Feature hotfixes (#274)

* hot fixes for data analyzer issues

* hotfixes for issues in DataAnalyzer

* changed comment wording

CHANGELOG.md

@@ -3,6 +3,17 @@
 ## Change History
 All notable changes to the Databricks Labs Data Generator will be documented in this file.
 
+
+### Version 0.3.6 Post 1
+
+#### Changed
+* Updated docs for complex data types / JSON to correct code examples
+* Updated license file in public docs
+
+#### Fixed 
+* Fixed scenario where `DataAnalyzer` is used on dataframe containing a column named `summary`
+
+
 ### Version 0.3.6
 
 #### Changed

README.md

@@ -64,7 +64,7 @@ details of use and many examples.
 
 Release notes and details of the latest changes for this specific release
 can be found in the GitHub repository
-[here](https://github.com/databrickslabs/dbldatagen/blob/release/v0.3.6/CHANGELOG.md)
+[here](https://github.com/databrickslabs/dbldatagen/blob/release/v0.3.6post1/CHANGELOG.md)
 
 # Installation
 

dbldatagen/_version.py

@@ -34,7 +34,7 @@ def get_version(version):
     return version_info
 
 
-__version__ = "0.3.6"  # DO NOT EDIT THIS DIRECTLY!  It is managed by bumpversion
+__version__ = "0.3.6post1"  # DO NOT EDIT THIS DIRECTLY!  It is managed by bumpversion
 __version_info__ = get_version(__version__)
 
 

dbldatagen/data_analyzer.py

@@ -7,14 +7,18 @@
 
 This code is experimental and both APIs and code generated is liable to change in future versions.
 """
-from pyspark.sql.types import LongType, FloatType, IntegerType, StringType, DoubleType, BooleanType, ShortType, \
-    TimestampType, DateType, DecimalType, ByteType, BinaryType, StructType, ArrayType, DataType
+import logging
 
 import pyspark.sql as ssql
-import pyspark.sql.functions as F
+from pyspark.sql.types import LongType, FloatType, IntegerType, StringType, DoubleType, BooleanType, ShortType, \
+    TimestampType, DateType, DecimalType, ByteType, BinaryType, StructType, ArrayType, DataType
 
-from .utils import strip_margins
 from .spark_singleton import SparkSingleton
+from .utils import strip_margins
+
+SUMMARY_FIELD_NAME = "summary"
+SUMMARY_FIELD_NAME_RENAMED = "__summary__"
+DATA_SUMMARY_FIELD_NAME = "__data_summary__"
 
 
 class DataAnalyzer:
@@ -23,6 +27,8 @@ class DataAnalyzer:
 
     :param df: Spark dataframe to analyze
     :param sparkSession: Spark session instance to use when performing spark operations
+    :param debug: If True, additional debug information is logged
+    :param verbose: If True, additional information is logged
 
     .. warning::
        Experimental
@@ -43,11 +49,17 @@ class DataAnalyzer:
                         |# Column definitions are stubs only - modify to generate correct data  
                         |#""", '|')
 
-    def __init__(self, df=None, sparkSession=None):
+    def __init__(self, df=None, sparkSession=None, debug=False, verbose=False):
         """ Constructor:
         :param df: Dataframe to analyze
         :param sparkSession: Spark session to use
         """
+        # set up logging
+        self.verbose = verbose
+        self.debug = debug
+
+        self._setupLogger()
+
         assert df is not None, "dataframe must be supplied"
 
         self._df = df
@@ -58,6 +70,19 @@ def __init__(self, df=None, sparkSession=None):
         self._sparkSession = sparkSession
         self._dataSummary = None
 
+    def _setupLogger(self):
+        """Set up logging
+
+        This will set the logger at warning, info or debug levels depending on the instance construction parameters
+        """
+        self.logger = logging.getLogger("DataAnalyzer")
+        if self.debug:
+            self.logger.setLevel(logging.DEBUG)
+        elif self.verbose:
+            self.logger.setLevel(logging.INFO)
+        else:
+            self.logger.setLevel(logging.WARNING)
+
     def _displayRow(self, row):
         """Display details for row"""
         results = []
@@ -95,6 +120,31 @@ def _addMeasureToSummary(self, measureName, summaryExpr="''", fieldExprs=None, d
 
         return dfResult
 
+    def _get_dataframe_describe_stats(self, df):
+        """ Get summary statistics for dataframe handling renaming of summary field if necessary"""
+        print("schema", df.schema)
+
+        src_fields = [fld.name for fld in df.schema.fields]
+        print("src_fields", src_fields)
+        renamed_summary = False
+
+        # get summary statistics handling the case where a field named 'summary' exists
+        # if the `summary` field name exists, we'll rename it to avoid a conflict
+        if SUMMARY_FIELD_NAME in src_fields:
+            renamed_summary = True
+            df = df.withColumnRenamed(SUMMARY_FIELD_NAME, SUMMARY_FIELD_NAME_RENAMED)
+
+        # The dataframe describe method produces a field named `summary`. We'll rename this to avoid conflict with
+        # any natural fields using the same name.
+        summary_df = df.describe().withColumnRenamed(SUMMARY_FIELD_NAME, DATA_SUMMARY_FIELD_NAME)
+
+        # if we renamed a field called `summary` in the data, we'll rename it back.
+        # The data summary field produced by the describe method has already been renamed so there will be no conflict.
+        if renamed_summary:
+            summary_df = summary_df.withColumnRenamed(SUMMARY_FIELD_NAME_RENAMED, SUMMARY_FIELD_NAME)
+
+        return summary_df
+
     def summarizeToDF(self):
         """ Generate summary analysis of data set as dataframe
 
@@ -154,11 +204,12 @@ def summarizeToDF(self):
             dfData=self._df,
             dfSummary=dfDataSummary)
 
-        descriptionDf = self._df.describe().where("summary in ('mean', 'stddev')")
+        descriptionDf = (self._get_dataframe_describe_stats(self._df)
+                         .where(f"{DATA_SUMMARY_FIELD_NAME} in ('mean', 'stddev')"))
         describeData = descriptionDf.collect()
 
         for row in describeData:
-            measure = row['summary']
+            measure = row[DATA_SUMMARY_FIELD_NAME]
 
             values = {k[0]: '' for k in dtypes}
 
@@ -401,7 +452,12 @@ def scriptDataGeneratorFromData(self, suppressOutput=False, name=None):
 
         """
         assert self._df is not None
-        assert type(self._df) is ssql.DataFrame, "sourceDf must be a valid Pyspark dataframe"
+
+        if not isinstance(self._df, ssql.DataFrame):
+            self.logger.warning(strip_margins(
+                """The parameter `sourceDf` should be a valid Pyspark dataframe.
+                   |Note this warning may false due to use of remote connection to a Spark cluster""",
+                '|'))
 
         if self._dataSummary is None:
             df_summary = self.summarizeToDF()

dbldatagen/utils.py

@@ -27,7 +27,6 @@ def deprecated(message=""):
     """
 
     # create closure around function that follows use of the decorator
-
     def deprecated_decorator(func):
         @functools.wraps(func)
         def deprecated_func(*args, **kwargs):
@@ -290,7 +289,6 @@ def split_list_matching_condition(lst, cond):
     x = ['id', 'city_name', 'id', 'city_id', 'city_pop', 'id', 'city_id', 'city_pop','city_id', 'city_pop','id']
     splitListOnCondition(x, lambda el: el == 'id')
 
-
     Result:
     `[['id'], ['city_name'], ['id'], ['city_id', 'city_pop'],
       ['id'], ['city_id', 'city_pop', 'city_id', 'city_pop'], ['id']]`

docs/source/conf.py

@@ -28,7 +28,7 @@
 author = 'Databricks Inc'
 
 # The full version, including alpha/beta/rc tags
-release = "0.3.6"  # DO NOT EDIT THIS DIRECTLY!  It is managed by bumpversion
+release = "0.3.6post1"  # DO NOT EDIT THIS DIRECTLY!  It is managed by bumpversion
 
 
 # -- General configuration ---------------------------------------------------

docs/source/generating_json_data.rst

@@ -7,6 +7,8 @@ Generating JSON and Structured Column Data
 This section explores generating JSON and structured column data. By structured columns,
 we mean columns that are some combination of `struct`, `array` and `map` of other types.
 
+*Note that some of the examples are code fragments for illustration purposes only.*
+
 Generating JSON data
 --------------------
 There are several methods for generating JSON data:
@@ -25,6 +27,7 @@ The following example illustrates the basic technique for generating JSON data f
 
    import dbldatagen as dg
 
+   device_population = 100000
 
    country_codes = ['CN', 'US', 'FR', 'CA', 'IN', 'JM', 'IE', 'PK', 'GB', 'IL', 'AU', 'SG',
                     'ES', 'GE', 'MX', 'ET', 'SA', 'LB', 'NL']
@@ -106,6 +109,7 @@ Note that in the current release, the `expr` attribute will override other colum
 
    import dbldatagen as dg
 
+   device_population = 100000
 
    country_codes = ['CN', 'US', 'FR', 'CA', 'IN', 'JM', 'IE', 'PK', 'GB', 'IL', 'AU', 'SG',
                     'ES', 'GE', 'MX', 'ET', 'SA', 'LB', 'NL']
@@ -221,6 +225,7 @@ functions such as `named_struct` and `to_json`.
 
    import dbldatagen as dg
 
+   device_population = 100000
 
    country_codes = ['CN', 'US', 'FR', 'CA', 'IN', 'JM', 'IE', 'PK', 'GB', 'IL', 'AU', 'SG',
                     'ES', 'GE', 'MX', 'ET', 'SA', 'LB', 'NL']
@@ -341,6 +346,25 @@ populated.
 
 The following example illustrates this:
 
+.. code-block:: python
+
+   import dbldatagen as dg
+
+   column_count = 10
+   data_rows = 10 * 1000
+   df_spec = (dg.DataGenerator(spark, name="test_data_set1", rows=data_rows)
+                .withIdOutput()
+                .withColumn("r", FloatType(), expr="floor(rand() * 350) * (86400 + 3600)",
+                            numColumns=column_count, structType="array")
+                .withColumn("code1", "integer", minValue=100, maxValue=200)
+                .withColumn("code2", "integer", minValue=0, maxValue=10)
+                .withColumn("code3", "string", values=['one', 'two', 'three'])
+                .withColumn("code4", "string", values=['one', 'two', 'three'])
+                .withColumn("code5", dg.INFER_DATATYPE, expr="current_date()")
+                .withColumn("code6", dg.INFER_DATATYPE, expr="code1 + code2")
+                .withColumn("code7", dg.INFER_DATATYPE, expr="concat(code3, code4)")
+                )
+
 
 Using multi feature columns to generate arrays
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.6
+current_version = 0.3.6post1
 commit = False
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+){0,1}(?P<release>\D*)(?P<build>\d*)

setup.py

@@ -31,7 +31,7 @@
 
 setuptools.setup(
     name="dbldatagen",
-    version="0.3.6",
+    version="0.3.6post1",
     author="Ronan Stokes, Databricks",
     description="Databricks Labs -  PySpark Synthetic Data Generator",
     long_description=long_description,

tests/test_generation_from_data.py

@@ -107,3 +107,9 @@ def test_summarize_to_df(self, generation_spec, testLogger):
         df = analyzer.summarizeToDF()
 
         df.show()
+
+    def test_df_containing_summary(self):
+        df = spark.range(10).withColumnRenamed("id", "summary")
+        summary_df = dg.DataAnalyzer(sparkSession=spark, df=df).summarizeToDF()
+
+        assert summary_df.count() == 10