For Chronosphere's distributed tracing feature, how do we optimize data retention periods while managing storage costs and maintaining performance?

Introduction

Optimizing data retention periods for Chronosphere's distributed tracing feature while managing storage costs and maintaining performance is a critical trade-off we need to address. This scenario involves balancing user needs, technical constraints, and business objectives. I'll analyze this trade-off by examining key factors, proposing metrics, and designing an experiment to inform our decision-making process.

Step 1

Clarifying Questions (3 minutes)

• Context: Based on the distributed tracing feature, I'm assuming this is a critical observability tool for our customers. Could you confirm if this is primarily used by DevOps teams or if it has broader usage across engineering organizations?

Why it matters: Helps understand the user base and potential impact of retention changes Expected answer: Primarily DevOps, but also used by software engineers for debugging Impact on approach: Would influence the retention period options and user communication strategy

• Business Context: I'm thinking this trade-off might be driven by increasing storage costs as our customer base grows. Is this correct, and can you share how significant these costs are relative to our overall operating expenses?

Why it matters: Helps prioritize cost-saving measures against potential user experience impacts Expected answer: Storage costs are becoming a concern, accounting for 15-20% of operating expenses Impact on approach: Would justify more aggressive retention period optimization if costs are high

• User Impact: Are we seeing any patterns in how far back users typically query trace data? For instance, do most queries cover the last 7 days, 30 days, or longer?

Why it matters: Helps identify the most valuable retention period for users Expected answer: Majority of queries are within 14 days, with occasional queries up to 60 days Impact on approach: Would inform the range of retention periods to consider in our experiment

• Technical: What's our current architecture for storing and retrieving trace data? Are there any technical limitations or opportunities for optimization beyond retention periods?

Why it matters: Identifies potential technical solutions to complement retention period changes Expected answer: Using distributed storage with hot and cold tiers, some room for query optimization Impact on approach: Might lead to a multi-faceted solution combining retention changes and technical improvements

• Timeline: Is there a specific deadline or event driving the urgency of this decision, such as a contract renewal with our storage provider or an upcoming product release?

Why it matters: Helps set the timeline for experimentation and implementation Expected answer: Aiming to implement changes before Q4 to impact next year's budget Impact on approach: Would influence the scope and duration of our experiment