Use Binary Protocols for High-Throughput and Low-Payload Scenarios Binary protocols (e.g. Protocol Buffers, MessagePack) encode data in compact binary form instead of text (JSON, XML). They typically reduce payload size by 50–80% and serialization/deserialization cost by 3–10x compared to JSON. The trade-off: less human-readable, requires a
Batch Network Requests to Reduce Round-Trips and Improve Performance Batching means sending multiple operations in one network request instead of one request per operation. Each network request has a fixed cost: round-trip time (RTT), connection setup, and per-request overhead. Doing one batch request instead of N separate requests can cut
Use Connection Pooling for Network and Database Connections Connection pooling reuses existing connections instead of creating new ones for each request, reducing connection overhead and improving performance. Creating a new connection (TCP handshake, TLS handshake, auth) is expensive; pooling can improve performance by 10x to 100x. The trade-off: pools require
Use Throttling and Rate Limiting to Prevent Overload and Improve Stability Throttling and rate limiting control the rate of operations (requests, calls, messages), preventing overload and improving system stability. By limiting how many operations are allowed per time window, you protect backends, databases, and external APIs from being overwhelmed. The
Use Cancellation Tokens for Cooperative Cancellation Cancellation tokens allow operations to be cancelled cooperatively, avoiding wasted resources on work that is no longer needed. Using cancellation tokens improves resource utilization, enables faster response to user cancellations, and prevents unnecessary work. The trade-off: code must check tokens and pass them through
Proper Thread Pool Sizing for Optimal Performance Thread pool size affects performance significantly. Too few threads limit parallelism and cause work items to queue up, while too many threads cause context switching overhead and resource contention. Properly sizing thread pools can improve performance by 20%–50% by optimizing the balance
Use Thread Pools for Concurrent Work Thread pools reuse threads instead of creating new ones, reducing the overhead of thread creation/destruction and improving performance. Thread pools can improve performance by 10x to 100x compared to manually creating threads. The trade-off: thread pools provide less control over individual threads and
Prefer Lock-Free Algorithms for High-Contention Scenarios Lock-free algorithms use atomic operations (like Compare-And-Swap) instead of locks, eliminating blocking and improving performance. Lock-free algorithms can improve performance by 20%–100% compared to locks in high-contention scenarios, with greater impact as thread count increases. The trade-off: lock-free algorithms are very complex to
Use Channels for Producer-Consumer Scenarios Channels provide an efficient, thread-safe way to communicate between producers (threads/tasks that generate data) and consumers (threads/tasks that process data), with automatic backpressure (flow control) to prevent producers from overwhelming consumers. Channels are 20%–50% faster than BlockingCollection and provide better scalability in
Avoid Exceptions for Control Flow Exceptions are expensive operations (stack unwinding, object creation, exception handling overhead). Using exceptions for normal control flow (e.g., checking if a value exists, validating input, handling expected errors) is extremely inefficient—100x to 10,000x slower than using return values, TryParse methods, or Result
Avoid Closures in Hot Paths to Reduce Allocations and GC Pressure Closures capture variables from the outer scope, creating heap-allocated objects (display classes) to maintain state. In hot paths (frequently executed code), these allocations accumulate, increasing GC pressure and reducing performance. Avoiding closures (by passing parameters explicitly, using static methods,
Use Buffered Streams to Reduce Syscall Overhead for Small I/O Operations Buffered streams maintain an internal buffer that batches multiple small read/write operations into fewer, larger syscalls, dramatically reducing syscall overhead. Without buffering, each small I/O operation (e.g., writing a single line) triggers a separate syscall,