How to import CSV files in Convex.dev

How to Import CSV Data into a Convex.dev App Using CSVBox (as of 2026)

Importing spreadsheet data remains a core need for many SaaS products — CRMs, dashboards, inventory systems, and finance tools all benefit from a robust CSV/XLSX import flow. Convex.dev provides a reactive, type-safe backend for real-time apps, but it doesn’t include a built-in CSV upload + parsing + mapping workflow.

This guide shows a production-ready integration pattern using CSVBox to handle the file, parsing, mapping, validation, and user feedback, and Convex to persist validated rows. The flow follows the simple file → map → validate → submit pattern that scales in production.

Who this is for

• Full-stack engineers and SaaS teams using Convex.dev for their backend
• Product and engineering teams building admin tooling or bulk onboarding flows
• Founders shipping MVPs who want a fast, reliable CSV import experience

Quick summary

• Use CSVBox to collect, parse, and validate spreadsheet uploads from end users.
• Embed CSVBox’s uploader widget in your frontend UI for mapping and preview.
• Receive CSVBox’s parsed payload via a webhook and forward validated rows to a Convex mutation.
• Keep import UX, errors, and retry logic on CSVBox; Convex receives clean JSON records.

Why use an external CSV importer with Convex.dev?

Convex excels at reactive database logic and type safety, but an external importer like CSVBox covers these missing pieces:

• File upload handling for large CSV/XLSX payloads
• Column mapping UI so end users can match their spreadsheet to your schema
• Row-level validation and per-row feedback
• Progress indicators, previews, and error reports

Outsourcing CSV parsing and UX to CSVBox lets your team focus on business logic inside Convex while keeping imports safe and auditable.

Integration overview (file → map → validate → submit)

1. Create an importer in CSVBox and configure expected columns/validation.
2. Embed the CSVBox uploader widget in your frontend so users can upload and map columns.
3. Implement a Convex mutation that accepts validated records and inserts them into your Convex DB.
4. Create a small webhook (serverless) that receives CSVBox’s POST and calls the Convex mutation.

Step-by-step: add CSV imports to a Convex.dev app

1) Create a CSVBox importer

In the CSVBox Dashboard:

• Go to Importers → Create Importer
• Give it a name (e.g., “Contact Upload”)
• Declare expected columns (Name, Email, Company) and mark required fields
• Configure validation rules and error messages
• Set the webhook URL to the serverless endpoint you’ll deploy in step 4

Copy the Importer ID for use when initializing the widget or API calls. See CSVBox docs for details: https://help.csvbox.io/getting-started/1.-create-importer

2) Embed the CSVBox uploader widget in your frontend

CSVBox provides a lightweight client script and widget you can open from any app (React, Next.js, plain HTML/JS). The widget handles file selection, column mapping, validation, and shows per-row errors before submit.

Example React usage:

import { useEffect } from 'react';

export default function CsvImportWidget() {
  useEffect(() => {
    const script = document.createElement('script');
    script.src = 'https://js.csvbox.io/0.8/csvbox.js';
    script.async = true;
    document.body.appendChild(script);
  }, []);

  const openUploader = () => {
    if (window.CSVBox) {
      window.CSVBox.importer('your-importer-id', {
        user: {
          user_id: '123',
          name: 'Jane Developer',
          email: 'jane@developer.com'
        }
      });
    }
  };

  return <button onClick={openUploader}>Upload CSV</button>;
}

Why use the widget?

• Users get a mapping UI and preview, reducing backend errors
• CSVBox validates types and required fields before submission
• The widget supports CSV and Excel files and scales for large uploads

3) Create a Convex mutation to persist validated rows

In Convex, write a mutation that accepts the array of validated records and inserts them into your table. Keep the schema strict so you fail fast on unexpected shapes.

Example mutation file convex/contacts.ts:

import { mutation } from './_generated/server';
import { v } from 'convex/values';

export const importContacts = mutation({
  args: {
    records: v.array(
      v.object({
        Name: v.string(),
        Email: v.string(),
        Company: v.optional(v.string())
      })
    )
  },
  handler: async (ctx, args) => {
    for (const row of args.records) {
      await ctx.db.insert('contacts', {
        name: row.Name,
        email: row.Email,
        company: row.Company || ''
      });
    }
  }
});

Run your Convex dev server or deploy your Convex functions: npx convex dev

This mutation expects CSVBox to send already-validated JSON records mapped to your column names.

4) Build a webhook bridge (serverless) to connect CSVBox → Convex

CSVBox sends parsed, validated rows to the webhook URL you configured. Convex projects don’t host arbitrary HTTP routes, so deploy a small serverless function (Vercel, Netlify, AWS Lambda) that receives CSVBox’s POST and forwards the data to your Convex mutation.

Example Next.js API route (pages/api/csvbox-webhook.ts):

import { ConvexHttpClient } from 'convex/browser';
import { api as convexApi } from '../../convex/_generated/api';

const convex = new ConvexHttpClient('https://<your-project>.convex.cloud');

export default async function handler(req, res) {
  if (req.method !== 'POST') {
    return res.status(405).end('Method Not Allowed');
  }

  const payload = req.body;

  if (!payload?.data) {
    return res.status(400).json({ error: 'Missing CSV data' });
  }

  // Forward validated rows to the Convex mutation
  await convex.mutation(convexApi.contacts.importContacts, {
    records: payload.data
  });

  return res.status(200).json({ status: 'Data saved' });
}

Deployment notes

• Deploy the serverless function to your host (e.g., Vercel)
• Use the full Convex deployment URL for ConvexHttpClient
• Update your CSVBox importer webhook to point at this endpoint
• Monitor serverless logs for errors and CSVBox import logs for per-row failures

Common troubleshooting (quick reference)

Where to check:

• CSVBox import logs and preview/errors in the dashboard
• Serverless function logs (Vercel, Netlify)
• Convex console or server logs for mutation errors

Why CSVBox + Convex is a practical choice in 2026

CSVBox offloads the heavy UX and validation work: file handling, mapping UI, row-level validation, and retry logic. That lets your Convex backend receive clean JSON that your mutation logic can persist reliably.

Benefits:

• File handling and parsing for both CSV and Excel formats
• UX for column mapping and preview reduces data-cleaning work
• Row-level validation and error messaging before backend insertion
• Developer-friendly JSON payloads to forward into Convex

For teams building import flows in 2026, this split of responsibilities (CSVBox for import UX + validation, Convex for business logic and persistence) speeds development and reduces production incidents.

Real-world scenarios and next steps

Common use cases:

• Bulk contact onboarding for a CRM
• Product catalog imports for e-commerce
• Time logs and financial report ingestion

After you implement the basic flow, consider:

• Creating separate importers for different object types (Contacts, Products)
• Adding import audit logs or notification hooks (Slack/email) when imports complete
• Running deduplication or enrichment transforms (serverless or Convex jobs) before final insertion

Resources

• CSVBox docs and support: https://help.csvbox.io
• Convex docs: https://docs.convex.dev

By combining CSVBox’s import UX and validation with Convex’s reactive backend, you can ship a reliable CSV import flow quickly and maintain clear, auditable data pipelines for your SaaS in 2026.