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Introduction to Babylon.js

Introduction to Babylon.js

This article was written over 18 months ago and may contain information that is out of date.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

Babylon.js is a powerful, open-source 3D engine capable of rendering interactive 3D and 2D graphics using JavaScript. It is a great choice for creating games, demos, visualizations, and other 3D applications for the web or even for VR.

Being free, open-source, and cross-platform, Babylon.js is a great alternative to proprietary 3D engines like Unity and Unreal Engine. It is also a great alternative to other open-source 3D engines like Three.js and PlayCanvas as it provides TypeScript types out of the box, is optimized for performance, and provides advanced debugging tools. Its developer experience is excellent, and it has a large and active community making it a great choice for creating 3D applications for the web for both beginners and experts alike.

Getting Started

Babylon.js supports both ES6 and CommonJS module imports:

  • CommonJS Babylon.js npm packages support CommonJS/ES6 imports, UMD, and AMD-imports​​.

  • For developers seeking optimization through tree shaking, Babylon.js offers ES6 packages. These include @babylonjs/core for the core functionalities, and additional modules like @babylonjs/materials, @babylonjs/loaders, @babylonjs/gui, etc. It's important not to mix ES6 and legacy packages​​.

If you want to use the CommonJS module imports, you can install Babylon.js as follows:

npm install --save babylonjs

Then, include it in your JavaScript or TypeScript file:

import * as BABYLON from "babylonjs";

If you want to use the ES6 module imports, you can install Babylon.js as follows:

npm install --save @babylonjs/core

Then, include it in your JavaScript or TypeScript file:

import { Engine, Scene } from "@babylonjs/core";

I prefer the ES6 module imports because they allow for tree shaking, which can significantly reduce the size of the final bundle. However, in this tutorial, I will be using the CommonJS module imports because that's what the Babylon.js playground uses.

Creating Your First Babylon.js Scene

The Babylon.js Playground is an essential tool for learning and developing with Babylon.js. It's a user-friendly environment where you can write code and immediately see the results in a live scene. The playground comes with a default scene, and experimenting with it is a great way to start.

Apart from playing around with the default scene, you can also search the playground if you want to see examples of specific features. For example, if you search for "physics," you will find several examples of physics in action.

The playground is also often used to share code snippets on the Babylon.js forum both when asking for help and when helping others. I often found myself googling specific Babylon.js problems or features and finding a playground example that helped me understand and solve my problem.

Creating and Modifying Meshes

Meshes are fundamental in 3D graphics. In Babylon.js, creating a basic mesh, like a sphere, involves a few lines of code:

var sphere = BABYLON.MeshBuilder.CreateSphere(
  "sphere",
  { diameter: 2, segments: 32 },
  scene
);
sphere.position.y = 1;

You can also create materials and assign them to meshes to change their appearance. For example, to make a ground plane red, you would write:

const groundMaterial = new BABYLON.StandardMaterial("Ground Material", scene);
groundMaterial.diffuseColor = BABYLON.Color3.Red();
ground.material = groundMaterial;

Textures can also be added to materials:

var groundTexture = new BABYLON.Texture(
  Assets.textures.checkerboard_basecolor_png.rootUrl,
  scene
);
groundMaterial.diffuseTexture = groundTexture;

Importing and Using Meshes

Babylon.js allows for the importation of complex meshes, which can be scaled and positioned within the scene:

BABYLON.SceneLoader.ImportMesh(
  "",
  Assets.meshes.MyCustomMesh.rootUrl,
  Assets.meshes.MyCustomMesh.filename,
  scene,
  function (newMeshes) {
    newMeshes[0].scaling = new BABYLON.Vector3(0.1, 0.1, 0.1);
  }
);

Making the Scene Interactive

Interactivity is a key aspect of web experiences. Attaching controls to a camera enables user interaction through click-and-drag operations.

var camera = new BABYLON.ArcRotateCamera(
  "Camera",
  0,
  0,
  10,
  BABYLON.Vector3.Zero(),
  scene
);
camera.attachControl(canvas, true);

Adding Virtual Reality Support

Virtual reality is an exciting new technology that allows users to experience 3D environments more immersively. Babylon.js has built-in support for virtual reality, which can be enabled with a few lines of code:

const experience = await scene.createDefaultXRExperienceAsync();
const camera = experience.baseExperience.camera;
camera.attachControl(true);

You just create an XR experience, get the camera from the base experience, and attach it to the canvas and you should be all set for VR. If you don't own a VR headset, you can still test your scene in VR using the Immersive Web Emulator Chrome Extension.

Adding Physics

Physics is an important part of many 3D applications. Babylon.js has a built-in physics engine that can be enabled with a few lines of code:

var gravityVector = new BABYLON.Vector3(0, -9.81, 0);
var physicsPlugin = new BABYLON.CannonJSPlugin();
scene.enablePhysics(gravityVector, physicsPlugin);

Physics can be applied to meshes by setting their physicsImpostor property:

sphere.physicsImpostor = new BABYLON.PhysicsImpostor(
  sphere,
  BABYLON.PhysicsImpostor.SphereImpostor,
  { mass: 1, restitution: 0.9 },
  scene
);

Adding Lights

Lights are essential for creating realistic scenes. In Babylon.js, there are four main types of lights, each offering unique properties and effects:

  1. Directional Light: Mimics sunlight, emitting parallel light rays across the entire scene. It's defined by a direction vector and has an infinite range.
  2. Point Light: Resembles a light bulb, radiating light equally in all directions from a single point in space.
  3. Spot Light: Functions like a flashlight, emitting a conical beam of light from a specific position in a given direction. Its illumination area and decay are controlled by angle and exponent parameters.
  4. Hemispheric Light: Simulates ambient environment lighting, defined by a direction, usually upwards. Its effect is influenced by setting different color properties.

Each light type can be customized using properties like intensity and range, and you can control which meshes they illuminate. For more complex lighting scenarios, lightmaps can be utilized to pre-calculate and store lighting effects.

For example, to add a directional light to the scene, you could write:

var light = new BABYLON.DirectionalLight(
  "DirectionalLight",
  new BABYLON.Vector3(0, -1, 0),
  scene
);

Adding Shadows

Shadows are an important part of creating realistic scenes as they can help convey the 3D structure of the scene by providing cues about the relative positions and distances of objects, enhancing the perception of depth and dimension. Babylon.js has several types of shadows, including PCF, PCFSoft, and PCSS. For example, to add PCF shadows to the scene, you would write:

var shadowGenerator = new BABYLON.ShadowGenerator(1024, light);
shadowGenerator.usePoissonSampling = true;
shadowGenerator.bias = 0.0001;
shadowGenerator.normalBias = 0.01;
shadowGenerator.setDarkness(0.5);
shadowGenerator.useBlurExponentialShadowMap = true;
shadowGenerator.blurKernel = 32;
shadowGenerator.blurScale = 2;
shadowGenerator.blurBoxOffset = 1;
shadowGenerator.useKernelBlur = true;

shadowGenerator.addShadowCaster(sphere);

Adding Audio

Audio is an important part of many 3D applications. Babylon.js has a built-in audio engine that can be enabled with a few lines of code:

var audioEngine = new BABYLON.AudioEngine();

Audio can be added to the scene by creating a sound object:

var sound = new BABYLON.Sound(
  "Sound",
  `${Assets.sound.cannonBlast.rootUrl}${Assets.sound.cannonBlast.filename}`,
  scene,
  null,
  { loop: true, autoplay: true }
);

This particular example would play a "cannon blast" sound on a loop in your scene. The cannon blast asset is preloaded in the Babylon.js playground. If you were to load a custom asset in your application, you would simply provide a URL pointing to the sound file on the filesystem. For more details, you can check the documentation on playing sounds in Babylon.js.

Adding User Interface Elements

User interface elements can be used to add interactivity to a scene. Babylon.js provides a GUI library extension built on top of the DynamicTexture. For example, to add a dialog to the scene containing a button, you would write:

var guiManager = new BABYLON.GUI.GUI3DManager(scene);

const slate = new BABYLON.GUI.HolographicSlate("down");
slate.minDimensions = new BABYLON.Vector2(5, 5);
slate.dimensions = new BABYLON.Vector2(5, 5);
slate.titleBarHeight = 0.75;
slate.title = "Button!";
guiManager.addControl(slate);

var button = BABYLON.GUI.Button.CreateSimpleButton("button", "Click me!");
button.width = 0.5;
button.height = 0.25;
button.background = "green";
button.textBlock.color = "white";
button.onPointerClickObservable.add(() => {
  alert("Hi!");
});

slate.content = button;
slate.position = new BABYLON.Vector3(-2, 2, 0);

To use the ES6 version, you'll need to install the @babylonjs/gui package:

npm install --save @babylonjs/gui

Then, import it into your JavaScript or TypeScript file like this:

import { GUI3DManager, HolographicSlate, Button } from "@babylonjs/gui";

Tip: In case you don't like the controls that allow users to tilt the HolographicSlate, you can disable them by setting slate._gizmo._rootMesh.setEnabled(false); after adding the slate to your scene.

Adding Animations

Animations can be used to add movement to a scene. Babylon.js has several types of animations, including keyframe, bone, and morph target. For example, to add a keyframe animation to the scene, you would write:

var animationBox = new BABYLON.Animation(
  "myAnimation",
  "scaling.x",
  30,
  BABYLON.Animation.ANIMATIONTYPE_FLOAT,
  BABYLON.Animation.ANIMATIONLOOPMODE_CYCLE
);
var keys = [];
keys.push({
  frame: 0,
  value: 1,
});
keys.push({
  frame: 20,
  value: 0.2,
});
keys.push({
  frame: 100,
  value: 1,
});
animationBox.setKeys(keys);
sphere.animations.push(animationBox);
scene.beginAnimation(sphere, 0, 100, true);

Here's an explanation of what the code does:

  1. Animation Creation: A BABYLON.Animation object named "myAnimation" is created to animate the scaling.x property, indicating the animation will affect the object's width. It runs at 30 frames per second, with values represented as floats, and loops continuously.

  2. Defining Keyframes: Three keyframes are defined:

    • At frame 0, the scale is 1 (original size).
    • At frame 20, the scale reduces to 0.2.
    • At frame 100, the scale returns to 1.
  3. Applying and Starting Animation: The animation is assigned to the sphere object and starts immediately, looping between frames 0 and 100. This creates a pulsating effect on the sphere's width.

For a deeper dive into animations, you can check out the Babylon.js Animation Documentation.

Debugging

Debugging is an important part of any development process. Babylon.js has a built-in debug layer that can be enabled with a few lines of code:

scene.debugLayer.show();

The debug layer provides a user-friendly interface for inspecting and modifying the scene. It can also show you the current frame rate and other performance metrics and lets you export the performance data.

In case you are using the ES6 module imports, you'll need to install the @babylonjs/inspector package:

npm install --save @babylonjs/inspector

Then, import it into your JavaScript or TypeScript file like this:

import "@babylonjs/inspector";

Hosting and Sharing Your Scene

Once you're satisfied with your creation, you can download it as an HTML file and host it on platforms like GitHub Pages, making it accessible to the world.

You can also share your scene with others by sharing the playground URL. For example, the URL for the default scene is https://playground.babylonjs.com/#6QY4X1#1.

In case you would like to integrate your scene with a framework of your choice, you can check out the Babylon.js External Libraries Documentation where you can find examples of Babylon.js being used with frameworks like React, Vue, or Ionic Angular.

Conclusion

Babylon.js is a powerful, open-source 3D engine capable of rendering interactive 3D and 2D graphics using JavaScript. It is well supported and maintained, providing a good developer experience with many interactive playground examples and a supportive community forum, making it a great choice for creating games, demos, visualizations, and other 3D applications for the web (and eventually even for native platforms as I've heard out of the box React Native support may be coming).

This blog post hopefully gave you an overview of some of its capabilities and how to get started with it along with pointers to more in-depth documentation.

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

The Importance of a Scientific Mindset in Software Engineering: Part 2 (Debugging) In the first part of my series on the importance of a scientific mindset in software engineering, we explored how the principles of the scientific method can help us evaluate sources and make informed decisions. Now, we will focus on how these principles can help us tackle one of the most crucial and challenging tasks in software engineering: debugging. In software engineering, debugging is often viewed as an art - an intuitive skill honed through experience and trial and error. In a way, it is - the same as a GP, even a very evidence-based one, will likely diagnose most of their patients based on their experience and intuition and not research scientific literature every time; a software engineer will often rely on their experience and intuition to identify and fix common bugs. However, an internist faced with a complex case will likely not be able to rely on their intuition alone and must apply the scientific method to diagnose the patient. Similarly, a software engineer can benefit from using the scientific method to identify and fix the problem when faced with a complex bug. From that perspective, treating engineering challenges like scientific inquiries can transform the way we tackle problems. Rather than resorting to guesswork or gut feelings, we can apply the principles of the scientific method—forming hypotheses, designing controlled experiments, gathering and evaluating evidence—to identify and eliminate bugs systematically. This approach, sometimes referred to as "scientific debugging," reframes debugging from a haphazard process into a structured, disciplined practice. It encourages us to be skeptical, methodical, and transparent in our reasoning. For instance, as Andreas Zeller notes in the book _Why Programs Fail_, the key aspect of scientific debugging is its explicitness: Using the scientific method, you make your assumptions and reasoning explicit, allowing you to understand your assumptions and often reveals hidden clues that can lead to the root cause of the problem on hand. Note: If you'd like to read an excerpt from the book, you can find it on Embedded.com. Scientific Debugging At its core, scientific debugging applies the principles of the scientific method to the process of finding and fixing software defects. Rather than attempting random fixes or relying on intuition, it encourages engineers to move systematically, guided by data, hypotheses, and controlled experimentation. By adopting debugging as a rigorous inquiry, we can reduce guesswork, speed up the resolution process, and ensure that our fixes are based on solid evidence. Just as a scientist begins with a well-defined research question, a software engineer starts by identifying the specific symptom or error condition. For instance, if our users report inconsistencies in the data they see across different parts of the application, our research question could be: _"Under what conditions does the application display outdated or incorrect user data?"_ From there, we can follow a structured debugging process that mirrors the scientific method: - 1. Observe and Define the Problem: First, we need to clearly state the bug's symptoms and the environment in which it occurs. We should isolate whether the issue is deterministic or intermittent and identify any known triggers if possible. Such a structured definition serves as the groundwork for further investigation. - 2. Formulate a Hypothesis: A hypothesis in debugging is a testable explanation for the observed behavior. 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. Establishing Clear Debugging Questions (Formulating a Hypothesis) A hypothesis is a proposed explanation for a phenomenon that can be tested through experimentation. In a debugging context, that phenomenon is the bug or issue you're trying to resolve. Having a clear, falsifiable statement that you can prove or disprove ensures that you stay focused on the real problem rather than jumping haphazardly between possible causes. A properly formulated hypothesis lets you design precise experiments to evaluate whether your explanation holds true. To formulate a hypothesis effectively, you can follow these steps: 1. Clearly Identify the Symptom(s) Before forming any hypothesis, pin down the specific issue users are experiencing. For instance: - "Users intermittently see outdated profile information after updating their accounts." - "Some newly created user profiles don't reflect changes in certain parts of the application." Having a well-defined problem statement keeps your hypothesis focused on the actual issue. Just like a research question in science, the clarity of your symptom definition directly influences the quality of your hypothesis. 2. Draft a Tentative Explanation Next, convert your symptom into a statement that describes a _possible root cause_, such as: - "Data inconsistency occurs because the caching layer isn't invalidating or refreshing user data properly when profiles are updated." - "Stale data is displayed because the cache timeout is too long under certain load conditions." This step makes your assumption about the root cause explicit. As with the scientific method, your hypothesis should be something you can test and either confirm or refute with data or experimentation. 3. Ensure Falsifiability A valid hypothesis must be falsifiable - meaning it can be proven _wrong_. You'll struggle to design meaningful experiments if a hypothesis is too vague or broad. 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....

Next.js + MongoDB Connection Storming cover image

Next.js + MongoDB Connection Storming

Building a Next.js application connected to MongoDB can feel like a match made in heaven. MongoDB stores all of its data as JSON objects, which don’t require transformation into JavaScript objects like relational SQL data does. However, when deploying your application to a serverless production environment such as Vercel, it is crucial to manage your database connections properly. If you encounter errors like these, you may be experiencing Connection Storming: * MongoServerSelectionError: connect ECONNREFUSED <IP_ADDRESS>:<PORT> * MongoNetworkError: failed to connect to server [<hostname>:<port>] on first connect * MongoTimeoutError: Server selection timed out after <x> ms * MongoTopologyClosedError: Topology is closed, please connect * Mongo Atlas: Connections % of configured limit has gone above 80 Connection storming occurs when your application has to mount a connection to Mongo for every serverless function or API endpoint call. Vercel executes your application’s code in a highly concurrent and isolated fashion. So, if you create new database connections on each request, your app might quickly exceed the connection limit of your database. We can leverage Vercel’s fluid compute model to keep our database connection objects warm across function invocations. Traditional serverless architecture was designed for quick, stateless web app transactions. Now, especially with the rise of LLM-oriented applications built with Next.js, interactions with applications are becoming more sequential. We just need to ensure that we assign our MongoDB connection to a global variable. Protip: Use global variables Vercel’s fluid compute model means all memory, including global constants like a MongoDB client, stays initialized between requests as long as the instance remains active. By assigning your MongoDB client to a global constant, you avoid redundant setup work and reduce the overhead of cold starts. This enables a more efficient approach to reusing connections for your application’s MongoDB client. The example below demonstrates how to retrieve an array of users from the users collection in MongoDB and either return them through an API request to /api/users or render them as an HTML list at the /users route. To support this, we initialize a global clientPromise variable that maintains the MongoDB connection across warm serverless executions, avoiding re-initialization on every request. ` Using this database connection in your API route code is easy: ` You can also use this database connection in your server-side rendered React components. ` In serverless environments like Vercel, managing database connections efficiently is key to avoiding connection storming. By reusing global variables and understanding the serverless execution model, you can ensure your Next.js app remains stable and performant....

The Quirks And Gotchas of PHP cover image

The Quirks And Gotchas of PHP

The Quirks And Gotchas of PHP If you come from a JavaScript background, you'll likely be familiar with some of its famous quirks, such as 1 + "1" equaling "11". Well, PHP has its own set of quirks and gotchas, too. Some are oddly similar to JavaScript's, while others can surprise a JavaScript developer. Let's start with the more familiar ones. 1. Type Juggling and Loose Comparisons Like JavaScript, PHP has two types of comparison operators: strict and loose. The loose comparison operator in PHP uses ==, while the strict comparison operator uses ===. Here's an example of a loose vs. strict comparison in PHP: ` PHP is a loosely typed language, meaning it will automatically convert variables from one type to another when necessary, just like JavaScript. This is not only when doing comparisons but also, for example, when doing numeric operations. Such conversions can lead to some unexpected results if you're not careful: ` As you can see, the type system has gotten a bit stricter in PHP 8, so it won't let you commit some of the "atrocities" that were possible in earlier versions, throwing a TypeError instead. PHP 8 introduced many changes that aim to eliminate some of the unpredictable behavior; we will cover some of them throughout this article. 1.1. Truthiness of Strings This is such a common gotcha in PHP that it deserves its own heading. By default, PHP considers an empty string as false and a non-empty string as true: ` But wait, there's more! PHP also considers the string "0" as false: ` You might think we're done here, but no! Try comparing a string such as "php" to 0: ` Until PHP7, any non-numeric string was converted to 0 when cast to an integer to compare it to the other integer. That's why this example will be evaluated as true. This quirk has been fixed in PHP 8. For a comprehensive comparison table of PHP's truthiness, check out the PHP documentation. 1.2. Switch Statements Switch statements in PHP use loose comparisons, so don't be surprised if you see some unexpected behavior when using them: ` The New Match Expression in PHP 8 PHP 8 introduced the match expression, which is similar to switch but uses strict comparisons (i.e., === under the hood) and returns a value: ` Unlike switch, there is no "fall-through" behavior in match, and each branch must return a value, making match a great alternative when you need a more precise or concise form of branching—especially if you want to avoid the loose comparisons of a traditional switch. 1.3 String to Number Conversion In earlier versions of PHP, string-to-number conversions were often done silently, even if the string wasn’t strictly numeric (like '123abc'). In PHP 7, this would typically result in 123 plus a Notice: ` In PHP 8, you’ll still get int(123), but now with a Warning, and in other scenarios (like extremely malformed strings), you might see a TypeError. This stricter behavior can reveal hidden bugs in code that relied on implicit type juggling. Stricter Type Checks & Warnings in PHP 8 - Performing arithmetic on non-numeric strings: As noted, in older versions, something like "123abc" + 0 would silently drop the non-numeric part, often producing 123 plus a PHP Notice. In PHP 8, such operations throw a more visible Warning or TypeError, depending on the exact scenario. - Null to Non-Nullable Internal Arguments: Passing null to a function parameter that’s internally declared as non-nullable will trigger a TypeError in PHP 8. Previously, this might have been silently accepted or triggered only a warning. - Internal Function Parameter Names: PHP 8 introduced named arguments but also made internal parameter names part of the public API. If you use named arguments with built-in functions, be aware that renaming or reordering parameters in future releases might break your code. Always match official parameter names as documented in the PHP manual. Union Types & Mixed Since PHP 8.0, we can declare union types, which allows you to specify that a parameter or return value can be one of multiple types. For example: ` Specifying the union of types your function accepts can help clarify your code’s intent and reveal incompatibilities if your existing code relies on looser type checking, preventing some of the conversion quirks we’ve discussed. 2. Operator Precedence and Associativity Operator precedence can lead to confusing situations if you’re not careful with parentheses. For instance, the . operator (string concatenation similar to + in JavaScript) has left-to-right associativity, but certain logical operators have lower precedence than assignment or concatenation, leading to puzzling results in PHP 7 and earlier: ` PHP 8 has fixed this issue by making the + and - operators take a higher precedence. 3. Variable Variables and Variable Functions Now, we're getting into unfamiliar territory as JavaScript Developers. PHP allows you to define variable variables and variable functions. This can be a powerful feature, but it can also lead to some confusing code: ` In this example, the variable $varName contains the string 'hello'. By using $$varName, we're creating a new variable with the name 'hello' and assigning it the value 'world'. Similarly, you can create variable functions: ` 4. Passing Variables by Reference You can pass variables by reference using the & operator in PHP. This means that any changes made to the variable inside the function will be reflected outside the function: ` While this example is straightforward, not knowing the pass-by-reference feature can lead to some confusion, and bugs can arise when you inadvertently pass variables by reference. 5. Array Handling PHP arrays are a bit different from JavaScript arrays. They can be used as both arrays and dictionaries, and they have some quirks that can catch you off guard. For example, if you try to access an element that doesn't exist in an array, PHP will return null instead of throwing an error: ` Furthermore, PHP arrays can contain both numerical and string keys at the same time, but numeric string keys can sometimes convert to integers, depending on the context> ` In this example: - "1" (string) and 1 (integer) collide, resulting in the array effectively having only one key: 1. - true is also cast to 1 as an integer, so it overwrites the same key. And last, but not least, let's go back to the topic of passing variables by reference. You can assign an array element by reference, which can feel quite unintuitive: ` 6 Checking for Variable Truthiness (isset, empty, and nullsafe operator) In PHP, you can use the empty() function to check if a variable is empty. But what does "empty" mean in PHP? The mental model of what's considered "empty" in PHP might differ from what you're used to in JavaScript. Let's clarify this: The following values are considered empty by the empty() function: - "" (an empty string) - 0 (0 as an integer) - 0.0 (0 as a float) - "0" (0 as a string) - null - false - [] (an empty array) This means that the following values are not considered empty: - "0" (a string containing "0") - " " (a string containing a space) - 0.0 (0 as a float) - new stdClass() (an empty object) Keep this in mind when using empty() in your code, otherwise, you might end up debugging some unexpected behavior. Undefined Variables and isset() Another little gotcha is that you might expect empty() to return true for undefined variables too - they contain nothing after all, right? Unfortunately, empty() will throw a notice in such case. To account for undefined variables, you may want to use the isset() function, which checks if a variable is set and not null: ` The Nullsafe Operator If you have a chain of properties or methods that you want to access, you may tend to check each step with isset() to avoid errors: ` In fact, because isset() is a special language construct and it doesn't fully evaluate an undefined part of the chain, it can be used to evaluate the whole chain at once: ` That's much nicer! However, it could be even more elegant with the nullsafe operator (?->) introduced in PHP 8: ` If you’ve used optional chaining in JavaScript or other languages, this should look familiar. It returns null if any part of the chain is null, which is handy but can also hide potential logic mistakes — if your application logic expects objects to exist, silently returning null may lead to subtle bugs. Conclusion While PHP shares a few loose typing quirks with JavaScript, it also has its own distinctive behaviors around type juggling, operator precedence, passing by reference, and array handling. Becoming familiar with these nuances — and with the newer, more predictable features in PHP 8 — will help you avoid subtle bugs and write clearer, more robust code. PHP continues to evolve, so always consult the official documentation to stay current on best practices and language changes....

Vercel BotID: The Invisible Bot Protection You Needed cover image

Vercel BotID: The Invisible Bot Protection You Needed

Nowadays, bots do not act like “bots”. They can execute JavaScript, solve CAPTCHAs, and navigate as real users. Traditional defenses often fail to meet expectations or frustrate genuine users. That’s why Vercel created BotID, an invisible CAPTCHA that has real-time protections against sophisticated bots that help you protect your critical endpoints. In this blog post, we will explore why you should care about this new tool, how to set it up, its use cases, and some key considerations to take into account. We will be using Next.js for our examples, but please note that this tool is not tied to this framework alone; the only requirement is that your app is deployed and running on Vercel. Why Should You Care? Think about these scenarios: - Checkout flows are overwhelmed by scalpers - Signup forms inundated with fake registrations - API endpoints draining resources with malicious requests They all impact you and your users in a negative way. For example, when bots flood your checkout page, real customers are unable to complete their purchases, resulting in your business losing money and damaging customer trust. Fake signups clutter the app, slowing things down and making user data unreliable. When someone deliberately overloads your app’s API, it can crash or become unusable, making users angry and creating a significant issue for you, the owner. BotID automatically detects and filters bots attempting to perform any of the above actions without interfering with real users. How does it work? A lightweight first-party script quickly gathers a high set of browser & environment signals (this takes ~30ms, really fast so no worry about performance issues), packages them into an opaque token, and sends that token with protected requests via the rewritten challenge/proxy path + header; Vercel’s edge scores it, attaches a verdict, and checkBotId() function simply reads that verdict so your code can allow or block. We will see how this is implemented in a second! But first, let’s get started. Getting Started in Minutes 1. Install the SDK: ` 1. Configure redirects Wrap your next.config.ts with BotID’s helper. This sets up the right rewrites so BotID can do its job (and not get blocked by ad blockers, extensions, etc.): ` 2. Integrate the client on public-facing pages (where BotID runs checks): Declare which routes are protected so BotID can attach special headers when a real user triggers those routes. We need to create instrumentation-client.ts (place it in the root of your application or inside a src folder) and initialize BotID once: ` instrumentation-client.ts runs before the app hydrates, so it’s a perfect place for a global setup! If we have an inferior Next.js version than 15.3, then we would need to use a different approach. We need to render the React component inside the pages or layouts you want to protect, specifying the protected routes: ` 3. Verify requests on your server or API: ` - NOTE: checkBotId() will fail if the route wasn’t listed on the client, because the client is what attaches the special headers that let the edge classify the request! You’re all set - your routes are now protected! In development, checkBotId() function will always return isBot = false so you can build without friction. To disable this, you can override the options for development: ` What happens on a failed check? In our example above, if the check failed, we return a 403, but it is mostly up to you what to do in this case; the most common approaches for this scenario are: - Hard block with a 403 for obviously automated traffic (just what we did in the example above) - Soft fail (generic error/“try again”) when you want to be cautious. - Step-up (require login, email verification, or other business logic). Remember, although rare, false positives can occur, so it’s up to you to determine how you want to balance your fail strategy between security, UX, telemetry, and attacker behavior. checkBotId() So far, we have seen how to use the property isBot from checkBotId(), but there are a few more properties that you can leverage from it. There are: isHuman (boolean): true when BotID classifies the request as a real human session (i.e., a clear “pass”). BotID is designed to return an unambiguous yes/no, so you can gate actions easily. isBot (boolean): We already saw this one. It will be true when the request is classified as automated traffic. isVerifiedBot (boolean): Here comes a less obvious property. Vercel maintains and continuously updates a comprehensive directory of known legitimate bots from across the internet. This directory is regularly updated to include new legitimate services as they emerge. This could be helpful for allowlists or custom logic per bot. We will see an example in a sec. verifiedBotName? (string): The name for the specific verified bot (e.g., “claude-user”). verifiedBotCategory? (string): The type of the verified bot (e.g., “webhook”, “advertising”, “ai_assistant”). bypassed (boolean): it is true if the request skipped BotID check due to a configured Firewall bypass (custom or system). You could use this flag to avoid taking bot-based actions when you’ve explicitly bypassed protection. Handling Verified Bots - NOTE: Handling verified bots is available in botid@1.5.0 and above. It might be the case that you don’t want to block some verified bots because they are not causing damage to you or your users, as it can sometimes be the case for AI-related bots that fetch your site to give information to a user. We can use the properties related to verified bots from checkBotId() to handle these scenarios: ` Choosing your BotID mode When leveraging BotID, you can choose between 2 modes: - Basic Mode: Instant session-based protection, available for all Vercel plans. - Deep Analysis Mode: Enhanced Kasada-powered detection, only available for Pro and Enterprise plan users. Using this mode, you will leverage a more advanced detection and will block the hardest to catch bots To specify the mode you want, you must do so in both the client and the server. This is important because if either of the two does not match, the verification will fail! ` Conclusion Stop chasing bots - let BotID handle them for you! Bots are and will get smarter and more sophisticated. BotID gives you a simple way to push back without slowing your customers down. It is simple to install, customize, and use. Stronger protection equals fewer headaches. Add BotID, ship with confidence, and let the bots trample into a wall without knowing what’s going on....

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