AWS

How to automatically deploy your full-stack JavaScript app with AWS CodePipeline

Sep 15, 2023

9 min read

How to host a full stack javascript app with AWS - 1 Part Series

How to automatically deploy your full-stack JavaScript app from an NX monorepo with AWS CodePipeline

In our previous blog post (How to host a full-stack JavaScript app with AWS CloudFront and Elastic Beanstalk) we set up a horizontally scalable deployment for our full-stack javascript app. In this article, we would like to show you how to set up AWS CodePipeline to automatically deploy changes to the application.

APP Structure

Our application is a simple front-end with an API back-end set up in an NX monorepo. The production built API code is hosted in Elastic Beanstalk, while the front-end is stored in S3 and hosted through CloudFront. Whenever we are ready to make a new release, we want to be able to deploy the new API and front-end versions to the existing distribution.

In this article, we will set up a CodePipeline to deploy changes to the main branch of our connected repository.

CodePipeline

CodeBuild and the buildspec file

First and foremost, we should set up the build job that will run the deploy logic. For this, we are going to need to use CodeBuild. Let's go into our repository and set up a build-and-deploy.buildspec.yml file. We put this file under the tools/aws/ folder.

version: 0.2

phases:
  install:
    runtime-versions:
      nodejs: 18
    on-failure: ABORT
    commands:
      - npm ci
  build:
    on-failure: ABORT
    commands:
      # Build the front-end and the back-end
      - npm run build:$ENVIRONMENT_TARGET
      # TODO: Push FE to S3
      # TODO: Push API to Elastic beanstalk

This buildspec file does not do much so far, we are going to extend it. In the installation phase, it will run npm ci to install the dependencies and in the build phase, we are going to run the build command using the ENVIRONMENT_TARGET variable. This is useful, because if you have more environments, like development and staging you can have different configurations and builds for those and still use the same buildspec file.

Let's go to the Codebuild page in our AWS console and create a build project. Add a descriptive name, such as your-appp-build-and-deploy. Please provide a meaningful description for your future self. For this example, we are going to restrict the number of concurrent builds to 1.

The next step is to set up the source for this job, so we can keep the buildspec file in the repository and make sure this job uses the steps declared in the yaml file. We use an access token that allows us to connect to GitHub. Here you can read more on setting up a GitHub connection with an access token. You can also connect with Oauth, or use an entirely different Git provider.

We set our provider to GitHub and provided the repository URL. We also set the Git clone depth to 1, because that makes checking out the repo faster.

In the Environment section, we recommend using an AWS CodeBuild managed image. We use the Ubuntu Standard runtime with the aws/codebuild/standard:7.0 version. This version uses Node 18. We want to always use the latest image version for this runtime and as the Environment type we are good with Linux EC2. We don't need elevated privileges, because we won't build docker images, but we do want to create a new service role.

In the Buildspec section select Use a buildspec file and give the path from your repository root as the Buildspec name. For our example, it is tools/aws/build-and-deploy.buildspec.yml. We leave the Batch configuration and the Artifacts sections as they are and in the Logs section we select how we want the logs to work. For this example, to reduce cost, we are going to use S3 logs and save the build logs in the aws-codebuild-build-logs bucket that we created for this purpose. We are finished, so let's create the build project.

CodePipeline setup

To set up automated deployment, we need to create a CodePipeline. Click on Create pipeline and give it a name. We also want a new service role to be created for this pipeline.

Next, we should set up the source stage. As the source provider, we need to use GitHub (version2) and set up a connection. You can read about how to do it here. After the connection is set up, select your repository and the branch you want to deploy from. We also want to start the pipeline if the source code changes. For the sake of simplicity, we want to have the Output artefact format as CodePipeline default.

At the Build stage, we select AWS CodeBuild as the build provider and let's select the build that we created above. Remember that we have the ENVIRONMENT_TARGET as a variable used in our build, so let's add it to this stage with the Plaintext value prod. This way the build will run the build:prod command from our package.json. As the Build type we want Single build.

We can skip the deployment stage because we are going to set up deployment in our build job. Review our build pipeline and create it. After it is created, it will run for the first time. At this time it will not deploy anything but it should run successfully.

Deployment prerequisites

To be able to deploy to S3 and Elastic Beanstalk, we need our CodeBuild job to be able to interact with those services. When we created the build, we created a service role for it. In this example, the service role is codebuild-aws-test-build-and-deploy-service-role. Let's go to the IAM page in the console and open the Roles page. Search for our codebuild role and let's add permissions to it. Click the Add permissions button and select Attach policies. We need two AWS-managed policies to be added to this service role. The AdministratorAccess-AWSElasticBeanstalk will allow us to deploy the API and the AmazonS3FullAccess will allow us to deploy the front-end. The CloudFrontFullAccess will allow us to invalidate the caches so CloudFront will send the new front-end files after the deployment is ready.

Deployment

Upload the front-end to S3

Uploading the front-end should be pretty straightforward. We use an AWS CodeBuild managed image in our pipeline, therefore, we have access to the aws command. Let's update our buildspec file with the following changes:

phases:
# ...
  build:
    on-failure: ABORT
    commands:
      # Build the front-end and the back-end
      - npm run build:$ENVIRONMENT_TARGET
      # Delete the current front-end and deploy the new version front-end
      - aws s3 sync dist/apps/frontend/ s3://$FRONT_END_BUCKET --delete
      # Invalidate cloudfront caches to immediately serve the new front-end files
      - aws cloudfront create-invalidation --distribution-id $CLOUDFRONT_DISTRIBUTION_ID --paths "/index.html"
      # TODO: Push API to Elastic beanstalk

First, we upload the fresh front-end build to the S3 bucket, and then we invalidate the caches for the index.html file, so CloudFront will immediately serve the changes. If you have more static files in your app, you might need to invalidate caches for those as well.

Before we push the above changes up, we need to update the environment variables in our CodePipeline. To do this open the pipeline and click on the Edit button. This will then enable us to edit the Build stage. Edit the build step by clicking on the edit button.

On this screen, we add the new environment variables. For this example, it is aws-hosting-prod as Plaintext for the FRONT_END_BUCKET and E3FV1Q1P98H4EZ as Plaintext for the CLOUDFRONT_DISTRIBUTION_ID

Now if we add changes to our index.html file, for example, change the button to <button id="hello">HELLO 2</button>, commit it and push it. It gets deployed.

Deploying the API to Elastic Beanstalk

We are going to need some environment variables passed down to the build pipeline to be able to deploy to different environments, like staging or prod. We gathered these below:

COMMIT_ID: #{SourceVariables.CommitId} - This will have the commit id from the checkout step. We include this, so we can always check what commit is deployed.
ELASTIC_BEANSTALK_APPLICATION_NAME: Test AWS App - This is the Elastic Beanstalk app which has your environment associated.
ELASTIC_BEANSTALK_ENVIRONMENT_NAME: TestAWSApp-prod - This is the Elastic Beanstalk environment you want to deploy to
API_VERSION_BUCKET: elasticbeanstalk-us-east-1-474671518642 - This is the S3 bucket that was created by Elastic Beanstalk

With the above variables, we can make some new variables during the build time, so we can make sure that every API version is unique and gets deployed. We set this up in the install phase.

# ...

phases:
  install:
    runtime-versions:
      nodejs: 18
    on-failure: ABORT
    commands:
      - APP_VERSION=`jq '.version' -j package.json`
      - API_VERSION=$APP_VERSION-build$CODEBUILD_BUILD_NUMBER
      - API_ZIP_KEY=$COMMIT_ID-api.zip
      - 'APP_VERSION_DESCRIPTION="$AP_VERSION: $COMMIT_ID"'
      - npm ci
# ...

The APP_VERSION variable is the version property from the package.json file. In a release process, the application's version is stored here. The API_VERSION variable will contain the APP_VERSION and as a suffix, we include the build number. We want to upload this API version by indicating the commit ID, so the API_ZIP_KEY will have this information. The APP_VERSION_DESCRIPTION will be the description of the deployed version in Elastic Beanstalk.

Finally, we are going to update the buildspec file with the actual Elastic Beanstalk deployment steps.

phases:
# ...
  build:
    on-failure: ABORT
    commands:
      # ...

      # ZIP the API
      - zip -r -j dist/apps/api.zip dist/apps/api
      # Upload the API bundle to S3
      - aws s3 cp dist/apps/api.zip s3://$API_VERSION_BUCKET/$ENVIRONMENT_TARGET/$API_ZIP_KEY
      # Create new API version in Elastic Beanstalk
      - aws elasticbeanstalk create-application-version --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --version-label "$API_VERSION" --description "$APP_VERSION_DESCRIPTION" --source-bundle "S3Bucket=$API_VERSION_BUCKET,S3Key=$ENVIRONMENT_TARGET/$API_ZIP_KEY"
      # Deploy new API version
      - aws elasticbeanstalk update-environment --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --version-label "$API_VERSION" --environment-name "$ELASTIC_BEANSTALK_ENVIRONMENT_NAME"
      # Wait until the Elastic Beanstalk environment is stable
      - aws elasticbeanstalk wait environment-updated --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --environment-name "$ELASTIC_BEANSTALK_ENVIRONMENT_NAME"

Let's make a change in the API, for example, the message sent back by the /api/hello endpoint and push up the changes.

Now every time a change is merged to the main branch, it gets pushed to our production deployment. Using these guides, you can set up multiple environments, and you can configure separate CodePipeline instances to deploy from different branches. I hope this guide proved to be helpful to you.

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Balázs Tápai

He is a Software Engineer with a passion for automated testing. He loves Angular on the Front-End and NestJS on the Back-End. He also uses Cypress to reduce developer anxiety before demo meetings.

@TapaiBalazs @TapaiBalazs

Intro to EdgeDB - The 10x ORM

Intro to EdgeDB - The 10x ORM I’ve written a couple of posts recently covering different TypeScript ORMs. One about Prisma, and another about Drizzle. ORM’s are a controversial topic in their own right - some people think they are evil, and others think they are great. I enjoy them quite a bit. They make it easy to interact with your databases. What is more important and magical for an application than data? SQL without an ORM is amazing as well, but there are some pain points with that approach. Today I’m excited to write about EdgeDB, which isn’t _exactly_ an ORM or a database from my perspective (although they call themselves one). It is, however an incredibly impressive piece of technology that solves these common pain points in a pretty novel way. So if it’s not an ORM or a database, what exactly is it? I don’t think I can answer that in one or two sentences, so we will explore the various pieces that make up EdgeDB in this article. From a high-level standpoint, though, an interface/query language that sits in front of PostgreSQL. This may seem like some less important implementation detail, but in my eyes, it’s a feature and one of the most compelling selling points. Data Modeling EdgeDB advertises itself as a “graph-relational database”. The core components of EdgeDB data modeling are the schema, type system, and relationship definitions. A schema will consist of objects that contain various typed attributes and links that connect the objects. In SQL, a table is analogous to an Object, and a foreign-key is associated with a link. Here’s what a simple schema in EdgeDB looks like ` There’s a few things to highlight here * We defined two different objects (tables) User and Post * Each object contains properties with their types defined * str is one of several scalar types (bool, int, float, json, datetime, etc) * the author property is a required link to the User object Defining relations / associations In our example above we defined a one-to-many relationship between a user and posts. All the relation types that you can define in traditional SQL are available. One interesting feature though is called backward links. These can be defined in your schema and it allows you to access related data from both sides of a relationship. ` likes are a many-to-many relationship between Tweet and User. With a backlink defined multi link likers := Tweet.<likes[is User]; - we can access likes from a User and likers from a Tweet. ` That's how we can access these relations in our queries. You might be looking at these queries and thinking it looks a lot like GraphQL. This is why they call it a ‘Graph-relational’ database. We’ve only scratched the surface of EdgeDB schema’s. Hopefully, I’ve at least managed to pique your interest. Computed properties Computed properties are a super powerful feature that can be added to your schema or queries. This example user schema creates a computed discriminator and username property. The discriminator uses an EdgeDB standard library function to generate a discriminator number and the username property is a combination between the name and discriminator properties. ` Globals and Access Policies EdgeDB allows you to define global variables as part of your schema. The most common use case I’ve seen for this is to power the access policy feature. You can define a global variable as part of your schema: global current_user: uuid; With a global variable defined, you can provide the value as a sort of context from your application by providing it into your EdgeDB driver/client. ` You can then add access policies directly to your schema, for example, to provide fine-grained access control to blog posts in your blogging application. ` Aside from access policies, you can use your global variables in your queries as well. For example, if you wanted a query to select the current user. ` Types and Sets EdgeDB is very type-centric. All of your data is strongly typed. We’ve touched on some of the types already. * Scalars - There are a lot of scalar types available out of the box * Custom scalars - Custom scalar types are user-defined extensions of existing types * Enums - Are supported out of the box - enum<Admin, Moderator, Member> * Arrays - Defined by passing the singular value type - array<str>; * Tuples - In EdgeD, tuples can contain more than 2 elements and come in named and unnamed varieties - <str, number>; tuple<name: str, jersey_number: float64, active: bool>; All queries return a Set which is a collection of values of a given type. In the query language, all values are Sets. Sets are a collection of values of a given type. A comma-separated list of values inside a set of {curly braces}. A query with no results will return an empty or singleton set. If we have no User values stored yet - select User returns {}. Paired with the query language types and set provides an incredibly powerful and expressive system for interacting with your data. If you thought TypeScript was cool, wait until you start writing EdgeQL! 🙂 EdgeQL Now to the fun stuff: the query language. We’ll use a schema from the docs and start by looking at some of those queries and build on those. The example schema has an abstract type Person with two sub-types based on it Hero and Villian. This is known as a polymorphic type in EdgeDB. The Movie type includes a 1:m association with Person ` Selecting properties / data Before we dig into some real queries we should just touch on how we select actual data from a query. It’s pretty obvious and GraphQL-like but worth mentioning. To specify properties to select, you attach a shape. This works for getting nested link / association data as well. Based on our schema, here’s how we could select fields from Movie, including data from the collection of related characters. ` There is also a feature called a splats that allows you to select all fields and/or all linked fields without specifying them individually. ` If you don’t specify any properties or splats, only id’s get returned select Movie; . Adding some objects with insert To get started, we can use insert to add objects to our database. We’ll start big by looking at the nested insert example. This example is interesting because it shows the creation of two objects in a single query. You’ll notice the simplicity of the syntax. Even though this is the first EdgeQL query we’re looking at, in my experience, it’s like this across the board. I’ve found EdgeQL queries to be simple and intuitive to the point where I’ve been able to intuit how to accomplish things in my head without having to reference the docs or ask the AI. This example adds a new Villian and a new Hero which gets assigned as a link or association to the nemesis field on our Villian. To accomplish this we see that we can nest queries by wrapping them in (). ` The next example is pretty similar, but instead of creating the linked property, we are select ing and adding several potential objects to the characters list of Movie since it is a multi link. This is a pretty complex query that is doing a lot of different things. It’s deceivingly succinct. To accomplish the same thing with SQL this would probably be about 3 different queries. This query finds the objects to add to the characters multi link by filtering on a collection of different strings to match against the name property. ` The last thing we’ll cover for insert is bulk inserts. This is particularly useful for things like seed scripts. In this example, you can just imagine that you have a JSON array of objects with hero names that gets passed in as an argument to your query ` Querying data with select We’ve already seen subqueries and a select in the last section where we found a collection of Person records with a filter. We’ll build on that and see what tools are available to us when it comes to querying data. This one covers a lot of ground. Very similar to SQL we have order and limit, and offset operators to support sorting and pagination. Also there is a whole standard library of functions and operators like count that can be used in our queries. This example returns a collection of villian names, excluding the first and last result. ` Most commonly, you will want to filter by an id ` Here’s another common example filtering by datetime. Since we’re using a string value here we need to cast it to the EdgeDB datetime type. ` You get a pretty similar toolbox to SQL when it comes to filtering with your common operators and things. Combined with all the tools in the standard library, you can get pretty creative with it. Changing values and links with update The update..filter..set statement is how we can update existing data with EdgeQL. set is followed by a shape with assignments of properties to be updated. ` You can replace links for an object ` or add additional ones ` An even more interesting example is removing links matched on a type. Since Villian is a sub-type of Person , this query will remove all characters linked of the Villian type. ` Deleting objects with delete Deleting is pretty straight forward. Using the delete command you can just filter for the objects that you would like to remove. ` When the EdgeQL pieces fall into place As you become more familiar with the EdgeQL query language chances are you’ll start writing very complex queries fluently because everything just makes sense once you’ve learned the building blocks. Domain and business concerns I don’t think they explicitly mention this as a goal anywhere but it’s something that I picked up on pretty quickly. EdgeDB nudges you to move more of what might have traditionally been application logic into your database layer. This is a topic that can bring a lot of division since even things like foreign keys and constraints in SQL are frowned upon in some circles. EdgeDB goes as far as providing constraints, global variables, contexts, and authorization support built into the database. I think that the ability to bake some of these concerns into your EdgeDB Schema is great. The way you model your schema and database in EdgeDB map to your domain in a much more intuitive way where domain concerns don’t really feel out of place there. Database Clients and Query Builders and Generators We’ve covered a lot so far to highlight what EdgeDB is and how to handle common use cases with the query language. To use it in your project though, you will need a client/driver library. There are clients available in several different languages. The one that they clearly have put the most investment into is the TypeScript query builder. We’ll briefly look at both options: simple driver/client and query builder. Whichever you end up choosing you will need to instantiate a driver and make sure you have a connection to your database instance configured. Basic client Although the TS query builder is very popular and pretty amazing, I couldn’t get away from just writing EdgeQL queries. In my application, I composed queries using template strings, and it worked great. The clients all have a set of methods available for passing in EdgeQL queries and parameters. querySingle is a method for queries where you are only expecting a single result. If your query will have multiple results you would use query instead. There is also a queryRequiredSingle which will throw an error if no results are found. There are some other methods available as well including one for running queries in a transaction ` The first argument is the query, and the second is a map of parameters. In this example we include the title parameter and it is accessed in our query via $title. TypeScript query builder If you have a TypeScript app and type-safety is important, you might prefer using the query builder. It is a pretty incredible feat of TypeScript magic initially developed by the same developer behind the popular library Zod. We can’t cover it in very much depth here but we’ll look at an example just to have an idea of what the query builder looks like in an application. ` The query builder is able to infer the result type automatically. It knows which fields you’ve selected, it knows that the result will be a single item. Query generator There are generators for queries and types. So even if you opt out of using the query builder you can still have queries that are strongly typed. It’s nice to have this option if you want to just write your queries as EdgeQL in .edgeql files. ` We end up with an exported function named getUser that is strongly typed. ` Tools and Utilities The team at EdgeDB puts a big emphasis on developer experience. It shows up all over the place. We’ve already seen some utilities with the generators that are available. There are some other tools available as well that help complete the entire experience. EdgeDB CLI The first and most important tool to mention is the CLI. If you’ve started using EdgeDB then you’ve most likely already installed and used it. The CLI is pretty extensive. It includes commands for things like migrations, managing EdgeDB versions and installations, managing projects and local/cloud database instances, dumps and restores, a repl, and more. The CLI makes managing EdgeDB a breeze. Admin UI The CLI includes a command to launch an admin UI for any project or database. The Admin UI includes a awesome interactive diagram of your database schema, a repl for running queries, and a table to inspect and make changes to the data stored in your database. Summary Adopting newer database technology is a tough sales pitch. Replacing your application’s database technology at any point in its lifecycle is not a problem that anyone wants to have. This is one of the reasons why EdgeDB being a superset of PostgreSQL is a huge feature in my opinion. The underlying database technology is tried and true, and EdgeDB is open-source. Based on this, I would feel confident using EdgeDB if it aligned well from a technical and business perspective. We’ve covered a lot of ground in this post. EdgeDB is feature-packed and powerful. Databases is a tough nut to crack, and I commend the team for all their hard work to help continue pushing forward one of the most important components of almost any application. I’m typically pretty conservative when it comes to databases, but EdgeDB took a great approach, in my opinion. I recommend at least giving it a try. You might catch the EdgeDB bug like I did!...

Apr 26, 2024

11 mins

JavaScriptEdgeDB

Understanding Vue.js's <Suspense> and Async Components

In this blog post, we will delve into how and async components work, their benefits, and practical implementation strategies to make your Vue.js applications more efficient and user-friendly. Without further ado, let’s get started! Suspense Let's kick off by explaining what Suspense components are. They are a new component that helps manage how your application handles components that need to await for some async resource to resolve, like fetching data from a server, waiting for images to load, or any other task that might take some time to complete before they can be properly rendered. Imagine you're building a web page that needs to load data from a server, and you have 2 components that fetch the data you need as they will show different things. Typically, you might see a loading spinner or a skeleton while the data is being fetched. Suspense components make it easier to handle these scenarios. Instead of manually managing loading states and error messages for each component that needs to fetch data, Suspense components let you wrap all these components together. Inside this wrapper, you can define: 1. What to show while the data is loading (like a loading spinner). 2. The actual content that should be displayed once the data is successfully fetched. This way, Vue Suspense simplifies the process of handling asynchronous operations (like data fetching) and improves the user (and the developer) experience by providing a more seamless and integrated way to show loading states and handle errors. There are two types of async dependencies that can wait on: - Components with an async setup() hook. This includes components using with top-level await expressions. *Note: These can only be used within a component.* - Async Components. Async components Vue's asynchronous components are like a smart loading system for your web app. Imagine your app as a big puzzle. Normally, you'd put together all the pieces at once, which can take time. But what if some pieces aren't needed right away? Asynchronous components help with this. Here's how they work: - Load Only What's Needed: Just like only picking up puzzle pieces you need right now, asynchronous components let your app load only the parts that are immediately necessary. Other parts can be loaded later, as needed. - Faster Start: Your app starts up faster because it doesn't have to load everything at once. It's like quickly starting with the border of a puzzle and filling in the rest later. - Save Resources: It uses your web resources (like internet data) more wisely, only grabbing what’s essential when it's essential. In short, asynchronous components make your app quicker to start and more efficient, improving the overall experience for your users. Example: ` Combining Async Components and Suspense Let's explore how combining asynchronous components with Vue's Suspense feature can enhance your application. When asynchronous components are used with Vue's Suspense, they form a powerful combination. The key point is that async components are "suspensable" by default. This means they can be easily integrated with Suspense to improve how your app handles loading and rendering components. When used together, you can do the following things: - Centralized Loading and Error Handling: With Suspense, you don't have to handle loading and error states individually for each async component. Instead, you can define a single loading indicator or error message within the Suspense component. This unified approach simplifies your code and ensures consistency across different parts of your app. - Flexible and Clean Code Structure: By combining async components with Suspense, your code becomes more organized and easier to maintain. An asynchronous component has the flexibility to operate independently of Suspense's oversight. By setting suspensible: false in its options, the component takes charge of its own loading behavior. This means that instead of relying on Suspense to manage when it appears, the component itself dictates its loading state and presentation. This option is particularly useful for components that have specific loading logic or visuals they need to maintain, separate from the broader Suspense-driven loading strategy in the application. In practice, this combo allows you to create a user interface that feels responsive and cohesive. Users see a well-timed loading indicator while the necessary components are being fetched, and if something goes wrong, a single, well-crafted error message is displayed. It's like ensuring that the entire puzzle is either revealed in its completed form or not at all rather than showing disjointed parts at different times. How it works When a component inside the boundary is waiting for something asynchronous, shows fallback content. This fallback content can be anything you choose, such as a loading spinner or a message indicating that data is being loaded. Example Usage Let’s use a simple example: In the visual example provided, imagine we have two Vue components: one showcasing a selected Pokémon, Eevee, and a carousel showcasing a variety of other Pokémon. Both components are designed to fetch data asynchronously. Without , while the data is being fetched, we would typically see two separate loading indicators: one for the Eevee Pokemon that is selected and another for the carousel. This can make the page look disjointed and be a less-than-ideal user experience. We could display a single, cohesive loading indicator by wrapping both components inside a boundary. This unified loading state would persist until all the data for both components—the single Pokémon display and the carousel—has been fetched and is ready to be rendered. Here's how you might structure the code for such a scenario: ` Here, is the component that's performing asynchronous operations. While loading, the text 'Loading...' is displayed to the user. Great! But what about when things don't go as planned and an error occurs? Currently, Vue's doesn't directly handle errors within its boundary. However, there's a neat workaround. You can use the onErrorCaptured() hook in the parent component of to catch and manage errors. Here's how it works: ` If we run this code, and let’s say that we had an error selecting our Pokemon, this is how it is going to display to the user: The error message is specifically tied to the component where the issue occurred, ensuring that it's the only part of your application that shows an error notification. Meanwhile, the rest of your components will continue to operate and display as intended, maintaining the overall user experience without widespread disruption. This targeted error handling keeps the application's functionality intact while indicating where the problem lies. Conclusion stands out as a formidable feature in Vue.js, transforming the management of asynchronous operations into a more streamlined and user-centric process. It not only elevates the user experience by ensuring smoother interactions during data loading phases but also enhances code maintainability and application performance. I hope you found this blog post enlightening and that it adds value to your Vue.js projects. As always, happy coding and continue to explore the vast possibilities Vue.js offers to make your applications more efficient and engaging!...

Apr 24, 2024

6 mins

VueWeb PerformanceUXJavaScript

Performance Analysis with Chrome DevTools

When it comes to performance, developers often use Lighthouse, Perfbuddy, or similar performance analysis tools. But when the target site has protection against bots, getting information is not that simple. In this blog post, we are going to focus on where to look for signs of performance bottlenecks by using Chrome Devtools. Preparations Even when access to automated performance analysis tools is restricted, the Network, Performance, and Performance Insights tabs of Chrome Devtools can still be leveraged. To do that, some preparations can be made. When starting our analysis, I recommend opening the page we want to analyse in incognito mode. So we separate the analysis from our regular browser habits, cookies, and possible browser extensions. When we load the page for the first time, let's make sure we disable caching in the Network tab, so resources are always fetched when we reload. Some pages heavily rely on client-side storage mechanisms such as indexedDB, localStorage, and sessionStorage. Cookies can also interfere. Therefore, it's good to leverage the "Application" tab's Clear site data button to make sure lingering data won't interfere with your results. Some antivirus software with network traffic filtering can also interfere with your requests. They can block, slow down, or even intercept and modify certain network requests, which can greatly affect loading time and the accuracy of your results. If the site under analysis is safe, we recommend disabling network traffic filtering temporarily. We strongly suggest just looking at the page and reloading it a few times to get a feeling of its performance. A lot of things cannot be detected by human eyes, but you can look for flickers and content shifts on the page. These performance issues can be good entry points to your investigation. Common bottlenecks: resources Let's start at the Network tab, where we can identify if resources are not optimised. After we reload the page, we can use the filters on the network tab to focus on image resources. Then we can see the information of these requests, including the size of the images, the time it took to load each image, and any errors that occurred. The waterfall chart is also useful. This is where you can see the timing of each image resource loading. We should look for evidence that the image resources are served from a CDN, with proper compression. We can check the resources one by one, and see if they contain Content-Encoding: gzip or Content-Encoding: br headers. If these headers are missing, we found one bottleneck that can be fixed by serving images using gzip or brotli compression while serving them. Headers on resource requests can tell other signs of errors. It can also happen that images are served from a CDN, such as fastly, but if there are fastly-io-error headers on the resources, it can mean that something is misconfigured. We also need to check the dimensions of the images. If an image is larger than the space it's being displayed in, it may be unnecessarily slowing down the page. If we find such bottlenecks, we can resize the images to match the actual dimensions of the space where they are being displayed to improve loading time. Server-side rendering can improve your SEO altogether, but it is worth checking the size of the index.html file because sometimes it can be counterproductive. It is recommended to try and keep HTML files under 100kb to keep the TTFB (Time To First Byte) metric under 1 second. If the page uses polyfills, it's worth checking what polyfills are in use. IE11 is no longer supported, and loading unnecessary polyfills for that browser slows down the page load time. Performance Insights Tab The performance Insights Tab in Chrome DevTools allows users to measure the page load of a website. This is done by running a performance analysis on the website and providing metrics on various aspects of the page load process, such as the time it takes for the page to be displayed, the time it takes for network resources to be loaded, and the time it takes for the page to be interactive. The performance analysis is run by simulating a user visiting the website and interacting with it, which allows the tool to accurately measure the performance of the page under real-world conditions. This information can then be used to identify areas of the website that may be causing slowdowns and to optimize the performance of the page. Follow the steps to run an analysis: 1. Open the Chrome Devtools 2. Select the "Performance insights" tab 3. Click on the Measure page load button The analysis provides us with a detailed waterfall representation of requests, color coded to the request types. It can help you identify requests that block/slow down the page rendering, and/or expensive function calls that block the main thread. It also provides you with information on important performance metrics, such as DCL (DOM Content Loaded), FCP (First Contentful Paint), LCP (Largest Contentful Paint) and TTI (Time To Interactive). You can also simulate network or CPU throttling, or enable the cache if your use case requires that. DCL refers to the time it takes for the initial HTML document to be parsed and for the DOM to be constructed. FCP refers to the time it takes for the page to display the first contentful element, such as an image or text. LCP is a metric that measures the loading speed of the largest element on a webpage, such as an image or a block of text. A fast LCP helps ensure that users see the page's main content as soon as possible, which can improve the overall user experience. TTI refers to the time it takes for the page to become fully interactive, meaning that all of the necessary resources have been loaded and the page is responsive to user input. Performance Tab The "start profiling and reload page" button in the Performance tab of Chrome DevTools allows users to run a performance analysis on a website and view detailed information about how the page is loading and rendering. By clicking this button, the tool will simulate a user visiting the website and interacting with it, and will then provide metrics and other information about the page load process. Follow the steps to run an analysis 1. Open the Chrome Devtools 2. Select the "Performance" tab 3. Click on the button with the "refresh" icon A very useful part of this view is the detailed information provided on the main thread. We can interact with call stacks and find functions that might run too long, blocking the main thread, and delaying the TTI (Time To Interactive) metric. Selecting a function gives all kinds of information on that function. You can see how long that function was running, and what other functions it called, and you can also directly open that function in the Sources tab. Identifying long-running, blocking functions is crucial in finding performance bottlenecks. One way to mitigate them is to move them into worker threads. --- Chrome DevTools is a powerful tool for analyzing the performance of web applications. By using the network tab, you can identify issues with resources that might slow down page load. With the Performance insights and Performance tabs, we can identify issues that may be causing the website to load slowly, and take steps to optimize the code for better performance. Whether you're a beginner or an experienced developer, Chrome DevTools is an essential tool for analyzing and improving the performance of web applications....

Jan 9, 2023

6 mins

DevToolsSEOWeb PerformanceWeb Development

How to Make Coding Both Your Hobby and Profession with Jason Lengsdorf @ CityJS Conf 2024

Join us backstage at CityJS Conf for this conversation with Jason Lengsdorf. In this discussion, we explore strategies for reducing stress and anxiety among engineers, ensuring they maintain relevance in the dynamic world of coding. Jason emphasizes the importance of self-assurance and the role of play in fostering continual engagement with evolving technologies. Moreover, we get a sneak peek into Jason's latest venture, "Web Lunch," a platform where he converses with developers who have found harmony between their careers and personal satisfaction. Coding isn't just a job; it's a passion and a lifestyle. Jason highlights the significance of pursuing work that excites and challenges us, echoing the sentiment that coding can be both a career and a hobby. By infusing enjoyment and creativity into our coding endeavors, we can shape our professional trajectories in alignment with our values and interests. This approach not only minimizes regret but also maximizes happiness and fulfillment in our careers. Central to our discussion is the pivotal role of effective leadership in creating a supportive and encouraging work environment. Leaders who prioritize the well-being and happiness of their team members foster a culture of collaboration and empowerment. Through mentorship, guidance, and opportunities for growth, these leaders enable developers to align their personal goals with organizational objectives, leading to increased job satisfaction and professional fulfillment. Adaptability is key to sustained success in software engineering. Jason emphasizes the value of remaining open-minded and continuously learning, even when it means stepping out of our comfort zones. Embracing discomfort is an integral part of personal growth, allowing us to overcome obstacles and unlock our full potential. By nurturing a spirit of curiosity and adaptability, developers can navigate the complexities of their careers with resilience and enthusiasm....

Apr 25, 2024

2 mins

JavaScriptMentorship

How to automatically deploy your full-stack JavaScript app with AWS CodePipeline

How to host a full stack javascript app with AWS - 1 Part Series

How to automatically deploy your full-stack JavaScript app from an NX monorepo with AWS CodePipeline

APP Structure

CodePipeline

CodeBuild and the buildspec file

CodePipeline setup

Deployment prerequisites

Deployment

Upload the front-end to S3

Deploying the API to Elastic Beanstalk

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