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Materializing Metrics using Python

Materializations are invoked through the interfaces and scheduled to run at a cadence. The configuration for the materialization must already exist in your Transform configurations in order for you to run this command. For more information on Materializations, see here.

Usage​

See API Reference Doc

Examples​

The materialization-name should map to the one committed in your Transform directory. If you don't provide a start and end-time, the materialization will run across all available data, which can be very expensive.

from transform import mql
mql.materialize("transaction_metrics") # synchronous
    /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
      warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')





    MqlMaterializeResp(schema='transform_sales_demo', table='transaction_metrics', query_id='240_1675213512554_0000155')
mql.create_materialization("transaction_metrics") # asynchronous
    MqlQueryStatusResp(query_id='240_1675213512554_0000156', status=<MqlQueryStatus.RUNNING: 'RUNNING'>, error=None, sql='', chart_value_min=None, chart_value_max=None, result=None, result_source=None, result_primary_time_granularity=None, warnings=[])

Providing a start and end time will bound the data for the materialization.

mql.materialize("transaction_metrics", start_time="2020-01-01", end_time="2020-01-31")
    MqlMaterializeResp(schema='transform_sales_demo', table='transaction_metrics', query_id='240_1675213512554_0000157')

By default, the materialization will write to the Transform schema and any other location you've specified in the destinations for your configuration. Optionally provide a specific schema and table to write the materialization to. Note this won't be managed by Transform once it's written outside our cache.

mql.materialize("transaction_metrics", start_time="2020-01-01", end_time="2020-01-31", output_table="my_schmea.my_table")
    MqlMaterializeResp(schema='my_schmea', table='my_table', query_id='240_1675213512554_0000160')