Service Supply Index: A Driver for Reliability

Buyer-first: Transferring from Hero Engineering to Reliability Engineering

From the start, Slack has all the time had a robust deal with the shopper expertise, and customer love is certainly one of our core values. Slack has grown from a small workforce to hundreds of workers over time and this buyer love has all the time included a deal with service reliability.

In a small startup, it’s manageable to have a reactive reliability focus. For instance, one engineer can troubleshoot and resolve a systemic problem — we all know them as Hero Engineers. You might also comprehend it as an operations workforce, or a small workforce of Website Reliability Engineers which might be 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 complicated to be managed by just a few of us.

With any quickly rising complicated product, it’s onerous to maneuver away from a reactionary deal with 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.

In case 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 have got 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 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 chief degree, and in observe is carried out fairly just by:

API Availability
availability api = profitable requests / complete requests

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

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

Since small adjustments in availability (~0.0001) can have a drastic impression 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 each 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 when it comes to the variety of days in 1 / 4 that we meet availability targets.

The Reliability Engineering workforce is basically chargeable for responding to and triaging regressions in availability that trigger or can probably trigger us to overlook these targets, however like several 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 Homeowners: Debug, perceive, and mitigate the basis explanation for regressions, enhancing the providers they personal over time
  • Reliability Engineering: Support service homeowners, develop tooling, and establish threats that have to be resolved to keep up availability

All events mix SDI-R regressions with incident and buyer impression knowledge to align on crucial 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 develop into incidents, we’ve been in a position to resolve reliability threats extra proactively. Points are:

  • Simpler to know and debug, because the variety of issues breaking directly is lowered
  • Recognized earlier, giving extra time to scope and implement any appropriate options
  • Usually solved earlier than clients 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 wherein 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 price.
  • Efficiency: Is our service delivering responses to clients well timed? Monitor API latency or shopper efficiency.
  • High quality: How shortly are we addressing open software program defects? Monitor ticket shut price.
  • Reliability: Is our service reliably delivering requests to clients? 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 large. We’ll discuss in regards to the Reliability program right here and the way we have been in a position to 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 effectively we’re assembly the SLA.

As soon as we now have visibility, we carry accountability. We publish this metric to a management group or firm large 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 providers, which could be mapped to a workforce. 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 price range 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 deal with availability. On this means, it helps us steadiness prioritization of investments throughout the corporate by means of a standard view of person impression.

Due to our sturdy perception in Service Possession, we’ve invested in instruments and processes that assist scale understanding and determination 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 have been value automating to make it simpler for service homeowners to keep up service degree goals (SLOs) and reply to regressions. The primary of which, Webapp Possession Device, is chargeable for automating the setup of alerts, SLOs, and dashboards for Slack API endpoints utilizing a standard 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 standard set of logging, metrics, and tracing to feed again information of availability into the Software program Growth Lifecycle. The second of which is Omni, Slack’s Service Catalog chargeable for being a system of file 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 mechanically route regressions to the suitable workforce. These instruments are very efficient in making certain response and determination of acute points.

We purpose to do the issues that finest serve our clients

Organizationally, it can be crucial that we set up the right boards and instruments to know ongoing regressions and for efficient re-prioritization of investments to strike the fitting steadiness 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 workforce degree aggregates of SDI-R that enable breakdown by organizational accountability and monitoring of success over time. Each of those assist ensure that our organization-wide objective can scale and that every one groups are aligned in direction of the shopper expertise. Usually we’ve discovered that groups self-service make the most of these studies to search out continual 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 ensure that it could carry most worth to our clients.

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 signify the shopper impression regressions in them might have. Usually we’d discover that regressions would have an effect on our largest clients first as they pushed the boundaries of our merchandise and infrastructure, however that with this breakdown we’d have the ability to resolve them proactively in the identical means 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 develop into more and more helpful 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 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 recent Slack merchandise and infrastructure as we scale, specifically for our GovSlack surroundings.

Our strategy should repeatedly evolve

Over time with new product launches, buyer wants, and adjustments to our infrastructure it can be crucial that we repeatedly iterate on our metrics and processes in order that we will maintain 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 troublesome to remain proactive whereas additionally prioritizing availability and product work collectively. By specializing in our clients, we’ve discovered SDI-R helpful in placing this delicate steadiness. For each product and infrastructure, the shopper is crucial factor and data-driven approaches mixed with the fitting processes are vital in direction of preserving our clients pleased and productive.


We wished to offer 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 clients first.


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