deno bench for benchmarking - Deep Dive

Overview - deno bench for benchmarking

What is it?

Deno bench is a built-in tool in the Deno runtime that helps you measure how fast your code runs. It lets you write small tests called benchmarks that run your code many times to see how long it takes. This helps you find slow parts and improve your program's speed. You write these benchmarks in simple JavaScript or TypeScript files and run them with a special Deno command.

Why it matters

Without benchmarking tools like deno bench, developers would guess which parts of their code are slow, often wasting time fixing the wrong things. Deno bench gives clear, reliable numbers about performance, so you can make your programs faster and more efficient. This matters especially when building apps that need to handle many users or process lots of data quickly.

Where it fits

Before learning deno bench, you should know basic JavaScript or TypeScript and how to run Deno programs. After mastering deno bench, you can explore advanced performance tuning, profiling tools, or benchmarking in other environments like Node.js or browsers.

Mental Model

Core Idea

Deno bench runs your code repeatedly to measure how long it takes, giving you accurate timing to find and fix slow parts.

Think of it like...

It's like timing how long it takes to bake a batch of cookies multiple times to find the perfect oven temperature and recipe for the fastest, tastiest batch.

┌───────────────┐
│  Benchmark    │
│  Function     │
└──────┬────────┘
       │ runs many times
       ▼
┌───────────────┐
│  Timer starts │
│  Code runs    │
│  Timer stops  │
└──────┬────────┘
       │ calculates average time
       ▼
┌───────────────┐
│  Results      │
│  Displayed    │
└───────────────┘

Build-Up - 6 Steps

1

Foundation

What is deno bench?

Concept: Introduction to the deno bench tool and its purpose.

Deno bench is a command-line tool built into Deno that lets you write and run benchmarks. Benchmarks are small pieces of code that measure how long a function or block of code takes to run. You write these benchmarks in a file and run them with the command `deno bench`.

Result

You get a report showing how long your code took to run, helping you understand its speed.

Knowing that deno bench is built-in means you don't need extra tools to start measuring performance.

2

Foundation

Writing your first benchmark

Concept: How to write a simple benchmark function using Deno's API.

Create a file named `bench.ts` and write: import { bench } from "https://deno.land/std@0.203.0/testing/bench.ts"; bench(function simple() { let sum = 0; for (let i = 0; i < 1000; i++) { sum += i; } }); Run it with `deno bench bench.ts`.

Result

The terminal shows how many times the function ran and the average time per run.

Understanding the simple syntax lets you quickly start measuring any code's speed.

3

Intermediate

Using async benchmarks

🤔Before reading on: do you think deno bench can measure asynchronous code directly or only synchronous code? Commit to your answer.

Concept: Deno bench supports async functions to measure code that waits for promises.

You can write benchmarks with async functions to test code that uses timers, fetch, or other async operations. Example: import { bench } from "https://deno.land/std@0.203.0/testing/bench.ts"; bench(async function asyncExample() { await new Promise((resolve) => setTimeout(resolve, 10)); }); Run with `deno bench` as usual.

Result

The benchmark reports include the time spent waiting inside async code.

Knowing async benchmarks exist lets you measure real-world code that often uses waiting or network calls.

4

Intermediate

Benchmark options and configuration

🤔Before reading on: do you think you can control how many times a benchmark runs or set time limits? Commit to your answer.

Concept: Deno bench lets you customize how benchmarks run with options like iterations and time limits.

You can pass options to the bench function: bench({ name: "custom", runs: 1000, baseline: true, fn() { // code to test }, }); - `runs` controls how many times the code runs. - `baseline` marks a benchmark as a baseline for comparison. Run with `deno bench` to see detailed results.

Result

Benchmarks run with your settings, giving more control over accuracy and comparison.

Controlling runs and baselines helps you compare different code versions fairly.

5

Advanced

Interpreting benchmark results

🤔Before reading on: do you think the fastest reported time always means the best code? Commit to your answer.

Concept: Understanding what the benchmark output means and its limitations.

Deno bench shows: - Number of runs - Average time per run - Relative speed compared to baseline But results can vary due to CPU load, caching, or randomness. Always run benchmarks multiple times and consider the environment.

Result

You learn to read results critically, not just trust the fastest number.

Knowing the limits of benchmarks prevents wrong conclusions and wasted effort.

6

Expert

Benchmark internals and timing precision

🤔Before reading on: do you think deno bench uses simple Date.now() or a more precise timer? Commit to your answer.

Concept: How deno bench measures time precisely and handles repeated runs internally.

Deno bench uses high-resolution timers provided by the Deno runtime, like `performance.now()`, which measures time in milliseconds with microsecond precision. It runs the benchmark function many times in a loop to reduce noise and averages the results. It also warms up the code to avoid startup delays affecting measurements.

Result

You understand why benchmark results are stable and precise despite JavaScript's usual timing limits.

Understanding timing precision and warm-up phases explains why benchmarks are reliable and how to trust their numbers.

Under the Hood

Deno bench runs your benchmark function repeatedly in a tight loop using high-resolution timers like performance.now(). It measures the total time taken and divides by the number of runs to get an average. Before timing, it runs the code a few times to warm up the JavaScript engine's optimizations. It also isolates benchmarks to avoid interference and reports results with statistical data.

Why designed this way?

This design ensures accurate and stable timing by reducing noise from startup costs, background processes, and timer resolution limits. Alternatives like single-run timing or low-resolution timers would give unreliable results. The repeated runs and warm-up mimic how real code behaves under load, making benchmarks meaningful.

┌───────────────┐
│ Start Benchmark│
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Warm-up Runs  │
│ (JIT optimize)│
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Timed Runs    │
│ (performance.now())
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Calculate Avg │
│ Time per Run  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Display Result│
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does running a benchmark once give an accurate speed measurement? Commit yes or no.

Common Belief: Running a benchmark once is enough to know how fast the code is.

Reality: A single run can be misleading due to startup delays, caching, or random system activity. Multiple runs averaged give a more accurate picture.

Why it matters: Relying on one run can cause wrong optimizations or miss real performance issues.

Quick: Do you think async benchmarks measure only the async wait time or the whole function time? Commit your answer.

Common Belief: Async benchmarks only measure the time spent waiting, ignoring the rest of the function.

Reality: Deno bench measures the total time the async function takes, including waiting and execution.

Why it matters: Misunderstanding this can lead to wrong conclusions about where time is spent in async code.

Quick: Does a faster benchmark result always mean better code? Commit yes or no.

Common Belief: The fastest benchmark result always means the best code.

Reality: Faster results might come from skipping important work or using less accurate methods. Correctness and context matter too.

Why it matters: Optimizing blindly for speed can break functionality or reduce code quality.

Quick: Is deno bench a replacement for profiling tools? Commit yes or no.

Common Belief: Deno bench replaces the need for profiling tools.

Reality: Deno bench measures speed but does not show where time is spent inside the code like profilers do.

Why it matters: Using only benchmarks can miss detailed performance bottlenecks.

Expert Zone

1

Benchmarks can be affected by CPU frequency scaling and background OS tasks, so running on a quiet system improves accuracy.

2

Using the `baseline` option allows comparing multiple benchmarks relative to a standard, helping spot regressions or improvements.

3

Deno bench internally uses microtask queues and event loop ticks carefully to avoid skewing async benchmark results.

When NOT to use

Deno bench is not suitable for measuring UI responsiveness or memory usage; use profiling or tracing tools instead. For very short or very long-running tasks, specialized tools or manual timing might be better.

Production Patterns

In production, developers use deno bench to compare different algorithm versions, check performance after code changes, and automate benchmarks in CI pipelines to catch slowdowns early.

Connections

Performance Profiling

Complementary tools

Benchmarking measures overall speed, while profiling breaks down where time is spent; knowing both gives a full performance picture.

Statistical Sampling

Shared principle of repeated measurement

Benchmarking uses repeated runs to reduce noise, similar to how statistical sampling averages data for accuracy.

Scientific Experimentation

Same pattern of controlled testing and measurement

Benchmarking code is like running experiments with controls and repeated trials to get reliable results.

Common Pitfalls

#1 Running benchmarks with other heavy programs active, causing noisy results.

Wrong approach:

deno bench my_bench.ts # while running many apps and downloads

Correct approach:

Close other programs and run: deno bench my_bench.ts

Root cause:

Not realizing external system load affects timing accuracy.

#2 Writing benchmarks that include setup code inside the timed function.

Wrong approach:

bench(() => {
const data = generateData();
processData(data);
});

Correct approach:

const data = generateData();
bench(() => {
processData(data);
});

Root cause:

Including setup in timing inflates results and hides real processing speed.

#3 Using synchronous benchmarks to test asynchronous code without awaiting.

Wrong approach:

bench(() => {
fetch('https://example.com');
});

Correct approach:

bench(async () => {
await fetch('https://example.com');
});

Root cause:

Not awaiting async calls causes benchmark to measure only call start, not completion.

Key Takeaways

Deno bench is a built-in tool that runs your code many times to measure its speed accurately.

Writing benchmarks with async support lets you measure real-world asynchronous code performance.

Benchmark results must be interpreted carefully, considering environment and code correctness.

Understanding how deno bench uses high-resolution timers and warm-up runs explains its reliable measurements.

Combining benchmarking with profiling and controlled testing leads to the best performance insights.

Practice

1. What is the main purpose of deno bench in Deno?

easy

A. To format code automatically

B. To check for syntax errors in code

C. To deploy Deno applications to the cloud

D. To measure how fast a piece of code runs

Solution

1. Step 1: Understand the role of benchmarking

   Benchmarking is used to measure the speed or performance of code.

2. Step 2: Identify what deno bench does

   deno bench runs benchmark tests to measure execution time of code snippets.

3. Final Answer:

   To measure how fast a piece of code runs -> Option D

4. Quick Check:

   Benchmarking = measuring speed [OK]

Hint: Benchmark means measuring speed or performance [OK]

Common Mistakes:

• Confusing benchmarking with syntax checking
• Thinking it deploys code
• Assuming it formats code automatically

2. Which of the following is the correct syntax to define a benchmark in Deno?

easy

A. bench({ name: "test", fn: () => { /* code */ } })

B. bench = function(name, fn) { /* code */ }

C. benchmark test() { /* code */ }

D. bench: { name = "test", fn() { /* code */ } }

Solution

1. Step 1: Recall the official benchmark syntax

   The correct way is to call bench with an object containing name and fn properties.

2. Step 2: Compare options to the correct pattern

   bench({ name: "test", fn: () => { /* code */ } }) matches the pattern bench({ name: "test", fn: () => { } }). Others are invalid syntax.

3. Final Answer:

   bench({ name: "test", fn: () => { /* code */ } }) -> Option A

4. Quick Check:

   Use object with name and fn keys [OK]

Hint: Use bench({ name, fn }) to define benchmarks [OK]

Common Mistakes:

• Using assignment instead of function call
• Incorrect object syntax
• Using invalid keywords like 'benchmark'

3. What will be the output behavior when running this benchmark code with deno bench?

bench({
  name: "sum test",
  fn() {
    let sum = 0;
    for (let i = 0; i < 1000; i++) {
      sum += i;
    }
  }
});

medium

A. It prints the time taken to run the loop 1000 times

B. It prints the sum value after the loop

C. It throws a syntax error because of missing return

D. It runs but does not show any output

Solution

1. Step 1: Understand what deno bench does

   It runs the benchmark function multiple times and reports the time taken.

2. Step 2: Analyze the code behavior

   The code sums numbers but does not return or print the sum. The benchmark tool measures execution time, not return values.

3. Final Answer:

   It prints the time taken to run the loop 1000 times -> Option A

4. Quick Check:

   Benchmarks show timing, not return values [OK]

Hint: Benchmarks report time, not return values [OK]

Common Mistakes:

• Expecting printed sum output
• Thinking a return is required
• Assuming no output is shown

4. Identify the error in this benchmark code:

bench({
  name: "test",
  fn: () => {
    let x = 0
    for (let i = 0; i < 10; i++) {
      x += i;
    }
  }
});

medium

A. Missing semicolon after let x = 0

B. Using arrow function instead of regular function

C. No error, code runs fine

D. Missing await keyword inside fn

Solution

1. Step 1: Check syntax carefully

   JavaScript allows optional semicolons. Automatic Semicolon Insertion (ASI) inserts a semicolon after let x = 0 before the for loop since for cannot continue the previous statement.

2. Step 2: Verify other potential issues

   Arrow functions are valid for fn. The code is synchronous, so no await is needed. The code is syntactically valid and runs fine.

3. Final Answer:

   No error, code runs fine -> Option C

4. Quick Check:

   ASI handles missing semicolon before for [OK]

Hint: Semicolons are optional in JS; ASI works before for loops [OK]

Common Mistakes:

• Thinking missing semicolon is a syntax error
• Believing arrow functions are invalid in benchmarks
• Assuming await is needed for synchronous loops

5. You want to benchmark two functions: one that sums numbers from 1 to 1000, and another that multiplies numbers from 1 to 1000. How should you write the benchmark code to compare their speeds correctly?

hard

A. Run one function inside bench and call the other outside

B. Define two separate bench calls each with a unique name and function

C. Put both functions inside one bench call and run them sequentially

D. Use bench only for the sum function and measure multiply manually

Solution

1. Step 1: Understand benchmarking multiple functions

   Each benchmark should be defined separately with a unique name to measure independently.

2. Step 2: Why separate bench calls are needed

   Running both in one bench mixes timings and does not isolate performance.

3. Final Answer:

   Define two separate bench calls each with a unique name and function -> Option B

4. Quick Check:

   Separate benchmarks for accurate comparison [OK]

Hint: Use separate bench calls with unique names [OK]

Common Mistakes:

• Combining multiple tests in one bench
• Measuring one function outside bench
• Skipping benchmark for one function