Why has Cribl Stream's data ingestion rate dropped by 30% over the past week?

Introduction

The recent 30% drop in Cribl Stream's data ingestion rate over the past week is a critical issue that demands immediate attention. As we analyze this product problem, we'll follow a systematic framework to identify, validate, and address the root cause while considering both immediate and long-term implications.

Our approach will involve a thorough examination of potential factors, data analysis, and hypothesis generation. We'll then validate our findings and propose actionable solutions to restore and improve Cribl Stream's performance.

Step 1

Clarifying Questions (3 minute)

• I'm noticing the timing of this issue. Would you say there have been any recent updates or changes to Cribl Stream in the past two weeks?

Why it matters: Recent changes could directly impact ingestion rates. Expected answer: Yes, there was a minor update. Impact on approach: If confirmed, we'd focus on the update's impact.

• Given the 30% drop, I'm wondering about the affected data sources. Has this decline been uniform across all data types, or are specific sources more impacted?

Why it matters: Helps isolate the problem to specific data types or sources. Expected answer: The drop is more pronounced in certain data types. Impact on approach: We'd prioritize investigating those specific data types.

• Considering user behavior, have there been any significant changes in the number of active users or their usage patterns during this period?

Why it matters: User behavior changes could explain the ingestion rate drop. Expected answer: No significant changes in user numbers or behavior. Impact on approach: We'd focus more on technical issues rather than user-related factors.

• I'm curious about any recent infrastructure changes. Have there been any modifications to the underlying hardware or cloud resources supporting Cribl Stream?

Why it matters: Infrastructure changes could affect ingestion capacity. Expected answer: Some cloud resources were optimized for cost savings. Impact on approach: We'd investigate the impact of these optimizations on performance.