Please see the definition for the parent KPI.
Measures the average time job openings take from open to close. This metric includes sourcing time of candidates compared to Time to Hire or Time to Offer Accept which only measures the time from when a candidate applies to when they accept.
Target: at or below 50 days
Health: Attention
The Corrective Actions (CAs) SLO focuses on the number of open severity::1/severity::2 Corrective Action Issues past their due date. Corrective Actions and their due dates are defined in our Incident Review process.
Target: below 0
Health: Okay
URL(s)
This is a subset of an existing KPI. Please see the definition for the parent KPI.
We remain efficient financially if we are hiring globally, working asynchronously, and hiring great people in low-cost regions where we pay market rates. We track an average location factor by function and department so managers can make tradeoffs and hire in an expensive region when they really need specific talent unavailable elsewhere, and offset it with great people who happen to be in low cost areas.
Target: equal or less than 0.58
Health: Attention
This is a subset of an existing KPI. Please see the definition for the parent KPI.
We need to spend our investors' money wisely. We also need to run a responsible business to be successful, and to one day go on the public market. Latest data is in Adaptive, data team importing to Sisense in FY22Q2
Target: Identified in Sisense Chart
Health: Unknown
This is a subset of an existing KPI. Please see the definition for the parent KPI.
The handbook is essential to working remote successfully, to keeping up our transparency, and to recruiting successfully. Our processes are constantly evolving and we need a way to make sure the handbook is being updated at a regular cadence. This data is retrieved by querying the API with a python script for merge requests that have files matching `/source/handbook/engineering/**` or `/source/handbook/support/**` over time.
Target: 0.55
Health: Attention
This is a subset of an existing KPI. Please see the definition for the parent KPI.
Infrastructure Department MR is a performance indicator showing how many changes the Infrastructure team implements in the GitLab product, as well as changes in support of the GitLab SaaS infrastructure. We currently count all members of the Infrastructure Department (Directors, Managers, ICs) in the denominator, because this is a team effort. The full definition of MR Rate is linked in the url section.
Target: Greater than 6 MRs per month
Health: Attention
URL(s)
This is a subset of an existing KPI. Please see the definition for the parent KPI.
We remain efficient financially if we are hiring globally, working asynchronously, and hiring great people in low-cost regions where we pay market rates. We track an average location factor for team members hired within the past 3 months so hiring managers can make tradeoffs and hire in an expensive region when they really need specific talent unavailable elsewhere, and offset it with great people who happen to be in more efficient location factor areas with another hire. The historical average location factor represents the average location factor for only new hires in the last three months, excluding internal hires and promotions. The calculation for the three-month rolling average location factor is the location factor of all new hires in the last three months divided by the number of new hires in the last three months for a given hire month. The data source is Workday data.
Target: 0.58
Health: Okay
This is a subset of an existing KPI. Please see the definition for the parent KPI.
The number of discretionary bonuses given divided by the total number of team members, in a given period as defined. This metric definition is taken from the People Success Discretionary Bonuses KPI.
Target: at or above 10%
Health: Okay
Measures the mean elapsed time (in hours) from the start of one production incident, to the start of the next production incident. It serves primarily as an indicator of the amount of disruption being experienced by users and by on-call engineers. This metric includes only Severity 1 & 2 incidents as these are most directly impactful to customers. This metric can be considered "MTBF of Incidents".
Target: more than 120 hours
Health: Okay
URL(s)
This metric aims to show the level of confidence we have of our insight into the significant GitLab services on GitLab.com. We are looking to capture the maturity on the scale of "manual check is necessary" to "the system informs us of an upcoming scaling need".
Target: TBD
Health: Unknown
URL(s)
For all customer-impacting services, measures the elapsed time (in hours) it takes us to resolve when an incident occurs. This serves as an indicator of our ability to execute said recoveries. This includes Severity 1 & Severity 2 incidents from production project
Target: less than 24 hours
Health: Attention
URL(s)
For all customer-impacting services, measures the elapsed time (in hours) it takes us to mitigate when an incident occurs. This serves as an indicator of our ability to mitigate production incidents. This includes Severity 1 & Severity 2 incidents from production project
Target: less than 1 hours
Health: Attention
URL(s)
It is critical that we continuously observe resource saturation normal growth as well as detect anomalies. This helps to ensure that we have the appropriate platform capacity in place. This metric uses the results of Tamland forecasting framework of non-horizontally scalable services, which end up as issues in Capacity Planning Project. This metric counts the number of open capacity issues in that project.
Target: at or below 5 open issues
Health: Attention
URL(s)
GCP Labels & AWS Tags allow us to segment our cloud spend into more meaningful buckets that align with our business units. This PI tracks our progress towards adopting our Labeling Standards which is very important for improved understanding of cloud spend. Currently, the metric only tracks adoption of gl_env_name label to replace currently used pet_name label for services, but eventually will grow to encompass all the required standard labels.
Target: at or above 80%
Health: Attention
Committed Use Discounts allow us to lower the rate we pay for Compute by comitting to a certain level of usage for a set amount of time. We aim to cover at least 80% of our total GCP Compute usage with Committed Use Discounts to optimize our spend. Our coverage level for each region & machine type depends on what makes sense for those use cases. We do not strive to hit 100% for this metric as that would mean we are likely over-subscribed to commits & end up paying more for non-static environments.
Target: at or above 80%
Health: Okay
URL(s)
We need to spend our investors' money wisely. As part of this we aim to follow industry standard targets for hosting cost as a % of overall revenue. In this case revenue is measured as MRR + one time monthly revenue from CI & Storage
Target: TBD
This KPI cannot be public.
Health: Confidential
URL(s)
The total number of promotions over a rolling 12 month period divided by the month end headcount. The target promotion rate is 12% of the population. This metric definition is taken from the People Success Team Member Promotion Rate PI.
Target: 12%
Health: Attention
| Value | Level | Meaning |
|---|---|---|
| 3 | Okay | The KPI is at an acceptable level compared to the threshold |
| 2 | Attention | This is a blip, or we’re going to watch it, or we just need to enact a proven intervention |
| 1 | Problem | We'll prioritize our efforts here |
| -1 | Confidential | Metric & metric health are confidential |
| 0 | Unknown | Unknown |
Pages, such as the Engineering Function Performance Indicators page are rendered by an ERB template that contains HTML code.
Other PI Pages sectionThese ERB templates calls custom helper functions that extract and transform data from the Performance Indicators data file.
kpi_list_by_org(org) helper function takes a required string argument named org (deparment or division level) that returns all the KPIs (pi.is_key == true) for a specific organization grouping (pi.org == org) from the Performance Indicators data file.pi_maturity_level(performance_indicator) helper function automatically assigns a maturity level based on the availability of certain data properties for a particular PI.pi_maturity_reasons(performance_indicator) helper function returns a reason for a PI maturity based on other data properties.performance_indicators(org) takes a required string argument named org (deparment or division level) that returns two lists - a list of all KPIs and a list of all PIs for a specific organization grouping (department/division).signed_periscope_url(data) takes in the sisense_data property information from Performance Indicators data files and returns a signed chart URL for embedding a Sisense chart into the handbook.The heart of pages like this are Performance Indicators data files which are YAML files. Each - denotes a dictionary of values for a new (K)PI. The current elements (or data properties) are:
| Property | Type | Description |
|---|---|---|
name |
Required | String value of the name of the (K)PI. For Product PIs, product hierarchy should be separate from name by " - " (Ex. {Stage Name}:{Group Name} - {PI Type} - {PI Name} |
base_path |
Required | Relative path to the performance indicator page that this (K)PI should live on |
definition |
Required | refer to Parts of a KPI |
parent |
Optional | should be used when a (K)PI is a subset of another PI. For example, we might care about Hiring vs Plan at the company level. The child would be the division and department levels, which would have the parent flag. |
target |
Required | The target or cap for the (K)PI. Please use Unknown until we reach maturity level 2 if this is not yet defined. For GMAU, the target should be quarterly. |
org |
Required | the organizational grouping (Ex: Engineering Function or Development Department). For Product Sections, ensure you have the word section (Ex : Dev Section) |
section |
Optional | the product section (Ex: dev) as defined in sections.yml |
stage |
Optional | the product stage (Ex: release) as defined in stages.yml |
group |
Optional | the product group (Ex: progressive_delivery) as defined in stages.yml |
category |
Optional | the product group (Ex: feature_flags) as defined in categories.yml |
is_key |
Required | boolean value (true/false) that indicates if it is a (key) performance indicator |
health |
Required | indicates the (K)PI health and reasons as nested attributes. This should be updated monthly before Key Reviews by the DRI. |
health.level |
Optional | indicates a value between 0 and 3 (inclusive) to represent the health of the (K)PI. This should be updated monthly before Key Reviews by the DRI. |
health.reasons |
Optional | indicates the reasons behind the health level. This should be updated monthly before Key Reviews by the DRI. Should be an array (indented lines starting with dashes) even if you only have one reason. |
urls |
Optional | list of urls associated with the (K)PI. Should be an array (indented lines starting with dashes) even if you only have one url |
funnel |
Optional | indicates there is a handbook link for a description of the funnel for this PI. Should be a URL |
sisense_data |
Optional | allows a Sisense dashboard to be embeded as part of the (K)PI using chart, dashboard, and embed as neseted attributes. |
sisense_data.chart |
Optional | indicates the numeric Sisense chart/widget ID. For example: 9090628 |
sisense_data.dashboard |
Optional | indicates the numeric Sisense dashboard ID. For example: 634200 |
sisense_data.shared_dashboard |
Optional | indicates the numeric Sisense shared_dashboard ID. For example: 185b8e19-a99e-4718-9aba-96cc5d3ea88b |
sisense_data.embed |
Optional | indicates the Sisense embed version. For example: v2 |
sisense_data_secondary |
Optional | allows a second Sisense dashboard to be embeded. Same as sisense data |
sisense_data_secondary.chart |
Optional | Same as sisense_data.chart |
sisense_data_secondary.dashboard |
Optional | Same as sisense_data.dashboard |
sisense_data_secondary.shared_dashboard |
Optional | Same as sisense_data.shared_dashboard |
sisense_data_secondary.embed |
Optional | Same as sisense_data.embed |
public |
Optional | boolean flag that can be set to false where a (K)PI does not meet the public guidelines. |
pi_type |
Optional | indicates the Product PI type (Ex: AMAU, GMAU, SMAU, Group PPI) |
product_analytics_type |
Optional | indicates if the metric is available on SaaS, SM (self-managed), or Both. |
is_primary |
Optional | boolean flag that indicates if this is the Primary PI for the Product Group. |
implementation |
Optional | indicates the implementation status and reasons as nested attributes. This should be updated monthly before Key Reviews by the DRI. |
implementation.status |
Optional | indicates the Implementation Status status. This should be updated monthly before Key Reviews by the DRI. |
implementation.reasons |
Optional | indicates the reasons behind the implementation status. This should be updated monthly before Key Reviews by the DRI. Should be an array (indented lines starting with dashes) even if you only have one reason. |
lessons |
Optional | indicates lessons learned from a K(PI) as a nested attribute. This should be updated monthly before Key Reviews by the DRI. |
lessons.learned |
Optional | learned is an attribute that can be nested under lessonsand indicates lessons learned from a K(PI). This should be updated monthly before Key Reviews by the DRI. Should be an array (indented lines starting with dashes) even if you only have one lesson learned |
monthly_focus |
Optional | indicates monthly focus goals from a K(PI) as a nested attribute. This should be updated monthly before Key Reviews by the DRI. |
monthly_focus.goals |
Optional | indicates monthly focus goals from a K(PI). This should be updated monthly before Key Reviews by the DRI. Should be an array (indented lines starting with dashes) even if you only have one goal |
metric_name |
Optional | indicates the name of the metric in Self-Managed implemenation. The SaaS representation of the Self-Managed implementation should use the same name. |
Please reference the Engineering Metrics Page for guidelines on chart visualization formatting.