Skip to content

Dq factory

Module containing the class definition of the Data Quality Factory.

DQFactory

Bases: object

Class for the Data Quality Factory.

Source code in mkdocs/lakehouse_engine/packages/dq_processors/dq_factory.py
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
class DQFactory(object):
    """Class for the Data Quality Factory."""

    _LOGGER = LoggingHandler(__name__).get_logger()
    _TIMESTAMP = datetime.now(timezone.utc).strftime("%Y%m%d%H%M%S")

    @classmethod
    def _add_critical_function_tag(cls, args: dict) -> dict:
        """Add tags to function considered critical.

        Adds a tag to each of the functions passed on the dq_specs to
        denote that they are critical_functions. This means that if any
        of them fails, the dq process will fail, even if the threshold
        is not surpassed.
        This is done by adding a tag to the meta dictionary of the
        expectation configuration.

        Args:
            args: arguments passed on the dq_spec

        Returns:
            A dictionary with the args with the critical function tag.
        """
        if "meta" in args.keys():
            meta = args["meta"]

            if isinstance(meta["notes"], str):
                meta["notes"] = meta["notes"] + " **Critical function**."
            else:
                meta["notes"]["content"] = (
                    meta["notes"]["content"] + " **Critical function**."
                )

            args["meta"] = meta
            return args

        else:
            args["meta"] = {
                "notes": {
                    "format": "markdown",
                    "content": "**Critical function**.",
                }
            }
            return args

    @classmethod
    def _configure_checkpoint(
        cls,
        context: EphemeralDataContext,
        dataframe_bd: DataFrame,
        suite: ExpectationSuite,
        dq_spec: DQSpec,
        data: DataFrame,
        checkpoint_run_time: str,
    ) -> Tuple[ExpectationSuiteValidationResult, Optional[list]]:
        """Create and configure the validation checkpoint.

        Creates and configures a validation definition based on the suite
        and then creates, configures and runs the checkpoint returning,
        at the end, the result as well as the primary key from the dq_specs.

        Args:
            context: The data context from GX
            dataframe_bd: The dataframe with the batch definition to validate
            suite: A group of expectations to validate
            dq_spec: The arguments directly passed from the acon in the dq_spec key
            data: Input dataframe to run the dq process on.
            checkpoint_run_time: A string with the time in miliseconds

        Returns:
            A tuple with the result from the checkpoint run and the primary key
            from the dq_spec.
        """
        validation_definition = context.validation_definitions.add(
            gx.ValidationDefinition(
                data=dataframe_bd,
                suite=suite,
                name=f"{dq_spec.spec_id}-{dq_spec.input_id}"
                f"-validation-{checkpoint_run_time}",
            )
        )

        source_pk = cls._get_unexpected_rows_pk(dq_spec)
        result_format: dict = {
            "result_format": DQResultFormat.COMPLETE.value,
        }

        # If the source primary key is defined, we add it to the result format
        # so that it is included in the results from GX.
        if source_pk:
            result_format = {
                **result_format,
                "unexpected_index_column_names": source_pk,
            }

        checkpoint = context.checkpoints.add(
            gx.Checkpoint(
                name=f"{dq_spec.spec_id}-{dq_spec.input_id}"
                f"-checkpoint-{checkpoint_run_time}"
                f"-{str(random.randint(1, 100))}",  # nosec B311
                validation_definitions=[validation_definition],
                actions=[],
                result_format=result_format,
            )
        )

        result = checkpoint.run(
            batch_parameters={"dataframe": data},
            run_id=RunIdentifier(
                run_name=f"{checkpoint_run_time}"
                f"-{dq_spec.spec_id}-{dq_spec.input_id}"
                f"-{str(random.randint(1, 100))}-checkpoint",  # nosec B311
                run_time=datetime.strptime(checkpoint_run_time, "%Y%m%d-%H%M%S%f"),
            ),
        )

        return result, source_pk

    @classmethod
    def _check_row_condition(
        cls, dq_spec: DQSpec, dq_function: DQFunctionSpec
    ) -> DQFunctionSpec:
        """Enables/disables row_conditions.

        Checks for row_codition arguments in the definition of expectations
        and enables/disables their usage based on the enable_row_condition
        argument. row_conditions allow you to filter the rows that are
        processed by the DQ functions. This is useful when you want to run the
        DQ functions only on a subset of the data.

        Args:
            dq_spec: The arguments directly passed from the acon in the dq_spec key
            dq_function: A DQFunctionSpec with the definition of a dq function.

        Returns:
            The definition of a dq_function with or without the row_condition key.
        """
        if (
            not dq_spec.enable_row_condition
            and "row_condition" in dq_function.args.keys()
        ):
            del dq_function.args["row_condition"]
            cls._LOGGER.info(
                f"Disabling row_condition for function: {dq_function.function}"
            )
        return dq_function

    @classmethod
    def _add_suite(
        cls, context: EphemeralDataContext, dq_spec: DQSpec, checkpoint_run_time: str
    ) -> ExpectationSuite:
        """Create and configure an ExpectationSuite.

        Creates and configures an expectation suite, adding the dq functions
        passed on the dq_spec as well as the dq_critical_functions also passed
        on the dq_spec, if they exist. Finally return the configured suite.

        Args:
            context: The data context from GX
            dq_spec: The arguments directly passed from the acon in the dq_spec key
            checkpoint_run_time: A string with the time in miliseconds

        Returns:
            A configured ExpectationSuite object.
        """
        expectation_suite_name = (
            dq_spec.expectation_suite_name
            if dq_spec.expectation_suite_name
            else f"{dq_spec.spec_id}-{dq_spec.input_id}"
            f"-{dq_spec.dq_type}-{checkpoint_run_time}"
        )
        suite = context.suites.add(gx.ExpectationSuite(name=expectation_suite_name))

        for dq_function in dq_spec.dq_functions:
            dq_function = cls._check_row_condition(dq_spec, dq_function)
            suite.add_expectation_configuration(
                ExpectationConfiguration(
                    type=dq_function.function,
                    kwargs=dq_function.args if dq_function.args else {},
                    meta=dq_function.args.get("meta") if dq_function.args else {},
                )
            )
        if dq_spec.critical_functions:
            for critical_function in dq_spec.critical_functions:
                meta_args = cls._add_critical_function_tag(critical_function.args)
                suite.add_expectation_configuration(
                    ExpectationConfiguration(
                        type=critical_function.function,
                        kwargs=(
                            critical_function.args if critical_function.args else {}
                        ),
                        meta=meta_args,
                    )
                )

        suite.save()
        return suite

    @classmethod
    def _check_expectation_result(cls, result_dict: dict) -> dict:
        """Add an empty dict if the unexpected_index_list key is empty.

        Checks if the unexpected_index_list key has any element, if it doesn't,
        add an empty dictionary to the result key. This is needed due to some
        edge cases that appeared due to the GX update to version 1.3.13 where
        the unexpected_index_list would sometimes exist even for successful
        validation runs.

        Args:
            result_dict: A dict with the result_dict from a checkpoint run.

        Returns:
            The configured result_dict
        """
        for expectation_result in result_dict["results"]:
            if "unexpected_index_list" in expectation_result["result"].keys():
                if len(expectation_result["result"]["unexpected_index_list"]) < 1:
                    expectation_result["result"] = {}
        return result_dict

    @classmethod
    def run_dq_process(cls, dq_spec: DQSpec, data: DataFrame) -> DataFrame:
        """Run the specified data quality process on a dataframe.

        Based on the dq_specs we apply the defined expectations on top of the dataframe
        in order to apply the necessary validations and then output the result of
        the data quality process.

        The logic of the function is as follows:
        1. Import the custom expectations defined in the engine.
        2. Create the context based on the dq_spec. - The context is the base class for
        the GX, an ephemeral context means that it does not store/load the
        configuration of the environment in a configuration file.
        3. Add the data source to the context. - This is the data source that will be
        used to run the dq process, in our case Spark.
        4. Create the dataframe asset and batch definition. - The asset represents the
        data where the expectations are applied and the batch definition is the
        way how the data should be split, in the case of dataframes it is always
        the whole dataframe.
        5. Create the expectation suite. - This is the group of expectations that will
        be applied to the data.
        6. Create the checkpoint and run it. - The checkpoint is the object that will
        run the expectations on the data and return the results.
        7. Transform the results and write them to the result sink. - The results are
        transformed to a more readable format and then written to the result sink.
        8. Log the results and raise an exception if needed. - The results are logged
        and if there are any failed expectations the process will raise an exception
        based on the dq_spec.
        9. Tag the source data if needed. - If the dq_spec has the tag_source_data
        argument set to True, the source data will be tagged with the dq results.

        Args:
            dq_spec: data quality specification.
            data: input dataframe to run the dq process on.

        Returns:
            The DataFrame containing the results of the DQ process.
        """
        # Creating the context
        if dq_spec.dq_type == "validator" or dq_spec.dq_type == "prisma":

            for expectation in DQDefaults.CUSTOM_EXPECTATION_LIST.value:
                importlib.__import__(
                    "lakehouse_engine.dq_processors.custom_expectations." + expectation
                )

            context = gx.get_context(
                cls._get_data_context_config(dq_spec), mode="ephemeral"
            )

            # Adding data source to context
            dataframe_data_source = context.data_sources.add_spark(
                name=f"{dq_spec.spec_id}-{dq_spec.input_id}-datasource",
                persist=False,
            )
            dataframe_asset = dataframe_data_source.add_dataframe_asset(
                name=f"{dq_spec.spec_id}-{dq_spec.input_id}-asset"
            )
            dataframe_bd = dataframe_asset.add_batch_definition_whole_dataframe(
                name=f"{dq_spec.spec_id}-{dq_spec.input_id}-batch"
            )

            checkpoint_run_time = datetime.today().strftime("%Y%m%d-%H%M%S%f")

            suite = cls._add_suite(context, dq_spec, checkpoint_run_time)

            result, source_pk = cls._configure_checkpoint(
                context, dataframe_bd, suite, dq_spec, data, checkpoint_run_time
            )

            expectation_result_key = list(result.run_results.keys())[0]

            result_dict = result.run_results[expectation_result_key].to_json_dict()

            result_dict = cls._check_expectation_result(result_dict)

            data = cls._transform_checkpoint_results(
                data, source_pk, result_dict, dq_spec
            )

            # Processed keys are only added for the PRISMA dq type
            # because they are being used to calculate the good
            # records that were processed in a run.
            if dq_spec.dq_type == DQType.PRISMA.value:

                keys = data.select(*source_pk)
                keys = keys.withColumn(
                    "run_name", lit(result_dict["meta"]["run_id"]["run_name"])
                )

                cls._write_to_location(dq_spec, keys, processed_keys=True)

        else:
            raise TypeError(
                f"Type of Data Quality '{dq_spec.dq_type}' is not supported."
            )

        return data

    @classmethod
    def _check_critical_functions_tags(cls, failed_expectations: dict) -> list:
        critical_failure = []

        for expectation in failed_expectations.values():
            meta = expectation["meta"]
            if meta and (
                ("notes" in meta.keys() and "Critical function" in meta["notes"])
                or (
                    "content" in meta["notes"].keys()
                    and "Critical function" in meta["notes"]["content"]
                )
            ):
                critical_failure.append(expectation["type"])

        return critical_failure

    @classmethod
    def _explode_results(
        cls,
        df: DataFrame,
        dq_spec: DQSpec,
    ) -> DataFrame:
        """Transform dq results dataframe exploding a set of columns.

        Args:
            df: dataframe with dq results to be exploded.
            dq_spec: data quality specification.
        """
        df = df.withColumn("validation_results", explode("results")).withColumn(
            "source", lit(dq_spec.source)
        )

        if (
            not df.schema["validation_results"]
            .dataType.fieldNames()  # type: ignore
            .__contains__("result")
        ):
            df = df.withColumn(
                "validation_results",
                col("validation_results").withField(
                    "result", struct(lit(None).alias("observed_value"))
                ),
            )

        kwargs_columns = [
            f"validation_results.expectation_config.kwargs.{col_name}"
            for col_name in df.select(
                "validation_results.expectation_config.kwargs.*"
            ).columns
        ]

        cols_to_cast = ["max_value", "min_value", "sum_total"]
        for col_name in kwargs_columns:
            if col_name.split(".")[-1] in cols_to_cast:
                df = df.withColumn(
                    "validation_results",
                    col("validation_results").withField(
                        "expectation_config",
                        col("validation_results.expectation_config").withField(
                            "kwargs",
                            col(
                                "validation_results.expectation_config.kwargs"
                            ).withField(
                                col_name.split(".")[-1],
                                col(col_name).cast(FloatType()),
                            ),
                        ),
                    ),
                )

        new_columns = [
            "validation_results.expectation_config.kwargs.*",
            "validation_results.expectation_config.type as expectation_type",
            "validation_results.success as expectation_success",
            "validation_results.exception_info",
            "statistics.*",
        ] + dq_spec.result_sink_extra_columns

        df_exploded = df.selectExpr(*df.columns, *new_columns).drop(
            *[c.replace(".*", "").split(" as")[0] for c in new_columns]
        )

        df_exploded = df_exploded.drop(
            "statistics", "id", "results", "meta", "suite_name"
        )

        if (
            "meta"
            in df_exploded.select("validation_results.expectation_config.*").columns
        ):
            df_exploded = df_exploded.withColumn(
                "meta", col("validation_results.expectation_config.meta")
            )

        schema = df_exploded.schema.simpleString()

        if (
            dq_spec.gx_result_format.upper() == DQResultFormat.COMPLETE.value
            and "unexpected_index_list" in schema
        ):
            df_exploded = df_exploded.withColumn(
                "unexpected_index_list",
                transform(
                    col("validation_results.result.unexpected_index_list"),
                    lambda y: y.withField("run_success", lit(False)),
                ),
            )

        if "observed_value" in schema:
            df_exploded = df_exploded.withColumn(
                "observed_value", col("validation_results.result.observed_value")
            )

        return (
            df_exploded.withColumn("run_time_year", year(to_timestamp("run_time")))
            .withColumn("run_time_month", month(to_timestamp("run_time")))
            .withColumn("run_time_day", dayofmonth(to_timestamp("run_time")))
            .withColumn(
                "kwargs", to_json(col("validation_results.expectation_config.kwargs"))
            )
            .withColumn("validation_results", to_json(col("validation_results")))
        )

    @classmethod
    def _get_data_context_config(cls, dq_spec: DQSpec) -> DataContextConfig:
        """Get the configuration of the data context.

        Based on the configuration it is possible to define the backend to be
        the file system (e.g. local file system) or S3, meaning that the DQ artefacts
        will be stored according to this configuration.

        Args:
            dq_spec: data quality process specification.

        Returns:
            The DataContextConfig object configuration.
        """
        store_backend: Union[FilesystemStoreBackendDefaults, S3StoreBackendDefaults]

        if dq_spec.store_backend == DQDefaults.FILE_SYSTEM_STORE.value:
            store_backend = FilesystemStoreBackendDefaults(
                root_directory=dq_spec.local_fs_root_dir
            )
        elif dq_spec.store_backend == DQDefaults.FILE_SYSTEM_S3_STORE.value:
            store_backend = S3StoreBackendDefaults(
                default_bucket_name=dq_spec.bucket,
                validations_store_prefix=dq_spec.validations_store_prefix,
                checkpoint_store_prefix=dq_spec.checkpoint_store_prefix,
                expectations_store_prefix=dq_spec.expectations_store_prefix,
            )

        # @todo we should find a way to create a datacontextconfig without
        # passing a local_fs_root_dir so that we wont have problems with
        # changing versions of the lakehouse-engine due to the marshmallow
        # library identifiyng new fields on the checkpoints

        return DataContextConfig(
            store_backend_defaults=store_backend,
            analytics_enabled=False,
        )

    @classmethod
    def _get_data_source_defaults(cls, dq_spec: DQSpec) -> dict:
        """Get the configuration for a datasource.

        Args:
            dq_spec: data quality specification.

        Returns:
            The python dictionary with the datasource configuration.
        """
        return {
            "name": f"{dq_spec.spec_id}-{dq_spec.input_id}-datasource",
            "class_name": DQDefaults.DATASOURCE_CLASS_NAME.value,
            "execution_engine": {
                "class_name": DQDefaults.DATASOURCE_EXECUTION_ENGINE.value,
                "persist": False,
            },
            "data_connectors": {
                f"{dq_spec.spec_id}-{dq_spec.input_id}-data_connector": {
                    "module_name": DQDefaults.DATA_CONNECTORS_MODULE_NAME.value,
                    "class_name": DQDefaults.DATA_CONNECTORS_CLASS_NAME.value,
                    "assets": {
                        (
                            dq_spec.data_asset_name
                            if dq_spec.data_asset_name
                            else f"{dq_spec.spec_id}-{dq_spec.input_id}"
                        ): {"batch_identifiers": DQDefaults.DQ_BATCH_IDENTIFIERS.value}
                    },
                }
            },
        }

    @classmethod
    def _get_failed_expectations(
        cls,
        results: dict,
        dq_spec: DQSpec,
        failed_expectations: dict,
        evaluated_expectations: dict,
        is_final_chunk: bool,
    ) -> Tuple[dict, dict]:
        """Get the failed expectations of a Checkpoint result.

        Args:
            results: the results of the DQ process.
            dq_spec: data quality specification.
            failed_expectations: dict of failed expectations.
            evaluated_expectations: dict of evaluated expectations.
            is_final_chunk: boolean indicating if this is the final chunk.

        Returns: a tuple with a dict of failed expectations
                and a dict of evaluated expectations.
        """
        expectations_results = results["results"]
        for result in expectations_results:
            evaluated_expectations[result["expectation_config"]["id"]] = result[
                "expectation_config"
            ]
            if not result["success"]:
                failed_expectations[result["expectation_config"]["id"]] = result[
                    "expectation_config"
                ]
                if result["exception_info"]["raised_exception"]:
                    cls._LOGGER.error(
                        f"""The expectation {str(result["expectation_config"])}
                        raised the following exception:
                        {result["exception_info"]["exception_message"]}"""
                    )
        cls._LOGGER.error(
            f"{len(failed_expectations)} out of {len(evaluated_expectations)} "
            f"Data Quality Expectation(s) have failed! Failed Expectations: "
            f"{failed_expectations}"
        )

        percentage_failure = 1 - (results["statistics"]["success_percent"] / 100)

        if (
            dq_spec.max_percentage_failure is not None
            and dq_spec.max_percentage_failure < percentage_failure
            and is_final_chunk
        ):
            raise DQValidationsFailedException(
                f"Max error threshold is being surpassed! "
                f"Expected: {dq_spec.max_percentage_failure} "
                f"Got: {percentage_failure}"
            )

        return failed_expectations, evaluated_expectations

    @classmethod
    def _get_unexpected_rows_pk(cls, dq_spec: DQSpec) -> Optional[list]:
        """Get primary key for using on rows failing DQ validations.

        Args:
            dq_spec: data quality specification.

        Returns: the list of columns that are part of the primary key.
        """
        if dq_spec.unexpected_rows_pk:
            return dq_spec.unexpected_rows_pk
        elif dq_spec.tbl_to_derive_pk:
            return TableManager(
                {"function": "get_tbl_pk", "table_or_view": dq_spec.tbl_to_derive_pk}
            ).get_tbl_pk()
        elif dq_spec.tag_source_data:
            raise ValueError(
                "You need to provide either the argument "
                "'unexpected_rows_pk' or 'tbl_to_derive_pk'."
            )
        else:
            return None

    @classmethod
    def _log_or_fail(
        cls,
        results: dict,
        dq_spec: DQSpec,
        failed_expectations: dict,
        evaluated_expectations: dict,
        is_final_chunk: bool,
    ) -> Tuple[dict, dict]:
        """Log the execution of the Data Quality process.

        Args:
            results: the results of the DQ process.
            dq_spec: data quality specification.
            failed_expectations: list of failed expectations.
            evaluated_expectations: list of evaluated expectations.
            is_final_chunk: boolean indicating if this is the final chunk.

        Returns: a tuple with a dict of failed expectations
                and a dict of evaluated expectations.
        """
        if results["success"]:
            cls._LOGGER.info(
                "The data passed all the expectations defined. Everything looks good!"
            )
        else:
            failed_expectations, evaluated_expectations = cls._get_failed_expectations(
                results,
                dq_spec,
                failed_expectations,
                evaluated_expectations,
                is_final_chunk,
            )

        if dq_spec.critical_functions and is_final_chunk:
            critical_failure = cls._check_critical_functions_tags(failed_expectations)

            if critical_failure:
                raise DQValidationsFailedException(
                    f"Data Quality Validations Failed, the following critical "
                    f"expectations failed: {critical_failure}."
                )
        if dq_spec.fail_on_error and is_final_chunk and failed_expectations:
            raise DQValidationsFailedException("Data Quality Validations Failed!")

        return failed_expectations, evaluated_expectations

    @classmethod
    def _transform_checkpoint_results(
        cls,
        data: DataFrame,
        source_pk: list,
        checkpoint_results: dict,
        dq_spec: DQSpec,
    ) -> DataFrame:
        """Transforms the checkpoint results and creates new entries.

        All the items of the dictionary are cast to a json like format.
        All columns are cast to json like format.
        After that the dictionary is converted into a dataframe.

        Args:
            data: input dataframe to run the dq process on.
            source_pk: list of columns that are part of the primary key.
            checkpoint_results: dict with results of the checkpoint run.
            dq_spec: data quality specification.
            checkpoint_run_time: A string with the time in miliseconds.

        Returns:
            Transformed results dataframe.
        """
        results_dict = loads(dumps(checkpoint_results))
        results_dict_list = []

        # Here we are splitting the results into chunks per expectation
        # and then we are splitting the unexpected_index_list into
        # chunks of size dq_spec.result_sink_chunk_size.
        for ele in results_dict["results"]:
            base_result = deepcopy(results_dict)

            if "unexpected_index_list" in ele["result"].keys():
                for key in ExecEnv.ENGINE_CONFIG.dq_result_sink_columns_to_delete:
                    del ele["result"][key]

                unexpected_index_list = ele["result"]["unexpected_index_list"]
                unexpected_index_list_chunks = cls.split_into_chunks(
                    unexpected_index_list, dq_spec.result_sink_chunk_size
                )

                del ele["result"]["unexpected_index_list"]

                for chunk in unexpected_index_list_chunks:
                    ele["result"]["unexpected_index_list"] = chunk
                    base_result["results"] = [ele]
                    results_dict_list.append(deepcopy(base_result))
            else:
                base_result["results"] = [ele]
                results_dict_list.append(base_result)

        index = 0

        failed_expectations: dict = {}
        evaluated_expectations: dict = {}

        # The processed chunk is removed from the list of results
        # so the memory is freed as soon as possible.
        while index < len(results_dict_list):
            is_final_chunk = len(results_dict_list) == 1
            data, failed_expectations, evaluated_expectations = cls._process_chunk(
                dq_spec,
                source_pk,
                results_dict_list[index],
                data,
                failed_expectations,
                evaluated_expectations,
                is_final_chunk,
            )
            del results_dict_list[index]

        return data

    @classmethod
    def _process_chunk(
        cls,
        dq_spec: DQSpec,
        source_pk: list[str],
        ele: dict,
        data: DataFrame,
        failed_expectations: dict,
        evaluated_expectations: dict,
        is_final_chunk: bool,
    ) -> Tuple[DataFrame, dict, dict]:
        """Process a chunk of the results.

        Args:
            dq_spec: data quality specification.
            source_pk: list of columns that are part of the primary key.
            ele: dictionary with the results of the dq process.
            data: input dataframe to run the dq process on.
            failed_expectations: list of failed expectations.
            evaluated_expectations: list of evaluated expectations.
            is_final_chunk: boolean indicating if this is the final chunk.

        Returns:
            A tuple with the processed data, failed expectations and evaluated
            expectations.
        """
        df = ExecEnv.SESSION.createDataFrame([json.dumps(ele)], schema=StringType())
        schema = schema_of_json(lit(json.dumps(ele)))
        df = (
            df.withColumn("value", from_json("value", schema))
            .select("value.*")
            .withColumn("spec_id", lit(dq_spec.spec_id))
            .withColumn("input_id", lit(dq_spec.input_id))
            .withColumn("run_name", col("meta.run_id.run_name"))
            .withColumn("run_time", col("meta.run_id.run_time"))
        )
        exploded_df = (
            cls._explode_results(df, dq_spec)
            if dq_spec.result_sink_explode
            else df.withColumn("validation_results", to_json(col("results"))).drop(
                "statistics", "meta", "suite_name", "results", "id"
            )
        )

        exploded_df = exploded_df.withColumn("source_primary_key", lit(source_pk))

        cls._write_to_location(dq_spec, exploded_df)

        failed_expectations, evaluated_expectations = cls._log_or_fail(
            ele, dq_spec, failed_expectations, evaluated_expectations, is_final_chunk
        )
        if (
            dq_spec.tag_source_data
            and dq_spec.result_sink_explode
            and dq_spec.fail_on_error is not True
        ):
            data = Validator.tag_source_with_dq(source_pk, data, exploded_df)
            return data, failed_expectations, evaluated_expectations
        return data, failed_expectations, evaluated_expectations

    @classmethod
    def _write_to_location(
        cls,
        dq_spec: DQSpec,
        df: DataFrame,
        processed_keys: bool = False,
    ) -> None:
        """Write dq results dataframe to a table or location.

        It can be written:
        - a raw output (having result_sink_explode set as False)
        - an exploded output (having result_sink_explode set as True), which
        is more prepared for analysis, with some columns exploded, flatten and
        transformed. It can also be set result_sink_extra_columns with other
        columns desired to have in the output table or location.
        - processed keys when running the dq process with the dq_type set as
        'prisma'.

        Args:
            dq_spec: data quality specification.
            df: dataframe with dq results to write.
            processed_keys: boolean indicating if the dataframe contains
                the processed keys.
        """
        if processed_keys:
            table = None
            location = dq_spec.processed_keys_location
            options = {"mergeSchema": "true"}
        else:
            table = dq_spec.result_sink_db_table
            location = dq_spec.result_sink_location
            options = {"mergeSchema": "true"} if dq_spec.result_sink_explode else {}

        if table or location:
            WriterFactory.get_writer(
                spec=OutputSpec(
                    spec_id="dq_result_sink",
                    input_id="dq_result",
                    db_table=table,
                    location=location,
                    partitions=(
                        dq_spec.result_sink_partitions
                        if dq_spec.result_sink_partitions
                        else []
                    ),
                    write_type=WriteType.APPEND.value,
                    data_format=dq_spec.result_sink_format,
                    options=(
                        options
                        if dq_spec.result_sink_options is None
                        else {**dq_spec.result_sink_options, **options}
                    ),
                ),
                df=df,
                data=None,
            ).write()

    @staticmethod
    def split_into_chunks(lst: list, chunk_size: int) -> list:
        """Split a list into chunks of a specified size.

        Args:
            lst: The list to be split.
            chunk_size: Number of records in each chunk.

        Returns:
            A list of lists, where each inner list is a chunk of the original list.
        """
        if chunk_size <= 0:
            raise ValueError("Chunk size must be a positive integer.")
        chunk_list = []
        for i in range(0, len(lst), chunk_size):
            chunk_list.append(lst[i : i + chunk_size])
        return chunk_list

run_dq_process(dq_spec, data) classmethod

Run the specified data quality process on a dataframe.

Based on the dq_specs we apply the defined expectations on top of the dataframe in order to apply the necessary validations and then output the result of the data quality process.

The logic of the function is as follows: 1. Import the custom expectations defined in the engine. 2. Create the context based on the dq_spec. - The context is the base class for the GX, an ephemeral context means that it does not store/load the configuration of the environment in a configuration file. 3. Add the data source to the context. - This is the data source that will be used to run the dq process, in our case Spark. 4. Create the dataframe asset and batch definition. - The asset represents the data where the expectations are applied and the batch definition is the way how the data should be split, in the case of dataframes it is always the whole dataframe. 5. Create the expectation suite. - This is the group of expectations that will be applied to the data. 6. Create the checkpoint and run it. - The checkpoint is the object that will run the expectations on the data and return the results. 7. Transform the results and write them to the result sink. - The results are transformed to a more readable format and then written to the result sink. 8. Log the results and raise an exception if needed. - The results are logged and if there are any failed expectations the process will raise an exception based on the dq_spec. 9. Tag the source data if needed. - If the dq_spec has the tag_source_data argument set to True, the source data will be tagged with the dq results.

Parameters:

Name Type Description Default
dq_spec DQSpec

data quality specification.

required
data DataFrame

input dataframe to run the dq process on.

required

Returns:

Type Description
DataFrame

The DataFrame containing the results of the DQ process.

Source code in mkdocs/lakehouse_engine/packages/dq_processors/dq_factory.py
@classmethod
def run_dq_process(cls, dq_spec: DQSpec, data: DataFrame) -> DataFrame:
    """Run the specified data quality process on a dataframe.

    Based on the dq_specs we apply the defined expectations on top of the dataframe
    in order to apply the necessary validations and then output the result of
    the data quality process.

    The logic of the function is as follows:
    1. Import the custom expectations defined in the engine.
    2. Create the context based on the dq_spec. - The context is the base class for
    the GX, an ephemeral context means that it does not store/load the
    configuration of the environment in a configuration file.
    3. Add the data source to the context. - This is the data source that will be
    used to run the dq process, in our case Spark.
    4. Create the dataframe asset and batch definition. - The asset represents the
    data where the expectations are applied and the batch definition is the
    way how the data should be split, in the case of dataframes it is always
    the whole dataframe.
    5. Create the expectation suite. - This is the group of expectations that will
    be applied to the data.
    6. Create the checkpoint and run it. - The checkpoint is the object that will
    run the expectations on the data and return the results.
    7. Transform the results and write them to the result sink. - The results are
    transformed to a more readable format and then written to the result sink.
    8. Log the results and raise an exception if needed. - The results are logged
    and if there are any failed expectations the process will raise an exception
    based on the dq_spec.
    9. Tag the source data if needed. - If the dq_spec has the tag_source_data
    argument set to True, the source data will be tagged with the dq results.

    Args:
        dq_spec: data quality specification.
        data: input dataframe to run the dq process on.

    Returns:
        The DataFrame containing the results of the DQ process.
    """
    # Creating the context
    if dq_spec.dq_type == "validator" or dq_spec.dq_type == "prisma":

        for expectation in DQDefaults.CUSTOM_EXPECTATION_LIST.value:
            importlib.__import__(
                "lakehouse_engine.dq_processors.custom_expectations." + expectation
            )

        context = gx.get_context(
            cls._get_data_context_config(dq_spec), mode="ephemeral"
        )

        # Adding data source to context
        dataframe_data_source = context.data_sources.add_spark(
            name=f"{dq_spec.spec_id}-{dq_spec.input_id}-datasource",
            persist=False,
        )
        dataframe_asset = dataframe_data_source.add_dataframe_asset(
            name=f"{dq_spec.spec_id}-{dq_spec.input_id}-asset"
        )
        dataframe_bd = dataframe_asset.add_batch_definition_whole_dataframe(
            name=f"{dq_spec.spec_id}-{dq_spec.input_id}-batch"
        )

        checkpoint_run_time = datetime.today().strftime("%Y%m%d-%H%M%S%f")

        suite = cls._add_suite(context, dq_spec, checkpoint_run_time)

        result, source_pk = cls._configure_checkpoint(
            context, dataframe_bd, suite, dq_spec, data, checkpoint_run_time
        )

        expectation_result_key = list(result.run_results.keys())[0]

        result_dict = result.run_results[expectation_result_key].to_json_dict()

        result_dict = cls._check_expectation_result(result_dict)

        data = cls._transform_checkpoint_results(
            data, source_pk, result_dict, dq_spec
        )

        # Processed keys are only added for the PRISMA dq type
        # because they are being used to calculate the good
        # records that were processed in a run.
        if dq_spec.dq_type == DQType.PRISMA.value:

            keys = data.select(*source_pk)
            keys = keys.withColumn(
                "run_name", lit(result_dict["meta"]["run_id"]["run_name"])
            )

            cls._write_to_location(dq_spec, keys, processed_keys=True)

    else:
        raise TypeError(
            f"Type of Data Quality '{dq_spec.dq_type}' is not supported."
        )

    return data

split_into_chunks(lst, chunk_size) staticmethod

Split a list into chunks of a specified size.

Parameters:

Name Type Description Default
lst list

The list to be split.

required
chunk_size int

Number of records in each chunk.

required

Returns:

Type Description
list

A list of lists, where each inner list is a chunk of the original list.

Source code in mkdocs/lakehouse_engine/packages/dq_processors/dq_factory.py
@staticmethod
def split_into_chunks(lst: list, chunk_size: int) -> list:
    """Split a list into chunks of a specified size.

    Args:
        lst: The list to be split.
        chunk_size: Number of records in each chunk.

    Returns:
        A list of lists, where each inner list is a chunk of the original list.
    """
    if chunk_size <= 0:
        raise ValueError("Chunk size must be a positive integer.")
    chunk_list = []
    for i in range(0, len(lst), chunk_size):
        chunk_list.append(lst[i : i + chunk_size])
    return chunk_list