[{"data":1,"prerenderedAt":708},["ShallowReactive",2],{"/en-us/blog/sharing-slis-across-departments/":3,"navigation-en-us":37,"banner-en-us":454,"footer-en-us":469,"Bob Van Landuyt":680,"next-steps-en-us":693},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":27,"_id":30,"_type":31,"title":32,"_source":33,"_file":34,"_stem":35,"_extension":36},"/en-us/blog/sharing-slis-across-departments","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"How we share SLIs across engineering departments","The Scalability team engages with the Development department for collaborating on SLIs. 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GitLab's features are divided amongst development groups,\nand every group has [their own dashboard](https://docs.gitlab.com/ee/development/stage_group_observability/index.html)\ndisplaying an availability score.\n\n![Stage group availability](https://about.gitlab.com/images/blogimages/2022-02-share-infrastructure-slis/2022-02-23-code_review_availability.png)\n\nWhen a group's availability goes below 99.95%, we work with the group\non figuring out why that is and how we can improve the performance or\nreliability of the features that caused their number to drop. The\n99.95% service level objective (SLO) is the same target the\ninfrastructure department has set for\n[GitLab.com availability](/handbook/engineering/infrastructure/performance-indicators/#gitlabcom-availability).\n\nBy providing specific data about how features perform on our production systems, it has become easier to recognize when it is important to prioritize performance and availability work.\n\n## Service availability on GitLab.com\n\nOur infrastructure is separated into multiple services, handling\ndifferent kinds of traffic but running the same monolithic Rails\napplication. Not all features have a similar usage pattern. For\nexample, on the service handling web requests for GitLab.com we see a\nlot more requests related to `code_review` or `team_planning` than we\ndo related to `source_code_management`. It's important that we\nlook at these in isolation as well as a service aggregate.\n\nThere's nobody who knows better how to interpret these numbers in\nfeature aggregations than the people who build these features.\n\nThis number is sourced by the same SLIs that we use to monitor\nGitLab.com's availability. We calculate this by dividing the number of\nsuccessful measurements by the total number of measurements over the\npast 28 days. A measurement could be several things, most commonly a\nrequest handled by our Rails application or a background job.\n\n## Monitoring feature and service availability\n\nFor monitoring GitLab.com we have Grafana dashboards, generated using\n[Grafonnet](https://grafana.github.io/grafonnet-lib/), that show these\nsource metrics in several dimensions. For example, these are error\nrates of our monolithic Rails application, separated by feature:\n\n![Puma SLI by feature](https://about.gitlab.com/images/blogimages/2022-02-share-infrastructure-slis/2022-02-23-puma_sli_per_feature.png)\n\nWe also generate [multiwindow, multi-burn-rate alerts](https://sre.google/workbook/alerting-on-slos/#short_and_long_windows_for_alerting)\nas defined in Google's SRE workbook.\n\n![Puma SLI error rate and requests per second](https://about.gitlab.com/images/blogimages/2022-02-share-infrastructure-slis/2022-02-23-puma_sli.png)\n\nThe red lines represent alerting thresholds for a burn rate. The\nthin threshold means we'll alert if the SLI has spent more than 5%\nof its monthly error budget in the past 6 hours. 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