I’m assuming that, like me, you ran into a challenge where you needed  to stream a large JSON response (not a file) without having to store  the entire JSON array in memory before processing and uploading it as a  document. Streaming a JSON response with Axios returns the chunks of data as  substrings of the entire JSON response, an approach would be to append  all the chunks into a single string and run JSON.parse on the final string. JSON.parse let data = '';
const response = await axios.get(url, { responseType: 'stream' });
response.data.on('data', (chunk) => {
    data += chunk;
});
const parsedData = JSON.parse(data); let data = '';
const response = await axios.get(url, { responseType: 'stream' });
response.data.on('data', (chunk) => {
    data += chunk;
});
const parsedData = JSON.parse(data); This approach certainly works, but depending on the size of the  records, this approach is not memory efficient; for over a hundred  thousand records, you could be seeing a memory usage of over 1GB. Having multiple users making concurrent requests with each request utilizing over 1GB is not an optimal use of system resources. 1GB 1GB To convert the chunks of data into a format that can be  uploaded to CSV, you’d need to parse the data from the text format to a  compatible one. This can be achieved by making use of AsyncParser from the @json2csv/node npm package. AsyncParser @json2csv/node The steps below highlight how to go about it. Step 1 Install the @json2csv/node package @json2csv/node npm i --save @json2csv/node npm i --save @json2csv/node Step 2 Define your parser const opts = {
  fields: ['header1', 'header2', 'header3']
};
const transformOpts = {};
const asyncOpts = {};
const parser = new AsyncParser(opts, asyncOpts, transformOpts); const opts = {
  fields: ['header1', 'header2', 'header3']
};
const transformOpts = {};
const asyncOpts = {};
const parser = new AsyncParser(opts, asyncOpts, transformOpts); For further configuration options, you can check out the doc doc Step 3 Make use of your parser to parse your stream data and generate a PassThrough stream PassThrough function generateStream(url) {
  // Create a PassThrough stream to pipe data through
  const passThroughStream = new PassThrough();

  // Stream JSON data from the URL using Axios 
  axios.get(url, { responseType: 'stream' }).then((response) => {
    // Pipe the parsed response stream to the PassThrough stream
    parser.parse(response.data).pipe(passThroughStream);
  });
  return passThroughStream;
} function generateStream(url) {
  // Create a PassThrough stream to pipe data through
  const passThroughStream = new PassThrough();

  // Stream JSON data from the URL using Axios 
  axios.get(url, { responseType: 'stream' }).then((response) => {
    // Pipe the parsed response stream to the PassThrough stream
    parser.parse(response.data).pipe(passThroughStream);
  });
  return passThroughStream;
} The generateStream function makes an API call with a { responseType: 'stream' }  to indicate to Axios that the data should be returned as a continuous  stream instead of a single chunk. Using a stream is memory efficient as  opposed to loading all the records in memory at once; you only load a  chunk into memory and process it. generateStream { responseType: 'stream' } Step 4 Upload your data to s3 const { S3Client } = require('@aws-sdk/client-s3');

const s3Client = new S3Client({
  region: 'region',
  credentials: {
    accessKeyId: 'access_key',
    secretAccessKey: 'secret_key',
  },
});
async function streamJsonToS3(url, s3Bucket, s3Key) {
  try {

    const passThroughStream = generateStream(url)


    // Create an S3 upload command
    const uploadParams = {
      Bucket: s3Bucket,
      Key: s3Key,
      Body: passThroughStream,
      ContentType: 'application/csv', // Set the content type to CSV
    };

    // Upload the data to S3
    const command = new Upload({
      client: s3Client,
      params: uploadParams
    });

     await command.done();


    console.log(`Successfully uploaded JSON data to S3 as CSV at ${s3Bucket}/${s3Key}`);
  } catch (error) {
    console.error('Error streaming and uploading data:', error);
  }
} const { S3Client } = require('@aws-sdk/client-s3');

const s3Client = new S3Client({
  region: 'region',
  credentials: {
    accessKeyId: 'access_key',
    secretAccessKey: 'secret_key',
  },
});
async function streamJsonToS3(url, s3Bucket, s3Key) {
  try {

    const passThroughStream = generateStream(url)


    // Create an S3 upload command
    const uploadParams = {
      Bucket: s3Bucket,
      Key: s3Key,
      Body: passThroughStream,
      ContentType: 'application/csv', // Set the content type to CSV
    };

    // Upload the data to S3
    const command = new Upload({
      client: s3Client,
      params: uploadParams
    });

     await command.done();


    console.log(`Successfully uploaded JSON data to S3 as CSV at ${s3Bucket}/${s3Key}`);
  } catch (error) {
    console.error('Error streaming and uploading data:', error);
  }
} The Passthrough stream generated is then passed on to S3 for upload. Conclusion By streaming data to AWS S3 as a CSV file using Axios, you can  significantly enhance the performance and efficiency of your  application. This method allows for handling large datasets without  overwhelming memory resources, as data is processed in manageable  chunks. Utilizing AsyncParser from the @json2csv/node  package ensures that the data is accurately parsed and formatted, while  using a PassThrough stream facilitates seamless data transfer to  S3. Overall, this approach optimizes resource usage and maintains  consistent performance regardless of the volume of data being processed. AsyncParser @json2csv/node

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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