Fundamentals of High Throughput System Design
This article introduces the core concepts of high throughput systems, distinguishing throughput from latency and explaining their common trade-offs. It emphasizes the importance of designing systems that can efficiently process large volumes of work within a given timeframe without succumbing to pressure, laying the groundwork for practical strategies to achieve this goal.
Read original on ByteByteGoUnderstanding Throughput and Latency
High throughput systems are engineered to process a significant amount of data or operations within a specific time period. Throughput is a measure of the total work completed, such as requests per second or transactions per minute. It is critical for applications like data processing pipelines, real-time analytics, and high-volume transaction systems where the sheer volume of operations matters most.
It's important to distinguish throughput from latency. Latency refers to the time it takes for a single operation to complete from start to finish. A system can have low latency but low throughput if it processes individual requests quickly but cannot handle many concurrently. Conversely, a system might exhibit high throughput but high latency if it processes many requests simultaneously, but each individual request takes a longer time.
Throughput vs. Latency
Throughput: Amount of work completed in a given time (e.g., 10K requests/sec). Latency: Time taken for a single operation (e.g., 200ms per request). These two metrics often have an inverse relationship.
The Throughput-Latency Trade-off
A common trade-off exists between throughput and latency. For instance, batching multiple operations together can significantly increase throughput because the system processes many items at once, amortizing overheads. However, this batching inherently introduces waiting time for individual operations, leading to increased latency. Conversely, processing every request immediately can reduce latency but might limit overall throughput if the system becomes overwhelmed by the individual processing overheads or resource contention. System designers must carefully evaluate these trade-offs based on the application's specific requirements.