sdi.clients package

Submodules

sdi.clients.custom_datatypes_client module

SDI - Semantic Data Interconnect APIs

class CustomDatatypesClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_data_types(request_object)

Custom datatype registration.

Initiates a registration for list of custom datatypes for given datatype name and regular expression patterns. There can be one or more pattern for a given custom data type. The custom datatype is being used by DataUpload process during schema generation. The custom datatype registration is rejected for the current tenant if invalid regular expression pattern is provided. The custom datatype applies to all the files ingested for the current tenant. The tenant can have maximum 200 datatypes per tenant and each datatype cannot exceed 10 regular expression pattern per datatypename. This returns unique **name* that can be used to retrieve the custom data type.

Parameters:request_object (DataTypesPostRequest) – It contains the below parameters –>
( dataTypeDefinition* - dataTypeDefinition ) Returns:DataTypeDefinition

create_suggest_patterns(request_object)

Generates the regular expression patterns for a given set of sample values

Generates the regular expression patterns for a given set of sample values. In this case sampleValues generates a number of regex patterns and tries to match with patterns provided in testValues. The response contains any of testValues that matches sampleValues as probable pattern match.

Parameters:request_object (SuggestPatternsPostRequest) – It contains the below parameters –>
( sampleValues* - sampleValues ),
( testValues* - testValues ) Returns:ListOfPatterns

delete_data_types_name(name)

Deletes custom datatype for a given datatype name

Deletes custom datatype for a given datatype name if it is not being used by any schema

Parameters:request_object (DataTypesNameDeleteRequest) – It contains the below parameters –>
( name* - name ) Returns:None

get_data_types(request_object)

Retrieves the custom datatypes for the current tenant.

Retrieves custom datatypes for the current tenant containing data type name and one or more registered patterns.

Parameters:request_object (DataTypesGetRequest) – It contains the below parameters –>
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ListOfDataTypeDefinition

get_data_types_name(request_object)

Retrieves datatype for a given datatype name

Retrieves custom datatype for a given datatype name

Parameters:request_object (DataTypesNameGetRequest) – It contains the below parameters –>
( name* - name ) Returns:DataTypeDefinition

update_data_types_name_add_patterns(name, request_object)

Updates Datatype

Update custom datatypes

Parameters:request_object (DataTypesNameAddPatternsPostRequest) – It contains the below parameters –>
( dataTypePattern* - dataTypePattern ),
( name* - name ) Returns:DataTypeDefinition

sdi.clients.data_ingest_client module

SDI - Semantic Data Interconnect APIs

class DataIngestClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_data_upload(file)

Upload the given file for the current tenant. The input file must be less than 100MB in size.

Initiate the file upload process for provided file under the current tenant.

Parameters:file (DataUploadPostRequest) – It contains the below parameters –>
( file* - Select the file to Upload for SDI process ) Returns:SdiFileUploadResponse

create_ingest_jobs(request_object)

Ingest the provided file and start SDI’s schema generation process.

Initiate the data ingestion and start the SDI’s schema generation process for the current tenant. This operation currently supports CSV and XML format files. The XML format file requires root element information that is entry point for this operation. This is provided as either rootTag parameter to this operation or registered as part of Data Registry API operation. There are two modes for data ingest. * Default: Allows performing the data ingest without need of any Data Registry. In this case service processes files with default policy for schema generation. The schema generated this way cannot be versioned or changed with different files. This mode is used for quick validating the generated schema. * Dataregistry: The operation uses Data Registry for ingested file to generate schema. This is preferred mode as it allows more validation against the Data Registry and create multiple schema based on different domains created under Data Registry. Using this mode customer can create combination of schemas from different domain and query them or use for analytical modelling. This works in combination with Data Registry API.

Parameters:request_object (IngestJobsPostRequest) – It contains the below parameters –>
( ingestData* - Specifies the file path and Data Registry information to initiate data ingest process. The ‘{filePath}’ is required parameter and valid file path used during file upload operations. The ‘{dataTag}’ and ‘{sourceName}’ are the valid Data Registry source name and data tag. The ‘{rootTag}’ is optional and applies to XML formatted files. ) Returns:SdiJobStatusResponse

get_ingest_job_status(request_object)

Retrieve a list of jobIds.

Get a list of jobIds that is ingested, this jobId can be used to find detailed status using ingestJobStatus.

Parameters:request_object (IngestJobStatusGetRequest) – It contains the below parameters –>
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ListOfJobIds

get_ingest_job_status_id(id)

Retreive the job status.

Retrieve the job status based on jobid for the current tenant. The jobid belongs to data ingestion process started for the current tenant.

Parameters:id (IngestJobStatusIdGetRequest) – It contains the below parameters –>
( id* - job ID ) Returns:SdiJobStatusResponse

sdi.clients.data_lake_client module

SDI - Semantic Data Interconnect APIs

class DataLakeClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_data_lakes(request_object)

Creates a data lake record with a given data lake type and base path

Creates a data lake record with given data lake type, name and base path. To use IDL as preferred data lake, use type as MindSphere and name as IDL. The possible values for type is MindSphere or Custom.

Parameters:request_object (DataLakesPostRequest) – It contains the below parameters –>
( createDataLakeRequest* - createDataLakeRequest ) Returns:CreateDataLakeResponse

get_data_lakes()

Retrieves all DataLakes

Retrieves base path for a given data lake type, this will return an empty array when no datalake is found.

Returns:DataLakeList

get_data_lakes_id(data_lake_id)

Retrieves DataLake for a given DataLake Id

Retrieves DataLake for a given DataLake Id

Parameters:request_object (DataLakesIdGetRequest) – It contains the below parameters –>
( id* - id of the data lake ) Returns:DataLakeResponse

update_data_lakes_id(id, request_object)

Updates base path for a given data lake type

Updates base path for a given data lake type

Parameters:request_object (DataLakesIdPatchRequest) – It contains the below parameters –>
( id* - id of the data lake ),
( updateDataLakeRequest* - updateDataLakeRequest ) Returns:DataLakeResponse

sdi.clients.data_query_operations_client module

SDI - Semantic Data Interconnect APIs

class DataQueryOperationsClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_queries(request_object)

Create new queries

Returns id of created query

Parameters:request_object (QueriesPostRequest) – It contains the below parameters –>
( dataQuerySQLRequest* - dataQuerySQLRequest ) Returns:DataQuerySQLResponse

create_queries_id_execution_jobs(id, request_object)

Create execution job for dynamic query. There is soft limit of upto 10 number of parameters and aliases.

Returns id of created job

Parameters:request_object (QueriesIdExecutionJobsPostRequest) – It contains the below parameters –>
( id* - query id ),
( dataQueryExecuteRequest* - dataQueryExecuteRequest ) Returns:DataQueryExecuteQueryResponse

delete_execution_jobs_id(id)

Delete job by job id

Returns NO CONTENT HTTP Status

Parameters:request_object (ExecutionJobsIdDeleteRequest) – It contains the below parameters –>
( id* - id ) Returns:str

delete_queries_id(id)

Delete query by query id

Returns NO CONTENT HTTP Status

Parameters:id (QueriesIdDeleteRequest) – It contains the below parameters –>
( id* - id ) Returns:str

get_execution_jobs(request_object)

Retrieve all jobs

Returns a list of Data Query Execution Model

Parameters:request_object (ExecutionJobsGetRequest) – It contains the below parameters –>
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ),
( queryId - Filter based on query id ),
( status - Filter based on job status ) Returns:ResponseAllDataQueryExecutionResponse

get_execution_jobs_id(id)

Retrieve job details by job id

Returns Data Query Execution Model

Parameters:id (ExecutionJobsIdGetRequest) – It contains the below parameters –>
( id* - execution jobId ) Returns:DataQueryExecutionResponse

get_execution_jobs_id_results(id, range)

Retrieve query results by job id

Returns query results

Parameters:request_object (ExecutionJobsIdResultsGetRequest) – It contains the below parameters –>
( id* - execution jobId ),
( Range - Part of a file to return in Bytes, eg bytes=200-600 ) Returns:QueryResult

get_queries(request_object)

Retrieve all queries

Returns a list of Data Query SQL Model

Parameters:request_object (QueriesGetRequest) – It contains the below parameters –>
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ),
( executable - Filter based on executable flag ),
( isDynamic - Filter based on isDynamic flag ),
( ontologyId - Filter based on ontology id ) Returns:ResponseAllDataSQLQuery

get_queries_id(request_object)

Retrieve query by query id

Returns a Data Query SQL Model

Parameters:request_object (QueriesIdGetRequest) – It contains the below parameters –>
( id* - id ) Returns:NativeQueryGetResponse

get_queries_id_execution_jobs_latest_results(id, range)

Retrieve latest query results by query id

Returns query results

Parameters:request_object (QueriesIdExecutionJobsLatestResultsGetRequest) – It contains the below parameters –>
( id* - query id ),
( Range - Part of a file to return in Bytes, eg bytes=200-600 ) Returns:QueryResult

update_queries_id(id, request_object)

Update query by query id

Returns Updated Data Query SQL Model

Parameters:request_object (QueriesIdPatchRequest) – It contains the below parameters –>
( DataQuerySQLUpdateRequest* - DataQuerySQLUpdateRequest ),
( id* - id ) Returns:DataQuerySQLResponse

sdi.clients.data_registry_client module

SDI - Semantic Data Interconnect APIs

class DataRegistryClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_data_registries(request_object)

Add a new Data Registry entry

Initiate creation of Data Registry for the current tenant. The Data Registry information is used during data ingest for the tenant. Only one Data Registry can be created for a request. The dataTag, sourceName and fileUploadStrategy is required to create Date Registry otherwise creation is rejected. DataUpload will allow only files to be uploaded matching this Data Registry. This returns unique registryId for each request that can be used to retrieve the created registry. The tenant cannot have more than 500 data registries in the system. The schemaFrozen flag must be set to false during creation of a registry. It can be set to true after creation of the initial schema for the registry. Data partitioning on the registry can be optionally enabled by specifying a single partitionKey at the time of registry creation. The property value can be set to “sdi-default-partition-key” to enable default partitioning based on the sdi_hour attribute. The property can also be set to a custom attribute present in the data to enable custom partitioning based on that attribute.

Parameters:request_object (DataRegistriesPostRequest) – It contains the below parameters –>
( createDataRegistryRequest* - createDataRegistryRequest ) Returns:DataRegistry

delete_data_registries_id(id)

Deletes Data Registry for a given registry id

Deletes registry for a registry id

get_data_registries(request_object)

Retrieves Data Registry entries

Retrieves all Data Registry entries, Data Registry based on sourceName, dataTag or combination of sourceName and dataTag.

Parameters:request_object (DataRegistriesGetRequest) – It contains the below parameters –>
( dataTag - dataTag ),
( sourceName - sourceName ),
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ListOfRegistryResponse

get_data_registries_id(id)

Retrieves Data Registry for a given registry id

Retrieves registry for a registry id

Parameters:request_object (DataRegistriesIdGetRequest) – It contains the below parameters –>
( id* - Unique identifier of the Data Registry. ) Returns:DataRegistry

update_data_registries_id(id, request_object)

Updates a Data Registry

Update Data Registry entries for a given Data Registry Id. The registry attributes sourceName, dataTag and partitionKeys cannot be updated.

Parameters:request_object (DataRegistriesIdPatchRequest) – It contains the below parameters –>
( id* - Unique identifier of the Data Registry. ),
( updateDataRegistryRequest* - updateDataRegistryRequest ) Returns:DataRegistry

sdi.clients.iot_dataregistry_client module

SDI - Semantic Data Interconnect APIs

class IotDataRegistryClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_iot_data_registries(request_object)

Add a new IoT Data Registry entry

Create new IoT Data Registry with MindSphereAssetId and AspectName

Parameters:request_object (IotDataRegistriesPostRequest) – It contains the below parameters –>
( IotDataRegistry* - IotDataRegistry object ) Returns:IotDataRegistryResponse

get_iot_data_registries(request_object)

Retrieves an IoT Data Registry entries

Retrieves an IoT Data Registry with MindSphere AssetId and AspectName

Parameters:request_object (IotDataRegistriesGetRequest) – It contains the below parameters –>
( filter - filter ),
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ListOfIoTRegistryResponse

sdi.clients.ontologies_client module

SDI - Semantic Data Interconnect APIs

class OntologiesClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

delete_ontologies_id(id)

Delete ontology

Delete ontology

Parameters:id (OntologiesIdDeleteRequest) – It contains the below parameters –>
( id* - id ) Returns:None

get_ontologies(request_object)

Retrieve all ontologies

Returns a list of ontologies

Parameters:request_object (OntologiesGetRequest) – It contains the below parameters –>
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ResponseAllOntologies

get_ontologies_id(id, range)

Retrieve ontology.

Retrieve ontology.

Parameters:request_object (OntologiesIdGetRequest) – It contains the below parameters –>
( id* - id ),
( Range - Part of a file to return in Bytes, eg bytes=200-600 ) Returns:OntologyResponseData

infer_ontology(request_object)

Infer semantic model based on two or more generated schemas. Currently it supports infer of upto 20 schemas.

Infer semantic model to create ontology based on two or more schemas

Parameters:request_object (InferOntologyPostRequest) – It contains the below parameters –>
( inferSchemaSearchRequest* - Provide two or more schemas to infer an ontology. ) Returns:None

sdi.clients.ontologies_operations_client module

SDI - Semantic Data Interconnect APIs

class OntologiesOperationsClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_ontology_jobs(request_object)

Upload file and submit job for create/update ontology

Upload file and submit job for create/update ontology

Parameters:request_object (OntologyJobsPostRequest) – It contains the below parameters –>
( ontologyName* - - Ontology name should be unique. ),
( ontologyDescription - - Ontology description. ),
( ontologyId - - Provide OntologyId for updating existing ontology. If empty then will create new ontology. ),
( keyMappingType - - Define keyMappingType for ontology. ),
( file - - We support JSON and OWL file pre-defined format. ) Returns:OntologyJob

get_ontology_jobs_id(id)

Retrieve status of ontology creation/updation job

Parameters:request_object (OntologyJobsIdGetRequest) – It contains the below parameters –>
( id* - Ontology job ID as obtained on Ontology creation/updation ) Returns:JobStatus

sdi.clients.schema_registry_client module

SDI - Semantic Data Interconnect APIs

class SchemaRegistryClient(rest_client_config=None, mindsphere_credentials=None)

Bases: object

create_search_schemas(query_parameters, request_object)

Search schema for ingested data.

The data ingestion API creates and stores the schemas for each ingested file. This schema can be retrieved based on schema name, or source name and data tag combination. SchemaSearchRequest is limited to 20 schemas atmost.

Parameters:request_object (SearchSchemasPostRequest) – It contains the below parameters –>
( schemaSearchRequest* - schemaSearchRequest ),
( pageToken - Selects next page. Value must be taken rom response body property ‘page.nextToken’. If omitted, first page is returned. ) Returns:ListOfSchemaRegistry

Module contents