What's causing the sudden spike in latency for Confluent's Kafka Connect connectors?

Introduction

The sudden spike in latency for Confluent's Kafka Connect connectors is a critical issue that demands immediate attention. This problem could significantly impact data integration workflows, potentially causing delays in real-time data processing and analytics for our customers. I'll approach this issue systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this might be related to a recent deployment. Has there been any significant update to the Kafka Connect infrastructure or connectors in the past week?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update to the connector framework. Impact on approach: If confirmed, we'd focus on the changes made in that update.

• Considering the nature of Kafka Connect, I'm wondering about data volume changes. Have we seen any unusual spikes in data ingestion or processing volume recently?

Why it matters: Increased data load could explain latency issues. Expected answer: Data volume has been relatively stable. Impact on approach: If stable, we'd shift focus to internal system issues rather than external factors.

• Given the complexity of Kafka ecosystems, I'm curious about the scope. Is this latency spike affecting all connectors or specific types?

Why it matters: Helps narrow down the problem area. Expected answer: The issue is more pronounced in sink connectors. Impact on approach: We'd prioritize investigating sink connector-specific components.

• Thinking about our monitoring setup, I'm wondering about the detection timeline. When was this latency spike first observed, and has it been consistent since then?

Why it matters: Helps establish a timeline for potential causes. Expected answer: Spike noticed 48 hours ago, consistently high since then. Impact on approach: We'd focus on events or changes around that timeframe.