August 2016 Summaries

After receiving an invite to use Honeycomb, the author applied the observability tool to monitor their consulting business's website, which handles about 30,000 requests per day, to proactively identify and troubleshoot anomalies such as latency spikes. They discovered the causes of these spikes by utilizing Honeycomb's SQL-like query interface and the honeytail tool to parse nginx logs, allowing them to identify and address two key issues: one caused by a poorly formed template that led to slow rendering and another by a misconfigured DNS directing unrelated requests to their server. By filtering and analyzing request patterns, they swiftly resolved these issues, enhancing their understanding of baseline query latency and request rates, thus equipping themselves to better evaluate future performance changes as new features are developed.

Honeycomb recently released a new API version and is focused on encouraging customers to upgrade by using their observability platform to track and analyze client usage patterns, particularly through the User-Agent string field within their dogfooding cluster. Despite the challenge of maintaining backward compatibility, Honeycomb enables detailed analysis by aggregating data across various dimensions, such as traffic volume and version distribution. This granular insight helps prioritize which clients to target first for upgrades, facilitating better resource allocation and customer engagement strategies. Honeycomb's flexibility allows users to compare performance and error rates across different API versions, supporting informed discussions about the benefits of upgrading. As they continue to improve their system, Honeycomb demonstrates the value of comprehensive observability tools in managing API versions and customer interactions effectively.

In this initial entry of a series on "dogfooding," Honeycomb illustrates how they use their own platform to address performance issues internally. The company was motivated to create Honeycomb due to dissatisfaction with the traditional method of using separate dashboard and log aggregation tools for system monitoring. They demonstrate this approach by resolving a user-reported issue regarding slow query performance. By filtering data and examining latency distribution, they identify a sharding problem where queries are incorrectly concentrated on a single partition instead of being evenly distributed across all partitions. Honeycomb's platform allows for extensive data capture without the constraints of cardinality limits, enabling rapid problem identification by adding attributes as needed. This process exemplifies their belief in Honeycomb's capability as a future-forward tool for metrics exploration, emphasizing its flexibility and efficiency in solving complex data issues.