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Enhancing server performance typically entails achieving one or more of the following objectives: reduce latency, increase number of requests per second (RPS), and minimize CPU and memory usage. These goals can be pursued through architectural modifications, such as eliminating some network hops, distributing data across multiple servers, upgrading to more powerful hardware, and so forth. This talk is not about that.
I categorize the primary sources of code performance degradation into three groups:
[*] Thread contention. For instance, too hot mutexes, overly strict order in lock-free operations, and false sharing.
[*] Heap utilization. Loss is often caused by frequent allocation and deallocation of large objects, and by the absence of intrusive containers at hand.
[*] Network IO. Socket reads and writes are expensive due to being system calls. Also they can block the thread for a long time, resulting in hacks like adding tens or hundreds more threads. Such measures intensify contention, as well as CPU and memory usage, while neglecting the underlying issue.
I present a series of concise and straightforward low-level recipes on how to gain performance via code optimizations. While often requiring just a handful of changes, the proposals might amplify the performance N-fold.
The suggestions target the mentioned bottlenecks caused by certain typical mistakes. Proposed optimizations might render architectural changes not necessary, or even allow to simplify the setup if existing servers start coping with the load effortlessly. As a side effect, the changes can make the code cleaner and reveal more bottlenecks to investigate.
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