Throughput

Picture a toll booth on a highway. Throughput in this scenario is the number of cars that can pass through the toll area in a given time period. If there are multiple lanes and fast payment methods, cars move through quickly, increasing throughput. If there's just one lane or slow payment processing, traffic builds up and fewer cars pass through, just as in a system with bottlenecks.

What is Throughput?

It refers to the amount of work a system can process in a given time frame. It's a critical metric in system design that indicates the efficiency and capacity of a system.

Definition:

Workload Processing:
Throughput measures how many operations, transactions, or data units a system can handle per unit of time. This could be requests per second, transactions per minute, or bits per second, depending on the context.

Suppose a programmer able to type 54 words per minute. then their throughput is 54 words/minute.

Formula:

Throughput = Total number of requests completed / Total time taken

Example:

If a server processes 10,000 requests in 5 seconds, then:

Throughput = 10,000 / 5 = 2,000 requests per second (RPS)

Throughput is usually measured in:

• Requests per second (RPS)
• Transactions per second (TPS)
• Messages per second
• MB/s or GB/s (for data transfer)

Analogy:

Latency: How long one car takes to pass a toll booth.

Throughput: How many cars pass the booth in one minute.

You can have:

• Low Latency but Low Throughput: One request processed fast, but system handles few at a time.
• High Throughput but High Latency: System handles many at once, but each takes longer.

A good design balances both.

Why Throughput Matters

Throughput defines your system's scalability and efficiency.

It tells you whether your architecture can handle millions of users, large data streams, or real-time workloads.

• For a web API, it's how many requests/second it can serve.
• For a database, it's how many reads/writes per second it supports.
• For a streaming service, it's how many MB/s of video data it delivers.
• For message queues, it's how many messages are processed per second.

Throughput directly affects:

• User experience under heavy load.
• Operational cost (fewer servers = cheaper).
• Reliability (systems that drop requests have poor throughput).

How to Increase Throughput

Improving throughput often means increasing parallelism, reducing bottlenecks, and optimizing I/O.

Here are proven techniques:

1 Horizontal Scaling

Add more servers or instances to distribute the load.

Example: Load balance across multiple API servers.

2 Asynchronous Processing

Don't block requests while waiting for long operations – use background jobs or queues.

Example: Process emails or analytics asynchronously.

3 Batching

Process multiple operations in one go instead of one at a time.

Example: Insert 1,000 records in one query instead of 1,000 separate inserts.

4 Caching

Serve frequently accessed data from cache to reduce load on the database.

5 Load Balancing

Distribute incoming traffic evenly to avoid overloading one server.

6 Database Optimization

• Use indexing and query tunning.
• Partition or shard large datasets.
• Use read replicas for heavy read workloads.

7 Concurrency and Threading

Use non-blocking I/O, thread pools, or async frameworks to handle multiple requests simultaneously.