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A large majority of computer systems have some state and are likely to depend on a storage system. My knowledge on databases accumulated over time, but along the way our design mistakes caused data loss and outages. In data-heavy systems, databases are at the core of system design goals and tradeoffs. Even though it is impossible to ignore how databases work, the problems that application develope
It is complicated to diagnose and debug complicated systems. It often takes multiple levels of diagnostics data to understand the possible causes of latency issues. A distributed system is made of many servers that are depending on each other to serve a user request. At any time, A process in the system might be handling a large number of requests.In highly concurrent servers, there is no easy way
Published also at https://jbd.dev/microservices-instrumentation/. What makes microservices observability different than observability of monolithic systems? Observability is the activities that involve measuring, collecting, and analyzing various diagnostics signals from a system. These signals may include metrics, traces, logs, events, profiles and more. In monolithic systems, the scope is a sing
The progression of microservices in the industry resembles me the way microservices progressed at Google. First, a common container format. Then, a way to express complicated systems in terms of containers. Tools to deploy them and services to schedule them. Core networking services to support the complicated networking requirements of our highly large systems with complex dependencies. Then, obse
Cindy Sridharan’s fantastic article on why everyone is not ops makes you rethink of the relationship between your development and operations teams. In this article, I am trying explain how Google approaches the problem and why it scales for our SRE team. At Google, SRE stands for site reliability engineer. Site reliability is about velocity and productivity of our engineers, the performance and re
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