691: Tests for CoW behavior in pandas

cdisc_rules_engine/rules_engine.py

@@ -2,6 +2,7 @@
 from typing import List, Union
 from dateutil.parser._parser import ParserError
 import traceback
+import pandas as pd
 
 from business_rules import export_rule_data
 from business_rules.engine import run
@@ -33,6 +34,7 @@
     DataServiceInterface,
 )
 from cdisc_rules_engine.models.actions import COREActions
+from cdisc_rules_engine.models.dataset import DaskDataset
 from cdisc_rules_engine.models.dataset.dataset_interface import DatasetInterface
 from cdisc_rules_engine.models.dataset_variable import DatasetVariable
 from cdisc_rules_engine.models.failed_validation_entity import FailedValidationEntity
@@ -59,6 +61,8 @@
 from cdisc_rules_engine.models.sdtm_dataset_metadata import SDTMDatasetMetadata
 from cdisc_rules_engine.enums.sensitivity import Sensitivity
 
+pd.options.mode.copy_on_write = True
+
 
 class RulesEngine:
     def __init__(
@@ -375,9 +379,9 @@ def execute_rule(
             rule["conditions"], dataset.columns.to_list()
         )
         rule_copy["conditions"].set_conditions(updated_conditions)
-        # Adding copy for now to avoid updating cached dataset
-        dataset = deepcopy(dataset)
         # preprocess dataset
+        if isinstance(dataset, DaskDataset):
+            dataset = deepcopy(dataset)
         dataset_preprocessor = DatasetPreprocessor(
             dataset, dataset_metadata, self.data_service, self.cache
         )

cdisc_rules_engine/services/cache/in_memory_cache_service.py

@@ -5,7 +5,7 @@
 from cdisc_rules_engine.interfaces import (
     CacheServiceInterface,
 )
-from cdisc_rules_engine.models.dataset import DatasetInterface
+from cdisc_rules_engine.models.dataset import DatasetInterface, PandasDataset
 from cachetools import LRUCache
 import psutil
 from multiprocessing import Lock
@@ -62,11 +62,16 @@ def add(self, cache_key, data):
             )
 
     def add_dataset(self, cache_key, data):
+        if get_data_size(data) > self.max_dataset_cache_size:
+            return
         with self.dataset_cache_lock:
             self.dataset_cache[cache_key] = data
 
     def get_dataset(self, cache_key):
-        return self.dataset_cache.get(cache_key, None)
+        cached = self.dataset_cache.get(cache_key)
+        if type(cached) is PandasDataset:
+            return PandasDataset(cached.data.copy(deep=False))
+        return cached
 
     def add_batch(
         self,
@@ -82,27 +87,32 @@ def add_batch(
             self.add(prefix + cache_key, item)
 
     def get(self, cache_key):
-        return self.cache.get(cache_key, None)
+        cached = self.cache.get(cache_key)
+        if type(cached) is PandasDataset:
+            return PandasDataset(cached.data.copy(deep=False))
+        return cached
 
     def get_all(self, cache_keys: List[str]):
-        return [self.cache.get(key) for key in cache_keys]
+        return [self.get(key) for key in cache_keys]
 
     def get_all_by_prefix(self, prefix):
-        items = []
-        for key in self.cache:
-            if key.startswith(prefix):
-                items.append(self.cache[key])
-        return items
+        with self.cache_lock:
+            keys = [key for key in self.cache.keys() if key.startswith(prefix)]
+        return [self.get(key) for key in keys]
 
     def dataset_keys(self):
         return self.dataset_cache.keys()
 
     def filter_cache(self, prefix: str) -> dict:
-        return {k: self.cache[k] for k in self.cache.keys() if k.startswith(prefix)}
+        with self.cache_lock:
+            keys = [k for k in self.cache.keys() if k.startswith(prefix)]
+        return {k: self.get(k) for k in keys}
 
     def get_by_regex(self, regex: str) -> dict:
         regex = regex.replace("*", ".*")
-        return {k: self.cache[k] for k in self.cache.keys() if re.search(regex, k)}
+        with self.cache_lock:
+            keys = [k for k in self.cache.keys() if re.search(regex, k)]
+        return {k: self.get(k) for k in keys}
 
     def exists(self, cache_key):
         return cache_key in self.cache
@@ -119,7 +129,7 @@ def clear_all(self, prefix: str = None):
             for key in keys_to_remove:
                 self.clear(key)
         else:
-            self.cache = LRUCache(maxsize=self.max_size, getsizeof=asizeof.asizeof)
+            self.cache = LRUCache(maxsize=self.max_size, getsizeof=cust_asizeof)
 
     def add_all(self, data: dict):
         for key, val in data.items():

docs/cli-reference.md

@@ -8,6 +8,9 @@
 
 Run conformance validation against a CDISC standard.
 
+Validate has pandas Copy-on-Write (CoW) enabled globally when using the rules engine.
+**Note**: In Pandas 2.x this is an opt-in feature, in Pandas 3.x, CoW is enabled by default.
+
 ```bash
 python core.py validate --help
 ```

tests/unit/test_services/test_cache/test_immutable_cache.py

@@ -0,0 +1,224 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from cdisc_rules_engine.models.dataset.pandas_dataset import PandasDataset
+from cdisc_rules_engine.services.cache.in_memory_cache_service import (
+    InMemoryCacheService,
+)
+
+
+@pytest.fixture(autouse=True)
+def reset_singleton():
+    InMemoryCacheService._instance = None
+    yield
+    InMemoryCacheService._instance = None
+
+
+@pytest.fixture
+def cache():
+    return InMemoryCacheService()
+
+
+@pytest.fixture
+def sample_dataset():
+    return PandasDataset(pd.DataFrame({"A": [1, 2, 3], "B": [10, 20, 30]}))
+
+
+class TestGet:
+    def test_returns_new_wrapper_not_cached_object(self, cache, sample_dataset):
+        cache.add("x", sample_dataset)
+        result = cache.get("x")
+        assert result is not cache.cache["x"]
+        assert result.data is not cache.cache["x"].data
+
+    def test_cow_does_not_modify_cache_on_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        retrieved = cache.get("x")
+        retrieved.data.loc[0, "A"] = 999
+        assert cache.cache["x"].data.loc[0, "A"] == 1
+
+    def test_shares_memory_before_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        retrieved = cache.get("x")
+        assert np.shares_memory(retrieved.data["A"], cache.cache["x"].data["A"])
+
+    def test_add_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        retrieved = cache.get("x")
+        retrieved.data = pd.concat(
+            [retrieved.data, pd.DataFrame({"A": [999], "B": [999]})],
+            ignore_index=True,
+        )
+        assert len(cache.cache["x"].data) == 3
+        assert len(retrieved.data) == 4
+
+    def test_drop_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        retrieved = cache.get("x")
+        retrieved.data = retrieved.data.drop(index=0).reset_index(drop=True)
+        assert len(cache.cache["x"].data) == 3
+        assert len(retrieved.data) == 2
+
+    def test_filter_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        retrieved = cache.get("x")
+        retrieved.data = retrieved.data[retrieved.data["A"] > 1].reset_index(drop=True)
+        assert len(cache.cache["x"].data) == 3
+        assert cache.cache["x"].data["A"].tolist() == [1, 2, 3]
+
+    def test_multiple_gets_are_independent(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        first = cache.get("x")
+        second = cache.get("x")
+        first.data = first.data.drop(index=0).reset_index(drop=True)
+        assert len(second.data) == 3
+        assert len(cache.cache["x"].data) == 3
+
+    def test_non_dataset_returns_as_is(self, cache):
+        cache.add("key", {"some": "dict"})
+        assert cache.get("key") == {"some": "dict"}
+
+    def test_object_dtype_nested_mutation_affects_cache(self, cache):
+        """CoW can't protect in nested mutations"""
+        df = pd.DataFrame({"A": [[1], [2], [3]]})
+        cache.add("x", PandasDataset(df))
+        retrieved = cache.get("x")
+        retrieved.data.loc[0, "A"].append(999)
+        assert cache.cache["x"].data.loc[0, "A"] == [1, 999]
+
+
+class TestGetDataset:
+    def test_returns_new_wrapper_not_cached_object(self, cache, sample_dataset):
+        cache.add_dataset("x", sample_dataset)
+        result = cache.get_dataset("x")
+        assert result is not cache.dataset_cache["x"]
+        assert result.data is not cache.dataset_cache["x"].data
+
+    def test_cow_does_not_modify_cache_on_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add_dataset("x", sample_dataset)
+        retrieved = cache.get_dataset("x")
+        retrieved.data.loc[0, "A"] = 999
+        assert cache.dataset_cache["x"].data.loc[0, "A"] == 1
+
+    def test_add_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add_dataset("x", sample_dataset)
+        retrieved = cache.get_dataset("x")
+        retrieved.data = pd.concat(
+            [retrieved.data, pd.DataFrame({"A": [999], "B": [999]})],
+            ignore_index=True,
+        )
+        assert len(cache.dataset_cache["x"].data) == 3
+        assert len(retrieved.data) == 4
+
+    def test_drop_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add_dataset("x", sample_dataset)
+        retrieved = cache.get_dataset("x")
+        retrieved.data = retrieved.data.drop(index=0).reset_index(drop=True)
+        assert len(cache.dataset_cache["x"].data) == 3
+        assert len(retrieved.data) == 2
+
+
+class TestGetAll:
+    def test_returns_new_wrappers(self, cache, sample_dataset):
+        cache.add("x", sample_dataset)
+        cache.add("y", sample_dataset)
+        results = cache.get_all(["x", "y"])
+        assert all(r is not cache.cache["x"] for r in results)
+        assert all(r.data is not cache.cache["x"].data for r in results)
+
+    def test_results_are_independent(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        cache.add("y", sample_dataset)
+        first, second = cache.get_all(["x", "y"])
+        first.data = first.data.drop(index=0).reset_index(drop=True)
+        assert len(second.data) == 3
+        assert len(cache.cache["x"].data) == 3
+
+    def test_cow_does_not_modify_cache_on_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("x", sample_dataset)
+        results = cache.get_all(["x"])
+        results[0].data.loc[0, "A"] = 999
+        assert cache.cache["x"].data.loc[0, "A"] == 1
+
+    def test_missing_key_returns_none(self, cache):
+        assert cache.get_all(["missing"]) == [None]
+
+
+class TestGetAllByPrefix:
+    def test_returns_only_matching_keys(self, cache, sample_dataset):
+        cache.add("ds/ae", sample_dataset)
+        cache.add("ds/lb", sample_dataset)
+        cache.add("other/ae", sample_dataset)
+        results = cache.get_all_by_prefix("ds/")
+        assert len(results) == 2
+
+    def test_returns_new_wrappers(self, cache, sample_dataset):
+        cache.add("ds/ae", sample_dataset)
+        results = cache.get_all_by_prefix("ds/")
+        assert results[0] is not cache.cache["ds/ae"]
+        assert results[0].data is not cache.cache["ds/ae"].data
+
+    def test_cow_does_not_modify_cache_on_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("ds/ae", sample_dataset)
+        results = cache.get_all_by_prefix("ds/")
+        results[0].data.loc[0, "A"] = 999
+        assert cache.cache["ds/ae"].data.loc[0, "A"] == 1
+
+    def test_drop_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("ds/ae", sample_dataset)
+        results = cache.get_all_by_prefix("ds/")
+        results[0].data = results[0].data.drop(index=0).reset_index(drop=True)
+        assert len(cache.cache["ds/ae"].data) == 3
+
+    def test_no_match_returns_empty(self, cache, sample_dataset):
+        cache.add("ds/ae", sample_dataset)
+        assert cache.get_all_by_prefix("other/") == []
+
+
+class TestGetByRegex:
+    def test_returns_matching_keys(self, cache, sample_dataset):
+        cache.add("ae_data", sample_dataset)
+        cache.add("lb_data", sample_dataset)
+        cache.add("ae_meta", sample_dataset)
+        result = cache.get_by_regex("ae_*")
+        assert set(result.keys()) == {"ae_data", "ae_meta"}
+
+    def test_returns_new_wrappers(self, cache, sample_dataset):
+        cache.add("ae_data", sample_dataset)
+        result = cache.get_by_regex("ae_*")
+        assert result["ae_data"] is not cache.cache["ae_data"]
+        assert result["ae_data"].data is not cache.cache["ae_data"].data
+
+    def test_cow_does_not_modify_cache_on_write(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("ae_data", sample_dataset)
+        result = cache.get_by_regex("ae_*")
+        result["ae_data"].data.loc[0, "A"] = 999
+        assert cache.cache["ae_data"].data.loc[0, "A"] == 1
+
+    def test_drop_rows_does_not_affect_cache(self, cache, sample_dataset):
+        pd.options.mode.copy_on_write = True
+        cache.add("ae_data", sample_dataset)
+        result = cache.get_by_regex("ae_*")
+        result["ae_data"].data = (
+            result["ae_data"].data.drop(index=0).reset_index(drop=True)
+        )
+        assert len(cache.cache["ae_data"].data) == 3
+
+    def test_no_match_returns_empty_dict(self, cache, sample_dataset):
+        cache.add("ae_data", sample_dataset)
+        assert cache.get_by_regex("lb_*") == {}