feat(integration/fivetran): Fivetran connector integration

metadata-ingestion/setup.py

@@ -384,6 +384,7 @@
     "powerbi-report-server": powerbi_report_server,
     "vertica": sql_common | {"vertica-sqlalchemy-dialect[vertica-python]==0.0.8"},
     "unity-catalog": databricks | sqllineage_lib,
+    "fivetran": {"requests"},
 }
 
 # This is mainly used to exclude plugins from the Docker image.
@@ -512,6 +513,7 @@
             "nifi",
             "vertica",
             "mode",
+            "fivetran",
             "kafka-connect",
         ]
         if plugin
@@ -616,6 +618,7 @@
         "unity-catalog = datahub.ingestion.source.unity.source:UnityCatalogSource",
         "gcs = datahub.ingestion.source.gcs.gcs_source:GCSSource",
         "sql-queries = datahub.ingestion.source.sql_queries:SqlQueriesSource",
+        "fivetran = datahub.ingestion.source.fivetran:FivetranSource",
     ],
     "datahub.ingestion.transformer.plugins": [
         "simple_remove_dataset_ownership = datahub.ingestion.transformer.remove_dataset_ownership:SimpleRemoveDatasetOwnership",

metadata-ingestion/src/datahub/ingestion/api/source_helpers.py

@@ -65,8 +65,10 @@ def auto_status_aspect(
     For all entities that don't have a status aspect, add one with removed set to false.
     """
 
+    skip_entities: Set[str] = {"dataProcessInstance"}
     all_urns: Set[str] = set()
     status_urns: Set[str] = set()
+    skip_urns: Set[str] = set()
     for wu in stream:
         urn = wu.get_urn()
         all_urns.add(urn)
@@ -89,9 +91,18 @@ def auto_status_aspect(
         else:
             raise ValueError(f"Unexpected type {type(wu.metadata)}")
 
+        if (
+            not isinstance(wu.metadata, MetadataChangeEventClass)
+            and wu.metadata.entityType in skip_entities
+        ):
+            # If any entity does not support aspect 'status' then skip that entity from adding status aspect.
+            # Example like dataProcessInstance doesn't suppport status aspect.
+            # If not skipped gives error: java.lang.RuntimeException: Unknown aspect status for entity dataProcessInstance
+            skip_urns.add(urn)
+
         yield wu
 
-    for urn in sorted(all_urns - status_urns):
+    for urn in sorted(all_urns - status_urns - skip_urns):
         yield MetadataChangeProposalWrapper(
             entityUrn=urn,
             aspect=StatusClass(removed=False),

metadata-ingestion/src/datahub/ingestion/source/fivetran/__init__.py

@@ -0,0 +1 @@
+from datahub.ingestion.source.fivetran.fivetran import FivetranSource

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

@@ -0,0 +1,135 @@
+import logging
+from dataclasses import dataclass, field as dataclass_field
+from typing import Dict, List, Optional
+
+import pydantic
+from pydantic import Field
+from pydantic.class_validators import root_validator
+
+from datahub.configuration.common import AllowDenyPattern, ConfigModel
+from datahub.configuration.source_common import DEFAULT_ENV, DatasetSourceConfigMixin
+from datahub.ingestion.source.state.stale_entity_removal_handler import (
+    StaleEntityRemovalSourceReport,
+    StatefulStaleMetadataRemovalConfig,
+)
+from datahub.ingestion.source.state.stateful_ingestion_base import (
+    StatefulIngestionConfigBase,
+)
+from datahub.ingestion.source_config.sql.snowflake import BaseSnowflakeConfig
+
+logger = logging.getLogger(__name__)
+
+
+class Constant:
+    """
+    keys used in fivetran plugin
+    """
+
+    ORCHESTRATOR = "fivetran"
+    # table column name
+    SOURCE_SCHEMA_NAME = "source_schema_name"
+    SOURCE_TABLE_NAME = "source_table_name"
+    DESTINATION_SCHEMA_NAME = "destination_schema_name"
+    DESTINATION_TABLE_NAME = "destination_table_name"
+    SYNC_ID = "sync_id"
+    MESSAGE_DATA = "message_data"
+    TIME_STAMP = "time_stamp"
+    STATUS = "status"
+    USER_ID = "user_id"
+    GIVEN_NAME = "given_name"
+    FAMILY_NAME = "family_name"
+    CONNECTOR_ID = "connector_id"
+    CONNECTOR_NAME = "connector_name"
+    CONNECTOR_TYPE_ID = "connector_type_id"
+    PAUSED = "paused"
+    SYNC_FREQUENCY = "sync_frequency"
+    DESTINATION_ID = "destination_id"
+    CONNECTING_USER_ID = "connecting_user_id"
+    # Job status constants
+    SUCCESSFUL = "SUCCESSFUL"
+    FAILURE_WITH_TASK = "FAILURE_WITH_TASK"
+    CANCELED = "CANCELED"
+
+
+SUPPORTED_DATA_PLATFORM_MAPPING = {
+    "postgres": "postgres",
+    "snowflake": "snowflake",
+    "mysql": "mysql",
+}
+
+
+class DestinationConfig(BaseSnowflakeConfig):
+    database: str = Field(description="The fivetran connector log database.")
+    log_schema: str = Field(description="The fivetran connector log schema.")
+
+
+class FivetranLogConfig(ConfigModel):
+    destination_platform: str = pydantic.Field(
+        default="snowflake",
+        description="The destination platform where fivetran connector log tables are dumped.",
+    )
+    destination_config: Optional[DestinationConfig] = pydantic.Field(
+        default=None,
+        description="If destination platform is 'snowflake', provide snowflake configuration.",
+    )
+
+    @root_validator(pre=True)
+    def validate_destination_platfrom_and_config(cls, values: Dict) -> Dict:
+        destination_platform = values["destination_platform"]
+        if destination_platform == "snowflake":
+            if "destination_config" not in values:
+                raise ValueError(
+                    "If destination platform is 'snowflake', user must provide snowflake destination configuration in the recipe."
+                )
+        else:
+            raise ValueError(
+                f"Destination platform '{destination_platform}' is not yet supported."
+            )
+        return values
+
+
+@dataclass
+class FivetranSourceReport(StaleEntityRemovalSourceReport):
+    connectors_scanned: int = 0
+    filtered_connectors: List[str] = dataclass_field(default_factory=list)
+
+    def report_connectors_scanned(self, count: int = 1) -> None:
+        self.connectors_scanned += count
+
+    def report_connectors_dropped(self, model: str) -> None:
+        self.filtered_connectors.append(model)
+
+
+class PlatformDetail(ConfigModel):
+    platform_instance: Optional[str] = pydantic.Field(
+        default=None,
+        description="The instance of the platform that all assets produced by this recipe belong to",
+    )
+    env: str = pydantic.Field(
+        default=DEFAULT_ENV,
+        description="The environment that all assets produced by DataHub platform ingestion source belong to",
+    )
+
+
+class FivetranSourceConfig(StatefulIngestionConfigBase, DatasetSourceConfigMixin):
+    fivetran_log_config: FivetranLogConfig = pydantic.Field(
+        description="Fivetran log connector destination server configurations.",
+    )
+    connector_patterns: AllowDenyPattern = Field(
+        default=AllowDenyPattern.allow_all(),
+        description="Regex patterns for connectors to filter in ingestion.",
+    )
+    # Configuration for stateful ingestion
+    stateful_ingestion: Optional[StatefulStaleMetadataRemovalConfig] = pydantic.Field(
+        default=None, description="Airbyte Stateful Ingestion Config."
+    )
+    # Fivetran connector all sources to platform instance mapping
+    sources_to_platform_instance: Dict[str, PlatformDetail] = pydantic.Field(
+        default={},
+        description="A mapping of the connector's all sources dataset to platform instance. Use connector id as key.",
+    )
+    # Fivetran destination to platform instance mapping
+    destination_to_platform_instance: Dict[str, PlatformDetail] = pydantic.Field(
+        default={},
+        description="A mapping of destination dataset to platform instance. Use destination id as key.",
+    )

metadata-ingestion/src/datahub/ingestion/source/fivetran/data_classes.py

@@ -0,0 +1,165 @@
+import json
+import logging
+from dataclasses import dataclass
+from typing import Any, Dict, List, Tuple
+
+from sqlalchemy import create_engine
+
+from datahub.ingestion.source.fivetran.config import (
+    Constant,
+    FivetranSourceConfig,
+    FivetranSourceReport,
+)
+from datahub.ingestion.source.fivetran.fivetran_query import FivetranLogQuery
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Connector:
+    connector_id: str
+    connector_name: str
+    connector_type: str
+    paused: bool
+    sync_frequency: int
+    destination_id: str
+    user_name: str
+    source_tables: List[str]
+    destination_tables: List[str]
+    jobs: List["Job"]
+
+
+@dataclass
+class Job:
+    job_id: str
+    start_time: int
+    end_time: int
+    status: str
+
+
+class FivetranLogDataDictionary:
+    def __init__(
+        self, config: FivetranSourceConfig, report: FivetranSourceReport
+    ) -> None:
+        self.logger = logger
+        self.config = config
+        self.report = report
+        self.engine = self._get_log_destination_engine()
+
+    def _get_log_destination_engine(self) -> Any:
+        destination_platform = self.config.fivetran_log_config.destination_platform
+        engine = None
+        if destination_platform == "snowflake":
+            snowflake_destination_config = (
+                self.config.fivetran_log_config.destination_config
+            )
+            if snowflake_destination_config is not None:
+                engine = create_engine(
+                    snowflake_destination_config.get_sql_alchemy_url(),
+                    **snowflake_destination_config.get_options(),
+                )
+                engine.execute(
+                    FivetranLogQuery.use_schema(
+                        snowflake_destination_config.database,
+                        snowflake_destination_config.log_schema,
+                    )
+                )
+        return engine
+
+    def _query(self, query: str) -> List[Dict]:
+        logger.debug("Query : {}".format(query))
+        resp = self.engine.execute(query)
+        return [row for row in resp]
+
+    def _get_table_lineage(self, connector_id: str) -> Tuple[List[str], List[str]]:
+        table_lineage = self._query(
+            FivetranLogQuery.get_table_lineage_query(connector_id=connector_id)
+        )
+        source_tables: List[str] = []
+        destination_tables: List[str] = []
+        for each in table_lineage:
+            source_tables.append(
+                f"{each[Constant.SOURCE_SCHEMA_NAME]}.{each[Constant.SOURCE_TABLE_NAME]}"
+            )
+            destination_tables.append(
+                f"{each[Constant.DESTINATION_SCHEMA_NAME]}.{each[Constant.DESTINATION_TABLE_NAME]}"
+            )
+        return source_tables, destination_tables
+
+    def _get_jobs_list(self, connector_id: str) -> List[Job]:
+        jobs: List[Job] = []
+        sync_start_logs = {
+            row[Constant.SYNC_ID]: row
+            for row in self._query(
+                FivetranLogQuery.get_sync_start_logs_query(connector_id=connector_id)
+            )
+        }
+        sync_end_logs = {
+            row[Constant.SYNC_ID]: row
+            for row in self._query(
+                FivetranLogQuery.get_sync_end_logs_query(connector_id=connector_id)
+            )
+        }
+        for sync_id in sync_start_logs.keys():
+            if sync_end_logs.get(sync_id) is None:
+                # If no sync-end event log for this sync id that means sync is still in progress
+                continue
+
+            message_data = json.loads(sync_end_logs[sync_id][Constant.MESSAGE_DATA])
+            if type(message_data) is str:
+                # Sometimes message_data contains json string inside string
+                # Ex: '"{\"status\":\"SUCCESSFUL\"}"'
+                # Hence, need to do json loads twice.
+                message_data = json.loads(message_data)
+
+            jobs.append(
+                Job(
+                    job_id=sync_id,
+                    start_time=round(
+                        sync_start_logs[sync_id][Constant.TIME_STAMP].timestamp()
+                    ),
+                    end_time=round(
+                        sync_end_logs[sync_id][Constant.TIME_STAMP].timestamp()
+                    ),
+                    status=message_data[Constant.STATUS],
+                )
+            )
+        return jobs
+
+    def _get_user_name(self, user_id: str) -> str:
+        user_details = self._query(FivetranLogQuery.get_user_query(user_id=user_id))[0]
+        return (
+            f"{user_details[Constant.GIVEN_NAME]} {user_details[Constant.FAMILY_NAME]}"
+        )
+
+    def get_connectors_list(self) -> List[Connector]:
+        connectors: List[Connector] = []
+        connector_list = self._query(FivetranLogQuery.get_connectors_query())
+        for connector in connector_list:
+            if not self.config.connector_patterns.allowed(
+                connector[Constant.CONNECTOR_NAME]
+            ):
+                self.report.report_connectors_dropped(connector[Constant.CONNECTOR_ID])
+                continue
+
+            source_tables, destination_tables = self._get_table_lineage(
+                connector[Constant.CONNECTOR_ID]
+            )
+
+            connectors.append(
+                Connector(
+                    connector_id=connector[Constant.CONNECTOR_ID],
+                    connector_name=connector[Constant.CONNECTOR_NAME],
+                    connector_type=connector[Constant.CONNECTOR_TYPE_ID],
+                    paused=connector[Constant.PAUSED],
+                    sync_frequency=connector[Constant.SYNC_FREQUENCY],
+                    destination_id=connector[Constant.DESTINATION_ID],
+                    user_name=self._get_user_name(
+                        connector[Constant.CONNECTING_USER_ID]
+                    ),
+                    source_tables=source_tables,
+                    destination_tables=destination_tables,
+                    jobs=self._get_jobs_list(connector[Constant.CONNECTOR_ID]),
+                )
+            )
+        return connectors