Merge branch 'datahub-project:master' into master

metadata-ingestion/docs/sources/powerbi/powerbi_pre.md

@@ -39,6 +39,7 @@ PowerBI Source supports M-Query expression for below listed PowerBI Data Sources
 3.  PostgreSQL
 4.  Microsoft SQL Server
 5.  Google BigQuery
+6.  Databricks
 
 Native SQL query parsing is supported for `Snowflake` and `Amazon Redshift` data-sources.
 

metadata-ingestion/src/datahub/ingestion/source/powerbi/config.py

@@ -206,6 +206,16 @@ class PlatformDetail(ConfigModel):
     )
 
 
+class DataBricksPlatformDetail(PlatformDetail):
+    """
+    metastore is an additional field used in Databricks connector to generate the dataset urn
+    """
+
+    metastore: str = pydantic.Field(
+        description="Databricks Unity Catalog metastore name.",
+    )
+
+
 class OwnershipMapping(ConfigModel):
     create_corp_user: bool = pydantic.Field(
         default=True, description="Whether ingest PowerBI user as Datahub Corpuser"
@@ -268,11 +278,14 @@ class PowerBiDashboardSourceConfig(
         hidden_from_docs=True,
     )
     # PowerBI datasource's server to platform instance mapping
-    server_to_platform_instance: Dict[str, PlatformDetail] = pydantic.Field(
+    server_to_platform_instance: Dict[
+        str, Union[PlatformDetail, DataBricksPlatformDetail]
+    ] = pydantic.Field(
         default={},
         description="A mapping of PowerBI datasource's server i.e host[:port] to Data platform instance."
-        " :port is optional and only needed if your datasource server is running on non-standard port."
-        "For Google BigQuery the datasource's server is google bigquery project name",
+        " :port is optional and only needed if your datasource server is running on non-standard port. "
+        "For Google BigQuery the datasource's server is google bigquery project name. "
+        "For Databricks Unity Catalog the datasource's server is workspace FQDN.",
     )
     # deprecated warning
     _dataset_type_mapping = pydantic_field_deprecated(

metadata-ingestion/src/datahub/ingestion/source/powerbi/m_query/parser.py

@@ -57,6 +57,7 @@ def get_upstream_tables(
     config: PowerBiDashboardSourceConfig,
     parameters: Dict[str, str] = {},
 ) -> List[resolver.Lineage]:
+
     if table.expression is None:
         logger.debug(f"Expression is none for table {table.full_name}")
         return []

metadata-ingestion/src/datahub/ingestion/source/powerbi/m_query/resolver.py

@@ -9,6 +9,7 @@
 import datahub.emitter.mce_builder as builder
 from datahub.ingestion.api.common import PipelineContext
 from datahub.ingestion.source.powerbi.config import (
+    DataBricksPlatformDetail,
     DataPlatformPair,
     PlatformDetail,
     PowerBiDashboardSourceConfig,
@@ -155,6 +156,17 @@ def get_db_detail_from_argument(
 
         return arguments[0], arguments[1]
 
+    @staticmethod
+    def get_tokens(
+        arg_list: Tree,
+    ) -> List[str]:
+        arguments: List[str] = tree_function.strip_char_from_list(
+            values=tree_function.remove_whitespaces_from_list(
+                tree_function.token_values(arg_list)
+            ),
+        )
+        return arguments
+
     def parse_custom_sql(
         self, query: str, server: str, database: Optional[str], schema: Optional[str]
     ) -> Lineage:
@@ -760,19 +772,69 @@ def create_lineage(
 
 
 class DatabrickDataPlatformTableCreator(AbstractDataPlatformTableCreator):
+    def form_qualified_table_name(
+        self,
+        value_dict: Dict[Any, Any],
+        catalog_name: str,
+        data_platform_pair: DataPlatformPair,
+        server: str,
+    ) -> str:
+        # database and catalog names are same in M-Query
+        db_name: str = (
+            catalog_name if "Database" not in value_dict else value_dict["Database"]
+        )
+
+        schema_name: str = value_dict["Schema"]
+
+        table_name: str = value_dict["Table"]
+
+        platform_detail: PlatformDetail = (
+            self.platform_instance_resolver.get_platform_instance(
+                PowerBIPlatformDetail(
+                    data_platform_pair=data_platform_pair,
+                    data_platform_server=server,
+                )
+            )
+        )
+
+        metastore: Optional[str] = None
+
+        qualified_table_name: str = f"{db_name}.{schema_name}.{table_name}"
+
+        if isinstance(platform_detail, DataBricksPlatformDetail):
+            metastore = platform_detail.metastore
+
+        if metastore is not None:
+            return f"{metastore}.{qualified_table_name}"
+
+        return qualified_table_name
+
     def create_lineage(
         self, data_access_func_detail: DataAccessFunctionDetail
     ) -> Lineage:
         logger.debug(
             f"Processing Databrick data-access function detail {data_access_func_detail}"
         )
-        value_dict = {}
+        value_dict: Dict[str, str] = {}
         temp_accessor: Optional[
             Union[IdentifierAccessor, AbstractIdentifierAccessor]
         ] = data_access_func_detail.identifier_accessor
+
         while temp_accessor:
             if isinstance(temp_accessor, IdentifierAccessor):
-                value_dict[temp_accessor.items["Kind"]] = temp_accessor.items["Name"]
+                # Condition to handle databricks M-query pattern where table, schema and database all are present in
+                # same invoke statement
+                if all(
+                    element in temp_accessor.items
+                    for element in ["Item", "Schema", "Catalog"]
+                ):
+                    value_dict["Schema"] = temp_accessor.items["Schema"]
+                    value_dict["Table"] = temp_accessor.items["Item"]
+                else:
+                    value_dict[temp_accessor.items["Kind"]] = temp_accessor.items[
+                        "Name"
+                    ]
+
                 if temp_accessor.next is not None:
                     temp_accessor = temp_accessor.next
                 else:
@@ -783,24 +845,30 @@ def create_lineage(
                 )
                 return Lineage.empty()
 
-        db_name: str = value_dict["Database"]
-        schema_name: str = value_dict["Schema"]
-        table_name: str = value_dict["Table"]
-
-        qualified_table_name: str = f"{db_name}.{schema_name}.{table_name}"
-
-        server, _ = self.get_db_detail_from_argument(data_access_func_detail.arg_list)
-        if server is None:
+        arguments = self.get_tokens(data_access_func_detail.arg_list)
+        if len(arguments) < 4:
             logger.info(
-                f"server information is not available for {qualified_table_name}. Skipping upstream table"
+                f"Databricks workspace and catalog information in arguments({arguments}). "
+                f"Skipping upstream table"
             )
             return Lineage.empty()
 
+        workspace_fqdn: str = arguments[0]
+
+        catalog_name: str = arguments[3]
+
+        qualified_table_name: str = self.form_qualified_table_name(
+            value_dict=value_dict,
+            catalog_name=catalog_name,
+            data_platform_pair=self.get_platform_pair(),
+            server=workspace_fqdn,
+        )
+
         urn = urn_creator(
             config=self.config,
             platform_instance_resolver=self.platform_instance_resolver,
             data_platform_pair=self.get_platform_pair(),
-            server=server,
+            server=workspace_fqdn,
             qualified_table_name=qualified_table_name,
         )
 
@@ -1063,14 +1131,20 @@ class FunctionName(Enum):
     DATABRICK_DATA_ACCESS = "Databricks.Catalogs"
     GOOGLE_BIGQUERY_DATA_ACCESS = "GoogleBigQuery.Database"
     AMAZON_REDSHIFT_DATA_ACCESS = "AmazonRedshift.Database"
+    DATABRICK_MULTI_CLOUD_DATA_ACCESS = "DatabricksMultiCloud.Catalogs"
 
 
 class SupportedResolver(Enum):
-    DATABRICK_QUERY = (
+    DATABRICKS_QUERY = (
         DatabrickDataPlatformTableCreator,
         FunctionName.DATABRICK_DATA_ACCESS,
     )
 
+    DATABRICKS_MULTI_CLOUD = (
+        DatabrickDataPlatformTableCreator,
+        FunctionName.DATABRICK_MULTI_CLOUD_DATA_ACCESS,
+    )
+
     POSTGRES_SQL = (
         PostgresDataPlatformTableCreator,
         FunctionName.POSTGRESQL_DATA_ACCESS,

metadata-ingestion/src/datahub/ingestion/source/powerbi/powerbi-lexical-grammar.rule

@@ -5,7 +5,7 @@
 // - Tweak unary_expression to allow arbitrary operators within it.
 //   This is necessary because unary_expression is the base for the
 //   whole relational_expression parse tree.
-
+// - Added letter_character_and_decimal_digit phrase and updated keyword_or_identifier phrase
 
 lexical_unit:   lexical_elements?
 
@@ -127,6 +127,7 @@ regular_identifier:     available_identifier
 available_identifier:   keyword_or_identifier
 
 keyword_or_identifier:  letter_character
+                  |     letter_character_and_decimal_digit
                   |     underscore_character
                   |     identifier_start_character identifier_part_characters
 
@@ -157,6 +158,8 @@ underscore_character:   "_"
 
 letter_character: /[_\-\p{Lu}\p{Ll}\p{Lt}\p{Lm}\p{Lo}\p{Nl}]+/
 
+letter_character_and_decimal_digit: /[_\-\p{Lu}\p{Ll}\p{Lt}\p{Lm}\p{Lo}\p{Nl}\p{Nd}]+/
+
 combining_character:    /[_\p{Mn}\p{Mc}]+/
 
 decimal_digit_character: /[\p{Nd}]+/

metadata-ingestion/tests/integration/powerbi/test_m_parser.py

@@ -46,6 +46,9 @@
     'let\n Source = Value.NativeQuery(AmazonRedshift.Database("redshift-url","dev"), "select * from dev.public.category", null, [EnableFolding=true]) \n in Source',
     'let\n    Source = Databricks.Catalogs("adb-123.azuredatabricks.net", "/sql/1.0/endpoints/12345dc91aa25844", [Catalog=null, Database=null]),\n    hive_metastore_Database = Source{[Name="hive_metastore",Kind="Database"]}[Data],\n    sandbox_revenue_Schema = hive_metastore_Database{[Name="sandbox_revenue",Kind="Schema"]}[Data],\n    public_consumer_price_index_Table = sandbox_revenue_Schema{[Name="public_consumer_price_index",Kind="Table"]}[Data],\n    #"Renamed Columns" = Table.RenameColumns(public_consumer_price_index_Table,{{"Country", "country"}, {"Metric", "metric"}}),\n #"Inserted Year" = Table.AddColumn(#"Renamed Columns", "ID", each Date.Year([date_id]) + Date.Month([date_id]), Text.Type),\n #"Added Custom" = Table.AddColumn(#"Inserted Year", "Custom", each Text.Combine({Number.ToText(Date.Year([date_id])), Number.ToText(Date.Month([date_id])), [country]})),\n    #"Removed Columns" = Table.RemoveColumns(#"Added Custom",{"ID"}),\n    #"Renamed Columns1" = Table.RenameColumns(#"Removed Columns",{{"Custom", "ID"}}),\n #"Filtered Rows" = Table.SelectRows(#"Renamed Columns1", each ([metric] = "Consumer Price Index") and (not Number.IsNaN([value])))\nin\n    #"Filtered Rows"',
     "let\n    Source = Value.NativeQuery(Snowflake.Databases(\"bu10758.ap-unknown-2.fakecomputing.com\",\"operations_analytics_warehouse_prod\",[Role=\"OPERATIONS_ANALYTICS_MEMBER\"]){[Name=\"OPERATIONS_ANALYTICS\"]}[Data], \"select #(lf)UPPER(REPLACE(AGENT_NAME,'-','')) AS CLIENT_DIRECTOR,#(lf)TIER,#(lf)UPPER(MANAGER),#(lf)TEAM_TYPE,#(lf)DATE_TARGET,#(lf)MONTHID,#(lf)TARGET_TEAM,#(lf)SELLER_EMAIL,#(lf)concat((UPPER(REPLACE(AGENT_NAME,'-',''))), MONTHID) as AGENT_KEY,#(lf)UNIT_TARGET AS SME_Quota,#(lf)AMV_TARGET AS Revenue_Quota,#(lf)SERVICE_QUOTA,#(lf)BL_TARGET,#(lf)SOFTWARE_QUOTA as Software_Quota#(lf)#(lf)from OPERATIONS_ANALYTICS.TRANSFORMED_PROD.V_SME_UNIT_TARGETS inner join OPERATIONS_ANALYTICS.TRANSFORMED_PROD.V_SME_UNIT #(lf)#(lf)where YEAR_TARGET >= 2022#(lf)and TEAM_TYPE = 'Accounting'#(lf)and TARGET_TEAM = 'Enterprise'#(lf)AND TIER = 'Client Director'\", null, [EnableFolding=true])\nin\n    Source",
+    'let\n Source = DatabricksMultiCloud.Catalogs("abc.cloud.databricks.com", "/sql/gh2cfe3fe1d4c7cd", [Catalog=null, Database=null, EnableAutomaticProxyDiscovery=null]),\n ml_prod_Database = Source{[Name="ml_prod",Kind="Database"]}[Data],\n membership_corn_Schema = ml_prod_Database{[Name="membership_corn",Kind="Schema"]}[Data],\n time_exp_data_v3_Table = membership_corn_Schema{[Name="time_exp_data_v3",Kind="Table"]}[Data],\n #"Renamed Columns" = Table.RenameColumns(time_exp_data_v3_Table)\nin\n #"Renamed Columns"',
+    'let\n Source = DatabricksMultiCloud.Catalogs("abc.cloud.databricks.com", "/sql/gh2cfe3fe1d4c7cd", [Catalog="data_analysis", Database="summary", EnableAutomaticProxyDiscovery=null]),\n committee_data_summary_dev = Source{[Item="committee_data",Schema="summary",Catalog="data_analysis"]}[Data],\n #"Added Index" = Table.AddIndexColumn(committee_data_summary_dev, "Index", 1, 1, Int64.Type),\n #"Renamed Columns" = Table.RenameColumns(#"Added Index",)\nin\n #"Renamed Columns"',
+    'let\n Source = DatabricksMultiCloud.Catalogs("abc.cloud.databricks.com", "/sql/gh2cfe3fe1d4c7cd", [Catalog="data_analysis", Database="summary", EnableAutomaticProxyDiscovery=null]),\n vips_data_summary_dev = Source{[Item="vips_data",Schema="summary",Catalog="data_analysis"]}[Data],\n #"Changed Type" = Table.TransformColumnTypes(vips_data_summary_dev,{{"vipstartDate", type date}, {"enteredDate", type datetime}, {"estDraftDate", type datetime}, {"estPublDate", type datetime}})\nin\n #"Changed Type"',
     'let\n Source = Value.NativeQuery(Snowflake.Databases("0DD93C6BD5A6.snowflakecomputing.com","sales_analytics_warehouse_prod",[Role="sales_analytics_member_ad"]){[Name="ORDERING"]}[Data], "SELECT#(lf) DISTINCT#(lf) T5.PRESENTMENT_START_DATE#(lf),T5.PRESENTMENT_END_DATE#(lf),T5.DISPLAY_NAME#(lf),T5.NAME#(tab)#(lf),T5.PROMO_DISPLAY_NAME#(lf),T5.REGION#(lf),T5.ID#(lf),T5.WALKOUT#(lf),T6.DEAL_ID#(lf),T6.TYPE#(lf),T5.FREE_PERIOD#(lf),T6.PRICE_MODIFICATION#(lf)#(lf)FROM#(lf)#(lf)(#(lf)    SELECT #(lf) T1.NAME#(lf),DATE(T1.CREATED_AT) as CREATED_AT#(lf),T1.PROMO_CODE#(lf),T1.STATUS#(lf),DATE(T1.UPDATED_AT) as UPDATED_AT#(lf),T1.ID#(lf),T1.DISPLAY_NAME as PROMO_DISPLAY_NAME#(lf),T4.*#(lf)FROM#(lf)(SELECT#(lf) DISTINCT#(lf) NAME#(lf),CREATED_AT#(lf),PROMO_CODE#(lf),STATUS#(lf),UPDATED_AT#(lf),ID#(lf),DISPLAY_NAME#(lf) FROM RAW.PROMOTIONS#(lf)#(lf)) T1#(lf)INNER JOIN#(lf)#(lf) (#(lf)    SELECT #(lf) T3.PRODUCT_STATUS#(lf),T3.CODE#(lf),T3.REGION#(lf),T3.DISPLAY_ORDER_SEQUENCE#(lf),T3.PRODUCT_LINE_ID#(lf),T3.DISPLAY_NAME#(lf),T3.PRODUCT_TYPE#(lf),T3.ID as PROD_TBL_ID#(lf),T3.NAME as PROD_TBL_NAME#(lf),DATE(T2.PRESENTMENT_END_DATE) as PRESENTMENT_END_DATE#(lf),T2.PRICE_COMMITMENT_PERIOD#(lf),T2.NAME as SEAL_TBL_NAME#(lf),DATE(T2.CREATED_AT) as SEAL_TBL_CREATED_AT#(lf),T2.DESCRIPTION#(lf),T2.FREE_PERIOD#(lf),T2.WALKOUT#(lf),T2.PRODUCT_CAT_ID#(lf),T2.PROMOTION_ID#(lf),DATE(T2.PRESENTMENT_START_DATE) as PRESENTMENT_START_DATE#(lf),YEAR(T2.PRESENTMENT_START_DATE) as DEAL_YEAR_START#(lf),MONTH(T2.PRESENTMENT_START_DATE) as DEAL_MONTH_START#(lf),T2.DEAL_TYPE#(lf),DATE(T2.UPDATED_AT) as SEAL_TBL_UPDATED_AT#(lf),T2.ID as SEAL_TBL_ID#(lf),T2.STATUS as SEAL_TBL_STATUS#(lf)FROM#(lf)(SELECT#(lf) DISTINCT#(lf) PRODUCT_STATUS#(lf),CODE#(lf),REGION#(lf),DISPLAY_ORDER_SEQUENCE#(lf),PRODUCT_LINE_ID#(lf),DISPLAY_NAME#(lf),PRODUCT_TYPE#(lf),ID #(lf),NAME #(lf) FROM#(lf) RAW.PRODUCTS#(lf)#(lf)) T3#(lf)INNER JOIN#(lf)(#(lf)    SELECT#(lf)    DISTINCT#(lf)    PRESENTMENT_END_DATE#(lf),PRICE_COMMITMENT_PERIOD#(lf),NAME#(lf),CREATED_AT#(lf),DESCRIPTION#(lf),FREE_PERIOD#(lf),WALKOUT#(lf),PRODUCT_CAT_ID#(lf),PROMOTION_ID#(lf),PRESENTMENT_START_DATE#(lf),DEAL_TYPE#(lf),UPDATED_AT#(lf),ID#(lf),STATUS#(lf)    FROM#(lf)    RAW.DEALS#(lf)#(lf)) T2#(lf)ON#(lf)T3.ID   =   T2.PRODUCT_CAT_ID   #(lf)WHERE#(lf)T2.PRESENTMENT_START_DATE >= \'2015-01-01\'#(lf)AND#(lf)T2.STATUS = \'active\'#(lf)#(lf))T4#(lf)ON#(lf)T1.ID   =   T4.PROMOTION_ID#(lf))T5#(lf)INNER JOIN#(lf)RAW.PRICE_MODIFICATIONS T6#(lf)ON#(lf)T5.SEAL_TBL_ID  =   T6.DEAL_ID", null, [EnableFolding=true]) \n in \n Source',
 ]
 
@@ -767,9 +770,93 @@ def test_sqlglot_parser():
         assert lineage[0].column_lineage[i].upstreams == []
 
 
-def test_sqlglot_parser_2():
+def test_databricks_multi_cloud():
     table: powerbi_data_classes.Table = powerbi_data_classes.Table(
         expression=M_QUERIES[25],
+        name="category",
+        full_name="dev.public.category",
+    )
+    reporter = PowerBiDashboardSourceReport()
+
+    ctx, config, platform_instance_resolver = get_default_instances()
+    data_platform_tables: List[DataPlatformTable] = parser.get_upstream_tables(
+        table,
+        reporter,
+        ctx=ctx,
+        config=config,
+        platform_instance_resolver=platform_instance_resolver,
+    )[0].upstreams
+
+    assert len(data_platform_tables) == 1
+
+    assert (
+        data_platform_tables[0].urn
+        == "urn:li:dataset:(urn:li:dataPlatform:databricks,ml_prod.membership_corn.time_exp_data_v3,PROD)"
+    )
+
+
+def test_databricks_catalog_pattern_1():
+    table: powerbi_data_classes.Table = powerbi_data_classes.Table(
+        expression=M_QUERIES[26],
+        name="category",
+        full_name="dev.public.category",
+    )
+    reporter = PowerBiDashboardSourceReport()
+
+    ctx, config, platform_instance_resolver = get_default_instances()
+    data_platform_tables: List[DataPlatformTable] = parser.get_upstream_tables(
+        table,
+        reporter,
+        ctx=ctx,
+        config=config,
+        platform_instance_resolver=platform_instance_resolver,
+    )[0].upstreams
+
+    assert len(data_platform_tables) == 1
+
+    assert (
+        data_platform_tables[0].urn
+        == "urn:li:dataset:(urn:li:dataPlatform:databricks,data_analysis.summary.committee_data,PROD)"
+    )
+
+
+def test_databricks_catalog_pattern_2():
+    table: powerbi_data_classes.Table = powerbi_data_classes.Table(
+        expression=M_QUERIES[27],
+        name="category",
+        full_name="dev.public.category",
+    )
+    reporter = PowerBiDashboardSourceReport()
+
+    ctx, config, platform_instance_resolver = get_default_instances(
+        override_config={
+            "server_to_platform_instance": {
+                "abc.cloud.databricks.com": {
+                    "metastore": "central_metastore",
+                    "platform_instance": "abc",
+                }
+            }
+        }
+    )
+    data_platform_tables: List[DataPlatformTable] = parser.get_upstream_tables(
+        table,
+        reporter,
+        ctx=ctx,
+        config=config,
+        platform_instance_resolver=platform_instance_resolver,
+    )[0].upstreams
+
+    assert len(data_platform_tables) == 1
+
+    assert (
+        data_platform_tables[0].urn
+        == "urn:li:dataset:(urn:li:dataPlatform:databricks,abc.central_metastore.data_analysis.summary.vips_data,PROD)"
+    )
+
+
+def test_sqlglot_parser_2():
+    table: powerbi_data_classes.Table = powerbi_data_classes.Table(
+        expression=M_QUERIES[28],
         name="SALES_TARGET",
         full_name="dev.public.sales",
     )