Creating an asset triggered by a sensor

Now that you’ve defined how the asset can be materialized, let’s create the ad hoc report asset.

Add the following imports to requests.py:

from dagster import asset, Config
from dagster_duckdb import DuckDBResource

import plotly.express as px
import plotly.io as pio

from . import constants

Create a new asset named adhoc_request with the following arguments:
1. config, type annotated to AdhocRequestConfig
2. the taxi_zones and taxi_trips assets as dependencies
3. database, type annotated to DuckDBResource to be able to query DuckDB.
The asset should look like this at this point:
```
@asset(
    deps=["taxi_zones", "taxi_trips"]
)
def adhoc_request(config: AdhocRequestConfig, database: DuckDBResource) -> None:
```
When the report is written to a file, it should have a similar name to the request. A template has been provided in assets/constants.py that contains a template named REQUEST_DESTINATION_TEMPLATE_FILE_PATH .
Generate the file name by using the template and stripping the .json extension from the request’s file name:
```
file_path = constants.REQUEST_DESTINATION_TEMPLATE_FILE_PATH.format(config.filename.split('.')[0])
```

Next, write a SQL query that:

Filters out trips that didn’t start in the named borough during the provided date range

Aggregates the data by the day of week and hour the trip started

For example:

query = f"""
  select
    date_part('hour', pickup_datetime) as hour_of_day,
    date_part('dayofweek', pickup_datetime) as day_of_week_num,
    case date_part('dayofweek', pickup_datetime)
      when 0 then 'Sunday'
      when 1 then 'Monday'
      when 2 then 'Tuesday'
      when 3 then 'Wednesday'
      when 4 then 'Thursday'
      when 5 then 'Friday'
      when 6 then 'Saturday'
    end as day_of_week,
    count(*) as num_trips
  from trips
  left join zones on trips.pickup_zone_id = zones.zone_id
  where pickup_datetime >= '{config.start_date}'
  and pickup_datetime < '{config.end_date}'
  and pickup_zone_id in (
    select zone_id
    from zones
    where borough = '{config.borough}'
  )
  group by 1, 2
  order by 1, 2 asc
"""

Next, run the query in DuckDB and store the results as a DataFrame:

with database.get_connection() as conn:
    results = conn.execute(query).fetch_df()

Now that the query data is stored as a DataFrame in the results variable, use the Plotly library to visualize the frequency of trips at each time of the day for the borough, stratified by the day of the week:

fig = px.bar(
    results,
    x="hour_of_day",
    y="num_trips",
    color="day_of_week",
    barmode="stack",
    title=f"Number of trips by hour of day in {config.borough}, from {config.start_date} to {config.end_date}",
    labels={
        "hour_of_day": "Hour of Day",
        "day_of_week": "Day of Week",
        "num_trips": "Number of Trips"
    }
)

Lastly, save the report to the destination file specified by the file_path variable you declared earlier:
```
pio.write_image(fig, file_path)
```

Verify that requests.py file looks like the code below:

from dagster import Config, asset
from dagster_duckdb import DuckDBResource

import plotly.express as px
import plotly.io as pio

from . import constants

class AdhocRequestConfig(Config):
    filename: str
    borough: str
    start_date: str
    end_date: str

@asset(
    deps=["taxi_zones", "taxi_trips"]
)
def adhoc_request(config: AdhocRequestConfig, database: DuckDBResource) -> None:
    """
      The response to an request made in the `requests` directory.
      See `requests/README.md` for more information.
    """

    # strip the file extension from the filename, and use it as the output filename
    file_path = constants.REQUEST_DESTINATION_TEMPLATE_FILE_PATH.format(config.filename.split('.')[0])

    # count the number of trips that picked up in a given borough, aggregated by time of day and hour of day
    query = f"""
        select
          date_part('hour', pickup_datetime) as hour_of_day,
          date_part('dayofweek', pickup_datetime) as day_of_week_num,
          case date_part('dayofweek', pickup_datetime)
            when 0 then 'Sunday'
            when 1 then 'Monday'
            when 2 then 'Tuesday'
            when 3 then 'Wednesday'
            when 4 then 'Thursday'
            when 5 then 'Friday'
            when 6 then 'Saturday'
          end as day_of_week,
          count(*) as num_trips
        from trips
        left join zones on trips.pickup_zone_id = zones.zone_id
        where pickup_datetime >= '{config.start_date}'
        and pickup_datetime < '{config.end_date}'
        and pickup_zone_id in (
          select zone_id
          from zones
          where borough = '{config.borough}'
        )
        group by 1, 2
        order by 1, 2 asc
    """

    with database.get_connection() as conn:
        results = conn.execute(query).fetch_df()

    fig = px.bar(
        results,
        x="hour_of_day",
        y="num_trips",
        color="day_of_week",
        barmode="stack",
        title=f"Number of trips by hour of day in {config.borough}, from {config.start_date} to {config.end_date}",
        labels={
            "hour_of_day": "Hour of Day",
            "day_of_week": "Day of Week",
            "num_trips": "Number of Trips"
        }
    )

    pio.write_image(fig, file_path)