Analytic Server Context

Returns the Pyspark python SparkContext instance:

cxt.getSparkContext() : SparkContext

Returns the Pyspark python SQLContext instance:

cxt.getSparkSQLContext() : SQLContext

Returns True to describe whether the execution is made only to compute the output data model. Otherwise returns False:

cxt.isComputeDataModelOnly() : Boolean

Returns True if the script is running in the Spark environment. Currently, it always returns True:

cxt.isSparkExecution() : Boolean

Loads input data from the upstream temporary file and generates the pyspark.sql.DataFrame instance:

cxt.getSparkInputData() : DataFrame

Returns a pyspark.sql.StructType instance generated from the input data model. Returns None if the input data model does not exist:

cxt.getSparkInputSchema() : StructType

Serializes the output data frame into Analytic Server context and returns the context:

cxt.setSparkOutputData( outDF) : AnalyticServerContext

Parameter:

• outDF (DataFrame) : The output data frame value

Exceptions:

• DataOutputNotSupported : If this interface is invoked in the function pyspark:buildmodel
• ASContextException : If the output data frame is None
• InconsistentOutputDataModel : The field names and storage type information common to both objects is inconsistent

Converts the outSchema StructType instance into a data model, serializes it into the Analytic Server context, and returns the context:

cxt.setSparkOutputSchema(outSchema) : AnalyticServerContext

Parameter:

• outSchema(StructType) : The output StructType object

Exceptions:

• ASContextException : If the output schema instance is None
• InconsistentOutputDataModel : The field names and storage type information common to both objects is inconsistent

Stores the location of model building output to the Analytic Server context and returns the context:

cxt.setModelContentFromPath(key, path, mimetype=None) : AnalyticServerContext

The path can be a directory path which should use the cxt.createTemporaryFolder() API to generate , when everything under the directory is packaged up as model content.

Parameters:

• key (string) : key string value
• path (string) : location of model building output string path
• mimetype (string, optional) : the MIME type of the content

Exceptions:

• ModelOutputNotSupported : When not invoking this API from the pyspark:buildmodel function
• KeyError : If the key attribute is None or the string is empty

Stores the model building content, metadata, or other attributes to the Analytic Server context and returns the context:

cxt.setModelContentFromString(key, value, mimetype=None) : AnalyticServerContext

Parameters:

• key (string) : key string value
• value (string) : the model metadata string value
• mimetype (string, optional) : the MIME type of the content

Exceptions:

• ModelOutputNotSupported : When not invoking this API from the pyspark:buildmodel function
• KeyError : If the key attribute is None or the string is empty

Returns the temporary folder location that is managed by Analytic Server; this can be used to store the model content:

cxt.createTemporaryFolder() : string

Exception:

• ModelOutputNotSupported : When not invoking this API from the pyspark:buildmodel function

Returns the location of the model which matches the input key:

cxt.getModelContentToPath(key) : string

Parameter:

• key (string) : key string value

Exceptions:

• ModelInputNotSupported : When not invoking this API from the pyspark:applymodel function
• KeyError : If the key attribute is None or the string is empty
• IncompatibleModelContentType : If the model content type is not a container

Returns the model content, metadata of the model, or other model attributes which match the input key:

cxt.getModelContentToString(key) : string

Parameter:

• key (string) : key string value

Exceptions:

• ModelInputNotSupported : When not invoking this API from the pyspark:applymodel function
• KeyError : If the key attribute is None, or the string is empty, or the key does not exist
• IncompatibleModelContentType : If the model content type is not consistent

Returns the mime-type assigned to the input key. It returns None if the specified content has no mime type:

cxt.getModelContentMimeType(key) : string

Parameter:

• key (string) : key string value

Exceptions:

• ModelInputNotSupported : When not invoking this API from the pyspark:applymodel function
• KeyError : If the key attribute is None, or the string is empty, or the key does not exist