pandas_batch_config

Summary

Declare a PandasBatchConfig for configuring a Batch Source with a Data Source Function. The function takes in an optional FilterContext, if supports_time_filtering=True. Returns a pandas.DataFrame.

Parameters

• data_delay (Optional[timedelta]) – By default, incremental materialization jobs run immediately at the end of the batch schedule period. This parameter configures how long they wait after the end of the period before starting, typically to ensure that all data has landed. For example, if a feature view has a batch_schedule of 1 day and one of the data source inputs has data_delay=timedelta(hours=1) set, then incremental materialization jobs will run at 01:00 UTC. (Default: datetime.timedelta(0))

• supports_time_filtering (bool) – Must be set to to True if one of the following conditions is met:

  • <data source>.get_dataframe() is called with start_time or end_time
  • A feature view wraps this Data Source with a FilteredSource

  If this parameter is set to true, Tecton passes a FilterContext object into the Data Source Function, which is expect to handle its own filtering. (Default: False)

• secrets Optional[Dict[str, Union[Secret, str]]]

  • A dictionary of Secret references that will be resolved and provided to the Data Source Function at runtime. During local development and testing, strings may be used instead Secret references.

Example

The following example shows how to use pandas_batch_config to read from a file in S3. This example uses Tecton Secrets to securely authenticate to a data source that requires a password or API key. If your data source supports AWS IAM authentication, then you can instead leverage a similar to the one shown here.

from tecton import pandas_batch_config, Secret


@pandas_batch_config(
    secrets={
        "aws_access_key": Secret(scope="dev", key="aws_access_key"),
        "aws_secret_key": Secret(scope="dev", key="aws_secret_key"),
    }
)
def parquet_data_source_function(secrets):
    import pyarrow.parquet as pq
    from pyarrow.fs import S3FileSystem

    # Suppose you need to pass a specific key-pair to access a bucket
    s3fs = S3FileSystem(access_key=secrets["aws_access_key"], secret_key=secrets["aws_secret_key"])
    dataset = pq.read_table(f"s3://bucket/pa../../../data.pq", filesystem=s3fs).to_pandas()
    return dataset