Hello,

This issue is quite common when attempting to generate a single invoice with a very large number of lines (e.g. 40,000+). Odoo is not optimized out-of-the-box to handle invoices of such scale due to performance limitations at both the ORM and database level. Here's some insight and recommendations:

⚠️ Why You're Seeing a Timeout

• Odoo tries to compute taxes, totals, and validations for each line item.
• Account move line creation, reconciliation, and posting all happen in one transaction.
• The HTTP timeout or even PostgreSQL query performance can become a bottleneck.
• The browser and backend server memory/CPU usage spikes significantly for such a massive invoice.

🛠 Possible Solutions

✅ 1. Batch Processing

Split the sales orders into multiple invoices, grouped logically (e.g. weekly or by SO tags).

• This reduces load per invoice and helps avoid timeouts.
• You can then group the invoices under a single customer statement or use a custom PDF merge (if needed for reporting).

✅ 2. Asynchronous Processing (Queue Job)

Use the queue_job module (OCA) or similar to run invoice generation as a background job, which:

• Runs outside the HTTP request-response cycle.
• Avoids timeouts and uses worker processes.
• Allows monitoring of job completion.

✅ 3. Optimize Backend Performance

• Increase server limits: request timeout, workers, and memory limits.
• Use PostgreSQL indexing and query tuning.
• If you're using Python loop + ORM, consider using cr.execute() for bulk inserts.

✅ 4. Invoice Consolidation Logic

Instead of one invoice with all lines, build a custom summary logic:

• Group similar lines (e.g. same product/tax) together.
• One line can represent multiple sales via quantity/description like “Service X (covered from SO001 to SO999)”.

✅ 5. Custom PDF Reporting

Generate one invoice per batch, but merge them only in the PDF output as a single report for the client — if that's the main goal.

❌ Things to Avoid

• Don't try to increase timeout arbitrarily — it can cause server instability.
• Avoid running this operation on production without testing on a staging environment first.