Best architecture for processing a 70k+ row spreadsheet with an LLM in Langflow?

[ary3tentos]

Hi everyone,

I need to process a spreadsheet with over 70,000 rows, running an LLM on each row (e.g., classification/extraction on a text column), and I'd like advice on the best way to architect this inside Langflow.

I've looked at Batch Run and the Loop component, but I'm unsure how they behave at this scale.

My questions:

1. Is Batch Run the recommended approach at this scale, or is there a better pattern?
2. What's the best way to handle checkpointing/resume so a failure at row 40,000 doesn't lose all progress?
3. Does Batch Run handle concurrency/rate limits well, or should I control that externally?
4. Any recommendations for running long jobs like this outside the playground (API, background execution, deployment setup)?
5. Any tips for managing Azure OpenAI token/rate limits within Langflow (retries, throttling, batching strategies)?

Thanks in advance!

[Ganesh-403]

@ary3tentos For a workload of 70,000+ independent rows, I'd recommend thinking of Langflow as the processing pipeline, not the job scheduler.

Each row is independent, so you want the workflow to be idempotent and restartable, with progress stored outside of Langflow. Langflow is well-suited for exposing a flow as an API or reusable workflow, but I wouldn't rely on a single long-running playground session for a job of this size. Langflow is designed to let you deploy flows as APIs or MCP servers, making external orchestration a natural fit. :contentReference[oaicite:0]{index=0}

Here's how I'd approach each of your questions:

1. Is Batch Run the right approach?

For tens of thousands of rows, I'd lean against one massive Batch Run.

From what I can tell, I couldn't find documentation stating that Batch Run provides durable checkpointing or automatic recovery for very large offline jobs. :contentReference[oaicite:1]{index=1}

Instead, I'd split the work into manageable chunks, for example:

• 500–2,000 rows per batch
• Each batch invokes the same Langflow flow
• Persist results immediately after each row (or small group of rows)

That way:

• failures only affect one batch
• retries are cheap
• progress is easy to monitor

---

2. Checkpointing / resume

I would not depend on in-memory state.

Instead, maintain a status column or table, for example:

id	status	result	processed_at
1	completed	...	...
2	completed	...	...
3	pending

Then your worker simply processes:

WHERE status = 'pending'

If the process crashes at row 40,000, restarting just continues from the remaining rows.

This approach is simple, durable, and independent of Langflow itself.

---

3. Concurrency & rate limits

I would control concurrency outside Langflow.

From what I could find, Langflow focuses on flow execution and deployment rather than acting as a distributed batch scheduler with built-in rate-limit management. :contentReference[oaicite:2]{index=2}

For Azure OpenAI especially, you'll typically want to:

• limit concurrent requests
• retry on HTTP 429
• use exponential backoff
• add jitter

For example:

Queue
   ↓
4 Workers
   ↓
Langflow API
   ↓
Azure OpenAI

Adjust the worker count until you stay comfortably under your TPM/RPM quotas.

---

4. Running long jobs

For a job this size I'd avoid the playground.

A more production-oriented setup would be:

Spreadsheet
      │
      ▼
Chunk rows (1000 each)
      │
      ▼
Worker / Python script
      │
      ▼
Langflow API
      │
      ▼
Azure OpenAI
      │
      ▼
Save results immediately

Benefits:

• restartable
• horizontally scalable
• easy logging
• easier monitoring
• works well in Docker/Kubernetes if needed

---

5. Azure OpenAI tips

A few practical suggestions:

• Retry only transient failures (429/5xx)
• Respect the Retry-After header when returned
• Keep prompts concise to reduce TPM consumption
• Save outputs immediately after each successful row
• If rows are very short, consider processing several rows in one prompt only if they are independent and you can reliably parse the structured response

Whether batching multiple rows into one prompt improves throughput depends on your prompt size, model context window, and Azure TPM limits.

---

Suggested architecture

70,000-row spreadsheet
          │
          ▼
 Split into chunks (500–2,000 rows)
          │
          ▼
 External worker(s)
          │
          ▼
 Langflow Flow API
          │
          ▼
 Azure OpenAI
          │
          ▼
 Persist each result immediately
          │
          ▼
 Resume by querying remaining "pending" rows

This keeps Langflow focused on what it does best—executing the AI workflow—while letting a dedicated worker handle scheduling, retries, checkpointing, and recovery.

One question that would help narrow the recommendation:

• Are you processing a CSV/Excel file once, or is this part of a recurring pipeline (e.g., new spreadsheets arriving daily)? The answer influences whether a simple Python worker is sufficient or whether it's worth introducing a queue (Celery, Azure Queue Storage, RabbitMQ, etc.) for long-term scalability.

If this solves your problem, feel free to mark it as the accepted answer so others can find it easily.