create_tabular_dataset_from_sql_query {azuremlsdk} | R Documentation |
Create a TabularDataset to represent tabular data in SQL databases.
Description
Create a TabularDataset to represent tabular data in SQL databases.
“from_sql_query“' creates a Tabular Dataset object , which defines the operations to
load data from SQL databases into tabular representation. For the data to be accessible
by Azure Machine Learning, the SQL database specified by query
must be located in
a Datastore and the datastore type must be of a SQL kind. Column data types are
read from data types in SQL query result. Providing 'set_column_types' will
override the data type for the specified columns in the returned Tabular Dataset.
Usage
create_tabular_dataset_from_sql_query(
query,
validate = TRUE,
set_column_types = NULL,
query_timeout = 30L
)
Arguments
query |
A SQL-kind datastore and a query |
validate |
Boolean to validate if data can be loaded from the returned dataset. Defaults to True. Validation requires that the data source is accessible from the current compute. |
set_column_types |
A named list to set column data type, where key is column name and value is data type. |
query_timeout |
Sets the wait time (as an int, in seconds) before terminating the attempt to execute a command and generating an error. The default is 30 seconds. |
Value
A TabularDataset
object
Examples
# create tabular dataset from a SQL database in datastore datastore <- get_datastore(ws, 'sql-db') query <- data_path(datastore, 'SELECT * FROM my_table') tab_ds <- create_tabular_dataset_from_sql_query(query, query_timeout = 10) # use `set_column_types` param to set column data types data_types <- list(ID = data_type_string(), Date = data_type_datetime('%d/%m/%Y %I:%M:%S %p'), Count = data_type_long(), Latitude = data_type_double(), Found = data_type_bool()) set_tab_ds <- create_tabular_dataset_from_sql_query(query, set_column_types = data_types)
See Also
data_path()
data_type_datetime()
data_type_bool()
data_type_double()
data_type_string()
data_type_long()