Hi. I have created a custom TrinoSQLDataSet, that: 1. Reads a query from a sql file 2. Injects some parameters into the query, .e.g.

{start_time_utc}

in the query replaced by the parameter

# catalog.yml
raw-query-dataset:
  type: <my-package>.io.TrinoSQLDataSet
  query_param_names:
    - start_time_utc

What the dataset does with

query_param_names

is to look for each of those keys in the kedro parameters and inject them into the query, so essentially this is a parameterised query

# parameters.yml
start_time_utc: 2023-01-01 00:00:00

In the dataset class itself, this is how i load the kedro parameters:

def _load_kedro_params() -> Dict[str, Any]:
    conf_path = str(settings.CONF_SOURCE)
    # get kedro run env to load the params from the correct conf dir
    env = os.getenv("KEDRO_ENV", "local")
    conf_loader = ConfigLoader(conf_source=conf_path, default_run_env=env)
    return conf_loader.get("parameters", "parameters*/**")

This means the yml file is read, but if I run the pipeline using

kedro run --params:"start_time_utc: 2023-03-01 13:00:00"

The dataset still loads the parameter from the file… Any way to access the merged dictionary of the parameters overwritten by the cli extra/runtime params? I cannot find a way to access the current session/context anymore

So the datasets shouldn’t be aware of IO

> So the datasets shouldn’t be aware of IO Not not quite sure what you mean by that. Limiting query size at runtime is preferred than filtering later

by design you shouldn’t be loading your own config loader

the right way to do all of this is to use a

before_pipeline_run

hook and pass the parameter to your catalog entry that way

ok, that would work 🙏 any example for adding onto the catalog?

Could it be achieved better with a customer resolver?

I don't see why there is a need for a custom dataset unless I miss something. You can use the built in ``SQL QueryDataset`` and the ``runtime_params`` resolver which are designed to do this

1. The query might be more than 1 line long, so having sql files that are read/loaded by the DataSet is cleaner 2. We are not using sqlalchemy to connect to our db (

trino

) 3. the

runtime_params

require the

OmegaConfigLoader

?

OmegaConfigLoader

is required and it will be the default in 0.19 series

ok great. tried it last night we use neptune for tracking our experiments and they have not adjusted their config to work wit omega so for now will just use a before_pipeline_run hook to reinstantiate my custom datasets (before they are loaded), ask neptune to update their config loader and then do what you suggested 🙂

Are you using

kedro-neptune

? Do they come with a custom config loader?

the current version works with

ConfigLoader

(and

TemplatedConfigLoader

) and have asked them to make it work with omega.. they will do it, but have not provided a time estimate

BTW I've tested it and it is totally possible to use a Trino backend with the

SQLQueryDataset

, you just need to install

pip install trino[sqlalchemy]

and set the connection in the credentials, so you can likely avoid the custom dataset.