One of the most important features of any software tool or web application is its reliability. Businesses that offer slow or unreliable software services always risk losing customers to better, more competent service providers. This makes it important for businesses to constantly monitor and enhance the performance and reliability of their digital systems. While this is relatively easier to do manually for small-to-medium-sized systems, it simply isn’t feasible for large businesses (with hundreds and even thousands of machines). SRE is a standard DevOps practice that serves to solve this problem through intelligent automation.
SRE, or Site Reliability Engineering, is the practice of using automation tools to dynamically monitor and manage digital applications. SRE helps organizations maintain a quick and reliable update cadence by allowing them to keep their applications stable and reliable despite frequent updates. It also allows them to scale applications quickly without having to worry about performance or reliability.
One of the most widely used SRE tools is an SRE dashboard - a dashboard that provides a high-level view of the reliability of any application, deployment, or code tiers within selected environments. SRE dashboards are an integral part of any SRE system. Let’s try to understand the different types of SRE dashboards and what makes them so important for development teams.
The need for SRE dashboards
Reliable digital systems often have appropriate observability measures in place which make it easier for developers to diagnose system issues (including performance lag or component failure/outage) in real-time. Observability is the ability of a system to be diagnosed for performance issues as soon as they happen. The ultimate goal of observability is to ensure that your development team has eyes on the system 24x7. This would help them identify and fix issues before they cause any customer-facing problems. Merely monitoring systems through timely performance reports and audits does not make them observable.
While monitoring and observability might seem like synonymous concepts, they have key differences. Monitoring is the retrospective diagnosis of system issues based on collected data. This can be done through comprehensive performance reports or by measuring specific parameters for each individual system component. Observability, on the other hand, is the active, real-time monitoring of systems using tools like performance dashboards. Observability is a more immediate and all-encompassing way of diagnosing system issues, which is crucial for efficient SRE processes.
From the point of view of a developer or a manager, monitoring digital systems means checking them for specific parameters like page load times, average response time, CPU usage, and downtime. The problem here is that large, complex systems can have repetitive issues. If a new system component keeps going bust in new ways each week, it can become difficult to settle on a list of parameters to include in your system reports.
Observability processes allow you to monitor system performance in real-time. Development teams can easily notice a lag in system performance using observability tools and go about diagnosing the problem. This makes observability a key feature of all SRE-optimized digital systems. SRE dashboards help development teams maintain smooth and efficient SRE processes by making the systems more visible through efficient visualization of budding system issues.
Different types of dashboarding tools
Development teams can use different types of dashboarding tools to build their custom SRE dashboards. Your choice of dashboarding tools depends entirely on the parameters you wish to actively monitor and the end goal of your SRE processes. This could be simply ensuring the infrastructural health and visibility of your digital systems, tracking the adoption of best methods and practices, or value stream management.
Observability and monitoring
Observability and monitoring tools allow you to keep track of operational metrics related to your services and system infrastructure. The primary purpose of these tools is to actively diagnose the operational and infrastructural health of digital systems by keeping an eye on tell-tale metrics like latency and page load times. These tools assist development teams in effective risk mitigation and incident management. A standard observability tool is expected to have three key components,
Metrics are a straightforward measure of key systems health parameters like memory usage, bandwidth utilization, and HTTP requests per second. Observability dashboards usually display an assortment of these metrics or their aggregates depending on the number of systems being observed.
Logs are simply collated records of everything that happens in a system or application. They are either stored as plain text, encrypted structured files, or binary text. Logs allow developers and system administrators to look at the last few events that occurred before a system issue was detected. They are essentially a map of all the dependencies present in the system.
Traces are flowcharts or representational profiles of entire processes as they are carried out within a digital system. They link all individual events that are carried out throughout the distributed system when a transaction request is received from the user. Traces help software development teams figure out how different services contend for network and storage resources and what’s causing them to lag.
Some of the most commonly used observability and monitoring tools are Datadog, Dynatrace, AppDynamics, and New Relic. Alternatively, you can also use open-source environments like Prometheus and Grafana to build custom observability tools.
Adoption of best practices and standards
This kind of dashboard is used to conduct a competitive analysis of the business landscape to ensure that all of your monitoring, observability, and SLO/SLI practices are up to date. Standard practices dashboards conduct a comprehensive analysis to track and define the best monitoring and SRE practices and determine to what degree they have been adopted by your teams. They essentially help you keep your systems competent and up-to-date, which is crucial for providing a quality customer experience that can help businesses gain and retain new customers. Tools like Cortex help businesses track the efficiency of their current systems (by measuring your processes against standard industry practices) by tracking several components such as service ownership, production readiness, and migration security.
Service Level Objectives dashboard
Service Level Objectives, or SLOs, are integral to the user experience provided by any service provider. SLOs are essentially the promises or guarantees a business makes to its users regarding the efficiency and effectiveness of its systems. For example, if a search engine makes a 99.99% uptime guarantee, it promises its users that they will be able to see the desired search results 99.99% of the time. SLOs depict the reliability of the service provided by a business. Service providers must ensure that their systems meet set SLOs. Failure to do so might damage a business’s reputation and hinder its ability to attract new customers.
Monitoring SLIs (Service Level Indicators, which are essentially SLO metrics) is a specialized SRE process that focuses solely on the ability of the system to constantly perform up to set standards. SLO dashboards can be customized to include specific parameters that allow developers to monitor relevant objectives - for instance, a set uptime guarantee or latency.
One important thing to remember here is to avoid complicating your SLOs. Keep in mind that SLOs should be tailored entirely around Service Level Agreements (SLAs), which are the actual user-facing guarantees that you make. For example, ensuring that all your users can store up to 256 GB of data per account without affecting system performance. Your SLO should then focus on what parameters need to be monitored here (uptime, latency, and storage space) to ensure that the SLA is fulfilled.
Things to keep in mind while developing your SRE dashboards
You can create one or more SRE dashboards depending on the depth and expanse of your SRE teams. Each dashboard should be a single-view dashboard that displays all relevant parameters (or their aggregates) in an easy-to-read format. For complex SRE processes, each individual API can be assigned its own SRE dashboards that measure its infrastructural and operational health. Data from each individual dashboard can then be aggregated and collated together for a central SRE dashboard that provides visibility into the overall health of the system.
Dashboards can also be segregated according to teams and subteams in an arrangement where each sub-team has its own dashboard that feeds into the aggregate dashboard that is referred to by the main SRE and system administration team. You should also avoid having too much overlap between two dashboards, say monitoring and SLO dashboards. This is because when too many metrics overlap, teams tend to instinctively rely on one of the dashboards instead of paying apt attention to all of them. This could cause them to overlook important vulnerabilities in the system that could try into bigger, client-facing issues.
Another thing to keep in mind is that the dashboards could display actionable recommendations for the SRE team and not just complex metrics. Simply displaying metrics without helpful signs like critical/threshold values, trigger notifications, and system warnings would defeat the core purpose of the SRE dashboard, which is to offer instant visibility into your system’s operational and infrastructural health. Benchmarks and associated parameters help teams spot system problems much easier and communicate actionable recommendations to concerned teams.
You can then add more granular visibility controls about the SRE workflow at your convenience. This can include special indicators that tell you whether the issue has been appropriately communicated to the development/troubleshooting team and whether they’re taken any correctional measures yet. The ultimate goal of your SRE dashboard(s) should be to simplify and optimize your SRE workflow by allowing for simple and effective messaging between teams. While it is important to keep track of all relevant performance and system health metrics, it is as crucial to keep the dashboards easy to read and interpret to avoid confusing your teams with data noise.
Dashboarding the human aspect of SRE
An important yet often overlooked aspect of an efficient SRE process is having reliable teams that are capable of responding to changes and adopting the best possible communication and operational practices along the way. Cultivating a culture of efficiency and reliability within your teams helps ensure that they perform up to expected standards and become capable of functioning and upgrading almost independently. Dashboarding tools like scorecards can prove to be a great help here. Scorecarding essentially divides any parameter into several broad categories (for example, project turnaround time can be divided into quick, average, slow, and very slow), each of which is associated with an indicative measure like a score out of 10 or card color. This helps teams keep track of important factors like employee efficiency, team efficiency, or the average time needed to finish a project. Scorecarding also informs teams of the efficiency of their current practices and promotes the adoption of best available practices for better software development and debugging lifecycles. Dashboarding should be viewed as a holistic management tool that helps businesses with several things like adopting best practices, proactive risk mitigation, and specialized SLO monitoring.
Four golden signals of SRE
When the concept of SRE was first developed at Google, the core objective of the team was to develop a practice that could help development teams gain further visibility into their systems’ operational and infrastructural health. The team was soon met with important questions - what exactly is system health? How do you define healthy systems? Without a somewhat standard definition, the team would just be adopting arbitrary benchmarks which may or may not be representative of the system’s actual capabilities and user demand. For instance, a 100% uptime is ideal but is it possible for any service to consistently achieve it? If not, what is the highest possible uptime a business should strive for? (99.99% is the industry standard).
The four golden signals of SRE were soon defined to establish a definition of a healthy system. SRE teams are recommended to establish benchmarks of a healthy system for each of these metrics. While they should always monitor more metrics and logs as a part of their SRE processes, these four golden signals are the basic, essential building blocks of any effective SRE strategy:
Latency
Latency is the average time your system takes to respond to a user request. It is a direct, user-facing measure of your system’s efficiency. Like uptime, system latency should also be measured against a well-defined benchmark as opposed to an impossibly high standard. Once you settle on an apt benchmark after observing your system and conducting competitive analysis, you can start monitoring the latency of successful requests across your system. Individually monitoring the latency of each service or module allows you to pinpoint problem areas quickly and make appropriate changes to improve your query response time. Similarly, the latency of failed requests can be measured to establish sound incident response protocols that help guide your development and troubleshooting teams.
Traffic
Traffic is simply the number of visitors or users your system sees at any given time. It is a measure of the demand stress that your system faces from the users. What counts as traffic is a question that is subjective to each business and its systems. While it might make more sense for some businesses to count the number of site visits as traffic, others might want to count specific user interactions like filing a webform or clicking on a link. Ideally, while monitoring the operational and infrastructural health of a digital system, the number of service requests received at any given time is considered to be traffic. By monitoring traffic-related metrics, SRE teams can monitor how the system responds to changing usage and demand.
Errors
The error rate of a system is the percentage of all user requests that fail. SRE teams are expected to monitor the average error rate for their system at both the overall system and individual service levels. You can define what counts as an error through manually defined logic while also flagging explicit errors such as failed HTTP requests. Errors help SRE teams estimate the overall operational health of a digital system. They can further classify errors under different categories such as critical or negligible to estimate the ‘true health’ of a system.
Saturation
Saturation, as the term suggests, is a measure of the capacity of a system to function at peak efficiency. This capacity can be measured by measuring any relevant parameter of choice like service requests, query response time, and other traffic-related metrics. It is important to know that most digital systems start to degrade when they function at their 100% capacity. This is why it is important to set a saturation benchmark around the point where the system starts losing its ability to function at its peak. Saturation allows SRE teams to understand what conditions need to be established to help the system perform at its peak capacity so that all SLOs are met comfortably.
At Cortex, we help development teams ensure that all their practices meet set industry standards. Developers can track key system health and security aspects like service maturity, production readiness, migration security, and overall engineering efficiency using custom Scorecards. In all, Cortex helps software engineering organizations gain visibility into their services and deliver high-quality software.
