Feature struct changes (#219)

* wip

* wip

* added support for inferred types

* added support for inferred types

* wip

* wip

* wip

* updates and fixes to unit tests

* wip

* updated pipfile due to upstream changes in pipenv

* additional tests

* wip

* wip

* wip

* wip

* removed old code

CHANGELOG.md

@@ -7,6 +7,8 @@ All notable changes to the Databricks Labs Data Generator will be documented in
 
 #### Changed
 * Added formatting of generated code as Html for script methods
+* Allow use of inferred types on `withColumn` method when `expr` attribute is used
+* Added ``withStructColumn`` method to allow simplified generation of struct and JSON columns
 * Modified pipfile to use newer version of package specifications
 
 ### Version 0.3.4 Post 3

dbldatagen/__init__.py

@@ -25,7 +25,8 @@
 
 from .data_generator import DataGenerator
 from .datagen_constants import DEFAULT_RANDOM_SEED, RANDOM_SEED_RANDOM, RANDOM_SEED_FIXED, \
-                               RANDOM_SEED_HASH_FIELD_NAME, MIN_PYTHON_VERSION, MIN_SPARK_VERSION
+                               RANDOM_SEED_HASH_FIELD_NAME, MIN_PYTHON_VERSION, MIN_SPARK_VERSION, \
+                               INFER_DATATYPE
 from .utils import ensure, topologicalSort, mkBoundsList, coalesce_values, \
     deprecated, parse_time_interval, DataGenError, split_list_matching_condition, strip_margins, \
     json_value_from_path, system_time_millis

dbldatagen/column_generation_spec.py

@@ -20,18 +20,20 @@
 
 from .column_spec_options import ColumnSpecOptions
 from .datagen_constants import RANDOM_SEED_FIXED, RANDOM_SEED_HASH_FIELD_NAME, RANDOM_SEED_RANDOM, \
-    DEFAULT_SEED_COLUMN, OPTION_RANDOM, OPTION_RANDOM_SEED, OPTION_RANDOM_SEED_METHOD
+    DEFAULT_SEED_COLUMN, OPTION_RANDOM, OPTION_RANDOM_SEED, OPTION_RANDOM_SEED_METHOD, INFER_DATATYPE
 
 from .daterange import DateRange
 from .distributions import Normal, DataDistribution
 from .nrange import NRange
 from .text_generators import TemplateGenerator
 from .utils import ensure, coalesce_values
+from .schema_parser import SchemaParser
 
 HASH_COMPUTE_METHOD = "hash"
 VALUES_COMPUTE_METHOD = "values"
 RAW_VALUES_COMPUTE_METHOD = "raw_values"
 AUTO_COMPUTE_METHOD = "auto"
+EXPR_OPTION = "expr"
 COMPUTE_METHOD_VALID_VALUES = [HASH_COMPUTE_METHOD,
                                AUTO_COMPUTE_METHOD,
                                VALUES_COMPUTE_METHOD,
@@ -107,8 +109,18 @@ def __init__(self, name, colType=None, minValue=0, maxValue=None, step=1, prefix
         # set up default range and type for column
         self._dataRange = NRange(None, None, None)  # by default range of values for  column is unconstrained
 
+        self._inferDataType = False
         if colType is None:  # default to integer field if none specified
             colType = IntegerType()
+        elif colType == INFER_DATATYPE:
+            colType = StringType()  # default inferred data type to string until exact type is known
+            self._inferDataType = True
+
+            if EXPR_OPTION not in kwargs:
+                raise ValueError("Column generation spec must have `expr` attribute specified if datatype is inferred")
+
+        elif type(colType) == str:
+            colType = SchemaParser.columnTypeFromString(colType)
 
         assert isinstance(colType, DataType), f"colType `{colType}` is not instance of DataType"
 
@@ -399,6 +411,12 @@ def textGenerator(self):
         """ Get the text generator for the column spec"""
         return self._textGenerator
 
+    @property
+    def inferDatatype(self):
+        """ If True indicates that datatype should be inferred to be result of computing SQL expression
+        """
+        return self._inferDataType
+
     @property
     def baseColumns(self):
         """ Return base columns as list of strings"""
@@ -1030,11 +1048,12 @@ def _makeSingleGenerationExpression(self, index=None, use_pandas_optimizations=T
             # TODO: add full support for date value generation
             if self.expr is not None:
                 # note use of SQL expression ignores range specifications
-                new_def = expr(self.expr).astype(self.datatype)
-
-                # record execution history
+                new_def = expr(self.expr)
                 self.executionHistory.append(f".. using SQL expression `{self.expr}` as base")
-                self.executionHistory.append(f".. casting to  `{self.datatype}`")
+
+                if not self._inferDataType:
+                    new_def = new_def.astype(self.datatype)
+                    self.executionHistory.append(f".. casting to  `{self.datatype}`")
             elif type(self.datatype) in [ArrayType, MapType, StructType] and self.values is None:
                 new_def = expr("NULL")
             elif self._dataRange is not None and self._dataRange.isFullyPopulated():
@@ -1083,6 +1102,22 @@ def _makeSingleGenerationExpression(self, index=None, use_pandas_optimizations=T
             new_def = self._applyComputePercentNullsExpression(new_def, percent_nulls)
         return new_def
 
+    def _onSelect(self, df):
+        """
+        The _onSelect method is called when the column specifications expression as produced by the
+        method ``_makeSingleGenerationExpression`` is used in a select statement.
+
+        :param df: Dataframe in which expression is used
+        :return: nothing
+
+        .. note:: The purpose of this method is to allow for introspection of information such as datatype
+                  which can only be determined when column specifications expression is used.
+        """
+        if self._inferDataType:
+            inferred_type = df.schema[self.name].dataType
+            self.logger.info("Inferred datatype for column %s as %s", self.name, str(inferred_type))
+            self._csOptions.options['type'] = inferred_type
+
     def _applyTextFormatExpression(self, new_def, sformat):
         # note :
         # while it seems like this could use a shared instance, this does not work if initialized
@@ -1141,6 +1176,9 @@ def _applyFinalCastExpression(self, col_type, new_def):
         # cast the result to the appropriate type. For dates, cast first to timestamp, then to date
         if type(col_type) is DateType:
             new_def = new_def.astype(TimestampType()).astype(col_type)
+        elif self._inferDataType:
+            # dont apply cast when column has an inferred data type
+            pass
         else:
             new_def = new_def.astype(col_type)