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 robust concentrate on the client expertise, and customer love is one in all our core values. Slack has grown from a small group to 1000’s of workers 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 clear up a systemic challenge — we all know them as Hero Engineers. You may additionally comprehend it as an operations group, or a small group of Website 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 data riddled with burnout because the system turns into too complicated to be managed by just a few of us.

With any quickly rising complicated 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 be taught from these points by means of 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.

If you happen to’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 could 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 web site. It’s a composite measure  of profitable API calls and content material supply (as measured on the edge), together with essential 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 chief degree, and in observe is carried out fairly just by:

API Availability
availability api = profitable requests / complete requests

Total Availability
availability general = uptime standing web site * availability api

Chances are you’ll be asking why uptime and availability are completely different; uptime is set by monitoring key workflows which are vital to Slack’s usability and if the supply of any of these vital person interactions drops under a predetermined threshold, we rely the minutes that the service is under that threshold to find out downtime.

Since small adjustments in availability (~0.0001) can have a drastic impression on the client 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 observe day by day and hourly aggregates of availability, monitoring it over time in order that we are able to spot traits and establish regressions and enhancements.

We keep company-wide targets on this metric when it comes to the variety of days in 1 / 4 that we meet availability targets.

The Reliability Engineering group is essentially answerable for responding to and triaging regressions in availability that trigger or can probably trigger us to overlook these targets, however like several essential 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 reason behind regressions, bettering the providers they personal over time
  • Reliability Engineering: Assist service house owners, develop tooling, and establish threats that should be resolved to keep up availability

All events mix SDI-R regressions with incident and buyer impression information 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 clear up reliability threats extra proactively. Points are:

  • Simpler to grasp and debug, because the variety of issues breaking without delay is decreased
  • Recognized earlier, giving extra time to scope and implement any appropriate options
  • Typically solved earlier than prospects even discover, stopping outages totally

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 through which he tried to quantify the standard of a service with 4 measurements: Safety, Efficiency, High quality, and Reliability.

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

Over time, every of these 4 areas have advanced into their very own separate applications and are tracked as key metrics firm extensive. We’ll discuss in regards to the Reliability program right here and the way we had been capable of set up a standard 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 facet of this system is visibility — we should perceive and see the sign of how properly we’re assembly the SLA.

As soon as now we have visibility, we deliver accountability. We publish this metric to a management group or firm extensive 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 are able to then set up a hyperlink between the SDI and groups. The SDI permits us to hyperlink regressions to providers, which may be mapped to a group. As soon as the connection is made, we are able to 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 determine how a lot time the corporate and particular person groups ought to spend on launching new options, and after we should cease characteristic work to concentrate on availability. On this manner, it helps us stability prioritization of investments throughout the corporate by means of a standard view of person impression.

Due to our robust 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 Individuals, in entrance of the Proper Downside, on the Proper Time

Monitoring, alerting, and observability instruments are essential 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 house owners to keep up service degree targets (SLOs) and reply to regressions. The primary of which, Webapp Possession Device, is answerable for automating the setup of alerts, SLOs, and dashboards for Slack API endpoints utilizing a standard set of metrics and infrastructure. Service house owners can usually reply to and resolve an alert earlier than it turns into an SDI-R regression, using a standard set of logging, metrics, and tracing to feed again data of availability into the Software program Improvement Lifecycle. The second of which is Omni, Slack’s Service Catalog answerable for being a system of file for possession and escalation. Omni consists of SDI-R information alongside owned APIs and infrastructure parts, enabling the escalation of points in dependencies and for us to routinely 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’s important that we set up the right boards and instruments to grasp ongoing regressions and for efficient re-prioritization of investments to strike the suitable stability between reliability and have work. The primary of those is the Engineering Monday Assembly, an everyday 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 degree aggregates of SDI-R that permit breakdown by organizational duty and monitoring of success over time. Each of those assist make it possible for our organization-wide purpose can scale and that each one groups are aligned in direction of the client expertise. Typically we’ve discovered that groups self-service make the most of these studies to search out persistent points that slowly degrade the client 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 advanced over time to make it possible for it may possibly 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 vital 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 signify the client impression regressions in them might have. Typically 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 difficulty occurred and when it impacted SDI-R. Nevertheless, we’ve discovered that as we’ve scaled SDI-R by means of service-specific alerting this has mattered much less, since by the point a difficulty was impacting SDI-R it will have already been captured by an alert.

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

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

Our strategy should constantly evolve

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


As organizations quickly develop, it’s usually tough 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 client is an important factor and data-driven approaches mixed with the suitable processes are vital in direction of maintaining our prospects joyful and productive.


We needed to present a shout out to all of the people 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.


Involved in taking up attention-grabbing tasks, making folks’s work lives simpler, or bettering our reliability? We’re hiring! 💼 Apply now