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ReactJS

Taming Forms With react-hook-form

Kevin Wolf

Kevin Wolf

Published Nov 19, 2020

Updated Feb 14, 2023

4 min read

Taming Forms With react-hook-form

This article was written over 18 months ago and may contain information that is out of date.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.

Taming Forms With react-hook-form

After some time of doing repetitive tasks like handling forms in React.js, you will notice that there is a lot of boilerplate code that can be abstracted into reusable custom hooks. Luckily, there is plenty of existing Open Source solutions. In this case, we will be using react-hook-form.

What is react-hook-form

react-hook-form is a performant, flexible, and extensible form handling library built for React. It exports a custom hook that you can call within your Functional Components, and returns both a register function that you pass as a ref to your input components, and a handleSubmit function to wrap your submit callback.

By returning a register function that will be added to the input component, we can leverage the Uncontrolled Component pattern to make our application faster and more performant, by avoiding unnecessary re-renders.

What are we going to build?

To get a better understanding of what react-hook-form can do, we will build a simple application showing a list of characters and a form to add them to our list.

Screen-Recording-2020-10-19-at-1

Application Setup

Before going right into react-hook-form, we will need to prepare our application with the basic file structure and functionality. For this, we will create a new react application (you can either use your preferred starter or cloud IDE).

If you want to skip the application setup, you can go ahead and fork this CodeSandbox, but I highly recommend you at least read this section to have a better understanding of what the app does.

1. Characters List

Let's start by creating a new component where we will display our characters.

character-list.js

import React from "react";

function CharacterList({ characters }) {
  return (
    <div>
      <h2>Character List</h2>

      {characters.length === 0 ? (
        <p>
          <em>Your character list is empty</em>
        </p>
      ) : (
        <ul>
          {characters.map((character, id) => (
            <li key={id}>
              {character.name} (<strong>{character.species}</strong>)
            </li>
          ))}
        </ul>
      )}
    </div>
  );
}

export default CharacterList;

If you have a basic understanding of React, you will notice our CharacterList component will receive a characters prop, which is an array of objects with the properties name and species. If the array is empty, we will render a placeholder. Elsewhere, we will render the list.

2. Add Character Form

The second step is to create a component that will render the form to add a new character to our list.

character-form.js

import React from "react";

function CharacterForm({ addCharacter }) {
  const onSubmit = (data) => {
    addCharacter(data);
  };

  return (
    <div>
      <h2>Add character</h2>
      <form onSubmit={onSubmit}>
        <div>
          <input name="name" placeholder="Character name" />
        </div>
        <div>
          <select name="species">
            <option value="sorcerer">Sorcerer</option>
            <option value="knight">Knight</option>
            <option value="human">Human</option>
          </select>
        </div>
        <div>
          <button type="submit">Add character</button>
        </div>
      </form>
    </div>
  );
}

export default CharacterForm;

By itself, this component won't do anything because we are not doing anything with the collected data, nor validating our fields. This will be the component where we will be working on the next section of this tutorial.

3. The App

Now, let's just create the App component where we will render CharacterList and CharacterForm.

app.js

import React from "react";

import CharacterList from "./character-list";
import CharacterForm from "./character-form";

function App() {
  const [characters, setCharacters] = React.useState([]);

  const addCharacter = (character) => {
    setCharacters((characters) => [...characters, character]);
  };

  return (
    <div>
      <CharacterList characters={characters} />
      <hr />
      <CharacterForm addCharacter={addCharacter} />
    </div>
  );
}

export default App;

We will be saving our character list in characters by using the React.useState hook, and passing them down to CharacterList. Also, we created an addCharacter function that will just add a new character at the end of the characters list, and pass it to CharacterForm via prop.

Let's get to it!

Now that we have our application setup, let's see how can we leverage react-hook-form to take our forms to the next level.

Install react-hook-form

yarn add react-hook-form

Add react-hook-form to your CharacterForm

Here comes the fun. First, let's import useForm from react-hook-form, call the hook in our component, destructure register and handleSubmit methods out of it (don't worry, I will explain what they do just in a while), wrap our onSubmit function with handleSubmit, and pass register as the ref for each one of our form controls.

character-form.js

import React from "react";
+import { useForm } from "react-hook-form";

function CharacterForm({ addCharacter }) {
+  const { register, handleSubmit } = useForm();
+
-  const onSubmit = (data) => {
-    addCharacter(data);
-  };
+  const onSubmit = handleSubmit((data) => {
+    addCharacter(data);
+  });

  return (
    <div>
      <h2>Add character</h2>
      <form onSubmit={onSubmit}>
        <div>
-          <input name="name" placeholder="Character name" />
+          <input ref={register} name="name" placeholder="Character name" />
        </div>
        <div>
-          <select name="species">
+          <select ref={register} name="species">
            <option value="sorcerer">Sorcerer</option>
            <option value="knight">Knight</option>
            <option value="human">Human</option>
          </select>
        </div>
        <div>
          <button type="submit">Add character</button>
        </div>
      </form>
    </div>
  );
}

export default CharacterForm;

The register method

By attaching the register ref to our form controls, we can start tracking some stuff like the field value, its validation status, and even if the field had been touched or not.

Important: the name prop is required when passing the register ref, and it should be unique. This way, react-hook-form will know where to assign the field value. For more information, check out the register documentation.

The handleSubmit method

This is a function that wraps our submit callback, and passes the actual form values to it. Under the hood, it also calls preventDefault on the form event to avoid full page reloads. It can also be an asynchronous function.

For more information, check out the handleSubmit documentation.

Add some validations

At this point, we have a working form that is able to add characters to our list. However, we are not checking if the field is filled, in order to avoid empty submissions.

With react-hook-form, it is as simple as calling the register function with a configuration object defining the validation rules. For our case, we will make the name field required. Also, we can extract errors from useForm to show the user if the field has errors.

import React from "react";
import { useForm } from "react-hook-form";

function CharacterForm({ addCharacter }) {
-  const { register, handleSubmit } = useForm();
+  const { register, handleSubmit, errors } = useForm();

  const onSubmit = handleSubmit((data) => {
    addCharacter(data);
  });

  return (
    <div>
      <h2>Add character</h2>
      <form onSubmit={onSubmit}>
        <div>
-          <input ref={register} name="name" placeholder="Character name" />
+          <input
+            ref={register({ required: true })}
+            name="name"
+            placeholder="Character name"
+          />
+          {errors.name && errors.name.type === "required"
+            ? "Name is required"
+            : null}
        </div>
        <div>
          <select ref={register} name="species">
            <option value="sorcerer">Sorcerer</option>
            <option value="knight">Knight</option>
            <option value="human">Human</option>
          </select>
        </div>
        <div>
          <button type="submit">Add character</button>
        </div>
      </form>
    </div>
  );
}

export default CharacterForm;

Reset the form status

The final step is to clear our form after successfully adding a character to our character list. For that, we will destructure a new method from the useForm hook: reset, and call it after addCharacter.

import React from "react";
import { useForm } from "react-hook-form";

function CharacterForm({ addCharacter }) {
-  const { register, handleSubmit, errors } = useForm();
+  const { register, handleSubmit, errors, reset } = useForm();

  const onSubmit = handleSubmit((data) => {
    addCharacter(data);
+    reset();
  });

  console.log(errors.nameRequired);

  return (
    <div>
      <h2>Add character</h2>
      <form onSubmit={onSubmit}>
        <div>
          <input
            ref={register({ required: true })}
            name="name"
            placeholder="Character name"
          />
          {errors.name && errors.name.type === "required"
            ? "Name is required"
            : null}
        </div>
        <div>
          <select ref={register} name="species">
            <option value="sorcerer">Sorcerer</option>
            <option value="knight">Knight</option>
            <option value="human">Human</option>
          </select>
        </div>
        <div>
          <button type="submit">Add character</button>
        </div>
      </form>
    </div>
  );
}

export default CharacterForm;

For more information, check out the reset documentation.

Moving forward

Now that you have a better sense of how to manage your React forms, you have unlocked a new world of possibilities by using battle-tested and community-validated libraries like react-hook-form.

You can take a look at more advanced use cases, additional resources or even take a look at the full API.

If you want a finished code sample, you can check out this CodeSandbox.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

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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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