> ## 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.

# Large experiment downloads timeout in UI

export const plans_0 = "Any"

export const deployments_0 = "Braintrust-hosted"

export const data_plane_version_0 = undefined

export const use_case_0 = "Use case - Exporting experiment data via UI with large datasets (>1000 rows)"

<Note>
  **Applies to:**

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

## Summary

**Issue:** Downloading JSON exports for large experiments from the Braintrust UI fails with a `malloc of size 1073741824 failed` error or times out during the download process.

**Cause:** The UI attempts to load the entire dataset into memory at once, causing memory allocation failures for experiments with thousands of records.

**Resolution:** Use the Braintrust API or SDK to paginate and export large experiment data programmatically.

## Resolution Steps

### Option 1: Export using TypeScript/JavaScript

#### Step 1: Create export script

Create a script that paginates through experiment data in chunks of 200 records.

```javascript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
import Braintrust from "braintrust";
import fs from "fs";

const BRAINTRUST_API_KEY = "your-api-key";
const EXPERIMENT_ID = "your-experiment-id";
const CHUNK_SIZE = 200;

const client = new Braintrust.APIClient({ apiKey: BRAINTRUST_API_KEY });

async function exportExperiment() {
  let offset = 0;
  let allData = [];

  while (true) {
    const response = await client.experiment(EXPERIMENT_ID).fetch({
      limit: CHUNK_SIZE,
      offset: offset
    });

    if (response.length === 0) break;
    allData.push(...response);
    offset += CHUNK_SIZE;
  }

  fs.writeFileSync("experiment_data.json", JSON.stringify(allData, null, 2));
}

exportExperiment();

```

#### Step 2: Run the script

Execute the script to generate `experiment_data.json` in your current directory.

### Option 2: Export using Python

#### Step 1: Create export script

Use the Python SDK to fetch and export experiment data with pagination.

```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
import braintrust
import json

BRAINTRUST_API_KEY = "your-api-key"
EXPERIMENT_ID = "your-experiment-id"
CHUNK_SIZE = 200

client = braintrust.init(api_key=BRAINTRUST_API_KEY)

all_data = []
offset = 0

while True:
    response = client.experiment(EXPERIMENT_ID).fetch(
        limit=CHUNK_SIZE,
        offset=offset
    )

    if not response:
        break

    all_data.extend(response)
    offset += CHUNK_SIZE

with open("experiment_data.json", "w") as f:
    json.dump(all_data, f, indent=2)

```

#### Step 2: Run the script

Execute the script to generate `experiment_data.json` in your current directory.

### Option 3: Export to CSV format

#### Step 1: Modify the script to write CSV

Replace JSON output with CSV writer for spreadsheet compatibility.

```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
import braintrust
import csv

BRAINTRUST_API_KEY = "your-api-key"
EXPERIMENT_ID = "your-experiment-id"

client = braintrust.init(api_key=BRAINTRUST_API_KEY)

with open("experiment_data.csv", "w", newline="") as f:
    writer = None
    offset = 0

    while True:
        response = client.experiment(EXPERIMENT_ID).fetch(limit=200, offset=offset)
        if not response:
            break

        if writer is None:
            writer = csv.DictWriter(f, fieldnames=response[0].keys())
            writer.writeheader()

        writer.writerows(response)
        offset += 200

```

## Additional Notes

A feature request exists to improve UI performance for large experiment downloads, including async export with email notification when ready. Until implemented, API-based exports are the recommended approach for datasets with more than 1000 records.