refactor(operation_config): change logging on is_profiling_enabled (#12416)

Co-authored-by: Harshal Sheth <hsheth2@gmail.com>

metadata-ingestion/setup.py

@@ -251,6 +251,7 @@ iceberg_common = {
     # Iceberg Python SDK
     # Kept at 0.4.0 due to higher versions requiring pydantic>2, as soon as we are fine with it, bump this dependency
     "pyiceberg>=0.4.0",
+    *cachetools_lib,
 }
 
 mssql_common = {
@@ -407,13 +408,14 @@ plugins: Dict[str, Set[str]] = {
     # UnsupportedProductError
     # https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/release-notes.html#rn-7-14-0
     # https://github.com/elastic/elasticsearch-py/issues/1639#issuecomment-883587433
-    "elasticsearch": {"elasticsearch==7.13.4"},
+    "elasticsearch": {"elasticsearch==7.13.4", *cachetools_lib},
     "cassandra": {
         "cassandra-driver>=3.28.0",
         # We were seeing an error like this `numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject`
         # with numpy 2.0. This likely indicates a mismatch between scikit-learn and numpy versions.
         # https://stackoverflow.com/questions/40845304/runtimewarning-numpy-dtype-size-changed-may-indicate-binary-incompatibility
         "numpy<2",
+        *cachetools_lib,
     },
     "feast": {
         "feast>=0.34.0,<1",
@@ -425,7 +427,7 @@ plugins: Dict[str, Set[str]] = {
         "numpy<2",
     },
     "grafana": {"requests"},
-    "glue": aws_common,
+    "glue": aws_common | cachetools_lib,
     # hdbcli is supported officially by SAP, sqlalchemy-hana is built on top but not officially supported
     "hana": sql_common
     | {
@@ -482,11 +484,11 @@ plugins: Dict[str, Set[str]] = {
     | classification_lib
     | {"db-dtypes"}  # Pandas extension data types
     | cachetools_lib,
-    "s3": {*s3_base, *data_lake_profiling},
+    "s3": {*s3_base, *data_lake_profiling, *cachetools_lib},
     "gcs": {*s3_base, *data_lake_profiling},
-    "abs": {*abs_base, *data_lake_profiling},
+    "abs": {*abs_base, *data_lake_profiling, *cachetools_lib},
     "sagemaker": aws_common,
-    "salesforce": {"simple-salesforce"},
+    "salesforce": {"simple-salesforce", *cachetools_lib},
     "snowflake": snowflake_common | usage_common | sqlglot_lib,
     "snowflake-summary": snowflake_common | usage_common | sqlglot_lib,
     "snowflake-queries": snowflake_common | usage_common | sqlglot_lib,

metadata-ingestion/src/datahub/ingestion/source/sql/sql_config.py

@@ -2,8 +2,6 @@ import logging
 from abc import abstractmethod
 from typing import Any, Dict, Optional
 
-import cachetools
-import cachetools.keys
 import pydantic
 from pydantic import Field
 from sqlalchemy.engine import URL
@@ -29,7 +27,6 @@ from datahub.ingestion.source.state.stateful_ingestion_base import (
     StatefulIngestionConfigBase,
 )
 from datahub.ingestion.source_config.operation_config import is_profiling_enabled
-from datahub.utilities.cachetools_keys import self_methodkey
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -118,13 +115,6 @@ class SQLCommonConfig(
     # Custom Stateful Ingestion settings
     stateful_ingestion: Optional[StatefulStaleMetadataRemovalConfig] = None
 
-    # TRICKY: The operation_config is time-dependent. Because we don't want to change
-    # whether or not we're running profiling mid-ingestion, we cache the result of this method.
-    # TODO: This decorator should be moved to the is_profiling_enabled(operation_config) method.
-    @cachetools.cached(
-        cache=cachetools.LRUCache(maxsize=1),
-        key=self_methodkey,
-    )
     def is_profiling_enabled(self) -> bool:
         return self.profiling.enabled and is_profiling_enabled(
             self.profiling.operation_config

metadata-ingestion/src/datahub/ingestion/source_config/operation_config.py

@@ -2,10 +2,12 @@ import datetime
 import logging
 from typing import Any, Dict, Optional
 
+import cachetools
 import pydantic
 from pydantic.fields import Field
 
 from datahub.configuration.common import ConfigModel
+from datahub.utilities.cachetools_keys import self_methodkey
 
 logger = logging.getLogger(__name__)
 
@@ -62,6 +64,13 @@ class OperationConfig(ConfigModel):
         return profile_date_of_month
 
 
+# TRICKY: The operation_config is time-dependent. Because we don't want to change
+# whether or not we're running profiling mid-ingestion, we cache the result of this method.
+# An additional benefit is that we only print the log lines on the first call.
+@cachetools.cached(
+    cache=cachetools.LRUCache(maxsize=1),
+    key=self_methodkey,
+)
 def is_profiling_enabled(operation_config: OperationConfig) -> bool:
     if operation_config.lower_freq_profile_enabled is False:
         return True