January 2017 Summaries

Honeycomb emphasizes reliability by implementing end-to-end checks that write and read a single data point within a specific time frame, retrying up to 30 times upon failure. An issue arose with one of these checks, specifically partition 5, where read durations were elevated, suggesting a problem not with the API server or storage but potentially with the Kafka-related processes. The investigation involved analyzing various metrics like cum_write_time and latency_api_msec to isolate the issue, demonstrating Honeycomb's ability to quickly slice and compare metrics to identify problems. This incident exemplifies Honeycomb's approach to systems observability, combining pre-aggregated time series metrics with log aggregator flexibility, and highlights its potential as a next-generation tool.

The third installment in a series on the limitations of pre-aggregated metrics delves into the challenges of relying on individual metrics for understanding system performance over time. It highlights the isolated nature of metrics as standalone data points, which can limit the ability to reconstruct the underlying reality of system behavior, especially in complex systems with multiple attributes. Using a datastore example, the text illustrates the problem of anticipating and managing metrics across various dimensions, such as operation type and namespace, and the impracticality of storing numerous cross products. Instead, the post advocates for a more flexible approach, using tools like Honeycomb, to iteratively explore data and reveal patterns and relationships between attributes, which can be more insightful than attempting to visually align trends from disparate graphs. This method allows for direct examination of anomalies and provides a clearer understanding of system dynamics, challenging the traditional reliance on pre-aggregated metrics for performance analysis.