Bulk commit to DB and Cache IDs

.pre-commit-config.yaml

@@ -23,14 +23,15 @@ repos:
     hooks:
       - id: black
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.5.1
+    rev: v1.7.1
     hooks:
       - id: mypy
         files: src
         additional_dependencies:
           - numpy>=1.21
           - sqlalchemy[mypy]
           - alembic
+          - types-cachetools
         args: [--install-types, --non-interactive]
         # Note that using the --install-types is problematic if running in
         # parallel as mutating the pre-commit env at runtime breaks cache.

pyproject.toml

@@ -45,6 +45,7 @@ dependencies = [
     "pandas >= 1.2",
     "scipy >= 1.5",
     "seaborn >= 0.11.0",
+    "cachetools >= 5.0",
 ]
 
   [project.optional-dependencies]

requirements.txt

@@ -1,4 +1,5 @@
 alembic==1.7.6
+cachetools==5.3.1
 contourpy==1.1.0
 cycler==0.11.0
 fonttools==4.42.1

src/insight/database/utils.py

@@ -1,9 +1,12 @@
 """Utils for fetching information from the backend DB."""
+
 import os
 import re
 import typing as ty
 
 import pandas as pd
+from cachetools import cached
+from cachetools.keys import hashkey
 from sqlalchemy import create_engine
 from sqlalchemy.exc import IntegrityError, SQLAlchemyError
 from sqlalchemy.orm import Session, sessionmaker
@@ -14,6 +17,8 @@
 NamedModelType = ty.TypeVar("NamedModelType", model.Dataset, model.Metric, model.Version)
 
 _database_fail_note = "Failure to communicate with the database"
+_DATASET_ID_MAPPING: ty.Optional[ty.Dict[str, int]] = None
+_METRIC_ID_MAPPING: ty.Optional[ty.Dict[str, int]] = None
 
 
 def get_df(url_or_path: str):
@@ -25,6 +30,7 @@ def get_df(url_or_path: str):
     return df
 
 
+@cached(cache={}, key=lambda df_name, session, **kwargs: hashkey(df_name))
 def get_df_id(
     df_name: str,
     session: Session,
@@ -40,6 +46,17 @@ def get_df_id(
         num_columns (int): The number of columns in the dataframe. Optional.
 
     """
+    global _DATASET_ID_MAPPING  # pylint: disable=global-statement
+    # create a mapping of df_names to session
+    if _DATASET_ID_MAPPING is None:
+        with session:
+            df_names = session.query(model.Dataset).all()
+            _DATASET_ID_MAPPING = {df.name: df.id for df in df_names if df.name is not None}
+
+    df_id = _DATASET_ID_MAPPING.get(df_name)
+    if df_id is not None:
+        return df_id
+
     dataset = get_object_from_db_by_name(df_name, session, model.Dataset)
     if dataset is None:
         with session:
@@ -51,6 +68,7 @@ def get_df_id(
     return int(dataset.id)
 
 
+@cached(cache={}, key=lambda metric, session, **kwargs: hashkey(metric))
 def get_metric_id(metric: str, session: Session, category: ty.Optional[str] = None) -> int:
     """Get the id of a metric in the database. If it doesn't exist, create it.
 
@@ -59,6 +77,17 @@ def get_metric_id(metric: str, session: Session, category: ty.Optional[str] = No
         session (Session): The database session.
         category (str): The category of the metric. Optional.
     """
+    global _METRIC_ID_MAPPING  # pylint: disable=global-statement
+    # create a mapping of df_names to session
+    if _METRIC_ID_MAPPING is None:
+        with session:
+            metrics = session.query(model.Dataset).all()
+            _METRIC_ID_MAPPING = {m.name: m.id for m in metrics if m.name is not None}
+
+    metric_id = _METRIC_ID_MAPPING.get(metric)
+    if metric_id is not None:
+        return metric_id
+
     db_metric = get_object_from_db_by_name(metric, session, model.Metric)
 
     if db_metric is None:
@@ -71,6 +100,7 @@ def get_metric_id(metric: str, session: Session, category: ty.Optional[str] = No
     return int(db_metric.id)
 
 
+@cached(cache={}, key=lambda version, session: hashkey(version))
 def get_version_id(version: str, session: Session) -> int:
     """Get the id of a version in the database. If it doesn't exist, create it.
 

src/insight/metrics/base.py

@@ -1,4 +1,5 @@
 """This module contains the base classes for the metrics used across synthesized."""
+
 import os
 import typing as ty
 from abc import ABC, abstractmethod
@@ -71,6 +72,7 @@ def _add_to_database(
         dataset_rows: ty.Optional[int] = None,
         dataset_cols: ty.Optional[int] = None,
         category: ty.Optional[str] = None,
+        session: ty.Optional[Session] = None,
     ):
         """
         Adds the metric result to the database. The metric result should be specified as value.
@@ -101,7 +103,23 @@ def _add_to_database(
         if hasattr(value, "item"):
             value = value.item()
 
-        with self._session as session:
+        if session is None:
+            with self._session as session:
+                metric_id = utils.get_metric_id(self.name, session, category=category)
+                version_id = utils.get_version_id(version, session)
+                dataset_id = utils.get_df_id(
+                    dataset_name, session, num_rows=dataset_rows, num_columns=dataset_cols
+                )
+                result = model.Result(
+                    metric_id=metric_id,
+                    dataset_id=dataset_id,
+                    version_id=version_id,
+                    value=value,
+                    run_id=run_id,
+                )
+                session.add(result)
+                session.commit()
+        else:
             metric_id = utils.get_metric_id(self.name, session, category=category)
             version_id = utils.get_version_id(version, session)
             dataset_id = utils.get_df_id(
@@ -115,7 +133,6 @@ def _add_to_database(
                 run_id=run_id,
             )
             session.add(result)
-            session.commit()
 
 
 class OneColumnMetric(_Metric):
@@ -167,7 +184,7 @@ def check_column_types(cls, sr: pd.Series, check: Check = ColumnCheck()) -> bool
     def _compute_metric(self, sr: pd.Series):
         ...
 
-    def __call__(self, sr: pd.Series, dataset_name: ty.Optional[str] = None):
+    def __call__(self, sr: pd.Series, dataset_name: ty.Optional[str] = None, session=None):
         if not self.check_column_types(sr, self.check):
             value = None
         else:
@@ -181,6 +198,7 @@ def __call__(self, sr: pd.Series, dataset_name: ty.Optional[str] = None):
                 dataset_rows=len(sr),
                 category="OneColumnMetric",
                 dataset_cols=1,
+                session=session,
             )
 
         return value
@@ -237,7 +255,9 @@ def check_column_types(cls, sr_a: pd.Series, sr_b: pd.Series, check: Check = Col
     def _compute_metric(self, sr_a: pd.Series, sr_b: pd.Series):
         ...
 
-    def __call__(self, sr_a: pd.Series, sr_b: pd.Series, dataset_name: ty.Optional[str] = None):
+    def __call__(
+        self, sr_a: pd.Series, sr_b: pd.Series, dataset_name: ty.Optional[str] = None, session=None
+    ):
         if not self.check_column_types(sr_a, sr_b, self.check):
             value = None
         else:
@@ -251,6 +271,7 @@ def __call__(self, sr_a: pd.Series, sr_b: pd.Series, dataset_name: ty.Optional[s
                 dataset_rows=len(sr_a),
                 category="TwoColumnMetric",
                 dataset_cols=1,
+                session=session,
             )
 
         return value

src/insight/metrics/metrics_usage.py

@@ -17,12 +17,23 @@ def __init__(self, metric: OneColumnMetric):
         self.name = f"{metric.name}_map"
 
     def _compute_result(self, df: pd.DataFrame) -> pd.DataFrame:
-        columns_map = {
-            col: self._metric(df[col], dataset_name=df.attrs.get("name", "") + f"_{col}")
-            for col in df.columns
-        }
-        result = pd.DataFrame(data=columns_map.values(), index=df.columns, columns=[self.name])
+        dataset_name = df.attrs.get("name", "")
+        if self._session is not None:
+            with self._session as session:
+                columns_map = {
+                    col: self._metric(
+                        df[col], dataset_name=f"{dataset_name}_{col}", session=session
+                    )
+                    for col in df.columns
+                }
+                session.commit()
+        else:
+            columns_map = {
+                col: self._metric(df[col], dataset_name=f"{dataset_name}_{col}", session=None)
+                for col in df.columns
+            }
 
+        result = pd.DataFrame(data=columns_map.values(), index=df.columns, columns=[self.name])
         result.name = self._metric.name
         return result
 
@@ -57,12 +68,24 @@ def _compute_result(self, df: pd.DataFrame) -> pd.DataFrame:
         columns = df.columns
         matrix = pd.DataFrame(index=columns, columns=columns)
 
-        for col_a, col_b in permutations(columns, 2):
-            matrix[col_a][col_b] = self._metric(
-                df[col_a],
-                df[col_b],
-                dataset_name=df.attrs.get("name", "") + f"_{col_a}_{col_b}",
-            )
+        if self._session is not None:
+            with self._session as session:
+                for col_a, col_b in permutations(columns, 2):
+                    matrix[col_a][col_b] = self._metric(
+                        df[col_a],
+                        df[col_b],
+                        dataset_name=df.attrs.get("name", "") + f"_{col_a}_{col_b}",
+                        session=session,
+                    )
+                session.commit()
+        else:
+            for col_a, col_b in permutations(columns, 2):
+                matrix[col_a][col_b] = self._metric(
+                    df[col_a],
+                    df[col_b],
+                    dataset_name=df.attrs.get("name", "") + f"_{col_a}_{col_b}",
+                    session=None,
+                )
 
         return pd.DataFrame(matrix.astype(np.float32))  # explicit casting for mypy
 
@@ -105,16 +128,31 @@ def __init__(self, metric: TwoColumnMetric):
         self.name = f"{metric.name}_map"
 
     def _compute_result(self, df_old: pd.DataFrame, df_new: pd.DataFrame) -> pd.DataFrame:
-        columns_map = {
-            col: self._metric(
-                df_old[col],
-                df_new[col],
-                dataset_name=df_old.attrs.get("name", "") + f"_{col}",
-            )
-            for col in df_old.columns
-        }
-        result = pd.DataFrame(data=columns_map.values(), index=df_old.columns, columns=[self.name])
 
+        if self._session is not None:
+            with self._session as session:
+                columns_map = {
+                    col: self._metric(
+                        df_old[col],
+                        df_new[col],
+                        dataset_name=df_old.attrs.get("name", "") + f"_{col}",
+                        session=session,
+                    )
+                    for col in df_old.columns
+                }
+                session.commit()
+        else:
+            columns_map = {
+                col: self._metric(
+                    df_old[col],
+                    df_new[col],
+                    dataset_name=df_old.attrs.get("name", "") + f"_{col}",
+                    session=None,
+                )
+                for col in df_old.columns
+            }
+
+        result = pd.DataFrame(data=columns_map.values(), index=df_old.columns, columns=[self.name])
         result.name = self._metric.name
         return result
 

tests/test_database/test_db.py

@@ -23,8 +23,18 @@ def tables(engine):
     Base.metadata.drop_all(engine)
 
 
-@pytest.fixture
-def db_session(engine, tables):
+@pytest.fixture(scope="function")
+def clear_utils_cache():
+    yield utils
+    utils.get_df_id.cache_clear()
+    utils.get_metric_id.cache_clear()
+    utils.get_version_id.cache_clear()
+    utils._DATASET_ID_MAPPING = None
+    utils._METRIC_ID_MAPPING = None
+
+
+@pytest.fixture(scope="function")
+def db_session(engine, tables, clear_utils_cache):
     connection = engine.connect()
     transaction = connection.begin()
     session = Session(bind=connection, expire_on_commit=False)
@@ -35,7 +45,6 @@ def db_session(engine, tables):
     base.TwoColumnMetric._session = session
     base.DataFrameMetric._session = session
     base.TwoDataFrameMetric._session = session
-
     yield session
 
     # Return class variables to their original state.