Site Reliability Engineering Quotes

When a team must allocate a disproportionate amount of time to resolving tickets at the cost of spending time improving the service, scalability and reliability suffer.

team size should not scale directly with service growth.

Hope is not a strategy.

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100% is the wrong reliability target for basically everything (pacemakers and anti-lock brakes being notable exceptions).

The launch cycle at Internet companies is markedly different. Launches and rapid iterations are far easier because new features can be rolled out on the server side, rather than requiring software rollout on individual customer workstations.

The Role of the Launch Coordination Engineer Our Launch Coordination Engineering team is composed of Launch Coordination Engineers (LCEs), who are either hired directly into this role, or are SREs with hands-on experience running Google services. LCEs are held to the same technical requirements as any other SRE, and are also expected to have strong communication and leadership skills

Any organization that aspires to be serious about running an effective SRE arm needs to consider training. Teaching SREs how to think in a complicated and fast-changing environment with a well-thought-out and well-executed training program has the promise of instilling best practices within a new hire’s first few weeks or months that otherwise would take months or years to accumulate.

Hiring SREs well is critical to having a high-functioning reliability organization, as explored in “Hiring Site Reliability Engineers” [Jon15]. Google’s hiring practices have been detailed in texts like Work Rules! [Boc15],1 but hiring SREs has its own set of particularities. Even by Google’s overall standards, SRE candidates are difficult to find and even harder to interview effectively.

Making the jump from a previous company or university, while changing job roles (from traditional software engineer or traditional systems administrator) to this nebulous Site Reliability Engineer role is often enough to knock students’ confidence down several times. For more introspective personalities (especially regarding questions #2 and #3), the uncertainties incurred by nebulous or less-than-clear answers can lead to slower development or retention problems.

Which backends of this server are considered “in the critical path,” and why? What aspects of this server could be simplified or automated? Where do you think the first bottleneck is in this architecture? If that bottleneck were to be saturated, what steps could you take to alleviate it?

In the course of their jobs, they will come across systems they’ve never seen before, so they need to have strong reverse engineering skills.

At scale, there will be anomalies that are hard to detect, so they’ll need the ability to think statistically, rather than procedurally, to uncloak problems.

When standard operating procedures break down, they’ll need to be able to improvise fully.

Those who cannot remember the past are condemned to repeat it.” George Santayana, philosopher and essayist

It’s important to establish credibility by delivering some product of value in a reasonable amount of time. Your first round of products should aim for relatively straightforward and achievable targets — ones without controversy or existing solutions. We

Production meetings are a special kind of meeting where an SRE team carefully articulates to itself — and to its invitees — the state of the service(s) in their charge, so as to increase general awareness among everyone who cares, and to improve the operation of the service(s).

Connecting the performance of the service with design decisions in a regular meeting is an immensely powerful feedback loop.

There’s a lot of evidence suggesting that diverse teams are simply better teams [Nel14]

the SRE Way in mind: thoroughness and dedication, belief in the value of preparation and documentation, and an awareness of what could go wrong, coupled with a strong desire to prevent it.

Don’t be afraid to provide white glove customer support for early adopters to help them through the onboarding process. Sometimes automation also entails a host of emotional concerns, such as fear that someone’s job will be replaced by a shell script. By working one-on-one with early users, you can address those fears personally, and demonstrate that rather than owning the toil of performing a tedious task manually, the team instead owns the configurations, processes, and ultimate results of their technical work. Later adopters are convinced by the happy examples of early adopters.

To this end, Google always strives to staff its SRE teams with a mix of engineers with traditional software development experience and engineers with systems engineering experience.

In order to remain reliable and to avoid scaling the number of SREs supporting a service linearly, the production environment has to run mostly unattended. To remain unattended, the environment must be resilient against minor faults.

And, as we all know, culture beats strategy every time: [Mer11]

Site Reliability Engineering is an approach to the operation and improvement of software applications pioneered by Google to deal with their global, multi-million-user systems. If adopted in full, SRE is significantly different from IT operations of the past, due to its focus on the “error budget” (namely defining what is an acceptable amount of downtime) and the ability of SRE teams to push back on poor software.

When an engineer with years of familiarity in a problem space begins designing a product, it’s easy to imagine a utopian end-state for the work. However, it’s important to differentiate aspirational goals of the product from minimum success criteria (or Minimum Viable Product). Projects can lose credibility and fail by promising too much,

Putting alerts into email and hoping that someone will read all of them and notice the important ones is the moral equivalent of piping them to /dev/null: they will eventually be ignored.

Software engineering has this in common with having children: the labor before the birth is painful and difficult, but the labor after the birth is where you actually spend most of your effort. Yet

Blameless culture originated in the healthcare and avionics industries where mistakes can be fatal. These industries nurture an environment where every “mistake” is seen as an opportunity to strengthen the system. When postmortems shift from allocating blame to investigating the systematic reasons why an individual or team had incomplete or incorrect information, effective prevention plans can be put in place. You can’t “fix” people, but you can fix systems and processes to better support people making the right choices when designing and maintaining complex systems.

A key principle of any effective software engineering, not only reliability-oriented engineering, simplicity is a quality that, once lost, can be extraordinarily difficult to recapture.

engage in development tasks, because the service basically runs and repairs itself: we want systems that are automatic, not just automated. In practice, scale and new features keep SREs on their toes.

In general, an SRE team is responsible for the availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning of their service(s). We

Google’s Site Reliability Engineering (SRE) team has a motto: “Hope is not a strategy.

Hope is not a strategy. Traditional SRE saying

By design, it is crucial that SRE teams are focused on engineering. Without constant engineering, operations load increases and teams will need more people just to keep pace with the workload.

If a human operator needs to touch your system during normal operations, you have a bug. The definition of normal changes as your systems grow. Carla Geisser, Google SRE

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The four golden signals of monitoring are latency, traffic, errors, and saturation. If you can only measure four metrics of your user-facing system, focus on these four.

And taking the historical view, who, then, looking back, might be the first SRE? We like to think that Margaret Hamilton, working on the Apollo program on loan from MIT, had all of the significant traits of the first SRE.5 In her own words, “part of the culture was to learn from everyone and everything, including from that which one would least expect.

Software engineering has this in common with having children: the labor before the birth is painful and difficult, but the labor after the birth is where you actually spend most of your effort.

In order to base these decisions on objective data, the two teams jointly define a quarterly error budget based on the service’s service level objective, or SLO (see Chapter 4). The error budget provides a clear, objective metric that determines how unreliable the service is allowed to be within a single quarter. This metric removes the politics from negotiations between the SREs and the product developers when deciding how much risk to allow.

Figure 2-4 shows how a user’s request is serviced: first, the user points their browser to shakespeare.google.com. To obtain the corresponding IP address, the user’s device resolves the address with its DNS server (1). This request ultimately ends up at Google’s DNS server, which talks to GSLB. As GSLB keeps track of traffic load among frontend servers across regions, it picks which server IP address to send to this user. Figure 2-4. The life of a request The browser connects to the HTTP server on this IP. This server (named the Google Frontend, or GFE) is a reverse proxy that terminates the TCP connection (2). The GFE looks up which service is required (web search, maps, or—in this case—Shakespeare). Again using GSLB, the server finds an available Shakespeare frontend server, and sends that server an RPC containing the HTTP request (3).

All of Google’s services communicate using a Remote Procedure Call (RPC) infrastructure named Stubby; an open source version, gRPC, is available. 3 Often, an RPC call is made even when a call to a subroutine in the local program needs to be performed. This makes it easier to refactor the call into a different server if more modularity is needed, or when a server’s codebase grows. GSLB can load balance RPCs in the same way it load balances externally visible services.

SRE’s goal is no longer “zero outages”; rather, SREs and product developers aim to spend the error budget getting maximum feature velocity.

When humans are necessary, we have found that thinking through and recording the best practices ahead of time in a “playbook” produces roughly a 3x improvement in MTTR as compared to the strategy of “winging it.

You might expect Google to try to build 100% reliable services—ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a cost: maximizing stability limits how fast new features can be developed and how quickly products can be delivered to users, and dramatically increases their cost, which in turn reduces the numbers of features a team can afford to offer. Further, users typically don’t notice the difference between high reliability and extreme reliability in a service, because the user experience is dominated by less reliable components like the cellular network or the device they are working with. Put simply, a user on a 99% reliable smartphone cannot tell the difference between 99.99% and 99.999% service reliability! With this in mind, rather than simply maximizing uptime, Site Reliability Engineering seeks to balance the risk of unavailability with the goals of rapid innovation and efficient service operations, so that users’ overall happiness—with features, service, and performance—is optimized.

If we were to build and operate these systems at one more nine of availability, what would our incremental increase in revenue be?

If we were to build and operate these systems at one more nine of availability, what would our incremental increase in revenue be? Does this additional revenue offset the cost of reaching that level of reliability?

Note that we can run multiple classes of services using identical hardware and software. We can provide vastly different service guarantees by adjusting a variety of service characteristics, such as the quantities of resources, the degree of redundancy, the geographical provisioning constraints, and, critically, the infrastructure software configuration.

a system that serves 2.5M requests in a day with a daily availability target of 99.99% can serve up to 250 errors and still hit its target for that given day.

A key principle of any effective software engineering, not only reliability-oriented engineering, simplicity is a quality that, once lost, can be extraordinarily difficult to recapture. Nevertheless, as the old adage goes, a complex system that works necessarily evolved from a simple system that works. Chapter 9, Simplicity, goes into this topic in detail.

For example, if product development wants to skimp on testing or increase push velocity and SRE is resistant, the error budget guides the decision. When the budget is large, the product developers can take more risks. When the budget is nearly drained, the product developers themselves will push for more testing or slower push velocity, as they don’t want to risk using up the budget and stall their launch.

Investing up front in the education and technical orientation of new SREs will shape them into better engineers. Such training will accelerate them to a state of proficiency faster, while making their skill set more robust and balanced.

Successful SRE teams are built on trust

How can we harness the enthusiasm and curiosity in our new hires to make sure that existing SREs benefit from it?

John is the newest member of the FooServer SRE team. Senior SREs on this team are tasked with a lot of grunt work, such as responding to tickets, dealing with alerts, and performing tedious binary rollouts.

An upfront investment in SRE training is absolutely worthwhile, both for the students eager to grasp their production environment and for the teams grateful to welcome students into the ranks of on-call.

If you currently assign tickets randomly to victims on your team, stop. Doing so is extremely disrespectful of your team’s time, and works completely counter to the principle of not being interruptible as much as possible.

The hero jack-of-all-trades on-call engineer does work, but the practiced on-call engineer armed with a playbook works much better

Best practices in this domain use automation to accomplish the following: Implementing progressive rollouts Quickly and accurately detecting problems Rolling back changes safely when problems arise

monitoring is an absolutely essential component of doing the right thing in production. If you can’t monitor a service, you don’t know what’s happening, and if you’re blind to what’s happening, you can’t be reliable.

Whenever you see leader election, critical shared state, or distributed locking, think about distributed consensus: any lesser approach is a ticking bomb waiting to explode in your systems.

(Synchronous consensus applies to real-time systems, in which dedicated hardware means that messages will always be passed with specific timing guarantees.)

[Dea07] J. Dean, “Software Engineering Advice from Building Large-Scale Distributed Systems”, Stanford CS297 class lecture, Spring 2007.

Introducing randomness is the best approach. Raft [Ong14], for example, has a well-thought-out method of approaching the leader election process.

Quorum leases [Mor14] are a recently developed distributed consensus performance optimization aimed at reducing latency and increasing throughput for read operations.

Quorum leases are particularly useful for read-heavy workloads in which reads for particular subsets of the data are concentrated in a single geographic region.

For non-Byzantine failures, the minimum number of replicas that can be deployed is three — if two are deployed, then there is no tolerance for failure of any process. Three replicas may tolerate one failure.

Automation is a force multiplier, not a panacea.

Use randomized exponential backoff on errors

Antagonistic neighbors Other processes (often completely unrelated and run by different teams) can have a significant impact on the performance of your processes. We’ve seen differences in performance of this nature of up to 20%. This difference mostly stems from competition for shared resources, such as space in memory caches or bandwidth, in ways that may not be directly obvious.

Often, sheer force of effort can help a rickety system achieve high availability, but this path is usually short-lived and fraught with burnout and dependence on a small number of heroic team members.

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Extreme reliability comes at a cost: maximizing stability limits how fast new features can be developed and how quickly products can be delivered to users, and dramatically increases their cost, which in turn reduces the numbers of features a team can afford to offer.

Google places a 50% cap on the aggregate “ops” work for all SREs —

An SLI is a service level indicator — a carefully defined quantitative measure of some aspect of the level of service that is provided.

An SLO is a service level objective: a target value or range of values for a service level that is measured by an SLI. A natural structure for SLOs is thus SLI ≤ target, or lower bound ≤ SLI ≤ upper bound.

SLAs are service level agreements: an explicit or implicit contract with your users that includes consequences of meeting (or missing) the SLOs they contain. The consequences are most easily recognized when they are financial — a rebate or a penalty — but they can take other forms.

Google Search is an example of an important service that doesn’t have an SLA for the public: we want everyone to use Search as fluidly and efficiently as possible, but we haven’t signed a contract with the whole world.

Big data systems, such as data processing pipelines, tend to care about throughput and end-to-end latency. In other words: How much data is being processed? How long does it take the data to progress from ingestion to completion? (Some pipelines may also have targets for latency on individual processing stages.)

You can always refine SLO definitions and targets over time as you learn about a system’s behavior. It’s better to start with a loose target that you tighten than to choose an overly strict target that has to be relaxed when you discover it’s unattainable.

If your service’s actual performance is much better than its stated SLO, users will come to rely on its current performance. You can avoid over-dependence by deliberately taking the system offline occasionally (Google’s Chubby service introduced planned outages in response to being overly available),3 throttling some requests, or designing the system so that it isn’t faster under light loads.

With explicitly delineated levels of service, the infrastructure providers can effectively externalize the difference in the cost it takes to provide service at a given level to clients. Exposing cost in this way motivates the clients to choose the level of service with the lowest cost that still meets their needs. For example, Google + can decide to put data critical to enforcing user privacy in a high-availability, globally consistent datastore (e.g., a globally replicated SQL-like system like Spanner [Cor12]), while putting optional data (data that isn’t critical, but that enhances the user experience) in a cheaper, less reliable, less fresh, and eventually consistent datastore (e.g., a NoSQL store with best-effort replication like Bigtable).

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Remember that the code path you never use is the code path that (often) doesn’t work.

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