Athena Query#

This example shows how to use a Flyte AthenaTask to execute a query.

from flytekit import kwtypes, task, workflow
from flytekit.types.schema import FlyteSchema
from flytekitplugins.athena import AthenaConfig, AthenaTask

This is the world’s simplest query. Note that in order for registration to work properly, you’ll need to give your Athena task a name that’s unique across your project/domain for your Flyte installation.

athena_task_no_io = AthenaTask(
        select 1

def no_io_wf():
    return athena_task_no_io()

Of course, in real world applications we are usually more interested in using Athena to query a dataset. In this case we’ve populated our vaccinations table with the publicly available dataset here. For a primer on how upload a dataset, checkout of the official AWS docs. The data is formatted according to this schema:

country (string)

iso_code (string)

date (string)

total_vaccinations (string)

people_vaccinated (string)

people_fully_vaccinated (string)

daily_vaccinations_raw (string)

daily_vaccinations (string)

total_vaccinations_per_hundred (string)

people_vaccinated_per_hundred (string)

people_fully_vaccinated_per_hundred (string)

daily_vaccinations_per_million (string)

vaccines (string)

source_name (string)

source_website (string)

Let’s look out how we can parameterize our query to filter results for a specific country, provided as a user input specifying a country iso code. We’ll produce a FlyteSchema that we can use in downstream flyte tasks for further analysis or manipulation. Note that we cache this output data so we don’t have to re-run the query in future workflow iterations should we decide to change how we manipulate data downstream

athena_task_templatized_query = AthenaTask(
    # Define inputs as well as their types that can be used to customize the query.
    task_config=AthenaConfig(workgroup="primary", catalog="AwsDataCatalog", database="vaccinations"),
    SELECT * FROM vaccinations where iso_code like  '{{ .Inputs.iso_code }}'
    # While we define a generic schema as the output here, Flyte supports more strongly typed schemas to provide
    # better compile-time checks for task compatibility. Refer to {py:class}`flytekit.FlyteSchema` for more details
    # Cache the output data so we don't have to re-run the query in future workflow iterations
    # should we decide to change how we manipulate data downstream.
    # For more information about caching, visit {ref}`Task Caching <task_cache>`

Now we (trivially) clean up and interact with the data produced from the above Athena query in a separate Flyte task.

def manipulate_athena_schema(s: FlyteSchema) -> FlyteSchema:
    df =
    return df

def full_athena_wf(country_iso_code: str) -> FlyteSchema:
    demo_schema = athena_task_templatized_query(iso_code=country_iso_code)
    return manipulate_athena_schema(s=demo_schema)