Javascript

Seeding Initial Data in Amplify

Published Sep 8, 2021

Updated Feb 14, 2023

6 min read

This article was written over 18 months ago and may contain information that is out of date.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

Challenges With Amplify

Amplify is a powerful framework and toolchain that enables developers to build their frontend applications with a serverless backend using all major languages and platforms. It relieves the developers from the effort of writing a backend from scratch - the entire backend stack is provided by AWS.

However, one of the things that does not come with Amplify out of the box, at least at this moment, is a way to perform initial database seeding right after setting up an environment. Database seeding is the process of populating an empty database with data. The data can be some fake placeholder data to use in development, or some necessary data such as a set of initial configuration properties required for the proper functioning of the application.

In this blog post, we'll walk you through our attempt to write our own database seeding script, while having some guiding principles in mind. First of all, the script should be generic - it should not be specific to a particular project. Anyone should be able to copy it and place it in their project with no or minimal adjustments required. Secondly, the database seeding process should be automatic and ideally not require any manual steps by the user - the user should just execute the script and the database should be populated after the script is finished running. The script is responsible for reading the Amplify environment, and putting the data in proper tables.

Prerequisites

Before proceeding further, note that some of the concepts presented here assume at least basic Amplify experience. Likewise, there should be an existing Amplify project. For this purpose, we scaffolded a simple Angular project using Amplify starter project instructions and the entire project is available on GitHub. The tutorial also assumes that a GraphQL API is generated as part of the project, just like in the starter project.

Looking at the official AWS guide for writing items to a DynamoDB database in batch, we know that we need several pieces of information before making a connection to the database:

AWS credentials
The profile under which AWS credentials are stored
Region
Amplify environment name
Database ID (Tables created by Amplify have the format of tableName-databaseId-environmentName)

A recommended way of reading AWS credentials is through a shared credentials file. The credentials file is located in the .aws folder of your user folder and contains your access key ID and secret access key which are used to sign programmatic requests to AWS. It should have been created after executing amplify configure, and before initializing the Amplify project. The file has the following format:

[your-profile]
aws_access_key_id = YOUR_ACCESS_KEY_ID
aws_secret_access_key = YOUR_SECRET_ACCESS_KEY

There is one additional file that contains your region preferences. It is named config, and it is also located in the .aws folder:

[profile your-profile]
region = eu-central-1

Extracting Information From Amplify Environment

Now that we have our credentials, we need to find the remaining information. Going through the amplify folder (generated by the Amplify CLI) we see that all information that we need is already there, but scattered across multiple files.

For example:

The environment name is located in amplify/.config/local-env-info.json
The profile name is located in amplify/.config/local-aws-info.json, assuming that the Amplify project was initialized using a local AWS profile name and not IAM keys
Database table ID and region is located in amplify/backend/amplify-meta.json

The risk in this approach is that this logic could change in the future if AWS decides to change the format of any of the above files, which aren't really meant for this kind of use. However, the benefit is ease and simplicity of use, and we feel that the benefits outweigh the risks. Should the files change in the future, we'll simply update the seeding script.

Writing the Script

Reading the environment and the profile name from local-env-info.json and local-aws-info.json is easy. Reading the database table ID and region is a bit tricky since we need to know the GraphQL API name first. Each GraphQL API will generate its own set of database tables on DynamoDB, so knowing GraphQL API name will easily get us the database table ID as well. Fortunately, amplify-meta.json has the list of APIs under the api field, and we'll iterate over this object to get API names.

Within the API object, there are also two fields that we are interested in. The first one is the name of the provider, found in the providerPlugin field. We'll use this to fetch the region from the providers root field in amplify-meta.json. The other one is output.GraphQLAPIIdOutput, which is the GraphQL API ID, and the same ID is used as the database ID for the tables.

Having everything we said so far in mind, the first version of the script is:

const AWS = require('aws-sdk');
const localEnvInfo = require('../../amplify/.config/local-env-info.json');
const localAwsInfo = require('../../amplify/.config/local-aws-info.json');
const amplifyMeta = require('../../amplify/backend/amplify-meta.json');

const environmentName = localEnvInfo.envName;
const profileName = localAwsInfo[environmentName]?.profileName;

if (!profileName) {
  throw Error('Please reinitialize your Amplify project using your AWS profile');
}

for (const [apiName] of Object.entries(amplifyMeta.api)) {
  const providerName = amplifyMeta.api[apiName].providerPlugin;
  const databaseId = amplifyMeta.api[apiName].output.GraphQLAPIIdOutput;
  const region = amplifyMeta.providers[providerName].Region;

  AWS.config.credentials = new AWS.SharedIniFileCredentials({ profile: profileName });
  AWS.config.update({ region: region })
  const documentClient = new AWS.DynamoDB.DocumentClient();

  // ToDo: read seed data and write to DynamoDB
}

The script reads the local Amplify environment from local-env-info.json, local-aws-info.json, amplify-meta.json, performs some safety checks, then iterates over each API found in amplify-meta.json. In the end, it creates an instance of the DocumentClient class which is used to write to DynamoDB.

Organizing Seed Data

The missing step is reading and writing seed data. Again, we don't want any manual configuration here - the script should just run, read the seed data and use the Amplify information we extracted earlier to insert the data to the DynamoDB database. For this to work, we'll organize our directories and files in a way that they are easily mapped to objects on AWS. The directory structure we're aiming for is this:

tools/
├─ seeder/
│  ├─ index.js (this is our seeding script)
│  ├─ fixtures/
│  │  ├─ [API 1 name]/
│  │  │  ├─ [Table 1 name].json
│  │  │  ├─ [Table 2 name].json
│  │  │  ├─ ...
│  │  ├─ [API 2 name]/
│  │  ├─ .../

For each API found in amplify-meta.json, the script will find the respective directory under fixtures, and iterate over JSON files found in that directory. Each file's name corresponds to the table name we're populating, and the content of the file is a JSON array with table items. For example, the JSON file to populate the Restaurant's table from our Angular starter project would be named Restaurants.json and would have the following contents:

[
  {
    "city": "Menton",
    "description": "",
    "name": "Mirazur"
  },
  {
    "city": "Copenhagen",
    "description": "",
    "name": "Noma"
  },
  {
    "city": "Axpe",
    "description": "",
    "name": "Asador Etxebarri"
  }
]

DynamoDB's DocumentClient

The next step is implementing the logic that reads JSON files, and writes them to DynamoDB. DocumentClient's batchWrite method allows providing multiple items for multiple tables, all in the same request. This means that the method will be called only once, and all items will be pushed in batch, which is very performant.

The structure of this write request looks as follows:

const writeParams = {
  RequestItems: {
    table1Name: [
      {
        PutRequest: {
          Item: {
            id: 1,
            field1:'field 1 value',
            field2: 'field 2 value',
            //...
          },
        }
      },
      {
        PutRequest: {
          Item: {
            id: 2,
            field1:'field 1 value',
            field2: 'field 2 value',
            //...
          },
        }
      },
      //...
    ]
  }
};

The table name is the complete table name, which is in the form of baseTableName-databaseId-environmentName when using Amplify. baseTableName normally comes from the GraphQL schema type, databaseId is equal to GraphQL API ID, while environmentName is the Amplify environment name (such as dev).

In the PutRequest, we should specify all fields that would normally be provisioned when using the GraphQL API. This includes some fields that Amplify uses internally, like:

id (primary key, which must be unique and can be generated using uuidv4() for example)
__typename (equal to type in the GraphQL schema, which is normally equal to baseTableName)
_lastChangedAt (can be the current date, written as Unix timestamp)
_version (can be 1)
createdAt (can be the current date, written as ISO-8601 string)
updatedAt (can be the current date, written as ISO-8601 string)

Other, external fields, can be provided from seed data.

Written in code, this piece of logic looks like this:

const writeParams = {
  RequestItems: {}
};
const baseTableNames = readdirSync(join(__dirname, 'fixtures', apiName)).map((filename) => parse(filename).name);
for (const baseTableName of baseTableNames) {
  const fullTableName = `${baseTableName}-${databaseId}-${environmentName}`;
  const tableItems = require(`./fixtures/${apiName}/${baseTableName}.json`);
  writeParams.RequestItems[fullTableName] = tableItems.map((tableItem) => ({
    PutRequest: {
      Item: {
        id: uuidv4(),
        __typename: baseTableName,
        _lastChangedAt: new Date().getTime(),
        _version: 1,
        createdAt: new Date().toISOString(),
        updatedAt: new Date().toISOString(),
        ...tableItem
      }
    }
  }));
}

documentClient.batchWrite(writeParams, (error, data) => {
  if (error) {
    console.error('Error in batch write', error);
  } else {
    console.error('Successfully executed batch write', data);
  }
});

Conclusion

And that's it - the script is complete. In just over 50 lines of code, we now have an efficient way of populating multiple DynamoDB tables with initial data. The initial data itself is stored in JSON files, which itself is quite readable and anyone can customize it to their project needs.

We hope you enjoyed this tutorial. Should you have any questions, do not hesitate to drop us a line.

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.

About the author(s)

Dario Djuric
Dario is a full-stack engineer who has spent most of his career doing enterprise Java projects. He has always had a hidden passion for frontend, though -- and he is now able to pursue that passion at This Dot. He spends most of his free time with his two sons, but occasionally, he manages to squeeze in some casual sports activities such as jogging, cycling, and soccer.
@dario_djuric @dariodjuric

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

In today’s blog post, we’ll build an AI Assistant using three different AI models: Whisper and TTS from OpenAI and Llama 3.1 from Meta. While exploring AI, I wanted to try different things and create an AI assistant that works by voice. This curiosity led me to combine OpenAI’s Whisper and TTS models with Meta’s Llama 3.1 to build a voice-activated assistant. Here’s how these models will work together: * First, we’ll send our audio to the Whisper model, which will convert it from speech to text. * Next, we’ll pass that text to the Llama 3.1 model. Llama will understand the text and generate a response. * Finally, we’ll take Llama’s response and send it to the TTS model, turning the text back into speech. We’ll then stream that audio back to the client. Let’s dive in and start building this excellent AI Assistant! Getting started We will use different tools to build our assistant. To build our client side, we will use Next.js. However, you could choose whichever framework you prefer. To use our OpenAI models, we will use their TypeScript / JavaScript SDK. To use this API, we require the following environmental variable: OPENAI_API_KEY— To get this key, we need to log in to the OpenAI dashboard and find the API keys section. Here, we can generate a new key. Awesome. Now, to use our Llama 3.1 model, we will use Ollama and the Vercel AI SDK, utilizing a provider called ollama-ai-provider. Ollama will allow us to download our preferred model (we could even use a different one, like Phi) and run it locally. The Vercel SDK will facilitate its use in our Next.js project. To use Ollama, we just need to download it and choose our preferred model. For this blog post, we are going to select Llama 3.1. After installing Ollama, we can verify if it is working by opening our terminal and writing the following command: Notice that I wrote “llama3.1” because that’s my chosen model, but you should use the one you downloaded. Kicking things off It's time to kick things off by setting up our Next.js app. Let's start with this command: ` After running the command, you’ll see a few prompts to set the app's details. Let's go step by step: * Name your app. * Enable app router. The other steps are optional and entirely up to you. In my case, I also chose to use TypeScript and Tailwind CSS. Now that’s done, let’s go into our project and install the dependencies that we need to run our models: ` Building our client logic Now, our goal is to record our voice, send it to the backend, and then receive a voice response from it. To record our audio, we need to use client-side functions, which means we need to use client components. In our case, we don’t want to transform our whole page to use client capabilities and have the whole tree in the client bundle; instead, we would prefer to use Server components and import our client components to progressively enhance our application. So, let’s create a separate component that will handle the client-side logic. Inside our app folder, let's create a components folder, and here, we will be creating our component: ` Let’s go ahead and initialize our component. I went ahead and added a button with some styles in it: ` And then import it into our Page Server component: ` Now, if we run our app, we should see the following: Awesome! Now, our button doesn’t do anything, but our goal is to record our audio and send it to someplace; for that, let us create a hook that will contain our logic: ` We will use two APIs to record our voice: navigator and MediaRecorder. The navigator API will give us information about the user’s media devices like the user media audio, and the MediaRecorder will help us record the audio from it. This is how they’re going to play out together: ` Let’s explain this code step by step. First, we create two new states. The first one is for keeping track of when we are recording, and the second one stores the instance of our MediaRecorder. ` Then, we’ll create our first method, startRecording. Here, we are going to have the logic to start recording our audio. We first check if the user has media devices available thanks to the navigator API that gives us information about the browser environment of our user: If we don’t have media devices to record our audio, we just return. If they do, then let us create a stream using their audio media device. ` Finally, we go ahead and create an instance of a MediaRecorder to record this audio: ` Then we need a method to stop our recording, which will be our stopRecording. Here, we will just stop our recording in case a media recorder exists. ` We are recording our audio, but we are not storing it anywhere. Let’s add a new useEffect and ref to accomplish this. We would need a new ref, and this is where our chunks of audio data will be stored. ` In our useEffect we are going to do two main things: store those chunks in our ref, and when it stops, we are going to create a new Blob of type audio/mp3: ` It is time to wire this hook with our AudioRecorder component: ` Let’s go to the other side of the coin, the backend! Setting up our Server side We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: ` Our route is called ‘chat’. In the route.ts file, we’ll set up our handler. Let’s start by setting up our OpenAI SDK. ` In this route, we’ll send the audio from the front end as a base64 string. Then, we’ll receive it and turn it into a Buffer object. ` It’s time to use our first model. We want to turn this audio into text and use OpenAI’s Whisper Speech-To-Text model. Whisper needs an audio file to create the text. Since we have a Buffer instead of a file, we’ll use their ‘toFile’ method to convert our audio Buffer into an audio file like this: ` Notice that we specified “mp3”. This is one of the many extensions that the Whisper model can use. You can see the full list of supported extensions here: https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-file Now that our file is ready, let’s pass it to Whisper! Using our OpenAI instance, this is how we will invoke our model: ` That’s it! Now, we can move on to the next step: using Llama 3.1 to interpret this text and give us an answer. We’ll use two methods for this. First, we’ll use ‘ollama’ from the ‘ollama-ai-provider’ package, which lets us use this model with our locally running Ollama. Then, we’ll use ‘generateText’ from the Vercel AI SDK to generate the text. Side note: To make our Ollama run locally, we need to write the following command in the terminal: ` ` Finally, we have our last model: TTS from OpenAI. We want to reply to our user with audio, so this model will be really helpful. It will turn our text into speech: ` The TTS model will turn our response into an audio file. We want to stream this audio back to the user like this: ` And that’s all the whole backend code! Now, back to the frontend to finish wiring everything up. Putting It All Together In our useRecordVoice.tsx hook, let's create a new method that will call our API endpoint. This method will also take the response back and play to the user the audio that we are streaming from the backend. ` Great! Now that we’re getting our streamed response, we need to handle it and play the audio back to the user. We’ll use the AudioContext API for this. This API allows us to store the audio, decode it and play it to the user once it’s ready: ` And that's it! Now the user should hear the audio response on their device. To wrap things up, let's make our app a bit nicer by adding a little loading indicator: ` Conclusion In this blog post, we saw how combining multiple AI models can help us achieve our goals. We learned to run AI models like Llama 3.1 locally and use them in our Next.js app. We also discovered how to send audio to these models and stream back a response, playing the audio back to the user. This is just one of many ways you can use AI—the possibilities are endless. AI models are amazing tools that let us create things that were once hard to achieve with such quality. Thanks for reading; now, it’s your turn to build something amazing with AI! You can find the complete demo on GitHub: AI Assistant with Whisper TTS and Ollama using Next.js...

Sep 27, 2024

8 mins

AINextJSJavaScript

Integrating Next.js with New Relic

Integrating Next.js with New Relic When it comes to application monitoring, New Relic is genuinely one of the veterans in the market. Founded in 2008, it offers a large set of tools designed for developers to help them analyze application performance, troubleshoot problems, and gain insight into user behavior through real-time analytics. We needed to integrate New Relic's application monitoring with a modern Next.js app built on top of the app router. This blog post will explain your options if you want to do the same in your project. Please keep in mind that depending on when you read this blog post, there may be some advances in how New Relic works with Next.js. You can always refer to the official New Relic website for up-to-date documentation. The approach is also different, depending on whether you use Vercel's managed Next.js hosting (or any other provider that is lambda-based) or self-host Next.js on an alternative provider such as Fly.io. We'll start with the typical steps you need to do for both ways of deploying; then we'll continue with the self-hosted option, which uses a complete set of features from New Relic, and finish with Vercel where we recommend the OpenTelemetry integration for now. Common Prerequisite Steps First, let's install the necessary packages. These were built by New Relic and are needed whether you self-host your Next.js app or host it on Vercel. ` The next thing you'll want to do is define your app name. The name will be different depending on the environment the app is running in, such as a local environment, staging, or production. This will ensure that the data you send to New Relic is always tagged with a proper app name and will not be mixed between environments. An environment-changing parameter, such as the app name, should always be included in an environment variable. In your local environment, that would be in .env.local, while staging/production or any other hosted environments would be configured in the provider. ` To get full coverage with New Relic, in Next.js, we need to cover the parts running on the client (in the browser) and on the server. The server-running part is different on Vercel's managed hosting, where all of the logic is executed within lambdas, and self-hosting variants, where the server logic is executed on a Node.js server. The client part is configured by going to the New Relic dashboard and clicking "Add Data" / "Browser Monitoring". Choose "place a JavaScript snippet in frontend code," name it the same name you used in the environment variable above, click "Continue" to the last step, and copy the part between the script tags: ` Then paste it to a variable inside any module of your code. For example, this is how we did it: ` Now, create a component that will use this script and put it to Next's Script component. Then you can use the component in your root layout: ` There are several things to note here. First, we need to set a unique ID for the Script component for Next.js to track and optimize the script. We also need to set the strategy to beforeInteractive so that the New Relic script is added to the document’s head element. Finally, we use some environment helper variables. IS_PRODUCTION and IS_STAGING are just helper variables that read the environment variables to determine if we are in a local environment, staging, or production. Now you can create an app router error page and report any uncaught errors to New Relic: ` With this in place, the client part is covered, and you should start seeing data in the New Relic dashboard under "All Entities" / "Browser Applications." Let's now focus on the server part, with both a self-hosted option and Vercel. Self-Hosting Next.js If you self-host Next.js, It will typically run through a Node server (containerized or not), meaning you can fully utilize New Relic's APM agent. Start by adding this to the Next.js config (next.config.js) to mark the newrelic package as external, otherwise you would not be able to import it. ` The next step that New Relic recommends is to copy the newrelic.js file from the newrelic library to the root of the folder. However, we want to keep our project root clean, and we'll store the file in o11y/newrelic.js (“o11y” is an abbreviation for observability). This will also demonstrate the configuration needed to make this possible. This file, by default, will look something like this. ` As you can see, some environment variables are involved, such as NEW_RELIC_APP_NAME and NEW_RELIC_LICENSE_KEY. These environment variables will be needed when the New Relic agent starts up. The trick here is that the agent doesn't start up with your app but *earlier* by preloading the library through another environment variable, NODE_OPTIONS. On the server, that would be done by modifying the start script to this: ` This ensures the agent is preloaded before starting the app, and the rest of the environment variables (such as NEW_RELIC_APP_NAME and NEW_RELIC_LICENSE_KEY) should already exist. Making sure this works in local development is not that easy due to different stages of loading environment variables. The environment variables from .env.local are loaded by the framework, which is too late for the New Relic agent (which is preloaded by the Node process). However, it is possible with a trick. First, add the following new environment variables to .env.local: ` (We have not mentioned NEW_RELIC_HOME so far, but the New Relic agent implicitly uses it to locate the newrelic.js file. We need to specify that since we moved it to the o11y folder.) Next, install the nodenv package. ` The nodenv package provides a cross-platform way of preloading environment variables before we start the Node process. If we provide it with the .env.local file, this means all of the environment variables from that file will be available to the New Relic agent! Change the scripts in package.json: ` And that's it. If you now start the local server using the dev command, nodenv will make environment variables available to the Node process. The Node process will preload the New Relic agent (because we have NODE_OPTIONS in .local.env), the agent will read all the environment variables needed by newrelic.js, and everything will work as expected. The first thing you might notice is the creation of the newrelic_agent.log. This is just a log file by the agent which you can use to troubleshoot if the agent is not working correctly. You can safely add this file to .gitignore. From this point on, the server-side part of the app can utilize New Relic's API (such as noticeError and recordLogEvent) and the agent will automatically be recording transactions made through the server. The data should be visible in the New Relic dashboard, under "All Entities" / "Services - APM". This is how you would log an error in your server action, for example: ` The full source of this type of integration is available on StackBlitz. Using Vercel's Managed Next.js Hosting Using the New Relic APM agent on Vercel is currently not possible, as the server part of Next.js runs within lambdas, and not within a typical Node server as in the self-hosted option. In the future, this may be possible. At the moment, the only way to integrate Vercel and New Relic is by using the Vercel OpenTelemetry collector, which is only available for some plans. The first step is to enable the integration in Vercel's dashboard and then install the following packages: ` Create an instrumentation.ts file in the root of your project and add the following code: ` In the the Next.js config (next.config.js), add: ` The OpenTelemetry integration does not work locally, hence the above conditional check. With the above configuration, the collected OpenTelemetry data should be visible in the New Relic dashboard under "All Entities" / "Services - OpenTelemetry". Optionally, Logging via New Relic API When using the Vercel OpenTelemetry integration, there is no easy way to submit custom logs to New Relic. New Relic offers a logging API, however, so to send custom logs from your backend, you could use a logger such as Winston, which has good library support for custom transports, such as the one for New Relic. Install the following packages: ` Then create a logger.ts file with the following code: ` NewRelicTransport utilizes the New Relic logging API to send application logs. Now you can use the serverLogger in your backend code, from server components to server actions. Conclusion As you can see, various options exist for integrating New Relic with the modern, app-router-based Next.js. Sadly, at the time of writing, no one-size-fits-all solution works equally for all hosting options, as Vercel's managed Next.js platform is still relatively young, and so is the app router in Next.js. If you liked this blog post, feel free to browse our other Next.js blog posts for more tips and tricks in other areas of the Next.js ecosystem....

Jul 19, 2024

7 mins

NextJS

D1 SQLite: Writing queries with the D1 Client API

Writing queries with the D1 Client API In the previous post we defined our database schema, got up and running with migrations, and loaded some seed data into our database. In this post we will be working with our new database and seed data. If you want to participate, make sure to follow the steps in the first post. We’ve been taking a minimal approach so far by using only wrangler and sql scripts for our workflow. The D1 Client API has a small surface area. Thanks to the power of SQL, we will have everything we need to construct all types of queries. Before we start writing our queries, let's touch on some important concepts. Prepared statements and parameter binding This is the first section of the docs and it highlights two different ways to write our SQL statements using the client API: prepared and static statements. Best practice is to use prepared statements because they are more performant and prevent SQL injection attacks. So we will write our queries using prepared statements. We need to use parameter binding to build our queries with prepared statements. This is pretty straightforward and there are two variations. By default we add ? ’s to our statement to represent a value to be filled in. The bind method will bind the parameters to each question mark by their index. The first ? is tied to the first parameter in bind, 2nd, etc. I would stick with this most of the time to avoid any confusion. ` I like this second method less as it feels like something I can imagine messing up very innocently. You can add a number directly after a question mark to indicate which number parameter it should be bound to. In this exampl, we reverse the previous binding. ` Reusing prepared statements If we take the first example above and not bind any values we have a statement that can be reused: ` Querying For the purposes of this post we will just build example queries by writing them out directly in our Worker fetch handler. If you are building an app I would recommend building functions or some other abstraction around your queries. select queries Let's write our first query against our data set to get our feet wet. Here’s the initial worker code and a query for all authors: ` We pass our SQL statement into prepare and use the all method to get all the rows. Notice that we are able to pass our types to a generic parameter in all. This allows us to get a fully typed response from our query. We can run our worker with npm run dev and access it at http://localhost:8787 by default. We’ll keep this simple workflow of writing queries and passing them as a json response for inspection in the browser. Opening the page we get our author results. joins Not using an ORM means we have full control over our own destiny. Like anything else though, this has tradeoffs. Let’s look at a query to fetch the list of posts that includes author and tags information. ` Let’s walk through each part of the query and highlight some pros and cons. ` * The query selects all columns from the posts table. * It also selects the name column from the authors table and renames it to author_name. * It aggregates the name column from the tags table into a JSON array. If there are no tags, it returns an empty JSON array. This aggregated result is renamed to tags. ` * The query starts by selecting data from the posts table. * It then joins the authors table to include author information for each post, matching posts to authors using the author_id column in posts and the id column in authors. * Next, it left joins the posts_tags table to include tag associations for each post, ensuring that all posts are included even if they have no tags. * Next, it left joins the tags table to include tag names, matching tags to posts using the tag_id column in posts_tags and the id column in tags. * Finally, group the results by the post id so that all rows with the same post id are combined in a single row SQL provides a lot of power to query our data in interesting ways. JOIN ’s will typically be more performant than performing additional queries.You could just as easily write a simpler version of this query that uses subqueries to fetch post tags and join all the data by hand with JavaScript. This is the nice thing about writing SQL, you’re free to fetch and handle your data how you please. Our results should look similar to this: ` This brings us to our next topic. Marshaling / coercing result data A couple of things we notice about the format of the result data our query provides: Rows are flat. We join the author directly onto the post and prefix its column names with author. ` Using an ORM we might get the data back as a child object: ` Another thing is that our tags data is a JSON string and not a JavaScript array. This means that we will need to parse it ourselves. ` This isn’t the end of the world but it is some more work on our end to coerce the result data into the format that we actually want. This problem is handled in most ORM’s and is their main selling point in my opinion. insert / update / delete Next, let’s write a function that will add a new post to our database. ` There’s a few queries involved in our create post function: * first we create the new post * next we run through the tags and either create or return an existing tag * finally, we add entries to our post_tags join table to associate our new post with the tags assigned We can test our new function by providing post content in query params on our index page and formatting them for our function. ` I gave it a run like this: http://localhost:8787authorId=1&tags=Food%2CReview&title=A+review+of+my+favorite+Italian+restaurant&content=I+got+the+sausage+orchette+and+it+was+amazing.+I+wish+that+instead+of+baby+broccoli+they+used+rapini.+Otherwise+it+was+a+perfect+dish+and+the+vibes+were+great And got a new post with the id 11. UPDATE and DELETE operations are pretty similar to what we’ve seen so far. Most complexity in your queries will be similar to what we’ve seen in the posts query where we want to JOIN or GROUP BY data in various ways. To update the post we can write a query that looks like this: ` COALESCE acts similarly to if we had written a ?? b in JavaScript. If the binded value that we provide is null it will fall back to the default. We can delete our new post with a simple DELETE query: ` Transactions / Batching One thing to note with D1 is that I don’t think the traditional style of SQLite transactions are supported. You can use the db.batch API to achieve similar functionality though. According to the docs: Batched statements are SQL transactions ↗. If a statement in the sequence fails, then an error is returned for that specific statement, and it aborts or rolls back the entire sequence. ` Summary In this post, we've taken a hands-on approach to exploring the D1 Client API, starting with defining our database schema and loading seed data. We then dove into writing queries, covering the basics of prepared statements and parameter binding, before moving on to more complex topics like joins and transactions. We saw how to construct and execute queries to fetch data from our database, including how to handle relationships between tables and marshal result data into a usable format. We also touched on inserting, updating, and deleting data, and how to use transactions to ensure data consistency. By working through these examples, we've gained a solid understanding of how to use the D1 Client API to interact with our database and build robust, data-driven applications....

Dec 23, 2024

6 mins

JavaScript

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Seeding Initial Data in Amplify

Challenges With Amplify

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Extracting Information From Amplify Environment

Writing the Script

Organizing Seed Data

DynamoDB's DocumentClient

Conclusion

Dario Djuric

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