---
title: "Bypassing Google Apps Script Limits: A Zero-Cost Pipeline for Large Datasets"
author: "Martin Hawksey"
date: 2026-07-20T07:03:27+00:00
modified: 2026-07-20T07:07:43+00:00
canonical_url: https://pulse.appsscript.info/p/2026/07/bypassing-google-apps-script-limits-a-zero-cost-pipeline-for-large-datasets/
markdown_url: https://pulse.appsscript.info/p/2026/07/bypassing-google-apps-script-limits-a-zero-cost-pipeline-for-large-datasets.md
categories:
  - "Drive"
  - "Solutions"
tags:
  - "itnext.io"
  - "Medium"
featured_image: https://pulse.appsscript.info/files/2026/07/huge-json.mmd_-scaled.png
description: "Dmitry Kostyuk has published a detailed guide on bypassing Google Apps Script quotas to process very large JSON datasets. Faced with the strict execution limit and and other restrictions, Dmitry designed a zero-cost pipeline to process over 5 million spreadsheet cells without timing out."
---

# Bypassing Google Apps Script Limits: A Zero-Cost Pipeline for Large Datasets

![Bypassing Google Apps Script Limits: A Zero-Cost Pipeline for Large Datasets](https://pulse.appsscript.info/files/2026/07/huge-json.mmd_-scaled.png)

![](https://pulse.appsscript.info/files/2026/07/huge-json.mmd_-1024x572.png)

> Google Apps Script is easily my favorite runtime, so much so that I wrote a book about it. Yet, being a low-code environment it comes with its quirks, quotas and limitations. In a recent project I needed to process 300 spreadsheets totalling over 5 million cells in over 80,000 rows. I could eithr abandon the platform and spin up a “real” backend, or get creative. Really creative.

Dmitry Kostyuk has published a detailed guide on bypassing Google Apps Script limits to process very large JSON datasets. Rather than completely abandoning Apps Script, Dmitry shares an example zero-cost pipeline to process over 5 million spreadsheet cells without timing out. Here’s the breakdown of his architecture:

1. Extract and compress the master data snapshot locally using Node.js and fflate (Dmitry highlights this could be hosted in Cloud Run).
2. Encode to `Base64` and slice it into text chunks.
3. Automate multipart file uploads to Google Drive using the open-source Google Workspace CLI (`gws`).
4. Use Apps Script as a thin proxy to fetch and return the text strings to the client browser.
5. Reassemble, decompress and parse the chunks in the browser using Alpine.js.

A key component of this workflow  is the efficiency of the open-source Google Workspace CLI (`gws`). Rather than writing custom API upload scripts, it uses the `gws +upload` helper to automatically detect MIME types and execute multipart Drive uploads in a single command.

Crucially, this solution moves heavy computation to the developer machine and the user browser, keeping the Apps Script runtime focused purely on serving data. This relies on `HtmlService` for client-side processing and you can deploy it in standalone web apps or custom sidebars and dialogue boxes for editors like Google Docs and Sheets.

Dmitry also uses his open-source Apps Script Engine Template, which provides a pre-configured Vite, clasp and environment management setup to speed up development and allow you to standardise your build workflow. 

Read Dmitry’s full post for the complete code snippets and a look at the technical trade-offs of this approach. It is a fantastic example of using platform constraints to define a more efficient architecture!

Source: *[How I Shipped 341 MB of JSON Through Google Apps Script — and Lived to Tell the Tale*](https://itnext.io/how-i-shipped-341-mb-of-json-through-google-apps-script-and-lived-to-tell-the-tale-ea364effc8b2?gi=42cd5c77e4ad&source=your_stories_outbox---writer_outbox_published-----------------------------------------)

---

*Published by [Martin Hawksey](https://pulse.appsscript.info/p/author/admin/) on [Pulse](https://pulse.appsscript.info/). Original article: [https://pulse.appsscript.info/p/2026/07/bypassing-google-apps-script-limits-a-zero-cost-pipeline-for-large-datasets/](https://pulse.appsscript.info/p/2026/07/bypassing-google-apps-script-limits-a-zero-cost-pipeline-for-large-datasets/)*