[dagster-fivetran] Support fetch_column_metadata in FivetranWorkspace…

….sync_and_poll

python_modules/libraries/dagster-fivetran/dagster_fivetran/resources.py

@@ -2,6 +2,7 @@
 import logging
 import os
 import time
+from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime, timedelta
 from functools import partial
 from typing import Any, Callable, Mapping, Optional, Sequence, Tuple, Type, Union
@@ -10,6 +11,7 @@
 import requests
 from dagster import (
     AssetExecutionContext,
+    AssetMaterialization,
     Definitions,
     Failure,
     InitResourceContext,
@@ -25,7 +27,10 @@
 from dagster._config.pythonic_config import ConfigurableResource
 from dagster._core.definitions.asset_spec import AssetSpec
 from dagster._core.definitions.definitions_load_context import StateBackedDefinitionsLoader
+from dagster._core.definitions.metadata.metadata_set import TableMetadataSet
+from dagster._core.definitions.metadata.table import TableColumn, TableSchema
 from dagster._core.definitions.resource_definition import dagster_maintained_resource
+from dagster._core.utils import imap
 from dagster._record import record
 from dagster._utils.cached_method import cached_method
 from dagster._vendored.dateutil import parser
@@ -55,6 +60,9 @@
 FIVETRAN_API_VERSION_PATH = f"{FIVETRAN_API_VERSION}/"
 FIVETRAN_CONNECTOR_PATH = f"{FIVETRAN_CONNECTOR_ENDPOINT}/"
 
+DAGSTER_FIVETRAN_FETCH_COLUMN_METADATA_KEY = "dagster-fivetran/fetch_column_metadata"
+DEFAULT_MAX_THREADPOOL_WORKERS = 10
+
 # default polling interval (in seconds)
 DEFAULT_POLL_INTERVAL = 10
 
@@ -626,6 +634,25 @@ def update_schedule_type_for_connector(
             method="PATCH", endpoint=connector_id, data=json.dumps({"schedule_type": schedule_type})
         )
 
+    def get_columns_for_table(
+        self, connector_id: str, schema_name: str, table_name: str
+    ) -> Mapping[str, Any]:
+        """Fetches the connector schema config for a given connector from the Fivetran API.
+
+        Args:
+            connector_id (str): The Fivetran Connector ID.
+            schema_name (str): The Fivetran Schema name.
+            table_name (str): The Fivetran Table name.
+
+        Returns:
+            Dict[str, Any]: Parsed json data from the response to this request.
+        """
+        self._make_connector_request(
+            method="GET",
+            endpoint=f"{connector_id}/schemas/{schema_name}/tables/{table_name}/columns",
+        )
+        return self._make_request("GET", f"connectors/{connector_id}/schemas")
+
     def start_sync(self, connector_id: str) -> None:
         """Initiates a sync of a Fivetran connector.
 
@@ -940,6 +967,9 @@ def sync_and_poll(
         # TODO: Add docstrings
         assets_def = context.assets_def
 
+        # TODO: Add op tags to fivetran_assets decorator and build_fivetran_assets_definitions factory
+        fetch_column_metadata = context.op.tags.get(DAGSTER_FIVETRAN_FETCH_COLUMN_METADATA_KEY)
+
         connector_id = next(
             check.not_none(FivetranMetadataSet.extract(spec.metadata).connector_id)
             for spec in assets_def.specs
@@ -952,27 +982,80 @@ def sync_and_poll(
 
         materialized_asset_keys = set()
 
-        # TODO: Create new asset materialization fn with assets and not asset key prefix
-        for materialization in generate_materializations(
-            fivetran_output,
-            asset_key_prefix=[],
-        ):
-            # scan through all tables actually created, if it was expected then emit an Output.
-            # otherwise, emit a runtime AssetMaterialization
-            if materialization.asset_key in context.selected_asset_keys:
-                yield Output(
-                    value=None,
-                    output_name=materialization.asset_key.to_python_identifier(),
-                    metadata=materialization.metadata,
-                )
-                materialized_asset_keys.add(materialization.asset_key)
-
-            else:
-                yield materialization
-
-        unmaterialized_asset_keys = context.selected_asset_keys - materialized_asset_keys
-        if unmaterialized_asset_keys:
-            context.log.warning(f"Assets were not materialized: {unmaterialized_asset_keys}")
+        _map_fn: Callable[[AssetMaterialization], AssetMaterialization] = (
+            lambda materialization: self._fetch_and_attach_col_metadata(
+                connector_id, materialization
+            )
+            if fetch_column_metadata
+            else materialization
+        )
+        with ThreadPoolExecutor(
+            max_workers=DEFAULT_MAX_THREADPOOL_WORKERS,
+            thread_name_prefix=f"fivetran_{connector_id}",
+        ) as executor:
+            for materialization in imap(
+                executor=executor,
+                # TODO: Create new asset materialization fn with assets and not asset key prefix
+                iterable=generate_materializations(
+                    fivetran_output,
+                    asset_key_prefix=[],
+                ),
+                func=_map_fn,
+            ):
+                # scan through all tables actually created, if it was expected then emit an Output.
+                # otherwise, emit a runtime AssetMaterialization
+                if materialization.asset_key in context.selected_asset_keys:
+                    yield Output(
+                        value=None,
+                        output_name=materialization.asset_key.to_python_identifier(),
+                        metadata=materialization.metadata,
+                    )
+                    materialized_asset_keys.add(materialization.asset_key)
+
+                else:
+                    yield materialization
+
+            unmaterialized_asset_keys = context.selected_asset_keys - materialized_asset_keys
+            if unmaterialized_asset_keys:
+                context.log.warning(f"Assets were not materialized: {unmaterialized_asset_keys}")
+
+    def _fetch_and_attach_col_metadata(
+        self, connector_id: str, materialization: AssetMaterialization
+    ) -> AssetMaterialization:
+        """Subroutine to fetch column metadata for a given table from the Fivetran API and attach it to the
+        materialization.
+        """
+        try:
+            schema_source_name = materialization.metadata["schema_source_name"].value
+            table_source_name = materialization.metadata["table_source_name"].value
+
+            table_conn_data = self.get_client().get_columns_for_table(
+                connector_id=connector_id,
+                schema_name=schema_source_name,
+                table_name=table_source_name,
+            )
+            columns = check.dict_elem(table_conn_data, "columns")
+            table_columns = sorted(
+                [
+                    TableColumn(name=col["name_in_destination"], type="")
+                    for col in columns.values()
+                    if "name_in_destination" in col and col.get("enabled")
+                ],
+                key=lambda col: col.name,
+            )
+            return materialization.with_metadata(
+                {
+                    **materialization.metadata,
+                    **TableMetadataSet(column_schema=TableSchema(table_columns)),
+                }
+            )
+        except Exception as e:
+            self._log.warning(
+                "An error occurred while fetching column metadata for table %s",
+                f"Exception: {e}",
+                exc_info=True,
+            )
+            return materialization
 
 
 @experimental