Introduction to Service Level Indicators (SLIs)

Introduction to Service Level Indicators (SLIs): Measuring What Matters

In the world of software and service delivery, providing a reliable and performant experience is paramount. But how do you objectively measure reliability and performance? That’s where Service Level Indicators (SLIs) come in. SLIs are the foundation of a robust system for managing service health and ensuring user satisfaction. They form the basis for Service Level Objectives (SLOs) and Service Level Agreements (SLAs), but understanding SLIs is the crucial first step.

This article provides a detailed introduction to SLIs, covering what they are, why they’re important, how to choose them, and best practices for implementing them.

What is a Service Level Indicator (SLI)?

A Service Level Indicator (SLI) is a carefully defined quantitative measure of some aspect of the level of service that is provided to a customer (internal or external). Crucially, SLIs focus on outcomes rather than internal implementation details. They represent what a user experiences when interacting with a service.

Think of it this way: an SLI answers the question, “How well is a specific part of our service performing from the user’s perspective?” It’s not about how the service works, but if it works effectively for the user.

Key Characteristics of a Good SLI:

• Quantitative: SLIs must be measurable and expressed numerically. Vague terms like “the service is fast” are not SLIs.
• User-Centric: The SLI should reflect something that directly impacts the user’s experience.
• Outcome-Oriented: Focus on the result of a user interaction, not the internal processes.
• Specific: Each SLI should measure a single, well-defined aspect of the service.
• Understandable: The SLI should be easily understood by both technical and non-technical stakeholders.
• Aggregatable: It should be possible to aggregate the SLI over time (e.g., daily, weekly, monthly) to track trends.
• Actionable: Changes in the SLI should trigger investigation and potential corrective actions.
• Proportion of Good Events: Often expressed as a ratio or percentage of “good” events over “valid” events.

Examples of Common SLIs:

Here are some examples of SLIs for different types of services, illustrating the concept of “good” events over “valid” events:

• Availability:

  • SLI: The percentage of successful HTTP requests over all HTTP requests.
    • Good Event: An HTTP request that returns a 2xx or 3xx status code.
    • Valid Event: Any HTTP request.
  • Example: 99.95% of HTTP requests return successfully.

• Latency (Response Time):

  • SLI: The percentage of requests that are processed in under 200ms.
    • Good Event: A request that completes within 200ms.
    • Valid Event: Any request.
  • Example: 99% of requests are processed in under 200ms. (Alternatively, you might use a percentile like “P95 latency is less than 200ms,” meaning 95% of requests are faster than 200ms.)

• Error Rate:

  • SLI: The percentage of requests that result in a 5xx error code.
    • Good Event: A request not returning a 5xx error.
    • Valid Event: Any request.
  • Example: The error rate is less than 0.1%.

• Throughput:

  • SLI: The number of successful requests processed per second.
    • Good Event: A successfully processed request.
    • Valid Event: A unit of time (one second).
    • Note: This is not a ratio, but a raw count. It’s still an SLI because it’s a quantitative measure of a user-relevant aspect.
  • Example: The service processes 1000 requests per second.

• Durability (for data storage):

  • SLI: The percentage of data successfully written and retrieved over a given period.
    • Good Event: Data is written and can be retrieved later without corruption.
    • Valid Event: Any data write operation.
    • Example: 99.9999% of data writes are durable.

• Freshness (for data pipelines):

  • SLI: The percentage of data updates that are processed within 5 minutes.
  • Good Event: A data update processed within 5 minutes.
  • Valid Event: Any data update.
  • Example: 95% of data updates are processed within 5 minutes.

• Correctness (for data processing):

  • SLI: The percentage of processed data that matches the expected output.
    • Good Event: Processed data that is correct.
    • Valid Event: All processed data.
  • Example: 99.9% of processed data is correct.

Why are SLIs Important?

SLIs are crucial for several reasons:

• Objective Measurement: They provide a data-driven way to assess service health, removing subjectivity and guesswork.
• User Focus: They ensure that you are monitoring and improving the things that actually matter to your users.
• Foundation for SLOs: SLIs are the building blocks of Service Level Objectives (SLOs). You cannot define an SLO without first defining the underlying SLI.
• Prioritization: They help you prioritize engineering efforts by identifying areas where the service is underperforming.
• Communication: They provide a clear and common language for discussing service performance with stakeholders.
• Alerting and Monitoring: SLIs are used to trigger alerts when the service is not meeting its performance targets.
• Capacity Planning: By tracking SLIs over time, you can gain insights into future capacity needs.
• Continuous Improvement: SLIs provide a framework for continuous improvement by allowing you to track the impact of changes and optimizations.

How to Choose the Right SLIs:

Choosing the right SLIs is critical. Too many SLIs can be overwhelming, while too few might not provide a complete picture. Here’s a process for selecting appropriate SLIs:

1. Identify Critical User Journeys (CUJs): Start by understanding the most important things users do with your service. What are the key interactions that drive value?
2. Map CUJs to Technical Components: Break down each CUJ into the underlying technical components and services involved.
3. Identify Potential Metrics: For each component, brainstorm potential metrics that could indicate the quality of service. Think about availability, latency, error rate, throughput, etc.
4. Evaluate and Select SLIs: From the potential metrics, select the ones that best meet the characteristics of a good SLI (listed above). Focus on the metrics that are most directly tied to user experience and are actionable.
5. Refine and Iterate: SLIs are not set in stone. As your service evolves and you gain more understanding, you may need to refine or replace your SLIs.

Best Practices for Implementing SLIs:

• Start Simple: Don’t try to measure everything at once. Begin with a small set of key SLIs and expand as needed.
• Document Everything: Clearly document each SLI, including its definition, how it’s measured, and the rationale behind it.
• Automate Measurement: Use monitoring tools and dashboards to automatically collect and visualize SLI data.
• Regularly Review: Periodically review your SLIs to ensure they are still relevant and effective.
• Use Percentiles: For latency, consider using percentiles (e.g., P95, P99) rather than averages. Averages can be misleading and mask outliers.
• Consider Different Time Windows: You may want to track SLIs over different time windows (e.g., hourly, daily, weekly) to identify different patterns.
• Don’t Confuse SLIs with Internal Metrics: Internal metrics (like CPU utilization or database query times) are not SLIs. They can be inputs to calculating SLIs, but they don’t directly reflect the user experience.
• Set Realistic Expectations: Don’t aim for 100% on all SLIs. There will always be some level of imperfection. The goal is to set realistic and achievable targets.

Conclusion:

Service Level Indicators (SLIs) are a fundamental building block for managing and improving the performance and reliability of your services. By carefully defining and monitoring SLIs, you can gain a clear, objective understanding of how well your service is meeting user needs, prioritize engineering efforts, and build a culture of continuous improvement. They are the first, crucial step in the journey towards defining SLOs and SLAs, ultimately leading to better user experiences and more reliable systems.