⏳[ROADMAP] Bulk CSV to Database Example

[nexconnectio]

Introduction

Bulk database updates are probably one of the most common real-world programming tasks. More specifically, importing and updating thousands of records from CSV files into your database would be a practical example - you'll need to handle memory efficiently, show progress to users, and manage database connections properly.

Why This Matters

Many Python devs spend time wrestling with raw threads or heavy frameworks like Celery and Redis, even when all they really need is to import a CSV with background progress. This upcoming example will show a middle ground: a simple, robust approach powered by PynneX, letting you keep your code straightforward while still getting concurrency “for free.”

The Example

The goal is to show that “no locks, no race conditions” approach in a scenario that many Python developers face on a regular basis: large CSV import, chunk processing, and real-time progress updates.

You'll learn how to:

• Set up SQLAlchemy,
• Generate or load large CSV files,
• Stream them into the database via PynneX Workers,
• And display a progress bar in either the console or a small web “Admin UI.”

Features

1. Chunk-Based Import

   • Reads a CSV file in configurable chunk sizes (e.g., 1,000 rows at a time) to keep memory usage low.
   • Commits each chunk to the database, allowing you to see partial results even before the entire file finishes.

2. Real-Time Progress Tracking

   • Uses PynneX emitters to send progress updates (like “30% done”) from the worker thread.
   • This ties cleanly into either a console or web-based dashboard (FastAPI + Socket.IO, for example).

3. Safe and Declarative Thread Model

   • Leverages @with_worker to handle all concurrency details behind the scenes.
   • No need to manually manage locks or call join(); everything is queued in an event-driven manner.

4. Adaptable to Multiple DBs

   • The example will use SQLAlchemy for easy switching between SQLite, PostgreSQL, MySQL, etc.
   • Demonstrates how to handle foreign keys, indices, or other custom logic via “chunk handlers.”

5. Error Handling & (Optional) Cancelation

   • Shows how to gracefully stop a long-running import if errors occur or the user manually cancels.
   • Rolls partial updates into separate commits so already-processed rows remain valid.

Example Flow

1. User selects a CSV file and chunk size
2. Worker thread starts reading lines in small batches.
3. After each batch is processed/committed, an emitter fires a “progress update” signal.
4. Main thread (or a web server) listens to the signal and updates a progress bar.
5. On completion, the worker emits a “finished” signal so the UI can show a success message.

A snippet of what the final code might look like:

@with_worker
class CSVImportWorker:
    @nx_emitter
    def progress_updated(self, ratio: float):
        pass

    @nx_emitter
    def import_finished(self):
        pass

    def start_import(self, csv_file):
        self.start()
        self.queue_task(lambda: self.run_import(csv_file))

    def run_import(self, csv_file):
        # Read lines in chunks, commit to DB
        for chunk_no, rows in enumerate(self.read_csv_in_chunks(csv_file)):
            self.process_chunk(rows)
            self.progress_updated.emit(self.processed_count / self.total_count)

        self.import_finished.emit()

Questions for You

While I'm finalizing the example, I'd love to hear your thoughts on a few key decisions:

1. How do you usually handle invalid CSV rows? Skip or fail fast?
2. Would a "resume from last position" feature be useful?

When?

The complete code will be available next week! Follow this discussion if you're interested.