RecallOps: Building Persistent Memory for Engineering Incident Response

In the world of software engineering, unexpected system failures are an unfortunate reality. These could manifest as slow APIs, database connection timeouts, or issues arising from new deployments. When such failures occur, engineers must rapidly diagnose the problem and implement a solution. However, a significant challenge in incident response lies in the fact that valuable insights from past incidents may not always be readily accessible when similar issues resurface.

This is where RecallOps comes into play, a system designed to provide engineering incident response with persistent memory.

RecallOps is engineered to enable engineers to tap into the team's accumulated experience when confronting new incidents. It employs a concept known as Hindsight, a central memory layer that retains engineering experiences for future reference. The core idea is straightforward: instead of starting from scratch every time, engineers can leverage past learnings to expedite the problem-solving process.

Consider a scenario where a Payment API is experiencing high latency due to a database connection timeout. An engineer investigating this issue might need to look into various factors, including the database, connection pool, recent deployments, queries, logs, and service configuration. If a similar problem had occurred previously, the engineering team might already possess data on its root cause and the effective resolution.

However, merely archiving old incident records is insufficient; this past knowledge must be readily available during a new and similar incident.

RecallOps tackles this challenge by establishing a direct link between current incidents and pertinent historical experiences through persistent memory. The system is anchored in an engineering incident command center where engineers can create and investigate incidents. Each incident includes essential details such as a title, description, service deployment, and severity.

For instance, an incident might be titled "API Latency Spike," with a description of "Database connection timeout causing high API latency," associated with the "Payment API" service, specifically version "v2.4.1," and rated as "Critical."

Upon creating an incident, engineers can utilize the 'Investigate with Recall' feature. This opens a dashboard where they can view the active incident, utilize the Recall feature, review analysis, and document the final outcome. This process is pivotal as RecallOps does not simply present a static list of past incidents. Instead, it leverages the current incident's symptoms and context to search Hindsight's persistent memory for relevant past experiences.

When the engineer clicks 'Investigate with Recall,' the system performs a targeted search in Hindsight for memories that align with this new incident.

The recalled information then becomes instrumental in guiding the engineering investigation. RecallOps presents three key areas derived from this recalled memory: the identified root cause pattern, recommended fixes, and historical lessons. The goal is not to replace the engineer's judgment but to augment it with historical context that can inform the investigation.

The effectiveness of this approach becomes evident when contrasting incident response scenarios with and without persistent memory. Without persistent memory, a new incident would require the engineer to start the investigation from the ground up—checking logs, examining the database, the connection pool, recent deployments, queries, and service configurations.

In contrast, with RecallOps and Hindsight, a new incident triggers a query that searches Hindsight for previous experiences. If a similar issue had been encountered before, the system can retrieve that experience, providing the engineer with a useful starting point, thus significantly reducing the time and effort needed to diagnose and resolve the problem.

This illustrates the practical value of persistent memory: past engineering experiences can be made useful during future incident investigations, transforming a repository of past incidents into a dynamic learning tool.

A critical component of RecallOps is the recording of the outcome. Engineers can document whether the recommended solution was effective, noting steps taken such as adjusting the database connection pool and optimizing slow database queries, which restored API latency to normal. This outcome is then stored, creating a feedback loop. If the fix was successful, the experience can inform future incidents; if not, the outcome still contributes valuable data on an approach that did not resolve the problem.

Technical implementation of RecallOps is built as a web-based application using Python Flask, HTML, CSS, JavaScript, and SQLite. Flask handles the application and API routes, ensuring seamless interaction between the user interface and the underlying database. The incorporation of Hindsight as the central memory layer is crucial, with two main operations—Recall and Retain—enabling the system to both retrieve relevant past experiences and store new learnings.

This technical foundation allows RecallOps to function as an effective tool in engineering incident response, harnessing past knowledge to enhance the speed and accuracy of troubleshooting new issues.