Explanation:

  1. Aggregation Pipeline:

    • {'$match': {'uuid': worksheet_uuid}}: Filters the Worksheet documents to find the one with the specified UUID.
    • {'$project': {'history_size': {'$size': '$history'}}}: Projects a new field history_size that contains the size of the history array.

  2. Execution:

    • For asynchronous execution, we use await Worksheet.asyncObjects.aggregate(*pipeline).to_list(length=None).
    • For synchronous execution, we use Worksheet.objects.aggregate(*pipeline).

  3. Result Extraction:

    • We check if the result is not empty and then extract the history_size value.
    • This value is the count of documents referenced in the history field.

Benefits:

  • Efficiency: Only the size of the history array is retrieved, not the entire array or the documents it references.
  • Speed: Aggregation pipelines are executed on the MongoDB server, which is optimized for such operations.
  • Non-blocking (Asynchronous): Using asyncObjects and await allows other operations to run concurrently, improving performance in asynchronous applications.