tecton.declarative.KinesisConfig

class tecton.declarative.KinesisConfig(stream_name, region, post_processor, timestamp_field, initial_stream_position, watermark_delay_threshold=datetime.timedelta(days=1), deduplication_columns=None, options=None)

Configuration used to reference a Kinesis stream.

The KinesisConfig class is used to create a reference to an AWS Kinesis stream.

This class used as an input to a StreamSource’s parameter stream_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 StreamSource that takes this configuration class instance as a parameter.

Methods

__init__

Instantiates a new KinesisConfig.

__init__(stream_name, region, post_processor, timestamp_field, initial_stream_position, watermark_delay_threshold=datetime.timedelta(days=1), deduplication_columns=None, options=None)

Instantiates a new KinesisConfig.

Parameters

• stream_name (str) – Name of the Kinesis stream.

• region (str) – AWS region of the stream, e.g: “us-west-2”.

• post_processor – 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. See an example of post_processor in the User Guide.

• timestamp_field (str) – Name of the column containing timestamp for watermarking.

• initial_stream_position (str) – Initial position in stream, e.g: “latest” or “trim_horizon”. More information available in Spark Kinesis Documentation.

• watermark_delay_threshold (timedelta) – (Default: 24h) Watermark time interval, e.g: timedelta(hours=36), used by Spark Structured Streaming to account for late-arriving data. See: Productionizing a Stream.

• deduplication_columns (Optional[List[str]]) – Columns in the stream data that uniquely identify data records. Used for de-duplicating.

• options (Optional[Dict[str, str]]) – A map of additional Spark readStream options

Returns

A KinesisConfig class instance.

Example of a KinesisConfig declaration:

import pyspark
from tecton import KinesisConfig


# Define our deserialization raw stream translator
def raw_data_deserialization(df:pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
    from pyspark.sql.functions import col, from_json, from_utc_timestamp
    from pyspark.sql.types import StructType, StringType

    payload_schema = (
      StructType()
            .add('amount', StringType(), False)
            .add('isFraud', StringType(), False)
            .add('timestamp', StringType(), False)
    )

    return (
        df.selectExpr('cast (data as STRING) jsonData')
        .select(from_json('jsonData', payload_schema).alias('payload'))
        .select(
            col('payload.amount').cast('long').alias('amount'),
            col('payload.isFraud').cast('long').alias('isFraud'),
            from_utc_timestamp('payload.timestamp', 'UTC').alias('timestamp')
        )
    )
# Declare KinesisConfig instance object that can be used as argument in `StreamSource`
stream_config = KinesisConfig(
                        stream_name='transaction_events',
                        region='us-west-2',
                        initial_stream_position='latest',
                        timestamp_field='timestamp',
                        post_processor=raw_data_deserialization,
                        options={'roleArn': 'arn:aws:iam::472542229217:role/demo-cross-account-kinesis-ro'}
)