wehub-resource-sync
519 lines
20 KiB
Python
519 lines
20 KiB
Python
import json
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import pytest
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from google.protobuf.timestamp_pb2 import Timestamp
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import mlflow
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from mlflow.entities import (
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AssessmentSource,
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Expectation,
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Feedback,
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Trace,
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TraceData,
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TraceInfo,
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TraceState,
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)
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from mlflow.entities.trace_location import (
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InferenceTableLocation,
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MlflowExperimentLocation,
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TraceLocation,
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TraceLocationType,
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UCSchemaLocation,
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UnityCatalog,
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)
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from mlflow.protos import assessments_pb2
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from mlflow.protos import databricks_tracing_pb2 as pb
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from mlflow.protos.assessments_pb2 import AssessmentSource as ProtoAssessmentSource
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from mlflow.tracing.constant import (
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TRACE_ID_V4_PREFIX,
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TRACE_SCHEMA_VERSION,
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TRACE_SCHEMA_VERSION_KEY,
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SpanAttributeKey,
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)
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from mlflow.tracing.utils import TraceMetadataKey, add_size_stats_to_trace_metadata
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from mlflow.utils.databricks_tracing_utils import (
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assessment_to_proto,
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get_trace_id_from_assessment_proto,
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inference_table_location_to_proto,
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mlflow_experiment_location_to_proto,
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parse_uc_location,
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trace_from_proto,
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trace_location_from_proto,
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trace_location_to_proto,
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trace_to_proto,
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uc_location_to_str,
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uc_schema_location_from_proto,
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uc_schema_location_to_proto,
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)
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def test_trace_location_to_proto_uc_schema():
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trace_location = TraceLocation.from_databricks_uc_schema(
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catalog_name="test_catalog", schema_name="test_schema"
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)
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proto = trace_location_to_proto(trace_location)
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assert proto.type == pb.TraceLocation.TraceLocationType.UC_SCHEMA
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assert proto.uc_schema.catalog_name == "test_catalog"
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assert proto.uc_schema.schema_name == "test_schema"
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def test_parse_uc_location():
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assert parse_uc_location("catalog.schema") == ("catalog", "schema", None)
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assert parse_uc_location("catalog.schema.prefix") == ("catalog", "schema", "prefix")
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with pytest.raises(ValueError, match="Invalid UC location"):
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parse_uc_location("a.b.c.d")
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def test_uc_location_to_str():
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assert uc_location_to_str("catalog", "schema") == "catalog.schema"
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assert uc_location_to_str("catalog", "schema", "prefix") == "catalog.schema.prefix"
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def test_trace_location_to_proto_mlflow_experiment():
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trace_location = TraceLocation.from_experiment_id(experiment_id="1234")
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proto = trace_location_to_proto(trace_location)
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assert proto.type == pb.TraceLocation.TraceLocationType.MLFLOW_EXPERIMENT
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assert proto.mlflow_experiment.experiment_id == "1234"
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def test_trace_location_to_proto_inference_table():
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trace_location = TraceLocation(
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type=TraceLocationType.INFERENCE_TABLE,
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inference_table=InferenceTableLocation(
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full_table_name="test_catalog.test_schema.test_table"
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),
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)
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proto = trace_location_to_proto(trace_location)
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assert proto.type == pb.TraceLocation.TraceLocationType.INFERENCE_TABLE
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assert proto.inference_table.full_table_name == "test_catalog.test_schema.test_table"
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def test_uc_schema_location_to_proto():
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schema_location = UCSchemaLocation(catalog_name="test_catalog", schema_name="test_schema")
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proto = uc_schema_location_to_proto(schema_location)
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assert proto.catalog_name == "test_catalog"
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assert proto.schema_name == "test_schema"
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def test_uc_schema_location_from_proto():
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proto = pb.UCSchemaLocation(
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catalog_name="test_catalog",
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schema_name="test_schema",
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otel_spans_table_name="test_spans",
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otel_logs_table_name="test_logs",
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)
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schema_location = uc_schema_location_from_proto(proto)
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assert schema_location.catalog_name == "test_catalog"
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assert schema_location.schema_name == "test_schema"
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assert schema_location.full_otel_spans_table_name == "test_catalog.test_schema.test_spans"
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assert schema_location.full_otel_logs_table_name == "test_catalog.test_schema.test_logs"
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def test_inference_table_location_to_proto():
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table_location = InferenceTableLocation(full_table_name="test_catalog.test_schema.test_table")
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proto = inference_table_location_to_proto(table_location)
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assert proto.full_table_name == "test_catalog.test_schema.test_table"
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def test_mlflow_experiment_location_to_proto():
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experiment_location = MlflowExperimentLocation(experiment_id="1234")
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proto = mlflow_experiment_location_to_proto(experiment_location)
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assert proto.experiment_id == "1234"
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def test_schema_location_to_proto():
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schema_location = UCSchemaLocation(
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catalog_name="test_catalog",
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schema_name="test_schema",
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)
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schema_location._otel_spans_table_name = "test_spans"
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schema_location._otel_logs_table_name = "test_logs"
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proto = uc_schema_location_to_proto(schema_location)
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assert proto.catalog_name == "test_catalog"
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assert proto.schema_name == "test_schema"
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assert proto.otel_spans_table_name == "test_spans"
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assert proto.otel_logs_table_name == "test_logs"
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def test_trace_location_from_proto_uc_schema():
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proto = pb.TraceLocation(
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type=pb.TraceLocation.TraceLocationType.UC_SCHEMA,
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uc_schema=pb.UCSchemaLocation(
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catalog_name="catalog",
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schema_name="schema",
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otel_spans_table_name="spans",
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otel_logs_table_name="logs",
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),
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)
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trace_location = trace_location_from_proto(proto)
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assert trace_location.uc_schema.catalog_name == "catalog"
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assert trace_location.uc_schema.schema_name == "schema"
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assert trace_location.uc_schema.full_otel_spans_table_name == "catalog.schema.spans"
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assert trace_location.uc_schema.full_otel_logs_table_name == "catalog.schema.logs"
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def test_trace_location_from_proto_mlflow_experiment():
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proto = pb.TraceLocation(
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type=pb.TraceLocation.TraceLocationType.MLFLOW_EXPERIMENT,
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mlflow_experiment=mlflow_experiment_location_to_proto(
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MlflowExperimentLocation(experiment_id="1234")
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),
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)
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trace_location = trace_location_from_proto(proto)
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assert trace_location.type == TraceLocationType.MLFLOW_EXPERIMENT
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assert trace_location.mlflow_experiment.experiment_id == "1234"
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def test_trace_location_from_proto_inference_table():
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proto = pb.TraceLocation(
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type=pb.TraceLocation.TraceLocationType.INFERENCE_TABLE,
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inference_table=inference_table_location_to_proto(
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InferenceTableLocation(full_table_name="test_catalog.test_schema.test_table")
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),
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)
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trace_location = trace_location_from_proto(proto)
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assert trace_location.type == TraceLocationType.INFERENCE_TABLE
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assert trace_location.inference_table.full_table_name == "test_catalog.test_schema.test_table"
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def test_trace_info_to_v4_proto():
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otel_trace_id = "2efb31387ff19263f92b2c0a61b0a8bc"
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trace_id = f"trace:/catalog.schema/{otel_trace_id}"
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trace_info = TraceInfo(
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trace_id=trace_id,
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trace_location=TraceLocation.from_databricks_uc_schema(
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catalog_name="catalog", schema_name="schema"
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),
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request_time=0,
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state=TraceState.OK,
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request_preview="request",
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response_preview="response",
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client_request_id="client_request_id",
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tags={"key": "value"},
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)
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proto_trace_info = trace_info.to_proto()
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assert proto_trace_info.trace_id == otel_trace_id
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assert proto_trace_info.trace_location.uc_schema.catalog_name == "catalog"
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assert proto_trace_info.trace_location.uc_schema.schema_name == "schema"
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assert proto_trace_info.state == 1
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assert proto_trace_info.request_preview == "request"
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assert proto_trace_info.response_preview == "response"
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assert proto_trace_info.client_request_id == "client_request_id"
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assert proto_trace_info.tags == {"key": "value"}
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assert len(proto_trace_info.assessments) == 0
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trace_info_from_proto = TraceInfo.from_proto(proto_trace_info)
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assert trace_info_from_proto == trace_info
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def test_trace_to_proto_and_from_proto():
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with mlflow.start_span() as span:
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otel_trace_id = span.trace_id.removeprefix("tr-")
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uc_schema = "catalog.schema"
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trace_id = f"trace:/{uc_schema}/{otel_trace_id}"
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span.set_attribute(SpanAttributeKey.REQUEST_ID, trace_id)
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mlflow_span = span.to_immutable_span()
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assert mlflow_span.trace_id == trace_id
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trace = Trace(
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info=TraceInfo(
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trace_id=trace_id,
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trace_location=TraceLocation.from_databricks_uc_schema(
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catalog_name="catalog", schema_name="schema"
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),
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request_time=0,
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state=TraceState.OK,
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request_preview="request",
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response_preview="response",
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client_request_id="client_request_id",
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tags={"key": "value"},
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),
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data=TraceData(spans=[mlflow_span]),
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)
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proto_trace_v4 = trace_to_proto(trace)
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assert proto_trace_v4.trace_info.trace_id == otel_trace_id
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assert proto_trace_v4.trace_info.trace_location.uc_schema.catalog_name == "catalog"
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assert proto_trace_v4.trace_info.trace_location.uc_schema.schema_name == "schema"
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assert len(proto_trace_v4.spans) == len(trace.data.spans)
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reconstructed_trace = trace_from_proto(proto_trace_v4, location_id="catalog.schema")
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assert reconstructed_trace.info.trace_id == trace_id
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assert reconstructed_trace.info.trace_location.uc_schema.catalog_name == "catalog"
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assert reconstructed_trace.info.trace_location.uc_schema.schema_name == "schema"
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assert len(reconstructed_trace.data.spans) == len(trace.data.spans)
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original_span = trace.data.spans[0]
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reconstructed_span = reconstructed_trace.data.spans[0]
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assert reconstructed_span.name == original_span.name
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assert reconstructed_span.span_id == original_span.span_id
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assert reconstructed_span.trace_id == original_span.trace_id
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assert reconstructed_span.inputs == original_span.inputs
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assert reconstructed_span.outputs == original_span.outputs
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assert reconstructed_span.get_attribute("custom") == original_span.get_attribute("custom")
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def test_trace_from_proto_with_location_preserves_v4_trace_id():
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with mlflow.start_span() as span:
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otel_trace_id = span.trace_id.removeprefix("tr-")
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uc_schema = "catalog.schema"
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trace_id_v4 = f"{TRACE_ID_V4_PREFIX}{uc_schema}/{otel_trace_id}"
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span.set_attribute(SpanAttributeKey.REQUEST_ID, trace_id_v4)
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mlflow_span = span.to_immutable_span()
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# Create trace with v4 trace ID
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trace = Trace(
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info=TraceInfo(
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trace_id=trace_id_v4,
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trace_location=TraceLocation.from_databricks_uc_schema(
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catalog_name="catalog", schema_name="schema"
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),
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request_time=0,
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state=TraceState.OK,
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),
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data=TraceData(spans=[mlflow_span]),
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)
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# Convert to proto
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proto_trace = trace_to_proto(trace)
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# Reconstruct with location parameter
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reconstructed_trace = trace_from_proto(proto_trace, location_id=uc_schema)
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# Verify that all spans have the correct v4 trace_id format
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for reconstructed_span in reconstructed_trace.data.spans:
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assert reconstructed_span.trace_id == trace_id_v4
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assert reconstructed_span.trace_id.startswith(TRACE_ID_V4_PREFIX)
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# Verify the REQUEST_ID attribute is also in v4 format
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request_id = reconstructed_span.get_attribute("mlflow.traceRequestId")
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assert request_id == trace_id_v4
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def test_trace_info_from_proto_handles_uc_schema_location():
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request_time = Timestamp()
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request_time.FromMilliseconds(1234567890)
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proto = pb.TraceInfo(
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trace_id="test_trace_id",
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trace_location=trace_location_to_proto(
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TraceLocation.from_databricks_uc_schema(catalog_name="catalog", schema_name="schema")
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),
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request_preview="test request",
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response_preview="test response",
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request_time=request_time,
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state=TraceState.OK.to_proto(),
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trace_metadata={
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TRACE_SCHEMA_VERSION_KEY: str(TRACE_SCHEMA_VERSION),
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"other_key": "other_value",
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},
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tags={"test_tag": "test_value"},
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)
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trace_info = TraceInfo.from_proto(proto)
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assert trace_info.trace_location.uc_schema.catalog_name == "catalog"
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assert trace_info.trace_location.uc_schema.schema_name == "schema"
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assert trace_info.trace_metadata[TRACE_SCHEMA_VERSION_KEY] == str(TRACE_SCHEMA_VERSION)
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assert trace_info.trace_metadata["other_key"] == "other_value"
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assert trace_info.tags == {"test_tag": "test_value"}
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def test_add_size_stats_to_trace_metadata_for_v4_trace():
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with mlflow.start_span() as span:
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otel_trace_id = span.trace_id.removeprefix("tr-")
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uc_schema = "catalog.schema"
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trace_id = f"trace:/{uc_schema}/{otel_trace_id}"
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span.set_attribute(SpanAttributeKey.REQUEST_ID, trace_id)
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mlflow_span = span.to_immutable_span()
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trace = Trace(
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info=TraceInfo(
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trace_id="test_trace_id",
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trace_location=TraceLocation.from_databricks_uc_schema(
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catalog_name="catalog", schema_name="schema"
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),
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request_time=0,
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state=TraceState.OK,
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request_preview="request",
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response_preview="response",
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client_request_id="client_request_id",
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tags={"key": "value"},
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),
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data=TraceData(spans=[mlflow_span]),
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)
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add_size_stats_to_trace_metadata(trace)
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assert TraceMetadataKey.SIZE_STATS in trace.info.trace_metadata
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def test_assessment_to_proto():
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# Test with Feedback assessment
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feedback = Feedback(
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name="correctness",
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value=0.95,
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source=AssessmentSource(source_type="LLM_JUDGE", source_id="gpt-4"),
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trace_id="trace:/catalog.schema/trace123",
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metadata={"model": "gpt-4", "temperature": "0.7"},
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span_id="span456",
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rationale="The response is accurate and complete",
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overrides="old_assessment_id",
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valid=False,
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)
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feedback.assessment_id = "assessment789"
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proto_v4 = assessment_to_proto(feedback)
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# Validate proto structure
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assert isinstance(proto_v4, pb.Assessment)
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assert proto_v4.assessment_name == "correctness"
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assert proto_v4.assessment_id == "assessment789"
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assert proto_v4.span_id == "span456"
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assert proto_v4.rationale == "The response is accurate and complete"
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assert proto_v4.overrides == "old_assessment_id"
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assert proto_v4.valid is False
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# Check TraceIdentifier
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assert proto_v4.trace_id == "trace123"
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assert proto_v4.trace_location.uc_schema.catalog_name == "catalog"
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assert proto_v4.trace_location.uc_schema.schema_name == "schema"
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# Check source
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assert proto_v4.source.source_type == ProtoAssessmentSource.SourceType.Value("LLM_JUDGE")
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assert proto_v4.source.source_id == "gpt-4"
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# Check metadata
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assert proto_v4.metadata["model"] == "gpt-4"
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assert proto_v4.metadata["temperature"] == "0.7"
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# Check feedback value
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assert proto_v4.HasField("feedback")
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assert proto_v4.feedback.value.number_value == 0.95
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# Test with Expectation assessment
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expectation = Expectation(
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name="expected_answer",
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value={"answer": "Paris", "confidence": 0.99},
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source=AssessmentSource(source_type="HUMAN", source_id="user@example.com"),
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trace_id="trace:/main.default/trace789",
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metadata={"question": "What is the capital of France?"},
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span_id="span111",
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)
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expectation.assessment_id = "exp_assessment123"
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proto_v4_exp = assessment_to_proto(expectation)
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assert isinstance(proto_v4_exp, pb.Assessment)
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assert proto_v4_exp.assessment_name == "expected_answer"
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assert proto_v4_exp.assessment_id == "exp_assessment123"
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assert proto_v4_exp.span_id == "span111"
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# Check TraceIdentifier for expectation
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assert proto_v4_exp.trace_id == "trace789"
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assert proto_v4_exp.trace_location.uc_schema.catalog_name == "main"
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assert proto_v4_exp.trace_location.uc_schema.schema_name == "default"
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# Check expectation value
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assert proto_v4_exp.HasField("expectation")
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assert proto_v4_exp.expectation.HasField("serialized_value")
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assert json.loads(proto_v4_exp.expectation.serialized_value.value) == {
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"answer": "Paris",
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"confidence": 0.99,
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}
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def test_get_trace_id_from_assessment_proto():
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proto = pb.Assessment(
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trace_id="1234",
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trace_location=trace_location_to_proto(
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TraceLocation.from_databricks_uc_schema(catalog_name="catalog", schema_name="schema")
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),
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)
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assert get_trace_id_from_assessment_proto(proto) == "trace:/catalog.schema/1234"
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proto = assessments_pb2.Assessment(
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trace_id="tr-123",
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)
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assert get_trace_id_from_assessment_proto(proto) == "tr-123"
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def test_trace_location_uc_table_prefix_proto_round_trip():
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location = UnityCatalog(
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catalog_name="catalog",
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schema_name="schema",
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table_prefix="prefix",
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)
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location._otel_spans_table_name = "catalog.schema.prefix_otel_spans"
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location._otel_logs_table_name = "catalog.schema.prefix_otel_logs"
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location._annotations_table_name = "catalog.schema.prefix_otel_annotations"
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trace_location = TraceLocation(type=TraceLocationType.UC_TABLE_PREFIX, uc_table_prefix=location)
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proto = trace_location_to_proto(trace_location)
|
|
assert proto.type == pb.TraceLocation.TraceLocationType.UC_TABLE_PREFIX
|
|
assert proto.uc_table_prefix.catalog_name == "catalog"
|
|
assert proto.uc_table_prefix.schema_name == "schema"
|
|
assert proto.uc_table_prefix.table_prefix == "prefix"
|
|
assert proto.uc_table_prefix.spans_table_name == "catalog.schema.prefix_otel_spans"
|
|
assert proto.uc_table_prefix.logs_table_name == "catalog.schema.prefix_otel_logs"
|
|
assert proto.uc_table_prefix.annotations_table_name == "catalog.schema.prefix_otel_annotations"
|
|
|
|
reconstructed = trace_location_from_proto(proto)
|
|
assert reconstructed.type == TraceLocationType.UC_TABLE_PREFIX
|
|
uc = reconstructed.uc_table_prefix
|
|
assert uc.catalog_name == "catalog"
|
|
assert uc.schema_name == "schema"
|
|
assert uc.table_prefix == "prefix"
|
|
assert uc.full_otel_spans_table_name == "catalog.schema.prefix_otel_spans"
|
|
assert uc.full_otel_logs_table_name == "catalog.schema.prefix_otel_logs"
|
|
assert uc.full_annotations_table_name == "catalog.schema.prefix_otel_annotations"
|
|
|
|
|
|
def test_trace_info_from_proto_handles_uc_table_prefix_location():
|
|
request_time = Timestamp()
|
|
request_time.FromMilliseconds(1234567890)
|
|
proto = pb.TraceInfo(
|
|
trace_id="test_trace_id",
|
|
trace_location=trace_location_to_proto(
|
|
TraceLocation.from_databricks_uc_table_prefix(
|
|
catalog_name="catalog", schema_name="schema", table_prefix="prefix"
|
|
)
|
|
),
|
|
request_preview="test request",
|
|
response_preview="test response",
|
|
request_time=request_time,
|
|
state=TraceState.OK.to_proto(),
|
|
trace_metadata={TRACE_SCHEMA_VERSION_KEY: str(TRACE_SCHEMA_VERSION)},
|
|
)
|
|
trace_info = TraceInfo.from_proto(proto)
|
|
assert trace_info.trace_id == "trace:/catalog.schema.prefix/test_trace_id"
|
|
assert trace_info.trace_location.type == TraceLocationType.UC_TABLE_PREFIX
|
|
assert trace_info.trace_location.uc_table_prefix.catalog_name == "catalog"
|
|
assert trace_info.trace_location.uc_table_prefix.schema_name == "schema"
|
|
assert trace_info.trace_location.uc_table_prefix.table_prefix == "prefix"
|
|
|
|
|
|
def test_assessment_to_proto_uc_table_prefix():
|
|
feedback = Feedback(
|
|
name="correctness",
|
|
value=0.95,
|
|
source=AssessmentSource(source_type="LLM_JUDGE", source_id="gpt-4"),
|
|
trace_id="trace:/catalog.schema.prefix/trace123",
|
|
)
|
|
proto = assessment_to_proto(feedback)
|
|
assert proto.trace_id == "trace123"
|
|
assert proto.trace_location.type == pb.TraceLocation.TraceLocationType.UC_TABLE_PREFIX
|
|
assert proto.trace_location.uc_table_prefix.catalog_name == "catalog"
|
|
assert proto.trace_location.uc_table_prefix.schema_name == "schema"
|
|
assert proto.trace_location.uc_table_prefix.table_prefix == "prefix"
|
|
|
|
|
|
def test_get_trace_id_from_assessment_proto_uc_table_prefix():
|
|
proto = pb.Assessment(
|
|
trace_id="1234",
|
|
trace_location=trace_location_to_proto(
|
|
TraceLocation.from_databricks_uc_table_prefix(
|
|
catalog_name="catalog", schema_name="schema", table_prefix="prefix"
|
|
)
|
|
),
|
|
)
|
|
assert get_trace_id_from_assessment_proto(proto) == "trace:/catalog.schema.prefix/1234"
|