> ## Documentation Index
> Fetch the complete documentation index at: https://braintrust.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# 500 error when iterating over a dataset

export const plans_0 = "Any"

export const deployments_0 = "Any"

export const data_plane_version_0 = undefined

export const use_case_0 = "Use case - Running evaluations that iterate over a Braintrust dataset when a transient backend error causes the fetch to fail with a 500 InternalServerError"

<Note>
  **Applies to:**

  * Plan - {plans_0}
  * Deployment - {deployments_0}
  * {data_plane_version_0}
  * {use_case_0}
</Note>

## Summary

**Issue:** Iterating over a Braintrust dataset with `for row in dataset` fails with a `500 Internal Server Error` from the `/btql` endpoint, raising `braintrust.util.AugmentedHTTPError`.

**Cause:** The default batch size of 1000 rows per paginated BTQL request can hit a transient backend timeout (e.g., S3 connectivity issue on a storage node), and the Python SDK does not automatically retry on `5xx` responses.

**Resolution:** Retry the evaluation — transient errors typically resolve on their own. If the error recurs, use `dataset.fetch(batch_size=500)` to reduce the size of each paginated request.

## Resolution steps

### If the error occurred once

#### Step 1: Retry the evaluation

Re-run the eval without changes. A transient backend timeout is not caused by your code or dataset size and should not persist.

### If the error recurs intermittently

#### Step 1: Replace dataset iteration with `fetch(batch_size=...)`

Replace `for row in dataset` with an explicit `fetch()` call using a smaller batch size. Start with `500`; reduce to `100`–`200` if errors continue.

```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
# Instead of: for row in dataset
dataset_rows = list(dataset.fetch(batch_size=500))
```

#### Step 2: Add retry logic for 5xx errors

The SDK does not retry `5xx` responses automatically. Wrap the fetch in a retry loop to handle transient failures without aborting the eval.

```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
import time
from braintrust.util import AugmentedHTTPError

def fetch_with_retry(dataset, batch_size=500, retries=3, delay=5):
    for attempt in range(retries):
        try:
            return list(dataset.fetch(batch_size=batch_size))
        except AugmentedHTTPError as e:
            if attempt < retries - 1:
                time.sleep(delay)
            else:
                raise
```