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

# Process Status

> Poll the status of an upload process from the Alphacast Python SDK to know when your data is ready.

When you upload data with `upload_data_from_df` or `upload_data_from_csv`, the call returns immediately with a **process** record. The actual merge runs in the background. Use the methods below to check progress and inspect the result.

<Info>
  For the full upload lifecycle (states, idempotency, notifications) see [Upload lifecycle](/uploading/overview#the-upload-lifecycle) in the Documentation tab.
</Info>

## List all processes for a dataset

`processes()` returns every upload process recorded for the dataset, most recent first. The response is the raw API body as bytes — decode and parse it as JSON:

```python theme={null}
import json

raw = alphacast.datasets.dataset(7938).processes()
processes = json.loads(raw)

for p in processes[:5]:
    print(p["id"], p["status"], p.get("statusDescription"))
```

Each entry includes at least:

| Field                 | Meaning                                                     |
| --------------------- | ----------------------------------------------------------- |
| `id`                  | Process ID.                                                 |
| `datasetId`           | The dataset this process belongs to.                        |
| `status`              | `Requested`, `Processing`, `Processed`, or `Error`.         |
| `statusDescription`   | Free-text summary (e.g., `1292 values added to database.`). |
| `deleteMissingFromDB` | The flag value used for this upload.                        |
| `onConflictUpdateDB`  | The flag value used for this upload.                        |
| `createdAt`           | When the upload was submitted.                              |
| `processedAt`         | When processing finished.                                   |

## Get a single process

If you saved the process ID returned by the upload call, fetch only that record with `process(id)`:

```python theme={null}
import json

raw = alphacast.datasets.dataset(7938).process(45141)
record = json.loads(raw)

print(record["status"], record.get("statusDescription"))
```

The response shape matches a single entry from `processes()`.

## Polling pattern

To wait for an upload to complete, poll until the status leaves `Requested`/`Processing`:

```python theme={null}
import json
import time

dataset_id = 7938
upload = alphacast.datasets.dataset(dataset_id).upload_data_from_df(df)
process_id = json.loads(upload)["id"]

while True:
    raw = alphacast.datasets.dataset(dataset_id).process(process_id)
    record = json.loads(raw)
    status = record["status"]

    if status in ("Processed", "Error"):
        break

    time.sleep(5)

if status == "Error":
    raise RuntimeError(f"Upload failed: {record.get('statusDescription')}")

print("Upload complete:", record.get("statusDescription"))
```

<Tip>
  Most uploads finish in seconds. For large CSVs (hundreds of MB) processing can take several minutes — only one process per dataset runs at a time, so subsequent uploads wait in `Requested` until the in-flight job finishes.
</Tip>

## Upload stats

After a successful run, the process record's `stats` field contains row-level counts: `insertedValues`, `updatedValues`, `deletedValues`, `changedValues`, `totalValues`, `minDate`, `maxDate`. Use these for logging or to drive your own monitoring:

```python theme={null}
record = json.loads(alphacast.datasets.dataset(7938).process(45141))
stats = record.get("stats", {})

print("Inserted:", stats.get("insertedValues"))
print("Updated:", stats.get("updatedValues"))
print("Deleted:", stats.get("deletedValues"))
```

## Error handling

When the worker rejects the upload — duplicated rows, manifest mismatch, missing date column — the process transitions to `Error` and `statusDescription` carries the message. The full list of validation rules is documented in [Validation rules](/uploading/validation#validation-rules).

```python theme={null}
record = json.loads(alphacast.datasets.dataset(7938).process(45141))

if record["status"] == "Error":
    print("Upload failed:", record["statusDescription"])
```

## Next steps

* Trigger a new upload — see [Uploading data](/python-sdk/uploading).
* Read the dataset back once processing finishes — see [Downloading data](/python-sdk/downloading).