Parallel PDF Generation

from ironpdf import *
import concurrent.futures
import uuid

# Instantiate Renderer
renderer = ChromePdfRenderer()

htmlStrings = ["<h1>Html#1</h1>", "<h1>Html#2</h1>", "<h1>Html#3</h1>"]

with concurrent.futures.ThreadPoolExecutor() as executor:
    futures = []
    for html in htmlStrings:
        futures.append(executor.submit(renderer.RenderHtmlAsPdf, html))

    # do something with each pdfDocument
    for future in concurrent.futures.as_completed(futures):
        pdf = future.result()
        pdf.SaveAs(f"{uuid.uuid4()}.pdf")
        # do something with each pdf

pip install ironpdf

pip install ironpdf

Parallel PDF Generation

IronPDF for Python supports multi-threaded PDF generation. A single ChromePdfRenderer instance is thread-safe and can be shared across concurrent tasks to render multiple PDFs in parallel.

Getting Started

Use Python's built-in concurrent.futures.ThreadPoolExecutor to submit render calls as parallel tasks. Each submitted task calls renderer.RenderHtmlAsPdf with a different HTML string, and the results are collected as they complete.

Understanding the Code

ChromePdfRenderer: A single instance can be reused across threads. IronPDF manages the underlying Chrome rendering engine safely under concurrent load.
ThreadPoolExecutor: Python's standard thread pool implementation. executor.submit schedules each render call as a non-blocking future.
concurrent.futures.as_completed(futures): Yields results as each thread finishes, regardless of submission order.
uuid.uuid4(): Generates a unique filename for each output PDF to avoid collisions when saving files concurrently.

Performance Considerations

Parallel rendering is most effective for batch jobs where many independent HTML documents need to be converted at the same time - for example, generating personalized reports or invoices for many recipients. The number of concurrent threads should be tuned based on the available CPU and memory on the host machine.

Learn to convert HTML to PDF with IronPDF for Python!