Efficient Data Transfer for API Communication Beyond 5 MB

Posted By :Razat Vir Singh |30th June 2023

Efficient data transfer is essential for optimizing web applications and ensuring smooth user experiences. However, transmitting large amounts of data exceeding 5 MB can be challenging due to API limitations and network constraints. In this blog post, we will explore how to overcome this challenge by utilizing pako.js, a powerful compression library that facilitates seamless data transfer while reducing network bandwidth consumption.


Understanding pako.js:

Pako.js is a JavaScript library that provides high-performance, in-memory data compression and decompression capabilities. It supports various compression algorithms such as Deflate, Gzip, and Zlib, giving developers the flexibility to choose the most suitable method for their specific use case. By leveraging pako.js, developers can significantly reduce the size of data payloads, making them more manageable for API transfers, particularly when the size exceeds the common 5 MB limit.


Step 1: Installing pako.js:
To get started, you need to include the pako.js library in your project. You can install it using npm or include it directly from a CDN. 

Step 2: Compressing Data:
Once pako.js is integrated into your project, you can compress the data you wish to transfer via the API. Let's assume we have a JSON object named `data` that we want to compress.

const data = { /* JSON data */ };

const compressedData = pako.deflate(JSON.stringify(data));

In the above code, we stringify the JSON data and pass it to the `deflate` method of pako.js, which compresses the data using the Deflate algorithm. The resulting `compressedData` variable will contain the compressed data.


Step 3: Sending the Compressed Data via API:
After compressing the data, you can send it via the API using a suitable HTTP client library such as Axios or the Fetch API. Here's an example using Axios:

axios.post('https://api.example.com/data', compressedData, {

headers: { 'Content-Encoding': 'deflate', 'Content-Type': 'application/json', },


In the above code, we use the `post` method of Axios to send a POST request to the API endpoint. The `compressedData` variable containing the compressed data is passed as the request body. The `'Content-Encoding': 'deflate'` header indicates that the data is compressed using the Deflate algorithm.


Step 4: Decompressing Data on the Server:
On the server-side, make the necessary adjustments to handle the compressed data. Most modern web frameworks provide built-in functionality to handle compressed payloads automatically. Here's an example using Node.js and Express:

const express = require('express');

const app = express();

const pako = require('pako');

app.post('/data', (req, res) => {

const compressedData = req.body;

// Assuming the compressed data is passed in the request body

const decompressedData = pako.inflate(compressedData, { to: 'string' });

// Process the decompressedData as required


In the above code, we handle the POST request to the `/data` endpoint and decompress the data using `pako.inflate`. The `{ to: 'string' }` option ensures that the decompressed data is returned as a string.


By utilizing pako.js, developers can efficiently compress data, reducing its size for API transfers beyond the typical 5 MB limit. This compression technique enhances web application performance and user experience. By following the steps outlined in this blog post, you can seamlessly integrate pako.js into your project, leveraging its powerful compression capabilities for efficient data transfer in API communications.

About Author

Razat Vir Singh

Razatvir Singh is an experienced MEAN stack developer with a diverse skill set and practical experience in using the latest tools and frameworks. His expertise includes Angular, Node.js, HTML, CSS, JavaScript, jQuery, and databases such as MongoDB and Opensearch. He has worked on various projects, such as E-Cart, a fully functional e-commerce website, MS-Excel Clone Project, a project where data is fetched from a popular sports website called "CricInfo.com" using Node.js packages and JavaScript, Konfer Project and he has also worked on other projects like Automation and Prediction of Stock Prices using Machine Learning. With his diverse skill set and expertise in the latest technologies, Razatvir is well-equipped to take on any MEAN stack development project and deliver high-quality solutions that meet the client's requirements.

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