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Convert Fledge Json to Process Control Data Model

FledgeOPCUAJsonToPCDMTransformer

Bases: TransformerInterface

Converts a Spark Dataframe column containing a json string created by Fledge to the Process Control Data Model.

Example

from rtdip_sdk.pipelines.transformers import FledgeOPCUAJsonToPCDMTransformer

fledge_opcua_json_to_pcdm_transfromer = FledgeOPCUAJsonToPCDMTransformer(
    data=df,
    souce_column_name="body",
    status_null_value="Good",
    change_type_value="insert",
    timestamp_formats=[
        "yyyy-MM-dd'T'HH:mm:ss.SSSX",
        "yyyy-MM-dd'T'HH:mm:ssX",
    ]
)

result = fledge_opcua_json_to_pcdm_transfromer.transform()

Parameters:

Name Type Description Default
data DataFrame

Dataframe containing the column with Json Fledge data

 required
source_column_name str

Spark Dataframe column containing the OPC Publisher Json OPC UA data

 required
status_null_value str

If populated, will replace 'Good' in the Status column with the specified value.

 'Good'
change_type_value optional str

If populated, will replace 'insert' in the ChangeType column with the specified value.

 'insert'
timestamp_formats list[str]

Specifies the timestamp formats to be used for converting the timestamp string to a Timestamp Type. For more information on formats, refer to this documentation.

 ["yyyy-MM-dd'T'HH:mm:ss.SSSX", "yyyy-MM-dd'T'HH:mm:ssX"]
Source code in src/sdk/python/rtdip_sdk/pipelines/transformers/spark/fledge_opcua_json_to_pcdm.py 
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class FledgeOPCUAJsonToPCDMTransformer(TransformerInterface):
    """
    Converts a Spark Dataframe column containing a json string created by Fledge to the Process Control Data Model.

    Example
    --------
    ```python
    from rtdip_sdk.pipelines.transformers import FledgeOPCUAJsonToPCDMTransformer

    fledge_opcua_json_to_pcdm_transfromer = FledgeOPCUAJsonToPCDMTransformer(
        data=df,
        souce_column_name="body",
        status_null_value="Good",
        change_type_value="insert",
        timestamp_formats=[
            "yyyy-MM-dd'T'HH:mm:ss.SSSX",
            "yyyy-MM-dd'T'HH:mm:ssX",
        ]
    )

    result = fledge_opcua_json_to_pcdm_transfromer.transform()
    ```

    Parameters:
        data (DataFrame): Dataframe containing the column with Json Fledge data
        source_column_name (str): Spark Dataframe column containing the OPC Publisher Json OPC UA data
        status_null_value (str): If populated, will replace 'Good' in the Status column with the specified value.
        change_type_value (optional str): If populated, will replace 'insert' in the ChangeType column with the specified value.
        timestamp_formats (list[str]): Specifies the timestamp formats to be used for converting the timestamp string to a Timestamp Type. For more information on formats, refer to this [documentation.](https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html)
    """

    data: DataFrame
    source_column_name: str
    status_null_value: str
    change_type_value: str
    timestamp_formats: list

    def __init__(
        self,
        data: DataFrame,
        source_column_name: str,
        status_null_value: str = "Good",
        change_type_value: str = "insert",
        timestamp_formats: list = [
            "yyyy-MM-dd'T'HH:mm:ss.SSSX",
            "yyyy-MM-dd'T'HH:mm:ssX",
        ],
    ) -> None:  # NOSONAR
        self.data = data
        self.source_column_name = source_column_name
        self.status_null_value = status_null_value
        self.change_type_value = change_type_value
        self.timestamp_formats = timestamp_formats

    @staticmethod
    def system_type():
        """
        Attributes:
            SystemType (Environment): Requires PYSPARK
        """
        return SystemType.PYSPARK

    @staticmethod
    def libraries():
        libraries = Libraries()
        return libraries

    @staticmethod
    def settings() -> dict:
        return {}

    def pre_transform_validation(self):
        return True

    def post_transform_validation(self):
        return True

    def transform(self) -> DataFrame:
        """
        Returns:
            DataFrame: A dataframe with the specified column converted to PCDM
        """
        df = (
            self.data.withColumn(
                self.source_column_name,
                from_json(self.source_column_name, FLEDGE_SCHEMA),
            )
            .selectExpr("inline({})".format(self.source_column_name))
            .select(explode("readings"), "timestamp")
            .withColumn(
                "EventTime",
                coalesce(
                    *[to_timestamp(col("timestamp"), f) for f in self.timestamp_formats]
                ),
            )
            .withColumnRenamed("key", "TagName")
            .withColumnRenamed("value", "Value")
            .withColumn("Status", lit(self.status_null_value))
            .withColumn(
                "ValueType",
                when(col("value").cast("float").isNotNull(), "float").when(
                    col("value").cast("float").isNull(), "string"
                ),
            )
            .withColumn("ChangeType", lit(self.change_type_value))
        )

        return df.select(
            "TagName", "EventTime", "Status", "Value", "ValueType", "ChangeType"
        )

system_type() staticmethod

Attributes:

Name Type Description
SystemType Environment

Requires PYSPARK
Source code in src/sdk/python/rtdip_sdk/pipelines/transformers/spark/fledge_opcua_json_to_pcdm.py 
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@staticmethod
def system_type():
    """
    Attributes:
        SystemType (Environment): Requires PYSPARK
    """
    return SystemType.PYSPARK

transform()

Returns:

Name Type Description
DataFrame DataFrame

A dataframe with the specified column converted to PCDM
Source code in src/sdk/python/rtdip_sdk/pipelines/transformers/spark/fledge_opcua_json_to_pcdm.py 
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def transform(self) -> DataFrame:
    """
    Returns:
        DataFrame: A dataframe with the specified column converted to PCDM
    """
    df = (
        self.data.withColumn(
            self.source_column_name,
            from_json(self.source_column_name, FLEDGE_SCHEMA),
        )
        .selectExpr("inline({})".format(self.source_column_name))
        .select(explode("readings"), "timestamp")
        .withColumn(
            "EventTime",
            coalesce(
                *[to_timestamp(col("timestamp"), f) for f in self.timestamp_formats]
            ),
        )
        .withColumnRenamed("key", "TagName")
        .withColumnRenamed("value", "Value")
        .withColumn("Status", lit(self.status_null_value))
        .withColumn(
            "ValueType",
            when(col("value").cast("float").isNotNull(), "float").when(
                col("value").cast("float").isNull(), "string"
            ),
        )
        .withColumn("ChangeType", lit(self.change_type_value))
    )

    return df.select(
        "TagName", "EventTime", "Status", "Value", "ValueType", "ChangeType"
    )