Benchmark: Jump vs switch case vs object literal

Tests:

switch case
function thing(j) { switch(j) { case 0: return "rah"; case 1: return "lah"; case 2: return "sah"; case 3: return "blah"; } } for(let i = 0; i < 100000; i++) { for(let j = 0; thing(j) != "blah"; j++) { } }
jump table
function rah() { return "rah"; } function lah() { return "lah"; } function sah() { return "sah"; } function blah() { return "blah"; } let jumpTable = [rah, lah, sah, blah]; for(let i = 0; i < 100000; i++) { for(let j = 0; jumpTable[j]() != "blah"; j++) { } }
object literal
const objectLiteral = { 0: () => 'rah', 1: () => 'lah', 2: () => 'sah', 3: () => 'blah' } for(let i = 0; i < 100000; i++) { for(let j = 0; objectLiteral[j]() != "blah"; j++) { } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
switch case
jump table
object literal

Fastest: N/A

Slowest: N/A

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Measuring the performance of different approaches to implement conditional logic in JavaScript can be fascinating.

The provided benchmarking test compares three approaches:

1. **Switch Case**: This approach uses a `switch` statement with multiple cases to determine which action to take based on a value.
2. **Jump Table**: This approach uses an array of functions, where each function corresponds to a specific value, and iterates through the array to find the matching function.
3. **Object Literal**: This approach uses an object literal with method references to implement the same logic as the switch case.

**Comparison of approaches:**

*   **Switch Case**:
    *   Pros:
        *   Easy to read and maintain
        *   Familiar syntax for most developers
    *   Cons:
        *   Can be slower due to the overhead of evaluating the `switch` statement
        *   May not be suitable for large numbers of cases or dynamic values
*   **Jump Table**:
    *   Pros:
        *   Fast and efficient, as it directly jumps to the correct function based on the value
        *   Suitable for large numbers of cases or dynamic values
    *   Cons:
        *   Can be harder to read and maintain, especially for complex logic
        *   May require additional setup to define the jump table
*   **Object Literal**:
    *   Pros:
        *   Concise and readable, as it uses a clear and familiar syntax
        *   Suitable for dynamic values or large numbers of cases
    *   Cons:
        *   May not be as efficient as a jump table for very large datasets

**Other considerations:**

*   **Library usage**: In the provided benchmarking test, none of the approaches use external libraries. However, in real-world scenarios, you might need to consider using libraries like Lodash or Ramda to simplify your code and improve performance.
*   **Special JavaScript features**: The benchmarking test does not highlight any special JavaScript features, such as async/await or generators. If you're interested in exploring these topics, I can provide more information.

**Alternatives:**

If you're looking for alternative approaches to implement conditional logic in JavaScript, consider the following:

*   **Ternary operator**: A concise and readable approach that uses a single expression to evaluate a condition.
*   **Conditional expressions**: Similar to ternary operators but can be used with multiple conditions and return values.
*   **Function composition**: A functional programming approach that combines functions to create more complex logic.

In conclusion, each approach has its pros and cons, and the choice of which one to use depends on your specific use case, performance requirements, and personal preference.

Related benchmarks:

Jump vs switch case vs object literal (version: 0)

Comparing performance of: switch case vs jump table vs object literal

Created: 3 years ago by: Guest

Jump to the latest result

switch case

jump table

object literal

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):