def _get_dataset_column_sample_values(
self, column_profile: DatasetFieldProfileClass,
column: str) -> None:
if self.config.include_field_sample_values:
# TODO do this without GE
self.dataset.set_config_value("interactive_evaluation", True)
res = self.dataset.expect_column_values_to_be_in_set(
column, [], result_format="SUMMARY").result
column_profile.sampleValues = [
str(v) for v in res["partial_unexpected_list"]
]
def __init__(
self,
dataframe: DataFrame,
spark: SparkSession,
profiling_config: DataLakeProfilerConfig,
report: DataLakeSourceReport,
file_path: str,
):
self.spark = spark
self.dataframe = dataframe
self.analyzer = AnalysisRunner(spark).onData(dataframe)
self.column_specs = []
self.row_count = dataframe.count()
self.profiling_config = profiling_config
self.file_path = file_path
self.columns_to_profile = []
self.ignored_columns = []
self.profile = DatasetProfileClass(timestampMillis=get_sys_time())
self.report = report
self.profile.rowCount = self.row_count
self.profile.columnCount = len(dataframe.columns)
column_types = {x.name: x.dataType for x in dataframe.schema.fields}
if self.profiling_config.profile_table_level_only:
return
# get column distinct counts
for column in dataframe.columns:
if not self.profiling_config.allow_deny_patterns.allowed(column):
self.ignored_columns.append(column)
continue
self.columns_to_profile.append(column)
# Normal CountDistinct is ridiculously slow
self.analyzer.addAnalyzer(ApproxCountDistinct(column))
if self.profiling_config.max_number_of_fields_to_profile is not None:
if (len(self.columns_to_profile) >
self.profiling_config.max_number_of_fields_to_profile):
columns_being_dropped = self.columns_to_profile[
self.profiling_config.max_number_of_fields_to_profile:]
self.columns_to_profile = self.columns_to_profile[:self.
profiling_config
.
max_number_of_fields_to_profile]
self.report.report_file_dropped(
f"The max_number_of_fields_to_profile={self.profiling_config.max_number_of_fields_to_profile} reached. Profile of columns {self.file_path}({', '.join(sorted(columns_being_dropped))})"
)
analysis_result = self.analyzer.run()
analysis_metrics = AnalyzerContext.successMetricsAsJson(
self.spark, analysis_result)
# reshape distinct counts into dictionary
column_distinct_counts = {
x["instance"]: int(x["value"])
for x in analysis_metrics if x["name"] == "ApproxCountDistinct"
}
select_numeric_null_counts = [
count(when(
isnan(c) | col(c).isNull(),
c,
)).alias(c) for c in self.columns_to_profile
if column_types[column] in [DoubleType, FloatType]
]
# PySpark doesn't support isnan() on non-float/double columns
select_nonnumeric_null_counts = [
count(when(
col(c).isNull(),
c,
)).alias(c) for c in self.columns_to_profile
if column_types[column] not in [DoubleType, FloatType]
]
null_counts = dataframe.select(select_numeric_null_counts +
select_nonnumeric_null_counts)
column_null_counts = null_counts.toPandas().T[0].to_dict()
column_null_fractions = {
c: column_null_counts[c] / self.row_count
for c in self.columns_to_profile
}
column_nonnull_counts = {
c: self.row_count - column_null_counts[c]
for c in self.columns_to_profile
}
column_unique_proportions = {
c:
(column_distinct_counts[c] /
column_nonnull_counts[c] if column_nonnull_counts[c] > 0 else 0)
for c in self.columns_to_profile
}
if self.profiling_config.include_field_sample_values:
# take sample and convert to Pandas DataFrame
if self.row_count < NUM_SAMPLE_ROWS:
# if row count is less than number to sample, just take all rows
rdd_sample = dataframe.rdd.take(self.row_count)
else:
rdd_sample = dataframe.rdd.takeSample(False,
NUM_SAMPLE_ROWS,
seed=0)
# init column specs with profiles
for column in self.columns_to_profile:
column_profile = DatasetFieldProfileClass(fieldPath=column)
column_spec = _SingleColumnSpec(column, column_profile)
column_profile.uniqueCount = column_distinct_counts.get(column)
column_profile.uniqueProportion = column_unique_proportions.get(
column)
column_profile.nullCount = column_null_counts.get(column)
column_profile.nullProportion = column_null_fractions.get(column)
if self.profiling_config.include_field_sample_values:
column_profile.sampleValues = [
str(x[column]) for x in rdd_sample
]
column_spec.type_ = column_types[column]
column_spec.cardinality = _convert_to_cardinality(
column_distinct_counts[column],
column_null_fractions[column],
)
self.column_specs.append(column_spec)
def _handle_convert_column_evrs( # noqa: C901 (complexity)
self,
profile: DatasetProfileClass,
column: str,
col_evrs: Iterable[ExpectationValidationResult],
pretty_name: str,
send_sample_values: bool,
) -> None:
# TRICKY: This method mutates the profile directly.
column_profile = DatasetFieldProfileClass(fieldPath=column)
profile.fieldProfiles = profile.fieldProfiles or []
profile.fieldProfiles.append(column_profile)
for evr in col_evrs:
exp: str = evr.expectation_config.expectation_type
res: dict = evr.result
if not res:
self.report.report_warning(f"profile of {pretty_name}",
f"{exp} did not yield any results")
continue
if exp == "expect_column_unique_value_count_to_be_between":
column_profile.uniqueCount = res["observed_value"]
elif exp == "expect_column_proportion_of_unique_values_to_be_between":
column_profile.uniqueProportion = res["observed_value"]
elif exp == "expect_column_values_to_not_be_null":
column_profile.nullCount = res["unexpected_count"]
if ("unexpected_percent" in res
and res["unexpected_percent"] is not None):
column_profile.nullProportion = res[
"unexpected_percent"] / 100
elif exp == "expect_column_values_to_not_match_regex":
# ignore; generally used for whitespace checks using regex r"^\s+|\s+$"
pass
elif exp == "expect_column_mean_to_be_between":
column_profile.mean = str(res["observed_value"])
elif exp == "expect_column_min_to_be_between":
column_profile.min = str(res["observed_value"])
elif exp == "expect_column_max_to_be_between":
column_profile.max = str(res["observed_value"])
elif exp == "expect_column_median_to_be_between":
column_profile.median = str(res["observed_value"])
elif exp == "expect_column_stdev_to_be_between":
column_profile.stdev = str(res["observed_value"])
elif exp == "expect_column_quantile_values_to_be_between":
if "observed_value" in res:
column_profile.quantiles = [
QuantileClass(quantile=str(quantile), value=str(value))
for quantile, value in zip(
res["observed_value"]["quantiles"],
res["observed_value"]["values"],
)
]
elif exp == "expect_column_values_to_be_in_set":
column_profile.sampleValues = [
str(v) for v in res["partial_unexpected_list"]
]
if not send_sample_values:
column_profile.sampleValues = []
elif exp == "expect_column_kl_divergence_to_be_less_than":
if "details" in res and "observed_partition" in res["details"]:
partition = res["details"]["observed_partition"]
column_profile.histogram = HistogramClass(
[str(v) for v in partition["bins"]],
[
partition["tail_weights"][0],
*partition["weights"],
partition["tail_weights"][1],
],
)
elif exp == "expect_column_distinct_values_to_be_in_set":
if "details" in res and "value_counts" in res["details"]:
# This can be used to produce a bar chart since it includes values and frequencies.
# As such, it is handled differently from expect_column_values_to_be_in_set, which
# is nonexhaustive.
column_profile.distinctValueFrequencies = [
ValueFrequencyClass(value=str(value), frequency=count)
for value, count in res["details"]
["value_counts"].items()
]
if not send_sample_values:
column_profile.distinctValueFrequencies = []
elif exp == "expect_column_values_to_be_in_type_list":
# ignore; we already know the types for each column via ingestion
pass
elif exp == "expect_column_values_to_be_unique":
# ignore; this is generally covered by the unique value count test
pass
else:
self.report.report_warning(
f"profile of {pretty_name}",
f"warning: unknown column mapper {exp} in col {column}",
)