tecton.declarative.HiveConfig

class tecton.declarative.HiveConfig(table, database, timestamp_field=None, timestamp_format=None, datetime_partition_columns=None, post_processor=None, data_delay=datetime.timedelta(0))

Configuration used to reference a Hive table.

The HiveConfig class is used to create a reference to a Hive Table.

This class used as an input to a BatchSource’s parameter batch_config. This class is not a Tecton Object: it is a grouping of parameters. Declaring this class alone will not register a data source. Instead, declare as part of BatchSource that takes this configuration class instance as a parameter.

Methods

__init__

Instantiates a new HiveConfig.
__init__(table, database, timestamp_field=None, timestamp_format=None, datetime_partition_columns=None, post_processor=None, data_delay=datetime.timedelta(0))

Instantiates a new HiveConfig.

Parameters

  • table (str) – A table registered in Hive MetaStore.

  • database (str) – A database registered in Hive MetaStore.

  • timestamp_field (Optional[str]) – The timestamp column in this data source that should be used by FilteredSource to filter data from this source, before any feature view transformations are applied. Only required if this source is used with FilteredSource.

  • timestamp_format (Optional[str]) – Format of string-encoded timestamp column (e.g. “yyyy-MM-dd’T’hh:mm:ss.SSS’Z’”). If the timestamp string cannot be parsed with this format, Tecton will fallback and attempt to use the default timestamp parser.

  • datetime_partition_columns (Optional[List[DatetimePartitionColumn]]) – List of DatetimePartitionColumn the raw data is partitioned by, otherwise None.

  • post_processor (Optional[Callable]) – Python user defined function f(DataFrame) -> DataFrame that takes in raw PySpark data source DataFrame and translates it to the DataFrame to be consumed by the Feature View.

  • data_delay (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.

Returns

A HiveConfig class instance.

Example of a HiveConfig declaration:

from tecton import HiveConfig
import pyspark

def convert_temperature(df: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
    from pyspark.sql.functions import udf,col
    from pyspark.sql.types import DoubleType

    # Convert the incoming PySpark DataFrame temperature Celsius to Fahrenheit
    udf_convert = udf(lambda x: x * 1.8 + 32.0, DoubleType())
    converted_df = df.withColumn("Fahrenheit", udf_convert(col("Temperature"))).drop("Temperature")
    return converted_df

# declare a HiveConfig instance, which can be used as a parameter to a BatchSource
batch_config=HiveConfig(database='global_temperatures',
                            table='us_cities',
                            timestamp_field='timestamp',
                            post_processor=convert_temperature)

Attributes

data_delay