Throughput in Computing Systems
Throughput is a performance metric that measures the amount of work a system can process in a given time period. It quantifies how much data can move through a system successfully in a specified timeframe.
Key Aspects
- Measurement Units: Often expressed as operations per second, requests per second, or data volume per unit time (MB/s, Gbps)
- System Capacity: Represents the maximum sustainable workload a system can handle
- Bottlenecks: Limited by the slowest component in a processing chain
- Optimization Goals: Typically balanced against latency, cost, and reliability requirements
Real-World Examples
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Network Throughput:
- Internet connections measured in Mbps or Gbps (100 Mbps home fiber, 10 Gbps datacenter links)
- CDNs like Cloudflare or Akamai optimizing content delivery throughput
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Database Throughput:
- PostgreSQL handling 10,000+ transactions per second
- Redis processing 100,000+ operations per second for caching workloads
- Cassandra clusters scaling to millions of writes per second
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Web Services:
- Nginx web server managing thousands of HTTP requests per second
- Amazon’s infrastructure handling over 60 million requests per second during peak periods
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Storage Systems:
- SSDs delivering 500+ MB/s read/write throughput
- Distributed file systems like HDFS processing gigabytes per second
- S3 handling trillions of objects with massive throughput capabilities
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Processing Systems:
- Apache Kafka streaming platforms processing millions of messages per second
- GPU processing thousands of parallel operations for ML workloads
- Hadoop clusters processing terabytes of data per hour
Throughput optimization often involves techniques like parallel processing, load balancing, caching, batching operations, and hardware upgrades targeted at specific bottlenecks.