feat(dbt): emit column lineage using sqlglot

docs/content/integrations/dbt/reference.mdx

@@ -530,18 +530,18 @@ def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
 
 ---
 
-## Emit column schema as materialization metadata <Experimental />
+## Emit column-level metadata as materialization metadata <Experimental />
 
 <Note>
   <strong>
-    Emitting column schema as materialization metadata is currently an
+    Emitting column-level metadata as materialization metadata is currently an
     experimental feature.{" "}
   </strong>{" "}
-  To use this feature, you'll need to be on at least `dagster==1.6.6` and
-  `dagster-dbt==0.22.6`.
+  To use this feature, you'll need to be on at least `dagster>=1.6.12` and
+  `dagster-dbt>=0.22.12`.
 </Note>
 
-Dagster allows you to emit column schema [materialization metadata](/concepts/assets/software-defined-assets#recording-materialization-metadata), which includes the column names and data types of your materialized dbt models, seeds, and snapshots.
+Dagster allows you to emit column-level metadata, like column schema and column dependencies, as [materialization metadata](/concepts/assets/software-defined-assets#recording-materialization-metadata).
 
 With this metadata, you can view documentation in Dagster for all columns, not just columns described in your dbt project.
 
@@ -554,20 +554,20 @@ packages:
     revision: DAGSTER_VERSION # replace with the version of `dagster` you are using.
 ```
 
-Then, enable the `dagster.log_columns_in_relation()` macro as a [post-hook](https://docs.getdbt.com/reference/resource-configs/pre-hook-post-hook) for the dbt resources that should emit column schema metadata. For example, adding the following snippet in `dbt_project.yml` enables this macro for all dbt models, seeds, and snapshots:
+Then, enable the `dagster.log_column_level_metadata()` macro as a [post-hook](https://docs.getdbt.com/reference/resource-configs/pre-hook-post-hook) for the dbt resources that should emit column schema metadata. For example, adding the following snippet in `dbt_project.yml` enables this macro for all dbt models, seeds, and snapshots:
 
 ```yaml
 models:
   +post-hook:
-    - "{{ dagster.log_columns_in_relation() }}"
+    - "{{ dagster.log_column_level_metadata() }}"
 
 seeds:
   +post-hook:
-    - "{{ dagster.log_columns_in_relation() }}"
+    - "{{ dagster.log_column_level_metadata() }}"
 
 snapshots:
   +post-hook:
-    - "{{ dagster.log_columns_in_relation() }}"
+    - "{{ dagster.log_column_level_metadata() }}"
 ```
 
 ---

pyright/alt-1/requirements-pinned.txt

@@ -17,6 +17,7 @@ asn1crypto==1.5.1
 astroid==3.1.0
 asttokens==2.4.1
 async-lru==2.0.4
+async-timeout==4.0.3
 attrs==23.2.0
 babel==2.14.0
 backoff==2.2.1
@@ -76,6 +77,7 @@ distlib==0.3.8
 docker==7.0.0
 docstring-parser==0.15
 duckdb==0.10.0
+exceptiongroup==1.2.0
 execnet==2.0.2
 executing==2.0.1
 fastjsonschema==2.19.1
@@ -110,7 +112,7 @@ httplib2==0.22.0
 httptools==0.6.1
 httpx==0.27.0
 humanfriendly==10.0
-hypothesis==6.99.2
+hypothesis==6.99.4
 idna==3.6
 importlib-metadata==6.11.0
 iniconfig==2.0.0
@@ -178,7 +180,7 @@ overrides==7.7.0
 packaging==24.0
 pandas==2.0.3
 pandas-stubs==2.2.0.240218
-pandera==0.18.2
+pandera==0.18.3
 pandocfilters==1.5.1
 parsedatetime==2.6
 parso==0.8.3
@@ -256,6 +258,8 @@ snowflake-sqlalchemy==1.5.1
 sortedcontainers==2.4.0
 soupsieve==2.5
 sqlalchemy==1.4.52
+sqlglot==22.3.1
+sqlglotrs==0.1.2
 sqlparse==0.4.4
 stack-data==0.6.3
 starlette==0.37.2
@@ -315,7 +319,7 @@ webcolors==1.13
 webencodings==0.5.1
 websocket-client==1.7.0
 websockets==12.0
-wheel==0.42.0
+wheel==0.43.0
 wrapt==1.16.0
 yarl==1.9.4
 zipp==3.17.0

pyright/master/requirements-pinned.txt

@@ -35,6 +35,7 @@ asn1crypto==1.5.1
 -e examples/assets_pandas_pyspark
 asttokens==2.4.1
 async-lru==2.0.4
+async-timeout==4.0.3
 attrs==23.2.0
 autodocsumm==0.2.12
 autoflake==2.3.0
@@ -56,8 +57,8 @@ bitmath==1.3.3.1
 bleach==6.1.0
 blinker==1.7.0
 bokeh==3.3.4
-boto3==1.34.59
-botocore==1.34.59
+boto3==1.34.60
+botocore==1.34.60
 buildkite-test-collector==0.1.7
 cachecontrol==0.14.0
 cached-property==1.5.2
@@ -195,6 +196,7 @@ duckdb==0.10.0
 ecdsa==0.18.0
 email-validator==1.3.1
 entrypoints==0.4
+exceptiongroup==1.2.0
 execnet==2.0.2
 executing==2.0.1
 expandvars==0.12.0
@@ -252,7 +254,7 @@ httplib2==0.22.0
 httptools==0.6.1
 httpx==0.27.0
 humanfriendly==10.0
-hypothesis==6.99.2
+hypothesis==6.99.4
 idna==3.6
 ijson==3.2.3
 imagesize==1.4.1
@@ -374,7 +376,7 @@ packaging==23.2
 pandas==2.0.3
 pandas-gbq==0.22.0
 pandas-stubs==2.2.0.240218
-pandera==0.18.2
+pandera==0.18.3
 pandocfilters==1.5.1
 papermill==2.5.0
 papermill-origami==0.0.30
@@ -507,6 +509,8 @@ sphinxcontrib-serializinghtml==1.1.10
 sqlalchemy==1.4.52
 sqlalchemy-jsonfield==1.0.2
 sqlalchemy-utils==0.41.1
+sqlglot==22.3.1
+sqlglotrs==0.1.2
 sqlparse==0.4.4
 sshpubkeys==3.3.1
 sshtunnel==0.4.0
@@ -587,7 +591,7 @@ webencodings==0.5.1
 websocket-client==1.7.0
 websockets==12.0
 werkzeug==2.2.3
-wheel==0.42.0
+wheel==0.43.0
 widgetsnbextension==4.0.10
 -e examples/with_airflow
 -e examples/with_great_expectations

python_modules/libraries/dagster-dbt/dagster_dbt/core/resources_v2.py

@@ -17,6 +17,8 @@
     Mapping,
     Optional,
     Sequence,
+    Set,
+    Tuple,
     Union,
     cast,
 )
@@ -27,10 +29,12 @@
     AssetCheckResult,
     AssetCheckSeverity,
     AssetExecutionContext,
+    AssetKey,
     AssetMaterialization,
     AssetObservation,
     AssetsDefinition,
     ConfigurableResource,
+    JsonMetadataValue,
     OpExecutionContext,
     Output,
     get_dagster_logger,
@@ -43,6 +47,13 @@
 from dbt.version import __version__ as dbt_version
 from packaging import version
 from pydantic import Field, validator
+from sqlglot import (
+    MappingSchema,
+    exp,
+    parse_one,
+)
+from sqlglot.lineage import lineage
+from sqlglot.optimizer import optimize
 from typing_extensions import Literal
 
 from ..asset_utils import (
@@ -102,19 +113,28 @@ def log_level(self) -> str:
         """The log level of the event."""
         return self.raw_event["info"]["level"]
 
+    @property
+    def has_column_lineage_metadata(self) -> bool:
+        """Whether the event has column level lineage metadata."""
+        return bool(self._event_history_metadata) and "parents" in self._event_history_metadata
+
     @public
     def to_default_asset_events(
         self,
         manifest: DbtManifestParam,
         dagster_dbt_translator: DagsterDbtTranslator = DagsterDbtTranslator(),
         context: Optional[OpExecutionContext] = None,
+        target_path: Optional[Path] = None,
     ) -> Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]:
         """Convert a dbt CLI event to a set of corresponding Dagster events.
 
         Args:
             manifest (Union[Mapping[str, Any], str, Path]): The dbt manifest blob.
             dagster_dbt_translator (DagsterDbtTranslator): Optionally, a custom translator for
                 linking dbt nodes to Dagster assets.
+            context (Optional[OpExecutionContext]): The execution context.
+            target_path (Optional[Path]): An explicit path to a target folder used to retrieve
+                dbt artifacts while generating events.
 
         Returns:
             Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]:
@@ -173,6 +193,16 @@ def to_default_asset_events(
             finished_at = dateutil.parser.isoparse(event_node_info["node_finished_at"])
             duration_seconds = (finished_at - started_at).total_seconds()
 
+            lineage_metadata = (
+                self._build_column_lineage_metadata(
+                    manifest=manifest,
+                    dagster_dbt_translator=dagster_dbt_translator,
+                    target_path=target_path,
+                )
+                if target_path
+                else {}
+            )
+
             if has_asset_def:
                 yield Output(
                     value=None,
@@ -181,6 +211,7 @@ def to_default_asset_events(
                         **default_metadata,
                         "Execution Duration": duration_seconds,
                         **adapter_response_metadata,
+                        **lineage_metadata,
                     },
                 )
             else:
@@ -193,6 +224,7 @@ def to_default_asset_events(
                         **default_metadata,
                         "Execution Duration": duration_seconds,
                         **adapter_response_metadata,
+                        **lineage_metadata,
                     },
                 )
         elif manifest and node_resource_type == NodeType.Test and is_node_finished:
@@ -284,6 +316,108 @@ def _process_adapter_response_metadata(
 
         return processed_adapter_response
 
+    def _build_column_lineage_metadata(
+        self,
+        manifest: Mapping[str, Any],
+        dagster_dbt_translator: DagsterDbtTranslator,
+        target_path: Path,
+    ) -> Dict[str, Any]:
+        """Process the lineage metadata for a dbt CLI event.
+
+        Args:
+            manifest (Mapping[str, Any]): The dbt manifest blob.
+            dagster_dbt_translator (DagsterDbtTranslator): The translator for dbt nodes to Dagster assets.
+            target_path (Path): The path to the dbt target folder.
+
+        Returns:
+            Dict[str, Any]: The lineage metadata.
+        """
+        if (
+            # The dbt project name is only available from the manifest in `dbt-core>=1.6`.
+            version.parse(dbt_version) < version.parse("1.6.0")
+            # Column lineage can only be built if initial metadata is provided.
+            or not self.has_column_lineage_metadata
+        ):
+            return {}
+
+        # TODO: this should be refactored as a Python object that renders the JSON metadata.
+        lineage_metadata: Dict[str, List[Tuple[AssetKey, str]]] = {}
+        sqlglot_mapping_schema = MappingSchema()
+
+        event_node_info: Dict[str, Any] = self.raw_event["data"].get("node_info")
+        unique_id: str = event_node_info["unique_id"]
+        dbt_resource_props: Dict[str, Any] = manifest["nodes"][unique_id]
+
+        # If the unique_id is a seed, then we don't need to process lineage.
+        if unique_id.startswith("seed"):
+            return {}
+
+        # 1. Retrieve the current node's SQL file and its parents' column schemas.
+        for relation_name, relation_metadata in self._event_history_metadata["parents"].items():
+            sqlglot_mapping_schema.add_table(
+                table=relation_name,
+                column_mapping={
+                    column_name: column_metadata["data_type"]
+                    for column_name, column_metadata in relation_metadata["columns"].items()
+                },
+            )
+
+        node_sql_path = target_path.joinpath(
+            "run", manifest["metadata"]["project_name"], dbt_resource_props["original_file_path"]
+        )
+        node_ast = parse_one(sql=node_sql_path.read_text()).expression
+        optimized_node_ast = cast(
+            exp.Query,
+            optimize(
+                node_ast,
+                schema=sqlglot_mapping_schema,
+                validate_qualify_columns=False,  # Don't throw an error if we can't qualify a column without ambiguity.
+            ),
+        )
+
+        # 2. Retrieve the column names from the current node.
+        column_names = cast(exp.Query, optimized_node_ast).named_selects
+
+        # 3. For each column, retrieve its dependencies on upstream columns from direct parents.
+        for column_name in column_names:
+            dbt_parent_resource_props_by_alias: Dict[str, Dict[str, Any]] = {
+                parent_dbt_resource_props["alias"]: parent_dbt_resource_props
+                for parent_dbt_resource_props in map(
+                    lambda parent_unique_id: manifest["nodes"][parent_unique_id],
+                    dbt_resource_props["depends_on"]["nodes"],
+                )
+            }
+
+            parent_columns: Set[Tuple[AssetKey, str]] = set()
+            for sqlglot_lineage_node in lineage(
+                column=column_name, sql=optimized_node_ast, schema=sqlglot_mapping_schema
+            ).walk():
+                column = sqlglot_lineage_node.expression.find(exp.Column)
+                if column and column.table in dbt_parent_resource_props_by_alias:
+                    parent_resource_props = dbt_parent_resource_props_by_alias[column.table]
+                    parent_asset_key = dagster_dbt_translator.get_asset_key(parent_resource_props)
+
+                    parent_columns.add((parent_asset_key, column.name))
+
+            lineage_metadata[column_name] = list(parent_columns)
+
+        # 4. Render the lineage as a JSON blob.
+        # TODO: this should just call a method on a Python object that renders the JSON metadata.
+        return {
+            "dagster/column_lineage": JsonMetadataValue(
+                {
+                    column_name: [
+                        {
+                            "upstream_asset_key": parent_asset_key,
+                            "upstream_column_name": parent_column_name,
+                        }
+                        for parent_asset_key, parent_column_name in parent_columns
+                    ]
+                    for column_name, parent_columns in lineage_metadata.items()
+                }
+            )
+        }
+
 
 @dataclass
 class DbtCliInvocation:
@@ -454,6 +588,7 @@ def my_dbt_assets(context, dbt: DbtCliResource):
                 manifest=self.manifest,
                 dagster_dbt_translator=self.dagster_dbt_translator,
                 context=self.context,
+                target_path=self.target_path,
             )
 
     @public
@@ -487,14 +622,15 @@ def stream_raw_events(self) -> Iterator[DbtCliEventMessage]:
                 if is_error_message:
                     self._error_messages.append(str(event))
 
-                # Attempt to parse the columns metadata from the event message.
+                # Attempt to parse the column level metadata from the event message.
                 # If it exists, save it as historical metadata to attach to the NodeFinished event.
                 if event.raw_event["info"]["name"] == "JinjaLogInfo":
                     with contextlib.suppress(orjson.JSONDecodeError):
-                        columns = orjson.loads(event.raw_event["info"]["msg"])
-                        event_history_metadata_by_unique_id[cast(str, unique_id)] = {
-                            "columns": columns
-                        }
+                        column_level_metadata = orjson.loads(event.raw_event["info"]["msg"])
+
+                        event_history_metadata_by_unique_id[cast(str, unique_id)] = (
+                            column_level_metadata
+                        )
 
                         # Don't show this message in stdout
                         continue