Service Supply Index: A Driver for Reliability

Buyer-first: Shifting from Hero Engineering to Reliability Engineering

From the start, Slack has all the time had a powerful concentrate on the shopper expertise, and customer love is one in every of our core values. Slack has grown from a small group to 1000’s of staff through the years and this buyer love has all the time included a concentrate on service reliability.

In a small startup, it’s manageable to have a reactive reliability focus. For instance, one engineer can troubleshoot and remedy a systemic problem — we all know them as Hero Engineers. You may additionally understand it as an operations group, or a small group of Web site Reliability Engineers which are all the time on-call. As the corporate grows, these tried and practiced measures fail to scale, and also you’re left with pockets of tribal information riddled with burnout because the system turns into too advanced to be managed by only some of us.

With any quickly rising advanced product, it’s arduous to maneuver away from a reactionary concentrate on user-impacting points.  Reliability practitioners at Slack have developed efficient methods to reply, mitigate, and study from these points via Incident Management and Response processes and fostering Service Possession — these contribute to a tradition of reliability first as an entire. One of many key parts of each the Incident Administration program and the Service Possession program is the Service Supply Index.

In the event you’re driving a reliability tradition in a service-oriented firm, you should have a measurement of your service reliability earlier than all else, and this metric is quintessential in driving decision-making processes and setting buyer expectations. It permits groups to talk the identical language of reliability when you will have one frequent understanding.

Introducing the Service Supply Index

A dashboard displaying the SDI-R of 5.15 today, 90,273 500 errors, 18,224 502 errors, 32,323 503 errors, and 1,955 504 errors since 00:00 UTC, and a chart with the SDI-R and a 30-day weighted average

The Service Supply Index – Reliability (SDI-R for brief) is a composite metric of the success of jobs-to-be-done by Slack’s customers and Slack’s uptime as reported on our Slack System Status website. It’s a composite measure  of profitable API calls and content material supply (as measured on the edge), together with necessary person workflows (e.g. sending a message, loading a channel, utilizing a huddle).

This can be a company-wide metric with visibility as much as the manager stage, and in apply is carried out fairly just by:

API Availability
availability api = profitable requests / complete requests

General Availability
availability total = uptime standing website * availability api

You could be asking why uptime and availability are totally different; uptime is decided by monitoring key workflows which are crucial to Slack’s usability and if the provision of any of these crucial person interactions drops beneath a predetermined threshold, we rely the minutes that the service is beneath that threshold to find out downtime.

Since small adjustments in availability (~0.0001) can have a drastic influence on the shopper expertise, we convert availability to a 9s illustration, the place 99% availability is 2 9s, 99.9% availability is 3 9s, and 99.99% availability is 4 9s, and so forth.

We monitor day by day and hourly aggregates of availability, monitoring it over time in order that we will spot tendencies and establish regressions and enhancements.

We preserve company-wide targets on this metric by way of the variety of days in 1 / 4 that we meet availability targets.

The Reliability Engineering group is basically liable for responding to and triaging regressions in availability that trigger or can doubtlessly trigger us to overlook these targets, however like all necessary effort we’re removed from alone in assembly our targets:

  • Engineering Management: Determine prioritization and unblock wanted options to regressions systemically and tactically
  • Service House owners: Debug, perceive, and mitigate the foundation explanation for regressions, bettering the companies they personal over time
  • Reliability Engineering: Support service homeowners, develop tooling, and establish threats that should be resolved to keep up availability

All events mix SDI-R regressions with incident and buyer influence knowledge to align on an important points and drive them to conclusion.

We’ve discovered that by treating SDI-R as a “canary within the coal mine” as an alternative of ready for points to grow to be incidents, we’ve been capable of remedy reliability threats extra proactively. Points are:

  • Simpler to grasp and debug, for the reason that variety of issues breaking without delay is diminished
  • Recognized earlier, giving extra time to scope and implement any appropriate options
  • Usually solved earlier than prospects even discover, stopping outages solely

Rising the Service Supply Index from an concept to a program: Adoption

The SDI got here to fruition from an idea by our Chief Architect Keith Adams by which he tried to quantify the standard of a service with 4 measurements: Safety, Efficiency, High quality, and Reliability.

  • Safety: How rapidly are we addressing safety vulnerabilities? Monitor ticket shut price.
  • Efficiency: Is our service delivering responses to prospects well timed? Monitor API latency or consumer efficiency.
  • High quality: How rapidly are we addressing open software program defects? Monitor ticket shut price.
  • Reliability: Is our service reliably delivering requests to prospects? Monitor error charges.

Over time, every of these 4 areas have developed into their very own separate packages and are tracked as key metrics firm huge. We’ll speak concerning the Reliability program right here and the way we had been capable of set up a typical language that groups perceive and use to prioritize their work.

Slack—as a customer-first group—established a excessive bar of high quality and maintains a 99.99% availability SLA in buyer agreements. This requires a program that ensures the metric is being tracked and that there’s accountability.

The primary side of this system is visibility — we should perceive and see the sign of how nicely we’re assembly the SLA.

As soon as we’ve visibility, we deliver accountability. We publish this metric to a management group or firm huge group of stakeholders, and set up an goal of Reliability in planning. As soon as the target is revealed, and the important thing result’s monitored, we will then set up a hyperlink between the SDI and groups. The SDI permits us to hyperlink regressions to companies, which could be mapped to a group. As soon as the connection is made, we will then prioritize fixes or tradeoffs to appropriate the regression earlier than it turns into a SLA breach.

Scaling motion, studying, and prioritization

SDI-R is successfully an error finances that helps us resolve how a lot time the corporate and particular person groups ought to spend on launching new options, and once we should cease function work to concentrate on availability. On this manner, it helps us stability prioritization of investments throughout the corporate via a typical view of person influence.

Due to our sturdy perception in Service Possession, we’ve invested in instruments and processes that assist scale understanding and backbone of SDI-R impacting points.

We purpose to get the Proper Folks, in entrance of the Proper Downside, on the Proper Time

Monitoring, alerting, and observability instruments are necessary to scale the engineering response to customer-impacting points. We noticed a number of frequent use instances that had been value automating to make it simpler for service homeowners to keep up service stage goals (SLOs) and reply to regressions. The primary of which, Webapp Possession Device, is liable for automating the setup of alerts, SLOs, and dashboards for Slack API endpoints utilizing a typical set of metrics and infrastructure. Service homeowners can usually reply to and resolve an alert earlier than it turns into an SDI-R regression, using a typical set of logging, metrics, and tracing to feed again information of availability into the Software program Improvement Lifecycle. The second of which is Omni, Slack’s Service Catalog liable for being a system of document for possession and escalation. Omni contains SDI-R knowledge alongside owned APIs and infrastructure parts, enabling the escalation of points in dependencies and for us to robotically route regressions to the suitable group. These instruments are very efficient in guaranteeing response and backbone of acute points.

We purpose to do the issues that greatest serve our prospects

Organizationally, it is vital that we set up the right boards and instruments to grasp ongoing regressions and for efficient re-prioritization of investments to strike the proper stability between reliability and have work. The primary of those is the Engineering Monday Assembly, a daily discussion board for re-prioritization of investments and understanding by engineering management of ongoing buyer points and SDI-R regressions. Secondly, we report group and group stage aggregates of SDI-R that permit breakdown by organizational duty and monitoring of success over time. Each of those assist be sure that our organization-wide aim can scale and that each one groups are aligned in the direction of the shopper expertise. Usually we’ve discovered that groups self-service make the most of these experiences to seek out power points that slowly degrade the shopper expertise, however are in any other case not caught in incidents or alerting.

Not each system is ideal; there have been many classes

As we’ve labored with SDI-R over a few years, it has developed over time to be sure that it may well deliver most worth to our prospects.

Not all API requests are the identical

One of many issues we realized is that not all API requests are the identical. We might encounter points for particular customers that might be important for them however not transfer the general metric. This led to the institution of a breakdown of SDI-R for under our largest organizations and a weighting of various APIs by significance to correctly characterize the shopper influence regressions in them could have. Usually we’d discover that regressions would have an effect on our largest prospects first as they pushed the bounds of our merchandise and infrastructure, however that with this breakdown we’d be capable of resolve them proactively in the identical manner as the general SDI-R rating.

Lengthy-term maintainability

The delayed nature of SDI-R reporting generally led to a disconnect between the time that a problem occurred and when it impacted SDI-R. Nevertheless, we’ve discovered that as we’ve scaled SDI-R via service-specific alerting this has mattered much less, since by the point a problem was impacting SDI-R it will have already been captured by an alert.

It has grow to be more and more useful to put money into sustaining availability headroom by proactively fixing points earlier than our availability targets are liable to being violated. This proactive nature not solely reduces operational toil, however can be common apply in debugging and different abilities essential to triage and perceive regressions.

SDI-R has been so profitable as an method we’ve adopted it to make sure the provision of latest Slack merchandise and infrastructure as we scale, particularly for our GovSlack atmosphere.

Our method should constantly evolve

Over time with new product launches, buyer wants, and adjustments to our infrastructure it is vital that we constantly iterate on our metrics and processes in order that we will hold determining one of the simplest ways to measure our personal success. No enterprise is static, and we should not be afraid to study from failures and iterate to enhance our reliability over time.


As organizations quickly develop, it’s usually troublesome to remain proactive whereas additionally prioritizing availability and product work collectively. By specializing in our prospects, we’ve discovered SDI-R helpful in hanging this delicate stability. For each product and infrastructure, the shopper is an important factor and data-driven approaches mixed with the proper processes are crucial in the direction of protecting our prospects joyful and productive.


We wished to provide a shout out to all of the those who have contributed to this journey: 

Adam Fuchs, Ajay Patel, John Suarez, Bipul Pramanick, Justin Jeon, Nandini Tata, Shivam Shukla and all of these at Slack who’ve put our prospects first.


Concerned about taking over fascinating initiatives, making individuals’s work lives simpler, or bettering our reliability? We’re hiring! 💼 Apply now