We are all familiar with the conventional wisdom that you can only choose two out of three options: good, fast, and cheap. This principle has held true in the realm of monitoring as well. Engineering teams, whether dealing with traditional data center setups or cloud-native systems, often had to compromise on the balance between performance, affordability, and the depth of data collected. This compromise frequently left someone unsatisfied. However, this scenario is evolving.
By reimagining established monitoring principles, it’s now possible to construct a monitoring workflow that swiftly delivers in-depth insights at a reasonable cost, satisfying the needs of all stakeholders.
Yesterday’s Paradigm
In the past, the dilemma of choosing between good, fast, and cheap didn’t exist. When services operated on systems equivalent to family cars in single locations, the complexity was minimal, and data collection, transmission, and storage weren’t substantial challenges. Integrating a plugin and extracting logs and metrics directly from the application was simple, quick, and nearly cost-free.
This monolithic approach had its merits. The consolidated nature of these systems had certain advantages, even amidst their single points of failure.
From Monolith to Microservice
Over the last decade, most technology teams have been migrating legacy systems and services to the cloud. While harnessing the cloud’s potential for improved performance, scalability, and cost-efficiency, a new set of challenges emerged.
What was once a single integration with a large system has now transformed into multiple microservice integrations across various locations, each with distinct requirements. Extracting logs from a single source has evolved into collecting data from numerous endpoints, each potentially different. Just as doubts about the cloud’s promises arise, additional costs like egress charges for data movement, especially between cloud providers, and storage fees come into play. Lower-cost storage tiers might be available, but handling large-scale data can accumulate expenses rapidly.
Wouldn’t it be ideal to expedite the process while also reducing costs? Opting to collect and transmit only necessary data instead of every available log seems logical. However, this approach requires advanced planning, risking incomplete datasets and redundant tasks that could worsen the existing scenario. Suddenly, the speed, cost, and quality dilemma resurfaces.
Finding the Sweet Spot
By redefining monitoring requirements, shifting focus from inputs to outcomes, it’s feasible to break free from the cycle of costs versus depth. The solution is straightforward: rather than accumulating all conceivable datasets for analysis or randomly sampling data as it flows, adopt an intelligent approach.
Identify relevant datasets at the source and transmit only the essential data. Convert this data into actionable metrics, triggering automated analysis processes upon arrival. This instrumentation technique involves encasing input data in code to govern monitoring software logic. While overly intricate logic can strain the process, an increased level of analysis prior to data transmission has a significant impact. This explains why traditional monitoring tools either extract all data or use random samples.
Such constraints can be surmounted with eBPF, resulting in reduced data volumes over networks, diminished processing requirements, and lower storage expenses. With a focus on data quality, the selected data for analysis becomes a reliable source for insights.
The Ascendancy of Cloud-Native Monitoring
Navigating cloud-native data management is intricate, with the quantity and intricacy of logs, metrics, and trace data on a continuous rise. Maintaining visibility without facing escalating storage and egress charges demands a change in perspective. This is the inspiration behind our innovation. A new breed of observability frameworks has arrived, transforming the equation. There was a time when you couldn’t have it all, but now you can. An observability ecosystem is within reach, allowing you to scale sustainably by pinpointing pertinent data at its source. This unleashes the power of the cloud, negating the cost, speed, and quality dilemma. It minimizes overhead on source services, augments efficiency, eliminates irrelevant data, reduces network usage, and in turn, decreases costs.
