Exploring the variance in performance between lodash's "get" function and traditional "if else" clauses

Imagine you have a TypeScript object with the possibility of elements being undefined. Navigating through a deeply nested structure requires multiple comparisons against undefined values.

I wanted to examine the performance difference between using regular if-else statements and the lodash function get for this purpose.

I recently came across a tool known as "jsben" that allows benchmarking different JavaScript code snippets. However, I am struggling to properly interpret the results.

In a specific test, it appears that lodash get is slightly faster. But in another scenario (see here), when the variable is defined in the Setup block instead of the Boilerplate code, the if-else approach outperforms by a significant margin.

What is the correct method for accurately benchmarking these techniques? How should one make sense of the findings? Given the potential readability issues, is the improved speed enough to advocate for if-else statements over using get from lodash?

Answer №1

Perhaps you're not approaching the right question.

Before delving deep into performance optimization, it's crucial to determine if the code in question is actually a bottleneck in your system. Focus on fixing the most critical bottlenecks first before getting lost in worrying about minor optimizations that may not significantly impact overall performance. Testing is key to validating assumptions and ensuring the effectiveness of any changes made.

When considering the speed differences between two implementations, it appears that while there may be some slight variations, the emphasis should be on efficient data access rather than data initialization for accurate benchmarking. The placement of initialization code can affect the results, with one method executing it within each benchmark block while the other runs it before every test without impacting the benchmark itself.

Although the technique involving deep object access shows a minimal performance advantage, the complexity and readability of the code must also be taken into account. While using specialized libraries like lodash or Ramda for such a simple function may seem unnecessary, leveraging existing tools can streamline development processes and enhance code expressiveness.

In terms of performance, native code typically outperforms generic library functions due to their tailored design. However, the decision to use libraries often comes down to writing more understandable and expressive code rather than solely focusing on performance gains. In this scenario, the use of a library like lodash proves beneficial for creating cleaner and more readable code.

Answer №2

How can I effectively benchmark this code?

When conducting benchmarks, focus on comparing the actual code you are testing and try to move external factors outside of the test block. Run each piece of code multiple times (even hundreds of thousands) to minimize the impact of unrelated elements.

What should be my approach in interpreting the benchmark results?

1) Verify the validity of the results:

Check if the results align with your expectations. If not, investigate any potential reasons for discrepancies. Ensure that the test case accurately reflects your real-world scenario.

2) Assess the relevance of the results:

Compare the execution time of the code with the actual runtime in your application. For instance, if your code typically takes 200ms to run and both tests complete in under ~1ms, the results may not be significant. However, if you aim to optimize code running frequently, even a 1ms improvement is valuable.

3) Evaluate the cost-benefit of performance optimization:

Determine if the potential performance enhancements justify the effort required for refactoring or additional coding. Consider whether the gains outweigh the time investment.

Is there a noticeable slowdown when using certain methods over others, even if it impacts readability?

In most cases, favor using _.get unless extremely high frequency of usage demands an alternative. Efficiency should not compromise code clarity excessively.

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