Current observability practice is largely based on manual instrumentation, which requires adding code in relevant points in the user’s business logic code to generate telemetry data. This can become quite burdensome and create a barrier to entry for many wishing to implement observability in their environment. This is especially true in Kubernetes environments and microservices architecture.
Communications Service Providers around the world have experienced an unprecedented explosion in demand for bandwidth due to several driving forces including more video streaming, the growing popularity in areas like cloud gaming and the increasing move to the cloud to name a few.
Transactions are sent when your service receives a request and sends a response, like an API call or a page load. Within each transaction is a series of operations. We built Operations Breakdown to help you, the developer, quickly see how much time was spent in each operation within a transaction. Why? Simple, so you can address the operations with the longest duration and likely causing annoying performance issues for your customer.
Being alerted to an issue with your application before your customers experience undue interruption is a goal of every development and operations team. While methods for identifying problems exist in many forms, including uptime checks and application tracing, alerts on logs is a prominent method for issue detection. Previously, Cloud Logging only supported alerts on error logs and log-based metrics, but that was not robust enough for most application teams.
While unit testing and integration testing can give you insight into the individual functionalities of an application, “at times you need some sort of monitoring or testing mechanism which also simulates a user’s behavior to test how the application would work or look to an actual user in the world,” says Grofers Software Development Engineer Yashvardhan Kukreja. That’s where synthetic monitoring comes in.
