1.10.0

.gitignore

@@ -66,7 +66,7 @@ venv/
 /site/
 /.cache/
 
-# DQO Examples
+# DQOps Examples
 .localsettings.dqosettings.yaml
 .data/
 .logs/
@@ -79,3 +79,5 @@ venv/
 /home/bin/.duckdb
 /home/bin/v0.10.0
 
+# paid components
+/home/dqopspaid/

.run/dqo run.run.xml

@@ -5,9 +5,9 @@
       <option name="region" />
       <option name="useCurrentConnection" value="false" />
     </extension>
-    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.9.0.jar" />
+    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.10.0.jar" />
     <option name="VM_PARAMETERS" value="-XX:MaxRAMPercentage=60.0 --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED" />
-    <option name="PROGRAM_PARAMETERS" value="--server.port=8888" />
+    <option name="PROGRAM_PARAMETERS" value="--server.port=8888 --dqo.python.debug-mode=silent" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
     <option name="ALTERNATIVE_JRE_PATH" value="temurin-17" />
     <envs>

.run/run app with paid extensions.run.xml

@@ -13,7 +13,7 @@
     </envs>
     <option name="MAIN_CLASS_NAME" value="com.dqops.cli.CliApplication" />
     <module name="dqo-dqops" />
-    <option name="PROGRAM_PARAMETERS" value="--server.port=8888 --dqo.webserver.authentication-method=oauth2" />
+    <option name="PROGRAM_PARAMETERS" value="--server.port=8888 --dqo.webserver.authentication-method=oauth2 --dqo.python.debug-mode=failed" />
     <shortenClasspath name="ARGS_FILE" />
     <option name="VM_PARAMETERS" value="-XX:MaxRAMPercentage=60.0 --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED" />
     <extension name="software.aws.toolkits.jetbrains.core.execution.JavaAwsConnectionExtension">

CHANGELOG.md

@@ -1,13 +1,11 @@
-# 1.9.0
+# 1.9.1
+
+* Data lineage editor suggests similar source and target tables
+* Fixes in the management of tabs
+* ClickHouse connector
+* Teradata connector
+* Small UI fixes
+* Performance improvements in the anomaly detection code
+* Other performance optimizations
+* Run Python rules in parallel to speed up anomaly detection when tables have a different number of enabled checks
 
-* SAP HANA support
-* IBM DB2 support
-* Improvements to the anomaly rules
-* Missing UI for the data domains added
-* Incident histogram shows time periods, not the execution day
-* Incident notifications fixed to send notifications used a dedicated queue and work even when fallback addresses are not configured
-* Data lineage flow diagrams
-* Table similarity search to build data lineage
-* Table and column screens have a new primary tab to show the data observability status, which is a shortened status
-* Basic statistics are scheduled by CRON and reuse the profiling schedule
-* Connections support configuring a CRON expression to import new tables

VERSION

@@ -1 +1 @@
-1.9.0
+1.10.0

distribution/pom.xml

@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-distribution</artifactId>
-    <version>1.9.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.10.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>dqo-distribution</name>
 	<description>DQOps Data Quality Operations Center final assembly</description>
 	<packaging>pom</packaging>

distribution/python/dqops/client/models/__init__.py

@@ -131,6 +131,8 @@
 from .check_template import CheckTemplate
 from .check_time_scale import CheckTimeScale
 from .check_type import CheckType
+from .click_house_parameters_spec import ClickHouseParametersSpec
+from .click_house_parameters_spec_properties import ClickHouseParametersSpecProperties
 from .cloud_synchronization_folders_status_model import (
     CloudSynchronizationFoldersStatusModel,
 )
@@ -1094,6 +1096,12 @@
 from .column_sql_import_custom_result_sensor_parameters_spec import (
     ColumnSqlImportCustomResultSensorParametersSpec,
 )
+from .column_sql_invalid_value_count_check_spec import (
+    ColumnSqlInvalidValueCountCheckSpec,
+)
+from .column_sql_invalid_value_count_sensor_parameters_spec import (
+    ColumnSqlInvalidValueCountSensorParametersSpec,
+)
 from .column_statistics_collectors_root_categories_spec import (
     ColumnStatisticsCollectorsRootCategoriesSpec,
 )
@@ -1552,6 +1560,8 @@
 from .json_records_type import JsonRecordsType
 from .label_model import LabelModel
 from .local_data_domain_model import LocalDataDomainModel
+from .maria_db_parameters_spec import MariaDbParametersSpec
+from .maria_db_parameters_spec_properties import MariaDbParametersSpecProperties
 from .max_count_rule_0_error_parameters_spec import MaxCountRule0ErrorParametersSpec
 from .max_count_rule_0_warning_parameters_spec import MaxCountRule0WarningParametersSpec
 from .max_count_rule_100_parameters_spec import MaxCountRule100ParametersSpec
@@ -1608,6 +1618,7 @@
 from .parquet_file_format_spec import ParquetFileFormatSpec
 from .partition_incremental_time_window_spec import PartitionIncrementalTimeWindowSpec
 from .physical_table_name import PhysicalTableName
+from .postgresql_engine_type import PostgresqlEngineType
 from .postgresql_parameters_spec import PostgresqlParametersSpec
 from .postgresql_parameters_spec_properties import PostgresqlParametersSpecProperties
 from .postgresql_ssl_mode import PostgresqlSslMode
@@ -1623,6 +1634,8 @@
 from .provider_sensor_runner_type import ProviderSensorRunnerType
 from .provider_type import ProviderType
 from .quality_category_model import QualityCategoryModel
+from .quest_db_parameters_spec import QuestDbParametersSpec
+from .quest_db_parameters_spec_properties import QuestDbParametersSpecProperties
 from .redshift_authentication_mode import RedshiftAuthenticationMode
 from .redshift_parameters_spec import RedshiftParametersSpec
 from .redshift_parameters_spec_properties import RedshiftParametersSpecProperties
@@ -1643,6 +1656,7 @@
 from .run_checks_parameters import RunChecksParameters
 from .run_checks_queue_job_result import RunChecksQueueJobResult
 from .run_checks_result import RunChecksResult
+from .run_checks_target import RunChecksTarget
 from .schedule_enabled_status_model import ScheduleEnabledStatusModel
 from .schema_model import SchemaModel
 from .schema_remote_model import SchemaRemoteModel
@@ -1986,6 +2000,12 @@
 from .table_sql_import_custom_result_sensor_parameters_spec import (
     TableSqlImportCustomResultSensorParametersSpec,
 )
+from .table_sql_invalid_record_count_check_spec import (
+    TableSqlInvalidRecordCountCheckSpec,
+)
+from .table_sql_invalid_record_count_sensor_parameters_spec import (
+    TableSqlInvalidRecordCountSensorParametersSpec,
+)
 from .table_statistics_collectors_root_categories_spec import (
     TableStatisticsCollectorsRootCategoriesSpec,
 )
@@ -2097,6 +2117,8 @@
 from .target_rule_severity_level import TargetRuleSeverityLevel
 from .target_table_pattern_spec import TargetTablePatternSpec
 from .temporal_unit import TemporalUnit
+from .teradata_parameters_spec import TeradataParametersSpec
+from .teradata_parameters_spec_properties import TeradataParametersSpecProperties
 from .text_built_in_date_formats import TextBuiltInDateFormats
 from .time_period_gradient import TimePeriodGradient
 from .time_window_filter_parameters import TimeWindowFilterParameters
@@ -2181,6 +2203,8 @@
     "CheckTemplate",
     "CheckTimeScale",
     "CheckType",
+    "ClickHouseParametersSpec",
+    "ClickHouseParametersSpecProperties",
     "CloudSynchronizationFoldersStatusModel",
     "CollectErrorSamplesOnTableParameters",
     "CollectErrorSamplesParameters",
@@ -2554,6 +2578,8 @@
     "ColumnSqlConditionPassedPercentSensorParametersSpec",
     "ColumnSqlImportCustomResultCheckSpec",
     "ColumnSqlImportCustomResultSensorParametersSpec",
+    "ColumnSqlInvalidValueCountCheckSpec",
+    "ColumnSqlInvalidValueCountSensorParametersSpec",
     "ColumnStatisticsCollectorsRootCategoriesSpec",
     "ColumnStatisticsModel",
     "ColumnStringsExpectedTextsInTopValuesCountSensorParametersSpec",
@@ -2796,6 +2822,8 @@
     "JsonRecordsType",
     "LabelModel",
     "LocalDataDomainModel",
+    "MariaDbParametersSpec",
+    "MariaDbParametersSpecProperties",
     "MaxCountRule0ErrorParametersSpec",
     "MaxCountRule0WarningParametersSpec",
     "MaxCountRule100ParametersSpec",
@@ -2840,6 +2868,7 @@
     "ParquetFileFormatSpec",
     "PartitionIncrementalTimeWindowSpec",
     "PhysicalTableName",
+    "PostgresqlEngineType",
     "PostgresqlParametersSpec",
     "PostgresqlParametersSpecProperties",
     "PostgresqlSslMode",
@@ -2853,6 +2882,8 @@
     "ProviderSensorRunnerType",
     "ProviderType",
     "QualityCategoryModel",
+    "QuestDbParametersSpec",
+    "QuestDbParametersSpecProperties",
     "RedshiftAuthenticationMode",
     "RedshiftParametersSpec",
     "RedshiftParametersSpecProperties",
@@ -2873,6 +2904,7 @@
     "RunChecksParameters",
     "RunChecksQueueJobResult",
     "RunChecksResult",
+    "RunChecksTarget",
     "ScheduleEnabledStatusModel",
     "SchemaModel",
     "SchemaRemoteModel",
@@ -3042,6 +3074,8 @@
     "TableSqlConditionPassedPercentSensorParametersSpec",
     "TableSqlImportCustomResultCheckSpec",
     "TableSqlImportCustomResultSensorParametersSpec",
+    "TableSqlInvalidRecordCountCheckSpec",
+    "TableSqlInvalidRecordCountSensorParametersSpec",
     "TableStatisticsCollectorsRootCategoriesSpec",
     "TableStatisticsModel",
     "TableTimelinessDailyMonitoringChecksSpec",
@@ -3085,6 +3119,8 @@
     "TargetRuleSeverityLevel",
     "TargetTablePatternSpec",
     "TemporalUnit",
+    "TeradataParametersSpec",
+    "TeradataParametersSpecProperties",
     "TextBuiltInDateFormats",
     "TimePeriodGradient",
     "TimestampColumnsSpec",

distribution/python/dqops/client/models/anomaly_differencing_percentile_moving_average_rule_error_05_pct_parameters_spec.py

@@ -17,19 +17,26 @@ class AnomalyDifferencingPercentileMovingAverageRuleError05PctParametersSpec:
         anomaly_percent (Union[Unset, float]): The probability (in percent) that the current sensor readout (measure) is
             an anomaly, because the value is outside the regular range of previous readouts. The default time window of 90
             time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation.
+        use_ai (Union[Unset, bool]): Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is
+            not supported in an open-source distribution of DQOps. Please contact DQOps support to upgrade your instance to
+            a closed-source DQOps distribution.
     """
 
     anomaly_percent: Union[Unset, float] = UNSET
+    use_ai: Union[Unset, bool] = UNSET
     additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)
 
     def to_dict(self) -> Dict[str, Any]:
         anomaly_percent = self.anomaly_percent
+        use_ai = self.use_ai
 
         field_dict: Dict[str, Any] = {}
         field_dict.update(self.additional_properties)
         field_dict.update({})
         if anomaly_percent is not UNSET:
             field_dict["anomaly_percent"] = anomaly_percent
+        if use_ai is not UNSET:
+            field_dict["use_ai"] = use_ai
 
         return field_dict
 
@@ -38,8 +45,11 @@ def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:
         d = src_dict.copy()
         anomaly_percent = d.pop("anomaly_percent", UNSET)
 
+        use_ai = d.pop("use_ai", UNSET)
+
         anomaly_differencing_percentile_moving_average_rule_error_05_pct_parameters_spec = cls(
             anomaly_percent=anomaly_percent,
+            use_ai=use_ai,
         )
 
         anomaly_differencing_percentile_moving_average_rule_error_05_pct_parameters_spec.additional_properties = (

distribution/python/dqops/client/models/anomaly_differencing_percentile_moving_average_rule_fatal_01_pct_parameters_spec.py

@@ -17,19 +17,26 @@ class AnomalyDifferencingPercentileMovingAverageRuleFatal01PctParametersSpec:
         anomaly_percent (Union[Unset, float]): The probability (in percent) that the current sensor readout (measure) is
             an anomaly, because the value is outside the regular range of previous readouts. The default time window of 90
             time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation.
+        use_ai (Union[Unset, bool]): Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is
+            not supported in an open-source distribution of DQOps. Please contact DQOps support to upgrade your instance to
+            a closed-source DQOps distribution.
     """
 
     anomaly_percent: Union[Unset, float] = UNSET
+    use_ai: Union[Unset, bool] = UNSET
     additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)
 
     def to_dict(self) -> Dict[str, Any]:
         anomaly_percent = self.anomaly_percent
+        use_ai = self.use_ai
 
         field_dict: Dict[str, Any] = {}
         field_dict.update(self.additional_properties)
         field_dict.update({})
         if anomaly_percent is not UNSET:
             field_dict["anomaly_percent"] = anomaly_percent
+        if use_ai is not UNSET:
+            field_dict["use_ai"] = use_ai
 
         return field_dict
 
@@ -38,8 +45,11 @@ def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:
         d = src_dict.copy()
         anomaly_percent = d.pop("anomaly_percent", UNSET)
 
+        use_ai = d.pop("use_ai", UNSET)
+
         anomaly_differencing_percentile_moving_average_rule_fatal_01_pct_parameters_spec = cls(
             anomaly_percent=anomaly_percent,
+            use_ai=use_ai,
         )
 
         anomaly_differencing_percentile_moving_average_rule_fatal_01_pct_parameters_spec.additional_properties = (

distribution/python/dqops/client/models/anomaly_differencing_percentile_moving_average_rule_warning_1_pct_parameters_spec.py

@@ -17,19 +17,26 @@ class AnomalyDifferencingPercentileMovingAverageRuleWarning1PctParametersSpec:
         anomaly_percent (Union[Unset, float]): The probability (in percent) that the current sensor readout (measure) is
             an anomaly, because the value is outside the regular range of previous readouts. The default time window of 90
             time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation.
+        use_ai (Union[Unset, bool]): Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is
+            not supported in an open-source distribution of DQOps. Please contact DQOps support to upgrade your instance to
+            a closed-source DQOps distribution.
     """
 
     anomaly_percent: Union[Unset, float] = UNSET
+    use_ai: Union[Unset, bool] = UNSET
     additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)
 
     def to_dict(self) -> Dict[str, Any]:
         anomaly_percent = self.anomaly_percent
+        use_ai = self.use_ai
 
         field_dict: Dict[str, Any] = {}
         field_dict.update(self.additional_properties)
         field_dict.update({})
         if anomaly_percent is not UNSET:
             field_dict["anomaly_percent"] = anomaly_percent
+        if use_ai is not UNSET:
+            field_dict["use_ai"] = use_ai
 
         return field_dict
 
@@ -38,8 +45,11 @@ def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:
         d = src_dict.copy()
         anomaly_percent = d.pop("anomaly_percent", UNSET)
 
+        use_ai = d.pop("use_ai", UNSET)
+
         anomaly_differencing_percentile_moving_average_rule_warning_1_pct_parameters_spec = cls(
             anomaly_percent=anomaly_percent,
+            use_ai=use_ai,
         )
 
         anomaly_differencing_percentile_moving_average_rule_warning_1_pct_parameters_spec.additional_properties = (

distribution/python/dqops/client/models/anomaly_partition_row_count_rule_error_05_pct_parameters_spec.py

@@ -15,19 +15,26 @@ class AnomalyPartitionRowCountRuleError05PctParametersSpec:
         anomaly_percent (Union[Unset, float]): The probability (in percent) that the current daily row count is an
             anomaly because the value is outside the regular range of previous partition volume measures. The default time
             window of 90 time periods (days, etc.) is used, but at least 30 readouts must exist to run the calculation.
+        use_ai (Union[Unset, bool]): Use an AI model to predict anomalies. WARNING: anomaly detection by AI models is
+            not supported in an open-source distribution of DQOps. Please contact DQOps support to upgrade your instance to
+            a closed-source DQOps distribution.
     """
 
     anomaly_percent: Union[Unset, float] = UNSET
+    use_ai: Union[Unset, bool] = UNSET
     additional_properties: Dict[str, Any] = _attrs_field(init=False, factory=dict)
 
     def to_dict(self) -> Dict[str, Any]:
         anomaly_percent = self.anomaly_percent
+        use_ai = self.use_ai
 
         field_dict: Dict[str, Any] = {}
         field_dict.update(self.additional_properties)
         field_dict.update({})
         if anomaly_percent is not UNSET:
             field_dict["anomaly_percent"] = anomaly_percent
+        if use_ai is not UNSET:
+            field_dict["use_ai"] = use_ai
 
         return field_dict
 
@@ -36,8 +43,11 @@ def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:
         d = src_dict.copy()
         anomaly_percent = d.pop("anomaly_percent", UNSET)
 
+        use_ai = d.pop("use_ai", UNSET)
+
         anomaly_partition_row_count_rule_error_05_pct_parameters_spec = cls(
             anomaly_percent=anomaly_percent,
+            use_ai=use_ai,
         )
 
         anomaly_partition_row_count_rule_error_05_pct_parameters_spec.additional_properties = (