from argparse import Namespace
from typing import (
AbstractSet,
Any,
Callable,
Dict,
Iterator,
Mapping,
Optional,
Sequence,
Union,
)
import dateutil
from dagster import (
AssetKey,
AssetMaterialization,
AssetObservation,
MetadataValue,
Output,
_check as check,
)
from dagster._core.definitions.metadata import RawMetadataValue
from .types import DbtOutput
# dbt resource types that may be considered assets
ASSET_RESOURCE_TYPES = ["model", "seed", "snapshot"]
def default_node_info_to_asset_key(node_info: Mapping[str, Any]) -> AssetKey:
return AssetKey(node_info["unique_id"].split("."))
def _resource_type(unique_id: str) -> str:
# returns the type of the node (e.g. model, test, snapshot)
return unique_id.split(".")[0]
def dagster_name_fn(dbt_resource_props: Mapping[str, Any]) -> str:
return dbt_resource_props["unique_id"].replace(".", "_").replace("-", "_").replace("*", "_star")
def _node_result_to_metadata(node_result: Mapping[str, Any]) -> Mapping[str, RawMetadataValue]:
return {
"Materialization Strategy": node_result["config"]["materialized"],
"Database": node_result["database"],
"Schema": node_result["schema"],
"Alias": node_result["alias"],
"Description": node_result["description"],
}
def _timing_to_metadata(timings: Sequence[Mapping[str, Any]]) -> Mapping[str, RawMetadataValue]:
metadata: Dict[str, RawMetadataValue] = {}
for timing in timings:
if timing["name"] == "execute":
desc = "Execution"
elif timing["name"] == "compile":
desc = "Compilation"
else:
continue
# dateutil does not properly expose its modules to static checkers
started_at = dateutil.parser.isoparse(timing["started_at"]) # type: ignore
completed_at = dateutil.parser.isoparse(timing["completed_at"]) # type: ignore
duration = completed_at - started_at
metadata.update(
{
f"{desc} Started At": started_at.isoformat(timespec="seconds"),
f"{desc} Completed At": started_at.isoformat(timespec="seconds"),
f"{desc} Duration": duration.total_seconds(),
}
)
return metadata
def result_to_events(
result: Mapping[str, Any],
docs_url: Optional[str] = None,
node_info_to_asset_key: Optional[Callable[[Mapping[str, Any]], AssetKey]] = None,
manifest_json: Optional[Mapping[str, Any]] = None,
extra_metadata: Optional[Mapping[str, RawMetadataValue]] = None,
generate_asset_outputs: bool = False,
) -> Iterator[Union[AssetMaterialization, AssetObservation, Output]]:
"""This is a hacky solution that attempts to consolidate parsing many of the potential formats
that dbt can provide its results in. This is known to work for CLI Outputs for dbt versions 0.18+,
as well as RPC responses for a similar time period, but as the RPC response schema is not documented
nor enforced, this can become out of date easily.
"""
node_info_to_asset_key = check.opt_callable_param(
node_info_to_asset_key, "node_info_to_asset_key", default=default_node_info_to_asset_key
)
# status comes from set of fields rather than "status"
if "fail" in result:
status = (
"fail"
if result.get("fail")
else "skip"
if result.get("skip")
else "error"
if result.get("error")
else "success"
)
else:
status = result["status"]
# all versions represent timing the same way
metadata = {"Status": status, "Execution Time (seconds)": result["execution_time"]}
metadata.update(_timing_to_metadata(result["timing"]))
# working with a response that contains the node block (RPC and CLI 0.18.x)
if "node" in result:
unique_id = result["node"]["unique_id"]
metadata.update(_node_result_to_metadata(result["node"]))
else:
unique_id = result["unique_id"]
if docs_url:
metadata["docs_url"] = MetadataValue.url(f"{docs_url}#!/model/{unique_id}")
if extra_metadata:
metadata.update(extra_metadata)
# if you have a manifest available, get the full node info, otherwise just populate unique_id
dbt_resource_props = (
manifest_json["nodes"][unique_id] if manifest_json else {"unique_id": unique_id}
)
node_resource_type = _resource_type(unique_id)
if node_resource_type in ASSET_RESOURCE_TYPES and status == "success":
if generate_asset_outputs:
yield Output(
value=None,
output_name=dagster_name_fn(dbt_resource_props),
metadata=metadata,
)
else:
yield AssetMaterialization(
asset_key=node_info_to_asset_key(dbt_resource_props),
description=f"dbt node: {unique_id}",
metadata=metadata,
)
# can only associate tests with assets if we have manifest_json available
elif node_resource_type == "test" and manifest_json and status != "skipped":
upstream_unique_ids = manifest_json["nodes"][unique_id]["depends_on"]["nodes"]
# tests can apply to multiple asset keys
for upstream_id in upstream_unique_ids:
# the upstream id can reference a node or a source
dbt_resource_props = manifest_json["nodes"].get(upstream_id) or manifest_json[
"sources"
].get(upstream_id)
if dbt_resource_props is None:
continue
upstream_asset_key = node_info_to_asset_key(dbt_resource_props)
yield AssetObservation(
asset_key=upstream_asset_key,
metadata={
"Test ID": result["unique_id"],
"Test Status": status,
"Test Message": result.get("message") or "",
},
)
def generate_events(
dbt_output: DbtOutput,
node_info_to_asset_key: Optional[Callable[[Mapping[str, Any]], AssetKey]] = None,
manifest_json: Optional[Mapping[str, Any]] = None,
) -> Iterator[Union[AssetMaterialization, AssetObservation]]:
"""This function yields :py:class:`dagster.AssetMaterialization` events for each model updated by
a dbt command, and :py:class:`dagster.AssetObservation` events for each test run.
Information parsed from a :py:class:`~DbtOutput` object.
"""
for result in dbt_output.result["results"]:
for event in result_to_events(
result,
docs_url=dbt_output.docs_url,
node_info_to_asset_key=node_info_to_asset_key,
manifest_json=manifest_json,
):
yield check.inst(
event,
(AssetMaterialization, AssetObservation),
)
[docs]def generate_materializations(
dbt_output: DbtOutput,
asset_key_prefix: Optional[Sequence[str]] = None,
) -> Iterator[AssetMaterialization]:
"""This function yields :py:class:`dagster.AssetMaterialization` events for each model updated by
a dbt command.
Information parsed from a :py:class:`~DbtOutput` object.
Examples:
.. code-block:: python
from dagster import op, Output
from dagster_dbt.utils import generate_materializations
from dagster_dbt import dbt_cli_resource
@op(required_resource_keys={"dbt"})
def my_custom_dbt_run(context):
dbt_output = context.resources.dbt.run()
for materialization in generate_materializations(dbt_output):
# you can modify the materialization object to add extra metadata, if desired
yield materialization
yield Output(my_dbt_output)
@job(resource_defs={{"dbt":dbt_cli_resource}})
def my_dbt_cli_job():
my_custom_dbt_run()
"""
asset_key_prefix = check.opt_sequence_param(asset_key_prefix, "asset_key_prefix", of_type=str)
for event in generate_events(
dbt_output,
node_info_to_asset_key=lambda info: AssetKey(
asset_key_prefix + info["unique_id"].split(".")
),
):
yield check.inst(event, AssetMaterialization)
def select_unique_ids_from_manifest(
select: str,
exclude: str,
manifest_json: Mapping[str, Any],
) -> AbstractSet[str]:
"""Method to apply a selection string to an existing manifest.json file."""
import dbt.graph.cli as graph_cli
import dbt.graph.selector as graph_selector
from dbt.contracts.graph.manifest import Manifest
from dbt.graph.selector_spec import IndirectSelection, SelectionSpec
from networkx import DiGraph
# NOTE: this was faster than calling `Manifest.from_dict`, so we are keeping this.
class _DictShim(dict):
"""Shim to enable hydrating a dictionary into a dot-accessible object. We need this because
dbt expects dataclasses that can be accessed with dot notation, not bare dictionaries.
See https://stackoverflow.com/a/23689767.
"""
def __getattr__(self, item):
ret = super().get(item)
# allow recursive access e.g. foo.bar.baz
return _DictShim(ret) if isinstance(ret, dict) else ret
manifest = Manifest(
nodes={
unique_id: _DictShim(info)
for unique_id, info in manifest_json["nodes"].items() # type: ignore
},
sources={
unique_id: _DictShim(info)
for unique_id, info in manifest_json["sources"].items() # type: ignore
},
metrics={
unique_id: _DictShim(info)
for unique_id, info in manifest_json["metrics"].items() # type: ignore
},
exposures={
unique_id: _DictShim(info)
for unique_id, info in manifest_json["exposures"].items() # type: ignore
},
**( # type: ignore
{
"semantic_models": {
unique_id: _DictShim(info)
for unique_id, info in manifest_json.get("semantic_models", {}).items()
}
}
if manifest_json.get("semantic_models")
else {}
),
)
child_map = manifest_json["child_map"]
graph = graph_selector.Graph(DiGraph(incoming_graph_data=child_map))
# create a parsed selection from the select string
_set_flag_attrs(
{
"INDIRECT_SELECTION": IndirectSelection.Eager,
"WARN_ERROR": True,
}
)
parsed_spec: SelectionSpec = graph_cli.parse_union([select], True)
if exclude:
parsed_exclude_spec = graph_cli.parse_union([exclude], False)
parsed_spec = graph_cli.SelectionDifference(components=[parsed_spec, parsed_exclude_spec])
# execute this selection against the graph
selector = graph_selector.NodeSelector(graph, manifest)
selected, _ = selector.select_nodes(parsed_spec)
return selected
def get_dbt_resource_props_by_dbt_unique_id_from_manifest(
manifest: Mapping[str, Any],
) -> Mapping[str, Mapping[str, Any]]:
"""A mapping of a dbt node's unique id to the node's dictionary representation in the manifest."""
return {
**manifest["nodes"],
**manifest["sources"],
**manifest["exposures"],
**manifest["metrics"],
**manifest.get("semantic_models", {}),
}
def _set_flag_attrs(kvs: Dict[str, Any]):
from dbt.flags import get_flag_dict, set_flags
new_flags = Namespace()
for global_key, global_value in get_flag_dict().items():
setattr(new_flags, global_key.upper(), global_value)
for key, value in kvs.items():
setattr(new_flags, key.upper(), value)
set_flags(new_flags)