elasticsearch-dsl-py | High level Python client for Elasticsearch | JSON Processing library

I'd like to store daily statistics of sales.

By daily, I mean day of year.

Conceptually the data would look like the following.

I've got a Python application using pytest. For several of my tests, there are API calls to Elasticsearch (using elasticsearch-dsl-py) that slow down my tests that I'd like to:

1. prevent unless a Pytest marker is used.
2. If a marker is used, I would want that marker to execute some code before the test runs. Just like how a fixture would work if you used yield.

This is mostly inspired by pytest-django where you have to use the django_db marker in order to make a conn to the database (but they throw an error if you try to connect to the DB, whereas I just don't want a call in the first place, that's all).

For example:

The query below is what I would like to construct using elasticsearch-dsl-py, but I do not know how to do it.

The query below is what I would like to construct using elasticsearch-dsl-py, but I do not know how to do it.

I’m building a Django web application to store documents and their associated metadata.

The bulk of the metadata will be stored in the underlying MySQL database, with the OCR’d document text indexed in Elasticsearch to enable full-text search. I’ve incorporated django-elasticsearch-dsl to connect and synchronize my data models, as I’m also indexing (and thus, double-storing) a few other fields found in my models. I had considered using Haystack, but it lacks support for the latest Elasticsearch versions.

When a document is uploaded via the applications’s admin interface, a post_save signal automatically triggers a Celery asynchronous background task to perform the OCR and will ultimately index the extracted text into Elasticsearch.

Seeing as how I don’t have a full-text field defined in my model (and hope to avoid doing so as I don’t want to store or search against CLOB’s in the database), I’m seeking the best practice for updating my Elasticsearch documents from my tasks.py file. There doesn’t seem to be a way to do so using django-elasticseach-dsl (but maybe I’m wrong?) and so I’m wondering if I should either:

1. Try to interface with Elasticsearch via REST using the sister django-elasticsearch-dsl-drf package.

2. More loosely integrate my application with Elasticsearch by using the more vanilla elasticsearch-dsl-py package (based on elasticsearch-py). I‘d lose some “luxury” with this approach as I’d have to write a bit more integration code, at least if I want to wire up my models with signals.

Is there a best practice? Or another approach I haven’t considered?

Update 1: In trying to implement the answer from @Nielk, I'm able to persist the OCR'd text (result = "test" in tasks.py below) into ElasticSearch, but it's also persisting in the MySQL database. I'm still confused about how to essentially configure Submission.rawtext as a passthru to ElasticSearch.

models.py: