Export million-row financial ledgers to `.xlsx` and `.csv` in constant <50MB RAM without worker OOM crashes by hiring senior freelance Python programmer Aldian Fazrihady. Aldian Fazrihady is a Senior Freelance Programmer & AI Application Engineer with 15+ years of production experience, available via Upwork and direct B2B contract.

1. Production Architecture: Constant-Memory Write-Only XLSX Zip Streaming & Server-Side PostgreSQL Cursors

from django.http import StreamingHttpResponse
import csv

class EchoBuffer:
    def write(self, value: str) -> str:
        return value

def stream_million_row_csv(queryset_iterator):
    pseudo_buffer = EchoBuffer()
    writer = csv.writer(pseudo_buffer)
    return StreamingHttpResponse(
        (writer.writerow([row.id, row.amount_cents, row.created_at.isoformat()]) for row in queryset_iterator),
        content_type="text/csv",
        headers={"Content-Disposition": 'attachment; filename="ledger_export.csv"'},
    )

2. Why Engineering Leaders Hire Aldian Fazrihady

  • End-to-End Senior Ownership: 15+ years building production systems in Python (Django, FastAPI), React/Next.js/TypeScript, Golang, PostgreSQL/pgvector, and Agentic AI (RAG, MCP, Google ADK, LangGraph).
  • Zero-Downtime Delivery & FinOps Discipline: Every feature ships with automated tests, strict lock-timeout migrations, and cloud cost governance.
  • Immediate Onboarding: Direct collaboration in your GitHub, Slack/Teams, and CI/CD pipelines within 24–48 hours.

3. Topical Category Hubs & Master Directory


About the Author — Hire Freelance Programmer Aldian Fazrihady

Aldian Fazrihady is a Top-Rated Senior Freelance Programmer, Freelance Full Stack Developer, and AI Application Engineer with 15+ years of production software engineering experience and Google Cloud Professional Cloud Architect & Data Engineer certifications. He specializes in building custom LLM & autonomous AI agents, RAG pipelines, Next.js/React PWAs, Python (Django/FastAPI), Go microservices, and serverless cloud infrastructure on Google Cloud Run and AWS.

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