#Compressing NextJS response

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cold dock
#

How does Nextjs handle compressing a response in a server action. Will the browser automatically know the returned repsonse is compressed and automatically decompress it on the client side? Here's my code compressing the response. I read that I need to set the header content-encoding to gzip in order for the browser to know to decompress it

export async function transformData(arrayBuffer: string) {
console.log("In server action");
const buffer = Buffer.from(arrayBuffer);
const data = buffer.toString('utf-8');
// console.log(data);
const result = Papa.parse(data, {
header: true,
}) as {data: Advertising[]};
const jsonData = result.data;
const modifiedData = await modifyData(jsonData) as Advertising[];
getPayloadSize(modifiedData, "Modified JSON");
const compressedData = await compressData(modifiedData);
return compressedData

}

async function compressData(data: Advertising[]) {
return new Promise((resolve, reject) => {
const jsonData = JSON.stringify(data);
gzip(new TextEncoder().encode(jsonData), (err, compressed) => {
if (err) {
reject(err);
} else {
resolve(compressed);
}
});
});
}

twilit sleetBOT
#

Hi @cold dock. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

#

Next.js uses gzip by default to compress rendered content and static files when using next start or a custom server. This is an optimization for applications that do not have compression configured. If compression is already configured in your application via a custom server, Next.js will not add compression. When hosting your application on Vercel, compression uses brotli first, then gzip (source (https://nextjs.org/docs/pages/api-reference/next-config-js/compress)).

You are correct that you need to set the Content-Encoding header to gzip for the browser to know to decompress it. The Content-Encoding header is used to inform the client of the encoding used so it can properly decompress and decode the response (source (https://nextjs.org/docs/pages/api-reference/next-config-js/compress)).

However, it's important to note that not all clients include the Accept-Encoding header by default. This includes custom applications, HTTP libraries, older browsers, and bots/crawlers. If the client doesn't include this header, it may not be able to properly handle the compressed response (source (https://vercel.com/docs/edge-network/compression)).

In your code, you are compressing the data but not setting the Content-Encoding header. You would need to set this header in the response for the client to know that the response is compressed and needs to be decompressed.

Please note that the code to set the Content-Encoding header is not provided in the knowledge sources, so I can't provide a code example. However, you would typically use the res.setHeader('Content-Encoding', 'gzip') method in Node.js to set this header.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

I don't have access to the res in a server action though? @twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

In the new App Router model of Next.js, you don't have direct access to the raw request (req) and response (res) objects in a server action. This is a design decision to enforce consistent and predictable behavior for layouts across different pages, which simplifies development and debugging (source (https://nextjs.org/docs/app)).

However, you can access headers and cookies through server-only functions (source (https://nextjs.org/docs/app)). For example, you can use the headers() function to retrieve request headers, and the cookies() function to retrieve cookies (source (https://nextjs.org/docs/app/building-your-application/upgrading/app-router-migration#accessing-request-object)).

If you need to modify the response headers, you might face limitations as some users have reported issues with this (source (https://github.com/vercel/next.js/issues/50914)). As of now, there's no direct way to modify the response headers in the new App Router structure after retrieving context during server-side rendering for a given page.

If you need to set response headers such as Content-Encoding, you might need to use a custom server setup or use the older pages directory structure where you can access the req and res objects directly in getServerSideProps (source (https://nextjs.org/docs/pages/building-your-application/upgrading/app-router-migration#accessing-request-object)).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

How should I best handle the compression/decompression of data because once my site is live on vercel my server action gets error 405 because the repsonse payload is greater than 4.5 MB (not compressed). @twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

If you're dealing with a response payload that exceeds the 4.5MB limit on Vercel, there are several strategies you can consider:

  1. Reduce payload size: If possible, reduce the size of the payload being sent from the function. This might include sending less data or compressing the data before sending it (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)).

  2. Split into multiple requests: If the payload data is too large to be sent in a single request, consider splitting the data into smaller chunks and sending multiple requests (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)).

  3. Use external storage: If the data is very large, consider using external storage solutions to handle the data instead of sending it directly in the request (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)).

  4. Client-side uploads: For large file uploads, consider using client-side uploads directly to Vercel Blob, where the file is sent securely from the client to Vercel Blob without going through the server (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)).

  5. Directly Request from the Source: If you have a large file like a video that you need to send to a client, you should consider storing those assets in a dedicated media host and make them retrievable with a presigned URL that contains access control policies directly in the URL (source (https://vercel.com/guides/how-to-bypass-vercel-body-size-limit-serverless-functions)).

#
  1. Upload Directly To The Source: You can upload large files directly to a media host from your browser without needing a Serverless Function as a proxy. Vercel Blob, Cloudinary, FaunaDB, and AWS S3 are all solutions for this among others (source (https://vercel.com/guides/how-to-bypass-vercel-body-size-limit-serverless-functions)).

Remember, if you're compressing the data, you need to set the Content-Encoding header to gzip for the browser to know to decompress it. However, in the new App Router model of Next.js, you don't have direct access to the raw request (req) and response (res) objects in a server action. If you need to set response headers such as Content-Encoding, you might need to use a custom server setup or use the older pages directory structure where you can access the req and res objects directly in getServerSideProps.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

How can I achieve this with my response?

Split into multiple requests: If the payload data is too large to be sent in a single request, consider splitting the data into smaller chunks and sending multiple requests (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)).

@twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Splitting data into smaller chunks and sending multiple requests involves breaking down your large data set into smaller parts and creating a mechanism to request these parts individually. Here's a general approach on how you can do this:

  1. Break down your data: In your server action, instead of returning the entire data set at once, you could break it down into smaller chunks. The size of these chunks would depend on your specific use case, but they should be small enough to not exceed the payload limit.

  2. Create an endpoint to retrieve individual chunks: You would then create an endpoint that can return these individual chunks of data. This endpoint would take an index or some identifier as a parameter to know which chunk of data to return.

  3. Request data in chunks on the client side: On the client side, instead of making a single request to get all the data, you would make multiple requests to get each chunk of data. You would then combine these chunks on the client side to get the full data set.

Please note that the exact implementation would depend on your specific use case and the structure of your data. Also, this approach might increase the complexity of your application as you would need to handle multiple requests and the combination of data on the client side.

Unfortunately, the knowledge sources provided do not include a specific code example for this scenario in the context of Next.js or Vercel. For a more detailed guide, you might want to refer to the Next.js and Vercel documentation or other resources on handling large data sets in web applications.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

@twilit sleet
What about this method if I stored the returned data in vercel blob for instance, how would I know about it on the client side and be able to retrieve it?

Use external storage: If the data is very large, consider using external storage solutions to handle the data instead of sending it directly in the request (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE))

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Using external storage like Vercel Blob is a great way to handle large data sets. Here's a general approach on how you can do this:

  1. Upload data to Vercel Blob: In your server action, instead of returning the large data set, you would upload this data to your Vercel Blob store. After the upload, you would get a unique URL for the uploaded object (source (https://vercel.com/docs/storage/vercel-blob/client-upload)).

  2. Return the Blob URL: You would then return this Blob URL from your server action. This URL is unique to the uploaded object and can be used to access the object.

  3. Retrieve data on the client side: On the client side, instead of receiving the large data set directly, you would receive the Blob URL. You can then use this URL to make a request to the Blob store and retrieve the data.

Here's a general example of how you can upload data to Vercel Blob:

import { Blob } from '@vercel/blob/client';

const blob = new Blob({
  token: process.env.BLOB_READ_WRITE_TOKEN,
});

const file = new File([data], 'filename.txt', { type: 'text/plain' });

const upload = await blob.upload({
  file,
  path: '/path/to/file.txt',
  onProgress: (progress) => console.log(progress),
});

console.log(upload.url); // This is the Blob URL

Please note that the exact implementation would depend on your specific use case and the structure of your data. Also, remember to handle errors and edge cases appropriately in your implementation.

#

Unfortunately, the knowledge sources provided do not include a specific code example for this scenario in the context of Next.js or Vercel. For a more detailed guide, you might want to refer to the Vercel Blob documentation (https://vercel.com/docs/storage/vercel-blob).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

@twilit sleet
Can I upload a file greater than 4.5 MB to vercel blob from my server action?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can upload files larger than 4.5 MB to Vercel Blob, but not directly from a server action due to the 4.5 MB request body size limit on Vercel Functions (source (https://vercel.com/docs/functions/serverless-functions/runtimes#request-body-size)).

For larger files, you should use client-side uploads, where the file is sent directly from the client (a browser, for example) to Vercel Blob. This method supports files up to 5 TB (5,000 GB) (source (https://vercel.com/docs/storage/vercel-blob#server-and-client-uploads)).

Here's a general example of how you can upload a file from the client side to Vercel Blob:

import { upload } from '@vercel/blob/client';

const file = inputFileRef.current.files[0];  
const newBlob = await upload(file.name, file, { 
  access: 'public', 
  handleUploadUrl: '/api/avatar/upload', 
});

(source (https://vercel.com/guides/how-to-bypass-vercel-body-size-limit-serverless-functions))

Please note that this transfer is done securely to not expose your Vercel Blob store to anonymous uploads. The security mechanism is based on a token exchange between your server and Vercel Blob (source (https://vercel.com/docs/storage/vercel-blob/client-upload)).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

can I send a response larger than 4.5 MB from a normal post route response on the server?

#

@twilit sleet
can I send a response larger than 4.5 MB from a normal post route response on the server?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

If you're using Next.js and deploying your application on Vercel, the maximum response size for Serverless Functions, which includes API Routes, is 4.5 MB (source (https://vercel.com/docs/errors/FUNCTION_PAYLOAD_TOO_LARGE)). This limit is set by Vercel and cannot be increased.

If you need to send a response larger than 4.5 MB, you might need to consider other strategies such as:

  1. Splitting the data into multiple responses: You can break down your large data set into smaller parts and create a mechanism to request these parts individually. The client would then make multiple requests to get each chunk of data and combine these chunks to get the full data set.

  2. Using external storage: If the data is very large, consider using external storage solutions to handle the data instead of sending it directly in the response. You can upload the data to a storage service like Vercel Blob and return a URL to the client. The client can then use this URL to download the data directly from the storage service.

Please note that the exact implementation would depend on your specific use case and the structure of your data. Also, this approach might increase the complexity of your application as you would need to handle multiple requests and the combination of data on the client side.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

I can't do this from the server action though how would i do that?

Using external storage: If the data is very large, consider using external storage solutions to handle the data instead of sending it directly in the response. You can upload the data to a storage service like Vercel Blob and return a URL to the client. The client can then use this URL to download the data directly from the storage service.

#

@twilit sleet
I can't do this from the server action though how would i do that?

Using external storage: If the data is very large, consider using external storage solutions to handle the data instead of sending it directly in the response. You can upload the data to a storage service like Vercel Blob and return a URL to the client. The client can then use this URL to download the data directly from the storage service.

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

If you're dealing with large data sets in a server action and you're hitting the 4.5MB limit on Vercel, you can consider using an external storage solution like Vercel Blob. However, due to the 4.5MB limit on server actions, you won't be able to upload the data directly from the server action if it exceeds this limit.

Instead, you can consider the following approach:

  1. Client-side uploads: You can upload the data directly from the client to Vercel Blob. This method supports files up to 5 TB (5,000 GB) (source (https://vercel.com/docs/storage/vercel-blob#server-and-client-uploads)).

Here's a general example of how you can upload a file from the client side to Vercel Blob:

import { upload } from '@vercel/blob/client';

const file = inputFileRef.current.files[0];  
const newBlob = await upload(file.name, file, { 
  access: 'public', 
  handleUploadUrl: '/api/avatar/upload', 
});

(source (https://vercel.com/guides/how-to-bypass-vercel-body-size-limit-serverless-functions))

  1. Return the Blob URL: After the upload, you would get a unique URL for the uploaded object. You can then use this URL in your server action to return to the client.

  2. Retrieve data on the client side: On the client side, instead of receiving the large data set directly, you would receive the Blob URL. You can then use this URL to make a request to the Blob store and retrieve the data.

Please note that this transfer is done securely to not expose your Vercel Blob store to anonymous uploads. The security mechanism is based on a token exchange between your server and Vercel Blob (source (https://vercel.com/docs/storage/vercel-blob/client-upload)).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

I need to perform transfomration on the data on the server though @twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

If you need to perform transformations on the data on the server side, you can still do so before sending the data to the client. However, if the transformed data exceeds the 4.5MB limit, you'll need to consider alternative strategies.

Here's a general approach:

  1. Perform transformations: In your server action, you would perform the necessary transformations on your data.

  2. Upload transformed data to external storage: If the transformed data is larger than 4.5MB, you would need to upload this data to an external storage solution like Vercel Blob. However, due to the 4.5MB limit on server actions, you won't be able to upload the data directly from the server action if it exceeds this limit. Instead, you can return the transformed data to the client and have the client upload the data to Vercel Blob.

  3. Client-side uploads: The client would then upload the transformed data to Vercel Blob. Here's a general example of how you can upload a file from the client side to Vercel Blob:

import {{ upload }} from '@vercel/blob/client';

const file = new File([transformedData], 'filename.txt', {{ type: 'text/plain' }});  
const newBlob = await upload(file.name, file, {{ 
  access: 'public', 
  handleUploadUrl: '/api/avatar/upload', 
}});

(source (https://vercel.com/guides/how-to-bypass-vercel-body-size-limit-serverless-functions))

  1. Return the Blob URL: After the upload, the client would get a unique URL for the uploaded object. This URL can then be used to retrieve the transformed data.

  2. Retrieve data on the client side: The client can then use the Blob URL to make a request to the Blob store and retrieve the transformed data.

#

Please note that this approach increases the complexity of your application as you would need to handle the upload and retrieval of data on the client side. Also, this transfer is done securely to not expose your Vercel Blob store to anonymous uploads. The security mechanism is based on a token exchange between your server and Vercel Blob (source (https://vercel.com/docs/storage/vercel-blob/client-upload)).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

I also can't return it to the client because its over 4.5 MB @twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

If both the request and response sizes exceed the 4.5MB limit imposed by Vercel, and you need to perform transformations on the server, you might need to consider a different approach.

One possible solution could be to use a database or an external storage system to temporarily store the transformed data. Here's a general approach:

  1. Perform transformations: In your server action, you would perform the necessary transformations on your data.

  2. Store transformed data: If the transformed data is larger than 4.5MB, you would need to store this data in a database or an external storage solution like Vercel Blob. You would then generate a unique identifier for this data.

  3. Return the identifier: Instead of returning the transformed data directly, you would return the unique identifier to the client.

  4. Retrieve data on the client side: The client would then use this identifier to make a separate request to retrieve the transformed data. This could be done through a separate API endpoint that retrieves the data based on the identifier.

Please note that this approach increases the complexity of your application as you would need to handle the storage and retrieval of large data sets. Also, you would need to ensure that the data is securely stored and only accessible to authorized clients.

Unfortunately, the knowledge sources provided do not include a specific code example for this scenario in the context of Next.js or Vercel. For a more detailed guide, you might want to refer to the Next.js, Vercel, and your chosen database or storage solution's documentation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

@twilit sleet How can I set my header in my post route to have Content-Encoding: 'gzip'? My post route is in folder app/api/routes.ts

/** @type {import('next').NextConfig} /
const nextConfig = {
experimental: {
serverActions: {
bodySizeLimit: '10mb',
allowedOrigins: ['amazon-ppc.vercel.app'],
}
},
async headers() {
return [
{
source: '/api/:path',
headers: [
{
key: 'Access-Control-Allow-Origin',
value: '',
},
{
key: 'Access-Control-Allow-Methods',
value: 'POST',
},
{
key: 'Access-Control-Allow-Headers',
value: 'X-Requested-With, Content-Type, Accept',
},
],
},
];
}

};

export default nextConfig;

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

To set the Content-Encoding header to 'gzip' in your post route, you can modify the response headers in your server action. Here's an example of how you can do this in your app/api/routes.ts file:

import { NextApiRequest, NextApiResponse } from 'next';

export default function handler(req: NextApiRequest, res: NextApiResponse) {
  // Your logic here...

  // Set the Content-Encoding header to 'gzip'
  res.setHeader('Content-Encoding', 'gzip');

  // Send the response
  res.send(/* Your response data here... */);
}

This will set the Content-Encoding header to 'gzip' for the responses from this route. Please note that setting the Content-Encoding header to 'gzip' indicates that the response body is compressed using gzip, so you should only do this if you're actually compressing the response body.

Unfortunately, the knowledge sources provided do not include a specific code example for compressing the response body in the context of Next.js or Vercel. For a more detailed guide, you might want to refer to the Next.js and Node.js documentation or other resources on handling gzip compression in Node.js applications.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

@twilit sleet is nextapireponse still relevant in the App router which I'm using?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, NextResponse is still relevant in the App Router model of Next.js. The NextResponse API allows you to redirect the incoming request to a different URL, rewrite the response by displaying a given URL, set request headers for API Routes, getServerSideProps, and rewrite destinations, set response cookies, and set response headers (source (https://nextjs.org/docs/app/building-your-application/routing/middleware#nextresponse)).

In the App Router, you can use NextResponse to produce a response from Middleware. You can either rewrite to a route that produces a response or return a NextResponse directly (source (https://nextjs.org/docs/app/building-your-application/routing/middleware#nextresponse)).

Here's an example of how you can use NextResponse in a server action:

import { NextResponse } from "next/server";

export async function GET() {
  // SOME LOGIC TO GET THE DATA(This api was used with prisma orm)
  return new NextResponse.json([YOUR_DATA], { status: 200 });
}

(source (https://github.com/vercel/next.js/issues/55672))

Please note that the exact usage of NextResponse would depend on your specific use case and the structure of your application.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

@twilit sleet Did I do this correctly?

export async function POST(req: NextRequest, res: NextResponse) {
const csvString = JSON.stringify(req.body);
const result = Papa.parse(csvString, {
header: true,
}) as {data: Advertising[]};
const jsonData = result.data;
const modifiedData = await modifyData(jsonData) as Advertising[];
getPayloadSize(modifiedData, "Modified JSON");
const compressedData = await compressData(modifiedData);
res.headers.set('Content-Type', 'application/json');
res.headers.set('Content-Encoding', 'gzip');
return NextResponse.json(compressedData, { status: 200 });

}

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Your code seems mostly correct, but there are a few things to note:

  1. NextResponse does not have a headers property. You should use the NextResponse constructor to set the headers.

  2. The NextResponse.json() method is used to create a new NextResponse object with a JSON body. However, since you're sending gzipped data, you should use the NextResponse constructor directly and set the body to the gzipped data.

  3. Make sure that compressData() function is actually compressing your data using gzip.

Here's how you can modify your code:

import { NextRequest, NextResponse } from "next/server";

export async function POST(req: NextRequest) {
    const csvString = JSON.stringify(req.body); 
    const result = Papa.parse(csvString, {
        header: true,
      }) as {data: Advertising[]};
    const jsonData = result.data;
    const modifiedData = await modifyData(jsonData) as Advertising[];
    getPayloadSize(modifiedData, "Modified JSON");
    const compressedData = await compressData(modifiedData);
    return new NextResponse({
      status: 200,
      headers: {
        'Content-Type': 'application/json',
        'Content-Encoding': 'gzip'
      },
      body: compressedData
    });
}

Please note that this code assumes that compressData() returns a Buffer or a string containing the gzipped data. If it returns a Promise, you should await it before passing it to the NextResponse constructor.

Also, remember to handle errors and edge cases appropriately in your implementation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

@twilit sleet
Object literal may only specify known properties, and 'body' does not exist in type 'ReadableStream<any> | Blob | ArrayBufferView | ArrayBuffer | FormData | URLSearchParams'.

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

The error message you're seeing is because the NextResponse constructor does not accept a body property directly. Instead, you should use one of the static methods provided by NextResponse to create a response with a body.

However, since you're trying to send gzipped data, you might need to convert the gzipped data to a ReadableStream first, as NextResponse currently only supports ReadableStream for the response body.

Here's an example of how you can convert a Buffer to a ReadableStream:

import { Readable } from 'stream';

function bufferToStream(buffer) {
  const stream = new Readable();
  stream.push(buffer);
  stream.push(null);
  return stream;
}

You can then use this function to convert your gzipped data to a ReadableStream and pass it to the NextResponse constructor:

import { NextRequest, NextResponse } from "next/server";

export async function POST(req: NextRequest) {
  const csvString = JSON.stringify(req.body); 
  const result = Papa.parse(csvString, {
    header: true,
  }) as {data: Advertising[]};
  const jsonData = result.data;
  const modifiedData = await modifyData(jsonData) as Advertising[];
  getPayloadSize(modifiedData, "Modified JSON");
  const compressedData = await compressData(modifiedData);
  const compressedStream = bufferToStream(compressedData);
  return new NextResponse({
    status: 200,
    headers: {
      'Content-Type': 'application/json',
      'Content-Encoding': 'gzip'
    },
    body: compressedStream
  });
}

Please note that this code assumes that compressData() returns a Buffer containing the gzipped data. If it returns a Promise, you should await it before passing it to bufferToStream().

#

Also, remember to handle errors and edge cases appropriately in your implementation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

@twilit sleet Can I use more than 4.5 MB on a node.js runtime

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can use more than 4.5 MB in a Node.js runtime. The 4.5 MB limit is specific to Vercel Serverless Functions for request and response bodies (source (https://vercel.com/docs/functions/runtimes#request-body-size)).

However, there are other size limits to consider when using Node.js on Vercel:

If you're dealing with large data sets or long-running processes, you might need to consider other strategies such as streaming data, using external storage, or breaking down your process into smaller parts.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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#

Does that 4.5 MB limit the case wheter the runtime is edge or node.js though?

@twilit sleet

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, the 4.5 MB limit applies to both Edge and Node.js runtimes on Vercel for the request body size. However, the maximum size for the response body differs between the two runtimes. For the Node.js runtime, the maximum response body size is also 4.5 MB. For the Edge runtime, the maximum response body size depends on the plan: Hobby (1 MB), Pro (2 MB), and Enterprise (4 MB) (source (https://vercel.com/docs/functions/runtimes#request-body-size)).

Please note that these limits are set by Vercel and cannot be increased. If you need to handle larger data sets, you might need to consider other strategies such as splitting the data into multiple requests/responses, using external storage, or streaming data.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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#

@twilit sleet Can I return the NextResponse object in a server action ?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can return a NextResponse object in a server action. The NextResponse API allows you to manipulate cookies, set headers, implement redirects, and rewrite paths in your server action (source (https://nextjs.org/docs/app/api-reference/file-conventions/middleware#nextresponse)).

Here's an example of how you can use NextResponse in a server action:

import { NextResponse } from 'next/server'
 
export async function GET(request: Request) {
 return NextResponse.json({ error: 'Internal Server Error' }, { status: 500 })
}

(source (https://nextjs.org/docs/app/api-reference/functions/next-response#json))

In this example, the GET server action is returning a NextResponse object with a JSON body and a status of 500.

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#

@twilit sleet
why does this happen:

Error: Hydration failed because the initial UI does not match what was rendered on the server.
See more info here: https://nextjs.org/docs/messages/react-hydration-error

Expected server HTML to contain a matching <table> in <div>.

...
http://localhost:3000/_next/static/chunks/node_modules_flowbite-react_dist_esm_3fcce0._.js:6471:204
<div>
^^^^^
<table>
^^^^^^^
Call Stack
React
throwOnHydrationMismatch
node_modules/next/dist/compiled/react-dom/cjs/react-dom.development.js (6981:9)
tryToClaimNextHydratableInstance
node_modules/next/dist/compiled/react-dom/cjs/react-dom.development.js (7040:7)
updateHostComponent$1
node_modules/next/dist/compiled/react-dom/cjs/react-dom.development.js (16621:5)
beginWork$1
node_modules/next/dist/compiled/react-dom/cjs/react-dom.development.js (18503:14)

Text content does not match server-rendered HTML
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

The error message "Hydration failed because the initial UI does not match what was rendered on the server" typically occurs when the HTML content rendered on the server differs from what is expected on the client. This discrepancy can cause issues with React's hydration process, which is the process of using server-rendered HTML as the starting point for a client-side React application.

In your case, the error message suggests that the server-rendered HTML contains a <table> element, but the client-side React application was expecting a <div> element at the same location. This mismatch can occur due to a variety of reasons, such as conditional rendering that behaves differently on the server and the client, or side effects that modify the DOM directly.

To resolve this issue, you should ensure that the same content is rendered on both the server and the client. This might involve checking your components for any conditional rendering or side effects that could cause a mismatch.

Here's an example of a potential issue:

function MyComponent() {
  const [isTable, setIsTable] = useState(false);

  useEffect(() => {
    setIsTable(true);
  }, []);

  return isTable ? <table>...</table> : <div>...</div>;
}

In this example, the component initially renders a <div> on the server, but then switches to a <table> on the client after the first render. This would cause a hydration mismatch error.

To fix this, you could ensure that the same content is rendered on both the server and the client, for example by initializing isTable to true.

#

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#

What's causing this? @twilit sleet
export default function Chemicals() {
const [openModal, setOpenModal] = useState(false);

const { data, error } = useQuery({
queryKey: ["chemicals"],
queryFn: () => getChemicalData(),
});
if (data !== undefined && data !== null) {
console.log(data);
}
if (error) {
console.log(error);
}

return (
<div className="overflow-x-auto">
<Modal show={openModal} onClose={() => setOpenModal(false)}>
<Modal.Header>Terms of Service</Modal.Header>
<Modal.Body>
<div className="space-y-6">
<p className="text-base leading-relaxed text-gray-500 dark:text-gray-400">
With less than a month to go before the European Union enacts new consumer privacy laws for its citizens,
companies around the world are updating their terms of service agreements to comply.
</p>
<p className="text-base leading-relaxed text-gray-500 dark:text-gray-400">
The European Union’s General Data Protection Regulation (G.D.P.R.) goes into effect on May 25 and is meant
to ensure a common set of data rights in the European Union. It requires organizations to notify users as
soon as possible of high-risk data breaches that could personally affect them.
</p>
</div>
</Modal.Body>
<Modal.Footer>
<Button onClick={() => setOpenModal(false)}>I accept</Button>
<Button color="gray" onClick={() => setOpenModal(false)}>
Decline
</Button>
</Modal.Footer>

  </Modal>
  <Table hoverable>
    <Table.Head>
      <Table.HeadCell className="p-4">
        <Checkbox />
      </Table.HeadCell>
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Based on the provided code, it's not immediately clear what could be causing the hydration error. However, the error message "Expected server HTML to contain a matching <table> in <div>" suggests that there might be a mismatch between the server-rendered HTML and the client-side React application.

Here are a few things you could check:

  1. Asynchronous Data Fetching: You're using useQuery to fetch data. If the data fetching is causing a change in your component's output after the server-side rendering, it could lead to a hydration mismatch. Make sure that the component's output is consistent between the server and the client, regardless of the state of your data fetching.

  2. Conditional Rendering: Check if there's any conditional rendering in your component that could cause a mismatch between the server and the client. For example, if you're conditionally rendering the <Table> component based on the state of your data fetching, it could cause a mismatch if the data is not available at the time of server-side rendering.

  3. State Initialization: You're using useState to control the visibility of a modal. If the initial state is different between the server and the client, it could cause a hydration mismatch. Make sure that the initial state is consistent between the server and the client.

  4. Third-Party Libraries: If you're using any third-party libraries (like the one providing the Modal and Table components), they could potentially cause hydration issues if they manipulate the DOM directly or have side effects that run on the client side.

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#

@twilit sleet
Could this be the issue?
<Table.Body className="divide-y">
{data?.map((item, index) => (
<Table.Row key={index} className="bg-white dark:border-gray-700 dark:bg-gray-800">
<Table.Cell className="p-4">
<Checkbox />
</Table.Cell>
<Table.Cell className="whitespace-nowrap font-medium text-gray-900 dark:text-white">
{item.classificationId}
</Table.Cell>
<Table.Cell>{item.description}</Table.Cell>
<Table.Cell>{item.nmfc}</Table.Cell>
<Table.Cell>{item.sub || ''}</Table.Cell>
<Table.Cell>{item.freightClass}</Table.Cell>
<Table.Cell>{item.hazardous ? 'Yes' : 'No'}</Table.Cell>
<Table.Cell>{item.hazardId || ''}</Table.Cell>
<Table.Cell>{item.packingGroup || ''}</Table.Cell>
<Table.Cell>
<button onClick={() => setOpenModal(true)}>
<a href="#" className="font-medium text-cyan-600 hover:underline dark:text-cyan-500">
Edit
</a>
</button>
</Table.Cell>
</Table.Row>
)

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, this could potentially be the issue. The hydration error could be caused by the asynchronous data fetching with useQuery. If the data isn't available at the time of server-side rendering, but then becomes available on the client-side, it could cause a mismatch between the server-rendered HTML and the client-side React application.

In your case, the <Table.Body> component is conditionally rendering table rows based on the data array. If this data array is not available at the time of server-side rendering (i.e., it's undefined or null), but then becomes available on the client-side, it could cause a hydration mismatch.

To resolve this issue, you could add a loading state to your component and render some placeholder content while the data is being fetched. This would ensure that the output of your component is consistent between the server and the client, regardless of the state of your data fetching.

Here's an example of how you could do this:

const { data, error, isLoading } = useQuery({
  queryKey: ["chemicals"],
  queryFn: () => getChemicalData(),
});

if (isLoading) {
  return <div>Loading...</div>; // Replace this with your actual loading state
}

// Rest of your component...

In this example, the component renders a loading state while the data is being fetched. Once the data becomes available, it renders the actual content. This ensures that the output of the component is consistent between the server and the client.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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#

@twilit sleet can I call a server action function from a api routes get request handler?

import Shipstation from 'shipstation-node';

export async function GET() {

}

Server action code I want to call getShipstationProducts:

"use server";

import { getEnrichedOrder } from "@/helpers/EnrichedOrder";
import type { ErrorResult } from "@/types/book-freight/action-types";
import type { paths } from "@/types/book-freight/mycarrierSchema";
import { optsSchema } from "@/types/optsSchema";
import createClient from "openapi-fetch";
import Shipstation from "shipstation-node";
import type { IOrderPaginationResult } from "shipstation-node/typings/models";
import { Client } from "@upstash/qstash";
import { get } from "http";

const qstashClient = new Client({
token: process.env.QSTASH_TOKEN!,
})
const schedules = qstashClient.schedules;

const credentials = {
key: process.env.VERCEL_SHIPSTATION_KEY,
secret: process.env.VERCEL_SHIPSTATION_SECRET,
};

const shipStation = new Shipstation({
apiKey: credentials.key,
apiSecret: credentials.secret,
});

const client = createClient<paths>({
baseUrl: process.env.MYCARRIER_BASE_URL,
});

const products = shipStation.request({
url: "/products",
})

export async function getShipstationProducts() {
const products = await shipStation.request({
url: "/products",
});
console.log(products.data);
}

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can call a server action function from an API route handler in Next.js. However, you need to import the server action function into your API route file first.

Here's how you can do it:

import { getShipstationProducts } from 'path-to-your-server-action-file';

export default async function handler(req, res) {
  if (req.method === 'GET') {
    try {
      const products = await getShipstationProducts();
      res.status(200).json(products);
    } catch (error) {
      res.status(500).json({ error: 'Error fetching products' });
    }
  } else {
    res.status(405).json({ error: 'Method not allowed' });
  }
}

In this example, the handler function is the default export for your API route. When a GET request is made to this route, it calls the getShipstationProducts server action function and sends the result as the response.

Please replace 'path-to-your-server-action-file' with the actual path to the file where getShipstationProducts is defined.

However, it's important to note that Next.js is moving towards a new file-based routing system called the App Router, which introduces new concepts like Route Handlers and Server Actions. In the new system, API Routes in the pages/api directory are replaced by Route Handlers in the app directory (source (https://nextjs.org/docs/pages/building-your-application/upgrading/app-router-migration#api-routes)). You might want to consider migrating to the new system for future development.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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#

@twilit sleet Can i return a status code in a nextresponse object?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can return a status code in a NextResponse object in Next.js. The NextResponse class extends the native Response class and adds some additional helper methods. You can set the status code by passing an options object as the second argument to the NextResponse constructor. The options object can have a status property, which sets the status code of the response.

Here's an example from one of the knowledge sources:

if (some_condition) {
  return new NextResponse(
    JSON.stringify({ message: 'service not available' }),
    {
      status: 451,
      headers: { 'content-type': 'application/json' }
    }
  );
}

In this example, a NextResponse object is returned with a status code of 451 (source (https://github.com/vercel/next.js/issues/50155)).

You can also use the NextResponse.next() helper method to continue the middleware chain and set a status code. Here's an example:

import { NextResponse } from 'next/server';

export default function middleware() {
 return NextResponse.next({
   status: 200,
 });
}

In this example, the NextResponse.next() method is used to return a 200 OK response (source (https://vercel.com/docs/functions/edge-middleware/middleware-api)).

#

Please note that when streaming, a 200 status code will be returned to signal that the request was successful. The server can still communicate errors or issues to the client within the streamed content itself, for example, when using redirect or notFound. Since the response headers have already been sent to the client, the status code of the response cannot be updated (source (https://nextjs.org/docs/app/building-your-application/routing/loading-ui-and-streaming#status-codes)).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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#

@twilit sleet is it possible to conditionally render the search component based on the page the user is on?

const searchClient = alogiliasearch('PRRV6UCPXG','3c47055a99693c67accdbba674a35f16')

export function SearchComponent() {
    const [query, setQuery] = useState("");

    const queries: MultipleQueriesQuery[] = [
        {
          indexName: "product",
          params: {
            query: query,
          },
        },
      ]
    const { data, isLoading, error } = useQuery({queryKey: ["searchResults", query], queryFn: () => searchClient.search(queries)})
    return (
        <div className="hidden lg:block lg:pl-2">
              <Label htmlFor="search" className="sr-only">
                Search
              </Label>
              <TextInput
                className="w-full lg:w-96"
                icon={HiSearch}
                id="search"
                name="search"
                placeholder="Search"
                required
                type="search"
                value={query}
                onChange={(e) => setQuery(e.target.value)}
              />
            </div>
    )
}
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, it is possible to conditionally render the SearchComponent based on the page the user is on. You can achieve this by using the useRouter hook from Next.js to get the current route and then conditionally render the component based on the route.

Here's an example of how you can do this:

import { useRouter } from 'next/router';
import { useState } from 'react';
import { useQuery } from 'react-query';
import algoliasearch from 'algoliasearch/lite';
import { Label, TextInput } from '@components/ui';
import { HiSearch } from 'react-icons/hi';

const searchClient = algoliasearch('PRRV6UCPXG', '3c47055a99693c67accdbba674a35f16');

export function SearchComponent() {
  const [query, setQuery] = useState("");
  const router = useRouter();

  const queries = [
    {
      indexName: "product",
      params: {
        query: query,
      },
    },
  ];

  const { data, isLoading, error } = useQuery({
    queryKey: ["searchResults", query],
    queryFn: () => searchClient.search(queries),
  });

  // Conditionally render the search component based on the current route
  if (router.pathname !== '/specific-page') {
    return null;
  }

  return (
    <div className="hidden lg:block lg:pl-2">
      <Label htmlFor="search" className="sr-only">
        Search
      </Label>
      <TextInput
        className="w-full lg:w-96"
        icon={HiSearch}
        id="search"
        name="search"
        placeholder="Search"
        required
        type="search"
        value={query}
        onChange={(e) => setQuery(e.target.value)}
      />
    </div>
  );
}

In this example, the SearchComponent will only be rendered if the user is on the /specific-page route. You can replace '/specific-page' with the route you want to conditionally render the search component on.

#

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#

@twilit sleet This is component I want to conditionally render the SearchComponent in

export function DashboardNavbar() {

  }

  return (
    <Navbar
      fluid
      className="fixed top-0 z-30 w-full border-b border-gray-200 bg-white p-0 dark:border-gray-700 dark:bg-gray-800 sm:p-0"
    >
      <div className="w-full p-3 pr-4">
        <div className="flex items-center justify-between">
          <div className="flex items-center">
            <button
              onClick={handleToggleSidebar}
              className="mr-3 cursor-pointer rounded p-2 text-gray-600 hover:bg-gray-100 hover:text-gray-900 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-white"
            >
              <span className="sr-only">Toggle sidebar</span>
              {/* mobile */}
              <div className="lg:hidden">
                {sidebar.mobile.isOpen ? (
                  <HiX className="h-6 w-6" />
                ) : (
                  <HiMenuAlt1 className="h-6 w-6" />
                )}
              </div>
              {/* desktop */}
              <div className="hidden lg:block">
                <HiMenuAlt1 className="h-6 w-6" />
              </div>
            </button>
            <Navbar.Brand as={Link} href="/" className="mr-14">
              <Image
                className="mr-3 h-8"
                alt=""
                src={
                  computedMode === "light"
                    ? "/images/alliancechemical.svg"
                    : "/images/alliancechemical_dark.svg"
                }
                width={200}
                height={200}
              />
            </Navbar.Brand>
            <SearchComponent />
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

To conditionally render the SearchComponent based on the current page, you can use the useRouter hook from Next.js within your DashboardNavbar component. Here's how you can do it:

import { useRouter } from 'next/router';
import { Navbar } from 'flowbite-react';
import { HiX, HiMenuAlt1 } from 'react-icons/hi';
import Link from 'next/link';
import Image from 'next/image';
import { SearchComponent } from './SearchComponent'; // Adjust the import path as needed

export function DashboardNavbar() {
  const router = useRouter();

  const handleToggleSidebar = () => {
    // Your toggle sidebar logic here
  };
#

return (
<Navbar
fluid
className="fixed top-0 z-30 w-full border-b border-gray-200 bg-white p-0 dark:border-gray-700 dark:bg-gray-800 sm:p-0"
>
<div className="w-full p-3 pr-4">
<div className="flex items-center justify-between">
<div className="flex items-center">
<button
onClick={handleToggleSidebar}
className="mr-3 cursor-pointer rounded p-2 text-gray-600 hover:bg-gray-100 hover:text-gray-900 dark:text-gray-400 dark:hover:bg-gray-700 dark:hover:text-white"
>
<span className="sr-only">Toggle sidebar</span>
{/* mobile /}
<div className="lg:hidden">
{sidebar.mobile.isOpen ? (
<HiX className="h-6 w-6" />
) : (
<HiMenuAlt1 className="h-6 w-6" />
)}
</div>
{/ desktop */}
<div className="hidden lg:block">
<HiMenuAlt1 className="h-6 w-6" />
</div>
</button>
<Navbar.Brand as={Link} href="/" className="mr-14">
<Image
className="mr-3 h-8"
alt=""
src={
computedMode === "light"
? "/images/alliancechemical.svg"
: "/images/alliancechemical_dark.svg"
}
width={200}
height={200}
/>
</Navbar.Brand>
{router.pathname === '/specific-page' && <SearchComponent />}
</div>
</div>
</div>
</Navbar>
);
}


In this example, the `SearchComponent` will only be rendered if the user is on the `/specific-page` route. You can replace `'/specific-page'` with the route you want to conditionally render the search component on.
#

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#

@twilit sleet Can I have pass data from a layout.tsx file as a prop to a child component inside it?

twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

No, you cannot pass data from a layout.tsx file as a prop to a child component inside it directly. According to the Next.js documentation, passing data between a parent layout and its children is not possible. However, you can fetch the same data in a route more than once, and React will automatically dedupe the requests without affecting performance (source (https://nextjs.org/docs/app/building-your-application/routing/layouts-and-templates#nesting-layouts)).

Here's the relevant excerpt from the documentation:

If you need to share data between a layout and its children, you can use a global state management solution like React Context, Redux, or any other state management library. This way, you can fetch the data in the layout and make it available to all child components through the global state.

For more information, you can refer to the Next.js documentation on layouts (https://nextjs.org/docs/app/building-your-application/routing/layouts-and-templates#nesting-layouts).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

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cold dock
#

@twilit sleet Can i export my react-query to another component to use its data for templating?

const searchClient = alogiliasearch('PRRV6UCPXG','3c47055a99693c67accdbba674a35f16')

export function SearchComponent() {
const [query, setQuery] = useState("");
const [searchTerm, setSearchTerm] = useState("");

const queries: MultipleQueriesQuery[] = [
    {
      indexName: "products",
      params: {
        query: searchTerm,
      },
    },
  ]
const { data, isLoading, error } = useQuery({queryKey: ["searchResults", searchTerm], queryFn: () => searchClient.search(queries)})

function handleSubmit(formData: FormData) {
    setSearchTerm(formData.get("search") as string)
}

return (
    <form action={handleSubmit}>
    <div className="hidden lg:bl
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, you can export your react-query logic to another component to use its data for templating. To achieve this, you can create a custom hook that encapsulates the react-query logic and then use this hook in any component where you need the data.

Here's how you can do it:

  1. Create a custom hook:
import { useState } from 'react';
import { useQuery } from 'react-query';
import algoliasearch from 'algoliasearch/lite';

const searchClient = algoliasearch('PRRV6UCPXG', '3c47055a99693c67accdbba674a35f16');

export function useSearch() {
  const [searchTerm, setSearchTerm] = useState("");

  const queries = [
    {
      indexName: "products",
      params: {
        query: searchTerm,
      },
    },
  ];

  const { data, isLoading, error } = useQuery({
    queryKey: ["searchResults", searchTerm],
    queryFn: () => searchClient.search(queries),
  });

  return { data, isLoading, error, setSearchTerm };
}
  1. Use the custom hook in your component:
import { useSearch } from './path-to-your-hook';

export function SearchComponent() {
  const [query, setQuery] = useState("");
  const { data, isLoading, error, setSearchTerm } = useSearch();

  function handleSubmit(formData: FormData) {
    setSearchTerm(formData.get("search") as string);
  }
#

return (
<form action={handleSubmit}>
<div className="hidden lg:block lg:pl-2">
<label htmlFor="search" className="sr-only">
Search
</label>
<input
className="w-full lg:w-96"
id="search"
name="search"
placeholder="Search"
required
type="search"
value={query}
onChange={(e) => setQuery(e.target.value)}
/>
<button type="submit">Search</button>
</div>
{isLoading && <p>Loading...</p>}
{error && <p>Error: {error.message}</p>}
{data && (
<div>
{/* Render your search results here */}
</div>
)}
</form>
);
}


3. **Use the custom hook in another component**:

```javascript
import { useSearch } from './path-to-your-hook';

export function AnotherComponent() {
  const { data, isLoading, error } = useSearch();

  return (
    <div>
      {isLoading && <p>Loading...</p>}
      {error && <p>Error: {error.message}</p>}
      {data && (
        <div>
          {/* Render your search results here */}
        </div>
      )}
    </div>
  );
}

By creating a custom hook, you encapsulate the react-query logic and make it reusable across different components. This approach helps keep your code clean and maintainable.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

@twilit sleet If I have two queries going on should I name them differently?

"use client";

import { getUnlinkedProducts, updateProductFreightLink } from "@/helpers/getData";
import { useQuery, useQueryClient } from "@tanstack/react-query";
import { Table, TextInput, Button, Checkbox, Pagination } from "flowbite-react";
import React, { useState } from "react";
import { useSearch} from "@/hooks/use-search";

export default function UnlinkedProducts() {
const [selectedRows, setSelectedRows] = useState<number[]>([]);
const [classificationIds, setClassifications] = useState<{[key: number]: string}>({});
const [currentPage, setCurrentPage] = useState(1);
const itemsPerPage = 9;
const queryClient = useQueryClient();

const { data, isLoading, error } = useSearch();
const { data, isLoading, error } = useQuery({queryKey: ['unlinkedProducts'], queryFn: () => getUnlinkedProducts()})
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

Yes, if you have two queries going on, you should name them differently to avoid conflicts and ensure that each query is uniquely identified. In your example, you have two queries with the same variable names (data, isLoading, error), which will cause conflicts.

Here's how you can rename them to avoid conflicts:

"use client";

import { getUnlinkedProducts, updateProductFreightLink } from "@/helpers/getData";
import { useQuery, useQueryClient } from "@tanstack/react-query";
import { Table, TextInput, Button, Checkbox, Pagination } from "flowbite-react";
import React, { useState } from "react";
import { useSearch } from "@/hooks/use-search";

export default function UnlinkedProducts() {
    const [selectedRows, setSelectedRows]  = useState<number[]>([]);
    const [classificationIds, setClassifications] = useState<{[key: number]: string}>({});
    const [currentPage, setCurrentPage] = useState(1);
    const itemsPerPage = 9;
    const queryClient = useQueryClient();
  
    const { data: searchData, isLoading: searchLoading, error: searchError } = useSearch();
    const { data: unlinkedProductsData, isLoading: unlinkedProductsLoading, error: unlinkedProductsError } = useQuery({
        queryKey: ['unlinkedProducts'],
        queryFn: () => getUnlinkedProducts()
    });

    return (
        <div>
            {/* Render your components here */}
            {searchLoading && <p>Loading search results...</p>}
            {searchError && <p>Error loading search results: {searchError.message}</p>}
            {searchData && (
                <div>
                    {/* Render search results */}
                </div>
            )}
#

{unlinkedProductsLoading && <p>Loading unlinked products...</p>}
{unlinkedProductsError && <p>Error loading unlinked products: {unlinkedProductsError.message}</p>}
{unlinkedProductsData && (
<div>
{/* Render unlinked products */}
</div>
)}
</div>
);
}


In this example, the results of the `useSearch` hook are renamed to `searchData`, `searchLoading`, and `searchError`, while the results of the `useQuery` hook for unlinked products are renamed to `unlinkedProductsData`, `unlinkedProductsLoading`, and `unlinkedProductsError`. This ensures that each query is uniquely identified and avoids conflicts.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
#

@cold dock kapa.ai is still learning and improving, please let us know how it did by reacting below

cold dock
#

@twilit sleet How can I put the input of a search component into the parameters of the url bar as the user types? Here's my input component

"use client";

import { useEffect, useState } from "react";
import { Label, TextInput } from "flowbite-react";
import { HiSearch } from "react-icons/hi";
import { useSearch } from "@/hooks/use-search";


export function SearchComponent() {
    const [query, setQuery] = useState("");
    const {setSearchTerm} = useSearch();  


    function handleSubmit(formData: FormData) {
        setSearchTerm(formData.get("search") as string)
    }

    return (
        <form action={handleSubmit}>
        <div className="hidden lg:block lg:pl-2">
              <Label htmlFor="search" className="sr-only">
                Search
              </Label>
              <TextInput
                className="w-full lg:w-96"
                icon={HiSearch}
                id="search"
                name="search"
                placeholder="Search"
                required
                type="search"
                value={query}
                onChange={(e) => setQuery(e.target.value)}
              />
            </div>
            </form>
    )
}
twilit sleetBOT
#

Let me check the knowledge base for you. One moment! :books:

#

To update the URL parameters as the user types in the search input, you can use the useRouter hook from Next.js to manipulate the query parameters. This way, the search term will be reflected in the URL bar in real-time.

Here's how you can modify your SearchComponent to achieve this:

"use client";

import { useEffect, useState } from "react";
import { Label, TextInput } from "flowbite-react";
import { HiSearch } from "react-icons/hi";
import { useSearch } from "@/hooks/use-search";
import { useRouter } from "next/router";

export function SearchComponent() {
    const [query, setQuery] = useState("");
    const { setSearchTerm } = useSearch();  
    const router = useRouter();

    useEffect(() => {
        const handleRouteChange = (url) => {
            const urlParams = new URLSearchParams(window.location.search);
            const searchQuery = urlParams.get("search") || "";
            setQuery(searchQuery);
            setSearchTerm(searchQuery);
        };

        // Update the search term when the component mounts
        handleRouteChange(window.location.href);

        // Listen for URL changes
        router.events.on("routeChangeComplete", handleRouteChange);

        return () => {
            router.events.off("routeChangeComplete", handleRouteChange);
        };
    }, [router, setSearchTerm]);

    const handleInputChange = (e) => {
        const newQuery = e.target.value;
        setQuery(newQuery);
        setSearchTerm(newQuery);

        // Update the URL parameters
        router.replace({
            pathname: router.pathname,
            query: { ...router.query, search: newQuery },
        }, undefined, { shallow: true });
    };
#

return (
<form>
<div className="hidden lg:block lg:pl-2">
<Label htmlFor="search" className="sr-only">
Search
</Label>
<TextInput
className="w-full lg:w-96"
icon={HiSearch}
id="search"
name="search"
placeholder="Search"
required
type="search"
value={query}
onChange={handleInputChange}
/>
</div>
</form>
);
}


In this example:

1. The `useRouter` hook is used to manipulate the URL parameters.
2. The `useEffect` hook is used to initialize the search term from the URL when the component mounts and to listen for URL changes.
3. The `handleInputChange` function updates the search term in the state and the URL parameters as the user types.

This approach ensures that the search term is reflected in the URL bar in real-time, and the state is kept in sync with the URL parameters.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------