# Botify Agent: Tables Query Export

**ID:** `tables_query_export`

Export BigQuery query results to CSV or Parquet and upload to GCS for download

Export query results to CSV or Parquet format with compression (gz or zstd).
    The query must be a single read-only statement: use tables_query to create
    or remove catalog tables.
    Always prefix tables with "catalog." (ex: catalog.crawl_pages_20251201).
    Give the user web_url. It works for users logged in to Botify with access to
    the project. To fetch the file from a program, use temporary_url. It needs no
    login and expires 15 minutes after the export.
    

## Authentication

All endpoints require a **Bearer token** in the `Authorization` header.

```
Authorization: Bearer <your-token>
```

## Calling this agent

This agent supports the following processing modes:

| Mode | Type | Description |
|------|------|-------------|
| **Process** | Synchronous | Single-item processing. Best for real-time requests with immediate response. |
| **Batch Process** | Synchronous | Process multiple items in a single request for efficiency. |
| **Async Process** | Asynchronous | Single-item processing for long-running tasks that exceed timeout limits. |
| **Async Batch Process** | Asynchronous | Large-scale batch jobs with background processing. |

# Synchronous Processing

Synchronous calls block until the result is ready. Use these for quick operations where you need immediate results.

## Single Item Processing

Process a single input and receive the result immediately.

```
POST https://agents.botify.com/{org}/{project}/tables_query_export/process
```

### Request body

```json
{
  "item": {
    "query": "<query>"
  }
}
```

### Response

Returns a single processed item (HTTP 200).

### cURL example

```bash
curl -X POST "https://agents.botify.com/{org}/{project}/tables_query_export/process" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "item": {
    "query": "<query>"
  }
}'
```

## Batch Processing

Process multiple inputs in a single synchronous request. More efficient than making individual calls. Items are processed concurrently on the server.

```
POST https://agents.botify.com/{org}/{project}/tables_query_export/batch_process
```

### Request body

```json
{
  "items": [
    {
      "query": "<query>"
    }
  ]
}
```

### Response

Returns an array of results. Each element is either a successful processed item (`"status": "success"`) or an error (`"status": "error"`).

### cURL example

```bash
curl -X POST "https://agents.botify.com/{org}/{project}/tables_query_export/batch_process" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "items": [
    {
      "query": "<query>"
    }
  ]
}'
```

### When to use synchronous calls

- Processing completes within timeout limits (typically 30 seconds)
- You need immediate results for user-facing features
- Small to medium batch sizes (up to ~100 items)

# Asynchronous Processing

Asynchronous calls return immediately with a batch ID. You then poll for results using the `async_batches/` endpoints.

### Async processing flow

1. **Submit job** -- `POST` to `async_process` or `async_batch_process`
2. **Receive batch ID** -- response contains `batch_id`
3. **Poll status** -- `HEAD` request to check readiness (lightweight, no body)
4. **Get results** -- `GET` request to retrieve processed results

## Async Single Item

Submit a single item for background processing. Use the `/single` endpoint to retrieve the result.

```
POST https://agents.botify.com/{org}/{project}/tables_query_export/async_process
```

### Request body

```json
{
  "item": {
    "query": "<query>"
  }
}
```

### cURL example

```bash
# Step 1: Submit the job
curl -X POST "https://agents.botify.com/{org}/{project}/tables_query_export/async_process" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "item": {
    "query": "<query>"
  }
}'

# Response: { "batch_id": "abc123" }

# Step 2: Check if batch is ready (HEAD request -- lightweight check)
curl -I -X HEAD "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/abc123" \
  -H "Authorization: Bearer $TOKEN"
# Returns 200 if ready, 204 if still processing

# Step 3: Get the result (for single-item async)
curl -X GET "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/abc123/single" \
  -H "Authorization: Bearer $TOKEN"
# Returns 200 with result, 202 if still processing, 404 if batch not found
```

## Async Batch Processing

Submit multiple items for background processing. The request body uses **JSONL** (newline-delimited JSON) format.

```
POST https://agents.botify.com/{org}/{project}/tables_query_export/async_batch_process
```

### Request body (JSONL, `Content-Type: text/plain`)

The first line contains `config` and `batch_config`. Each subsequent line is an item with a unique `id`.

```
{"config": {}, "batch_config": {}}
{"id": "item_1", "item": {"query": "<query>"}}
```

### Response

Returns a JSON object with `batch_id`, `status`, and a `Location` header.

### cURL example

```bash
# Step 1: Submit the batch job
curl -X POST "https://agents.botify.com/{org}/{project}/tables_query_export/async_batch_process" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: text/plain" \
  -d '{"config": {}, "batch_config": {}}\n{"id": "item_1", "item": {"query": "<query>"}}'

# Response: { "batch_id": "xyz789" }

# Step 2: Check if batch is ready (HEAD request)
curl -I -X HEAD "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/xyz789" \
  -H "Authorization: Bearer $TOKEN"
# Returns 200 if ready, 204 if still processing

# Step 3: Get all results
curl -X GET "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/xyz789" \
  -H "Authorization: Bearer $TOKEN"
```

## Checking Batch Status

Use a lightweight `HEAD` request to check if your batch is ready without transferring data.

```
HEAD https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/{batch_id}
```

```bash
curl -I -X HEAD "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/{batch_id}" \
  -H "Authorization: Bearer $TOKEN"

# Response headers indicate status:
# HTTP/1.1 200 OK        -> Batch is complete, results ready
# HTTP/1.1 204 No Content -> Still processing
# HTTP/1.1 404 Not Found -> Batch does not exist
```

## Retrieving Results

Once the batch is ready, fetch results using the appropriate endpoint.

```bash
# For async_process (single item) -- use /single endpoint
curl -X GET "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/{batch_id}/single" \
  -H "Authorization: Bearer $TOKEN"

# For async_batch_process (multiple items) -- use base endpoint
curl -X GET "https://agents.botify.com/{org}/{project}/tables_query_export/async_batches/{batch_id}" \
  -H "Authorization: Bearer $TOKEN"
# Returns streaming JSONL where each line is a processed item or error
```

### Async response codes

| Code | Endpoint | Meaning | Action |
|------|----------|---------|--------|
| 200 | `HEAD` / `GET` | Batch complete, results ready | Read results from response body |
| 204 | `HEAD` | Still processing | Continue polling |
| 202 | `GET /single` | Still processing | Continue polling |
| 400 | `GET /single` | All items failed (client error) | Check error in response body |
| 404 | `HEAD` / `GET` | Batch does not exist | Verify `batch_id` is correct |

> **HEAD vs GET:** Use `HEAD` requests for lightweight status checks (no response body). Use `GET` only when ready to retrieve results to minimize bandwidth.

### When to use asynchronous calls

- Processing takes longer than 30 seconds
- Large batch sizes (100+ items)
- Background processing where immediate results aren't required
- Integration with job queues or workflow systems

## Python Example

A complete Python example showing both synchronous and asynchronous patterns.

```python
import requests
import time

API_TOKEN = "your_api_token"
BASE_URL = "https://agents.botify.com/{organization}/{project}"
AGENT = "tables_query_export"

headers = {
    "Authorization": f"Bearer {API_TOKEN}",
    "Content-Type": "application/json"
}

single_payload = {
  "item": {
    "query": "<query>"
  }
}


def process_sync(payload: dict) -> dict:
    """Synchronous single-item processing."""
    response = requests.post(
        f"{BASE_URL}/{AGENT}/process",
        headers=headers,
        json=payload
    )
    response.raise_for_status()
    return response.json()


def process_async(
    payload: dict,
    poll_interval: int = 5,
    max_retries: int = 60
) -> dict:
    """Asynchronous single-item processing with polling."""
    # Submit job
    response = requests.post(
        f"{BASE_URL}/{AGENT}/async_process",
        headers=headers,
        json=payload
    )
    batch_id = response.json()["batch_id"]

    # Poll for results using HEAD (lightweight check)
    for _ in range(max_retries):
        status = requests.head(
            f"{BASE_URL}/{AGENT}/async_batches/{batch_id}",
            headers=headers
        )

        if status.status_code == 200:
            result = requests.get(
                f"{BASE_URL}/{AGENT}/async_batches/{batch_id}/single",
                headers=headers
            )
            return result.json()
        elif status.status_code == 204:
            time.sleep(poll_interval)
        else:
            raise Exception(f"Unexpected status: {status.status_code}")

    raise Exception("Max retries exceeded")


def process_async_batch(
    items: list,
    config: dict | None = None,
    poll_interval: int = 5
) -> dict:
    """Asynchronous batch processing with polling."""
    import json as _json

    # Build JSONL payload: first line is config, subsequent lines are items
    lines = [_json.dumps({"config": config or {}, "batch_config": {}})]
    for i, item in enumerate(items):
        lines.append(_json.dumps({"id": f"item_{i}", "item": item}))
    body = "\n".join(lines)

    # Submit batch (JSONL, text/plain)
    response = requests.post(
        f"{BASE_URL}/{AGENT}/async_batch_process",
        headers={**headers, "Content-Type": "text/plain"},
        data=body
    )
    batch_id = response.json()["batch_id"]

    # Poll until ready
    while True:
        status = requests.head(
            f"{BASE_URL}/{AGENT}/async_batches/{batch_id}",
            headers=headers
        )

        if status.status_code == 200:
            results = requests.get(
                f"{BASE_URL}/{AGENT}/async_batches/{batch_id}",
                headers=headers
            )
            return results.json()

        time.sleep(poll_interval)


# Example usage
# result = process_sync(single_payload)
# result = process_async(single_payload)
# results = process_async_batch([{"input": "item1"}, {"input": "item2"}])
```

## Billing

Fixed cost per item requested, plus the data the query scans. Analytical queries are billed on how much data they read, not on how many rows come back, so a narrow filter over a large table can still scan the whole of it. Restricting the columns and the date range you ask for is what keeps this down. A query run in your organization's own cloud project (see bigquery_billing) has its scan billed by your cloud provider instead, and costs a flat fee per query here.

These usage SKUs can be charged on a call.

| SKU | Credits | Description |
| --- | ------- | ----------- |
| Tool call | 1 per request | Charged once per successful item, on top of any usage below. |
| Data scanned | 6,250 per TiB scanned | Analytical queries are billed on the volume of data the query reads, not on the rows it returns: a narrow filter over a large table can still scan the whole table. |
| Query billed to your cloud project | 4 per query | A flat fee per query run in your organization's own cloud project, in place of the charge on the data it scans. |


## Schemas

### Item

```json
{
  "type": "object",
  "properties": {
    "query": {
      "description": "BigQuery SQL query to execute (a single SELECT statement)",
      "title": "Query",
      "type": "string"
    },
    "format": {
      "default": "csv",
      "description": "Export format: csv or parquet",
      "enum": [
        "csv",
        "parquet"
      ],
      "title": "Format",
      "type": "string"
    },
    "compression": {
      "default": "gz",
      "description": "Compression type: gz or zstd",
      "enum": [
        "gz",
        "zstd"
      ],
      "title": "Compression",
      "type": "string"
    },
    "max_rows": {
      "anyOf": [
        {
          "minimum": 1,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Maximum number of rows to export (no limit if null)",
      "title": "Max Rows"
    }
  },
  "required": [
    "query"
  ]
}
```

### Config

```json
{
  "type": "object",
  "properties": {
    "bigquery_billing": {
      "default": "auto",
      "description": "Who pays BigQuery for the query. 'botify': Botify runs it, and the data it scans is charged in credits. 'organization': it runs in your organization's own GCP project, configured by Botify, which Google bills directly; a flat fee per query is charged in credits instead of the scan. 'auto': your organization's project when one is configured, else Botify.",
      "enum": [
        "auto",
        "botify",
        "organization"
      ],
      "title": "Bigquery Billing",
      "type": "string"
    }
  },
  "required": []
}
```

### Response

```json
{
  "properties": {
    "download_url": {
      "description": "Original storage URL for the file",
      "title": "Download Url",
      "type": "string"
    },
    "web_url": {
      "description": "Download URL served by the Botify API. Open it in a browser while logged in to Botify with access to the project.",
      "title": "Web Url",
      "type": "string"
    },
    "temporary_url": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Download URL that needs no login, for a program that fetches the file. It expires 15 minutes after the export. Null when the URL could not be signed.",
      "title": "Temporary Url"
    },
    "format": {
      "description": "Export format used",
      "title": "Format",
      "type": "string"
    },
    "compression": {
      "description": "Compression type used",
      "title": "Compression",
      "type": "string"
    },
    "row_count": {
      "description": "Number of rows exported",
      "title": "Row Count",
      "type": "integer"
    }
  },
  "required": [
    "download_url",
    "web_url",
    "format",
    "compression",
    "row_count"
  ],
  "title": "TablesQueryExportProcessedItem",
  "type": "object"
}
```

## Best Practices

**Use exponential backoff for polling.** Start with short intervals (1-2 seconds) and increase the delay between polls to reduce API load.

**Set reasonable timeouts.** For synchronous calls, configure your HTTP client with appropriate timeout values (30-60 seconds).

**Handle rate limits gracefully.** Implement retry logic with backoff when you receive 429 (Too Many Requests) responses.

**Batch when possible.** Use batch endpoints to reduce the number of API calls and improve throughput.