September 2022 Summaries

Observe has integrated "dogfooding" into its operations by heavily utilizing its own observability tool, O2, to monitor and improve its SaaS application. O2 serves as a distinct deployment environment that is separate from production, staging, and engineering, allowing for isolated testing and observation of the entire infrastructure, including cloud services and microservices. This environment not only supports engineering efforts but also aids other departments like sales and product management in replicating customer issues and analyzing feature usage. O2 processes over 5 TB of data daily from various sources, including infrastructure telemetry from platforms like AWS and Kubernetes, and application-specific data from Observe itself. This data is critical for troubleshooting, feature planning, and understanding customer usage patterns. The discussion of O2's functionality and data ingestion is part of a multi-part series, with future posts promising to delve deeper into data utilization, user engagement, and advanced troubleshooting techniques.

Observe has significantly enhanced its dashboard capabilities, transforming them from static data displays into interactive portals for data exploration. This update, announced at the Observe Direct Spring 2022 event, integrates Graphlink to allow users to delve deeper into datasets directly from dashboard visualizations, making them a gateway to the organization's data universe. Users can now auto-generate dashboards or start from scratch, utilizing improved customization features like drag-and-drop functionality and parameter filtering, to tailor dashboards to specific needs without redundancy. The integration with Graphlink enables seamless navigation between related data sets, reducing mean time to resolution (MTTR) and enhancing data observability. Additionally, upcoming features like Observe apps and a metric expression builder promise to further enrich dashboard functionality by offering automatic, relevant metrics and custom metric definitions to streamline the user experience.

In the OPAL programming environment, handling incoming data often involves testing expressions using four primary functions: if(), if_null(), case(), and coalesce(). The if() function operates like a ternary operator, allowing for conditional logic to choose between two values based on a boolean expression. The if_null() function simplifies handling null values by providing a default replacement for missing data. The case() function evaluates a series of conditions and returns a value based on the first true condition, with the option to set a default value. Lastly, coalesce() is used to return the first non-null value from a list, making it particularly useful in complex data structures like JSON objects. These functions are essential for data filtering and dynamic updates within OPAL, offering flexibility in data processing and manipulation.