ReactJS

Transitioning from React class components to function components with hooks

Published Mar 10, 2020

Updated Feb 14, 2023

5 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

It has been approximately one year since React v16.8 was released, marking the introduction of Hooks. Yet, there are still people used to React class components who still haven't experienced the full potential of this new feature, along with functional components, including myself. The aim of this article is to summarize and encompass the most distinguishable features of the class components, and respectively show their alternatives when using React hooks.

Functional components

Before we start with Hooks examples, we will shortly discuss functional components in case you aren't familiar. They provide an easy way to create new units without the need of creating a new class and extending React.Component.

Note: Keep in mind that functional components have been part of React since it's creation.

Here is a very simple example of a functional component:

const Element = () => (
  <div className="element">
    My Element
  </div>
);

And just like class components, we can access the properties. They are provided as the first argument of the function.

const Element = ({ text }) => (
  <div className="element">
    {text}
  </div>
);

However, these type of components--while very convenient for simple UI elements--used to be very limited in terms of life cycle control, and usage of state. This is the main reason they had been neglected until React v16.8.

Component state

Let's take a look at the familiar way of how we add state to our object-oriented components. The example will represent a component which renders a space scene with stars; they have the same color. We are going to use few utility functions for both functional and class components.

createStars(width: number): Star[] - Creates an array with the star objects that are ready for rendering. The number of stars depends on the window width.
renderStars(stars: Star[], color: string): JSX.Element - Builds and returns the actual stars markup.
logColorChange(color: string) - Logs when the color of the space has been changed.

and some less important like calculateDistancesAmongStars(stars: Star[]): Object.

We won't implement these. Consider them as black boxes. The names should be sufficient enough to understand their purpose.

Note: You may find a lot of demonstrated things unnecesary. The main reason I included this is to showcase the hooks in a single component.

And the example:

Class components

class Space extends React.Component {
  constructor(props) {
    super(props);

    this.state = {
      stars: createStars(window.innerWidth)
    };
  }

  render() {
    return (
      <div className="space">
        {renderStars(this.state.stars, this.props.color)}
      </div>
    );
  }
}

Functional components

The same can be achieved with the help of the first React Hook that we are going to introduce--useState. The usage is as follows: const [name, setName] = useState(INITIAL_VALUE). As you can see, it uses array destructuring in order to provide the value and the set function:

const Space = ({ color }) => {
  const [stars, setStars] = useState(createStars(window.innerWidth));

  return (
    <div className="space">
      {renderStars(stars, color)}
    </div>
  );
};

The usage of the property is trivial, while setStars(stars) will be equivalent to this.setState({ stars }).

Component initialization

Another prominent limitation of functional components was the inability to hook to lifecycle events. Unlike class components, where you could simply define the componentDidMount method, if you want to execute code on component creation, you can't hook to lifecycle events. Let's extend our demo by adding a resize listener to window which will change the number of rendered stars in our space when the user changes the width of the browser:

Class components

class Space extends React.Component {
  constructor(props) { ... }

  componentDidMount() {
    window.addEventListener('resize', () => {
      const stars = createStars(window.innerWidth, this.props.color);
      this.setState({ stars });
    });
  }

  render() { ... }
}

Functional components

You may say: "We can attach the listener right above the return statement", and you will be partially correct. However, think about the functional component as the render method of a class component. Would you attach the event listener there? No. Just like render, the function of a functional component could be executed multiple times throughout the the lifecycle of the instance. This is why we are going to use the useEffect hook.

It is a bit different from componentDidMount though--it incorporates componentDidUpdate, and componentDidUnmount as well. In other words, the provided callback to useEffect is executed on every update. Anyhow, you can have certain control with the second argument of useState - it represents an array with the values/dependencies which are monitored for change. If they do, the hook is executed. In case the array is empty, the hook will be executed only once, during initialization, since after that there won't be any values to be observed for change.

const Space = ({ color }) => {
  const [stars, setStars] = useState(createStars(window.innerWidth));

  useEffect(() => {
    window.addEventListener('resize', () => {
      const stars = createStars(window.innerWidth, color);
      setStars(stars);
    });
  }, []); // <-- Note the empty array

  return (
    ...
  );
};

Component destruction

We added an event listener to window, so we will have to remove it on component unmount in order to save us from memory leaks. Respectively, that'll require keeping a reference to the callback:

Class components

class Space extends React.Component {
  constructor(props) { ... }

  componentDidMount() {
    window.addEventListener('resize', this.__resizeListenerCb = () => {
      const stars = createStars(window.innerWidth, this.props.color);
      this.setState({ stars });
    });
  }

  componentDidUnmount() {
    window.removeEventListener('resize', this.__resizeListenerCb);
  }

  render() { ... }
}

Functional component

For the equivalent version of the class component, the useEffect hook will execute the returned function from the provided callback when the component is about to be destroyed. Here's the code:

const Space = ({ color }) => {
  const [stars, setStars] = useState(createStars(window.innerWidth));

  useEffect(() => {
    let resizeListenerCb;

    window.addEventListener('resize', resizeListenerCb = () => {
      const stars = createStars(window.innerWidth, color);
      setStars(stars);
    });

    return () => window.removeEventListener('resize', resizeListenerCb);
  }, []); // <-- Note the empty array

  return (
    ...
  );
};

An important remark

It is worth mentioning that, when you work with event listeners or any other methods that defer the execution in the future of a callback/function, you should take into account that the state provided to them is not mutable.

Taking the window listener we use in our demo as an example; if we used thestars state inside the callback, we would get the exact value at the moment of the definition (callback), which means that, when the callback is executed, we risk having a stale state.

There are various ways to handle that, one of which is to re-register the listener every time the stars are changed, by providing the stars value to the observed dependency array of useEffect.

Changed properties

We already went through useEffect in the sections above. Now, we will briefly show an example of componentDidUpdate. Let's say that we want to log the occurances of color change to the console:

Class components

class Space extends React.Component {
  ...

  componentDidUpdate(prevProps) {
    if (this.props.color !== prevProps.color) {
      logColorChange(this.props.color);
    }
  }

  ...
}

Functional components

We'll introduce another useEffect hook:

const Space = ({ color }) => {
  ...

  useEffect(() => {
    logColorChange(color);
  }, [color]); // <-- Note that this time we add `color` as observed dependency

  ...
};

Simple as that!

Changed properties and memoization

Just as an addition to the example above, we will quickly showcase useMemo; it provides an easy way to optimize your component when you have to perform a heavy calculation only when certain dependencies change:

const result = useMemo(() => expensiveCalculation(), [color]);

References

Due to the nature of functional components, it becomes hard to keep a reference to an object between renders. With class components, we can simply save one with a class property, like:

class Space extends React.Component {
  ...

  methodThatIsCalledOnceInALifetime() {
    this.__distRef = calculateDistancesAmongStars(this.state.stars);
  }

  ...
}

However, here is an example with a functional component that might look correct but it isn't:

const Space = ({ color }) => {
  ...

  let distRef; // Declared on every render.

  function thatIsCalledOnceInALifetime() {
    distRef = caclulateDistancesAmongStars(stars);
  }

  ...
};

As you can see, we won't be able to preserve the output object with a simple variable. In order to do that, we will take a look at yet another hook named useRef, which will solve our problem:

const Space = ({ color }) => {
  ...
  const distRef = useRef();

  function thatIsCalledOnceInALifetime() {
    // `current` keeps the same reference
    // throughout the lifetime of the component instance
    distRef.current = caclulateDistancesAmongStars(stars);
  }

  ...
}

The same hook is used when we want to keep a reference to a DOM element.

Conclusion

Hopefully this should give you a starting point when it comes to using React Hooks for the things that you are already used to doing with class components. Obviously, there are more hooks to explore, including the definition of custom ones. For all of that, you can head to the official docs. Give them a try and experience the potential of functional React!

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Improving INP in React and Next.js

Improving INP in React and Next.js In one of my previous articles, I've explained what INP is, how it works, and how it may affect your website. I also promised you to follow up with more concrete advice on how to improve your INP in your favorite framework. This is the follow-up article, where I'll focus on how to improve your INP score in React and Next.js. How to prepare for INP in React and Next.js? The first thing to do is to ensure you're using the latest version of React. The React team has been working on making React more INP-friendly and has already made some improvements in the latest versions. To enhance your INP score, consider fully taking advantage of new features introduced in React 18, such as Concurrent Rendering, Automatic Batching, and Selective Hydration. However, there are also some general areas to focus on, such as SSR and SSG in Next.js, Web Workers, or optimizing your hooks and state management. Concurrent Rendering The Concurrent Mode in React uses an algorithm that breaks rendering down into so-called "fiber nodes" and schedules the renders based on their expiration and priority. This effectively allows the user to interact with the page while the rendering is still in progress. In previous React versions, all updates, such as setState calls were treated as "urgent" and once the re-render had started, there was no way to interrupt it. Concurrent Mode changes this by being able to prioritize the updates and interrupt a non-blocking state update started with startTransition. For a simple explanation of concurrency in React, you can check out Dan Abramov's explanation. As part of the Concurrent Mode, React introduced several lifecycle methods that allow you to prioritize the rendering of certain parts of your UI, such as: - useTransition hook that allows you to update the state without blocking the UI, - useDeferredValue hook that allows you to defer the rendering of certain parts of your UI, - startTransition API that, similarly to the useTransition hook lets you mark a state update as non-blocking. It lacks, however, an indication of whether it's still pending. Automatic Batching Introduced in React 18, Automatic Batching reduces the number of re-renders that happen on state changes even when they happen outside of event handlers, e.g. in a setTimeout or Promise callback. This feature comes out of the box and you don't have to do anything to enable it, and it makes a great argument for upgrading to React 18. Selective Hydration Selective Hydration allows you to take hydration off the main thread by wrapping your components in a Suspense boundary. This way, components can become interactive faster as the browser can do other work on the main thread while the hydration is happening. To fully take advantage of selective hydration, consider the following: - Prioritizing Above-the-Fold Content: Use Suspense boundaries strategically around any parts of your application that may take the server longer to deliver to ensure they don’t block critical content from becoming interactive as soon as possible. - Hydration on Interaction: Implementing hydration upon user interaction for non-critical components can drastically reduce the main thread's workload, enhancing INP. Vercel even has a small case study showing how using selective hydration improved the performance of a Next.js site. Server-Side Rendering (SSR) and Static Site Generation (SSG) in Next.js Not everything has to run client-side. Next.js excels in SSR and SSG capabilities, which can significantly impact INP by delivering content to users faster. Optimizing SSR with techniques like incremental static regeneration (ISR) or leveraging SSG for static pages ensures that users can interact with content faster, improving the perceived performance. Workers Offloading heavy computations to Web Workers can free up the main thread, enhancing the responsiveness of React and Next.js applications. This strategy is especially useful when dealing with third-party scripts. Offloading such scripts in Next.js can be easily done by specifying the "worker" strategy on your Script component. Be aware that this feature is not yet stable and does not work with the app directory, though. If you want to take things one step further, you could use Partytown, which helps you offload any resource-intensive scripts to Web Workers. It comes with a React component that you can use to wrap your third-party scripts and offload them to a Web Worker, and it's compatible with Next.js as well. Hooks and State Management State management in React applications can easily get out of hand, leading to unnecessary re-renders and effectively an increased INP. Sometimes, using a state management library like Redux or MobX can help you consolidate your state and reduce the number of re-renders. However, they are not silver bullets and can also introduce performance issues if not used properly. If you are dealing with a lot of re-renders due to prop changes, make sure you are leveraging memoization. As of now, you may need to work with useMemo and useCallback hooks to memoize your values and functions, respectively. The upcoming React 19’s Forget Compiler, however, will apparently memoize everything under the hood, making these hooks obsolete. Using memoization properly can help you reduce the number of re-renders and improve your INP. To investigate your hook dependencies and re-renders, you can leverage React Developer Tools or use this handy helper hook I found on the internet to trace your re-renders: ` Conclusion Improving INP in React and Next.js is not easy and can require much investigation and fine-tuning. Still, it's worth doing to avoid being penalized by Google in its search results and provide a better experience for your users. Adopting React 18's new features, leveraging SSR and SSG in Next.js, utilizing Web Workers, and optimizing hooks and state management can significantly boost your INP score and deliver a faster application to your users. Remember, INP is just one among many performance metrics emphasizing the need for a comprehensive approach to performance optimization...

Apr 19, 2024

5 mins

Web PerformanceReactNextJSJavaScript

Communication Between Client Components in Next.js

Communication Between Client Components in Next.js In recent years, Next.js has become one of the most popular React frameworks for building server-rendered applications. With the introduction of the App Router in Next.js 13, the framework has taken a major leap forward by embracing a new approach to building web applications: the concept of server components and client components. This separation of concerns allows developers to strategically decide which parts of their application should be rendered on the server and which are then hydrated in the browser for interactivity. The Challenge: Communicating Between Client Components While server components offer numerous benefits, they also introduce a new challenge: how can client components within different boundaries communicate with each other? For instance, let's consider a scenario where you have a button (a client component) and a separate client component that displays the number of times the button has been clicked. In a regular React application, this would typically be accomplished by lifting the state to a common ancestor component, allowing the button to update the state, which is then passed down to the counter display component. However, the traditional approach may not be as straightforward in a Next.js application that heavily relies on server components, with client components scattered across the page. This blog post will explore three ways to facilitate communication between client components in Next.js, depending on where you want to store the state. Lifting State to a Common Client Component One approach to communicating between client components is to lift the state to a common client component. This could be a React context provider, a state management system like Zustand, or any other solution that allows you to share state across components. The key aspect is that this wrapper component should be higher up in the tree (perhaps even in the layout) and accept server components as children. Next.js allows you to interleave client and server components as much as you want, as long as server components are passed to client components as props or as component children. Here's how this approach might look in practice. First of all, we'd create a wrapper client component that holds the state: ` This wrapper component can be included in the layout: ` Any server components can be rendered within the layout, potentially nesting them several levels deep. Finally, we'd create two client components, one for the button and one for the counter display: ` ` The entire client and server component tree is rendered on the server, and the client components are then hydrated in the browser and initialized. From then on, the communication between the client components works just like in any regular React application. Check out the page using this pattern in the embedded Stackblitz window below: Using Query Params for State Management Another approach is to use query params instead of a wrapper client component and store the state in the URL. In this scenario, you have two client components: the button and the counter display. The counter value (the state) is stored in a query param, such as counterValue. The client components can read the current counter value using the useSearchParams hook. Once read, the useRouter hook can update the query param, effectively updating the counter value. However, there's one gotcha to this approach. If a route is statically rendered, calling useSearchParams will cause the client component tree up to the closest Suspense boundary to be client-side rendered. Next.js recommends wrapping the client component that uses useSearchParams in a boundary. Here's an example of how this approach might look. The button reads the current counter value and updates it on click by using the router's replace function: ` The counter display component is relatively simple, only reading the counter value: ` And here is the page that is a server component, hosting both of the above client components: ` Feel free to check out the above page in the embedded Stackblitz below: Storing State on the Server The third approach is to store the state on the server. In this case, the counter display component accepts the counter value as a prop, where the counter value is passed by a parent server component that reads the counter value from the database. The button component, when clicked, calls a server action that updates the counter value and calls revalidatePath() so that the counter value is refreshed, and consequently, the counter display component is re-rendered. It's worth noting that in this approach, unless you need some interactivity in the counter display component, it doesn't need to be a client component – it can be purely server-rendered. However, if both components need to be client components, here's an example of how this approach might look. First, we'll implement a server action that updates the counter value. We won't get into the mechanics of updating it, which in a real app would require a call to the database or an external API - so we're only commenting that part. After that, we revalidate the path so that the Next.js caches are purged, and the counter value is retrieved again in server components that read it. ` The button is a simple client component that calls the above server action when clicked. ` The counter display component reads the counter value from the parent: ` While the parent is a server component that reads the counter value from the database or an external API. ` This pattern can be seen in the embedded Stackblitz below: Conclusion Next.js is a powerful framework that offers various choices for implementing communication patterns between client components. Whether you lift state to a common client component, use query params for state management, or store the state on the server, Next.js provides all the tools you need to add a communication path between two separate client component boundaries. We hope this blog post has been useful in demonstrating how to facilitate communication between client components in Next.js. Check out other Next.js blog posts we've written for more insights and best practices. You can also view the entire codebase for the above snippets on StackBlitz....

Jun 21, 2024

5 mins

NextJSReact

“Music and code have a lot in common,” freeCodeCamp’s Jessica Wilkins on what the tech community is doing right to onboard new software engineers

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Apr 25, 2025

3 mins

JavaScriptSoftware Engineering

The Importance of a Scientific Mindset in Software Engineering: Part 2 (Debugging)

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For instance, you might hypothesize: _"The data inconsistency occurs because a caching layer is serving stale data when certain user profiles are updated."_ The key is that this explanation must be falsifiable; if experiments don't support the hypothesis, it must be refined or discarded. - 3. Collect Evidence and Data: Evidence often includes logs, system metrics, error messages, and runtime traces. Similar to reviewing primary sources in academic research, treat your raw debugging data as crucial evidence. Evaluating these data points can reveal patterns. In our example, such patterns could be whether the bug correlates with specific caching mechanisms, increased memory usage, or database query latency. During this step, it's essential to approach data critically, just as you would analyze the quality and credibility of sources in a research literature review. Don't forget that even logs can be misleading, incomplete, or even incorrect, so cross-referencing multiple sources is key. - 4. Design and Run Experiments: Design minimal, controlled tests to confirm or refute your hypothesis. In our example, you may try disabling or shortening the cache's time-to-live (TTL) to see if more recent data is displayed correctly. By manipulating one variable at a time - such as cache invalidation intervals - you gain clearer insights into causation. Tools such as profilers, debuggers, or specialized test harnesses can help isolate factors and gather precise measurements. - 5. Analyze Results and Refine Hypotheses: If the experiment's outcome doesn't align with your hypothesis, treat it as a stepping stone, not a dead end. Adjust your explanation, form a new hypothesis, or consider additional variables (for example, whether certain API calls bypass caching). Each iteration should bring you closer to a better understanding of the bug's root cause. Remember, the goal is not to prove an initial guess right but to arrive at a verifiable explanation. - 6. Implement and Verify the Fix: Once you're confident in the identified cause, you can implement the fix. Verification doesn't stop at deployment - re-test under the same conditions and, if possible, beyond them. By confirming the fix in a controlled manner, you ensure that the solution is backed by evidence rather than wishful thinking. - Personally, I consider implementing end-to-end tests (e.g., with Playwright) that reproduce the bug and verify the fix to be a crucial part of this step. This both ensures that the bug doesn't reappear in the future due to changes in the codebase and avoids possible imprecisions of manual testing. Now, we can explore these steps in more detail, highlighting how the scientific method can guide us through the debugging process. 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For example: - Not Falsifiable: "Occasionally, the application just shows weird data." - Falsifiable: "Users see stale data when the cache is not invalidated within 30 seconds of profile updates." Making your hypothesis specific enough to fail a test will pave the way for more precise debugging. 4. Align with Available Evidence Match your hypothesis to what you already know - logs, stack traces, metrics, and user reports. For example: - If logs reveal that cache invalidation events aren't firing, form a hypothesis explaining why those events fail or never occur. - If metrics show that data served from the cache is older than the configured TTL, hypothesize about how or why the TTL is being ignored. If your current explanation contradicts existing data, refine your hypothesis until it fits. 5. Plan for Controlled Tests Once you have a testable hypothesis, figure out how you'll attempt to _disprove_ it. This might involve: - Reproducing the environment: Set up a staging/local system that closely mimics production. For instance with the same cache layer configurations. - Varying one condition at a time: For example, only adjust cache invalidation policies or TTLs and then observe how data freshness changes. - Monitoring metrics: In our example, such monitoring would involve tracking user profile updates, cache hits/misses, and response times. These metrics should lead to confirming or rejecting your explanation. These plans become your blueprint for experiments in further debugging stages. Collecting and Evaluating Evidence After formulating a clear, testable hypothesis, the next crucial step is to gather data that can either support or refute it. This mirrors how scientists collect observations in a literature review or initial experiments. 1. Identify "Primary Sources" (Logs, Stack Traces, Code History): - Logs and Stack Traces: These are your direct pieces of evidence - treat them like raw experimental data. For instance, look closely at timestamps, caching-related events (e.g., invalidation triggers), and any error messages related to stale reads. - Code History: Look for related changes in your source control, e.g. using Git bisect. In our example, we would look for changes to caching mechanisms or references to cache libraries in commits, which could pinpoint when the inconsistency was introduced. Sometimes, reverting a commit that altered cache settings helps confirm whether the bug originated there. 2. Corroborate with "Secondary Sources" (Documentation, Q&A Forums): - Documentation: Check official docs for known behavior or configuration details that might differ from your assumptions. - Community Knowledge: Similar issues reported on GitHub or StackOverflow may reveal known pitfalls in a library you're using. 3. Assess Data Quality and Relevance: - Look for Patterns: For instance, does stale data appear only after certain update frequencies or at specific times of day? - Check Environmental Factors: For instance, does the bug happen only with particular deployment setups, container configurations, or memory constraints? - Watch Out for Biases: Avoid seeking only the data that confirms your hypothesis. Look for contradictory logs or metrics that might point to other root causes. You keep your hypothesis grounded in real-world system behavior by treating logs, stack traces, and code history as primary data - akin to raw experimental results. This evidence-first approach reduces guesswork and guides more precise experiments. Designing and Running Experiments With a hypothesis in hand and evidence gathered, it's time to test it through controlled experiments - much like scientists isolate variables to verify or debunk an explanation. 1. Set Up a Reproducible Environment: - Testing Environments: Replicate production conditions as closely as possible. In our example, that would involve ensuring the same caching configuration, library versions, and relevant data sets are in place. - Version Control Branches: Use a dedicated branch to experiment with different settings or configuration, e.g., cache invalidation strategies. This streamlines reverting changes if needed. 2. Control Variables One at a Time: - For instance, if you suspect data inconsistency is tied to cache invalidation events, first adjust only the invalidation timeout and re-test. - Or, if concurrency could be a factor (e.g., multiple requests updating user data simultaneously), test different concurrency levels to see if stale data issues become more pronounced. 3. Measure and Record Outcomes: - Automated Tests: Tests provide a great way to formalize and verify your assumptions. For instance, you could develop tests that intentionally update user profiles and check if the displayed data matches the latest state. - Monitoring Tools: Monitor relevant metrics before, during, and after each experiment. In our example, we might want to track cache hit rates, TTL durations, and query times. - Repeat Trials: Consistency across multiple runs boosts confidence in your findings. 4. Validate Against a Baseline: - If baseline tests manifest normal behavior, but your experimental changes manifest the bug, you've isolated the variable causing the issue. E.g. if the baseline tests show that data is consistently fresh under normal caching conditions but your experimental changes cause stale data. - Conversely, if your change eliminates the buggy behavior, it supports your hypothesis - e.g. that the cache configuration was the root cause. Each experiment outcome is a data point supporting or contradicting your hypothesis. Over time, these data points guide you toward the true cause. Analyzing Results and Iterating In scientific debugging, an unexpected result isn't a failure - it's valuable feedback that brings you closer to the right explanation. 1. Compare Outcomes to the hypothesis. For instance: - Did user data stay consistent after you reduced the cache TTL or fixed invalidation logic? - Did logs show caching events firing as expected, or did they reveal unexpected errors? - Are there only partial improvements that suggest multiple overlapping issues? 2. Incorporate Unexpected Observations: - Sometimes, debugging uncovers side effects - e.g. performance bottlenecks exposed by more frequent cache invalidations. Note these for future work. - If your hypothesis is disproven, revise it. For example, the cache may only be part of the problem, and a separate load balancer setting also needs attention. 3. Avoid Confirmation Bias: - Don't dismiss contrary data. For instance, if you see evidence that updates are fresh in some modules but stale in others, you may have found a more nuanced root cause (e.g., partial cache invalidation). - Consider other credible explanations if your teammates propose them. Test those with the same rigor. 4. Decide If You Need More Data: - If results aren't conclusive, add deeper instrumentation or enable debug modes to capture more detailed logs. - For production-only issues, implement distributed tracing or sampling logs to diagnose real-world usage patterns. 5. Document Each Iteration: - Record the results of each experiment, including any unexpected findings or new hypotheses that arise. - Through iterative experimentation and analysis, each cycle refines your understanding. By letting evidence shape your hypothesis, you ensure that your final conclusion aligns with reality. Implementing and Verifying the Fix Once you've identified the likely culprit - say, a misconfigured or missing cache invalidation policy - the next step is to implement a fix and verify its resilience. 1. Implementing the Change: - Scoped Changes: Adjust just the component pinpointed in your experiments. Avoid large-scale refactoring that might introduce other issues. - Code Reviews: Peer reviews can catch overlooked logic gaps or confirm that your changes align with best practices. 2. Regression Testing: - Re-run the same experiments that initially exposed the issue. In our stale data example, confirm that the data remains fresh under various conditions. - Conduct broader tests - like integration or end-to-end tests - to ensure no new bugs are introduced. 3. Monitoring in Production: - Even with positive test results, real-world scenarios can differ. Monitor logs and metrics (e.g. cache hit rates, user error reports) closely post-deployment. - If the buggy behavior reappears, revisit your hypothesis or consider additional factors, such as unpredicted user behavior. 4. Benchmarking and Performance Checks (If Relevant): - When making changes that affect the frequency of certain processes - such as how often a cache is refreshed - be sure to measure the performance impact. Verify you meet any latency or resource usage requirements. - Keep an eye on the trade-offs: For instance, more frequent cache invalidations might solve stale data but could also raise system load. By systematically verifying your fix - similar to confirming experimental results in research - you ensure that you've addressed the true cause and maintained overall software stability. Documenting the Debugging Process Good science relies on transparency, and so does effective debugging. Thorough documentation guarantees your findings are reproducible and valuable to future team members. 1. Record Your Hypothesis and Experiments: - Keep a concise log of your main hypothesis, the tests you performed, and the outcomes. - A simple markdown file within the repo can capture critical insights without being cumbersome. 2. Highlight Key Evidence and Observations: - Note the logs or metrics that were most instrumental - e.g., seeing repeated stale cache hits 10 minutes after updates. - Document any edge cases discovered along the way. 3. List Follow-Up Actions or Potential Risks: - If you discover additional issues - like memory spikes from more frequent invalidation - note them for future sprints. - Identify parts of the code that might need deeper testing or refactoring to prevent similar issues. 4. Share with Your Team: - Publish your debugging report on an internal wiki or ticket system. A well-documented troubleshooting narrative helps educate other developers. - Encouraging open discussion of the debugging process fosters a culture of continuous learning and collaboration. By paralleling scientific publication practices in your documentation, you establish a knowledge base to guide future debugging efforts and accelerate collective problem-solving. Conclusion Debugging can be as much a rigorous, methodical exercise as an art shaped by intuition and experience. By adopting the principles of scientific inquiry - forming hypotheses, designing controlled experiments, gathering evidence, and transparently documenting your process - you make your debugging approach both systematic and repeatable. The explicitness and structure of scientific debugging offer several benefits: - Better Root-Cause Discovery: Structured, hypothesis-driven debugging sheds light on the _true_ underlying factors causing defects rather than simply masking symptoms. - Informed Decisions: Data and evidence lead the way, minimizing guesswork and reducing the chance of reintroducing similar issues. - Knowledge Sharing: As in scientific research, detailed documentation of methods and outcomes helps others learn from your process and fosters a collaborative culture. Ultimately, whether you are diagnosing an intermittent crash or chasing elusive performance bottlenecks, scientific debugging brings clarity and objectivity to your workflow. By aligning your debugging practices with the scientific method, you build confidence in your solutions and empower your team to tackle complex software challenges with precision and reliability. But most importantly, do not get discouraged by the number of rigorous steps outlined above or by the fact you won't always manage to follow them all religiously. Debugging is a complex and often frustrating process, and it's okay to rely on your intuition and experience when needed. Feel free to adapt the debugging process to your needs and constraints, and as long as you keep the scientific mindset at heart, you'll be on the right track....

May 9, 2025

13 mins

JavaScript

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Transitioning from React class components to function components with hooks

Functional components

Component state

Class components

Functional components

Component initialization

Class components

Functional components

Component destruction

Class components

Functional component

An important remark

Changed properties

Class components

Functional components

Changed properties and memoization

References

Conclusion

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