perf(tableau): memoize filter+sort+postprocess pipeline (#72)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/utils/table.py

@@ -10,7 +10,7 @@ from src.db import query_marches, schema
 from src.utils import logger
 from src.utils.data import DATA_SCHEMA
 from src.utils.frontend import get_button_properties
-from src.utils.tracking import track_search  # noqa: F401
+from src.utils.tracking import track_search
 
 
 def split_filter_part(filter_part):
@@ -420,66 +420,60 @@ def prepare_table_data(
     :param source_table:
     :return:
     """
+    logger.debug(" + + + + + + + + + + + + + + + + + + ")
 
-    if os.getenv("DEVELOPMENT").lower() == "true":
-        logger.debug(" + + + + + + + + + + + + + + + + + + ")
-
-    trigger_cleanup = no_update
-
-    # Récupération des données
-    if isinstance(data, list):
-        lff: pl.LazyFrame = pl.LazyFrame(data, strict=False, infer_schema_length=5000)
-    elif isinstance(data, pl.LazyFrame):
-        lff = data
-    else:
-        lff: pl.LazyFrame = query_marches().lazy()
-
-    # Application des filtres
     if filter_query:
-        lff = filter_table_data(lff, filter_query, source_table)
+        track_search(filter_query, source_table)
-        trigger_cleanup = no_update if source_table == "tableau" else str(uuid.uuid4())
 
-    # Application des tris
+    trigger_cleanup = no_update if source_table == "tableau" else str(uuid.uuid4())
-    if sort_by and len(sort_by) > 0:
+
-        lff = sort_table_data(lff, sort_by)
+    if data is None:
+        sort_by_key = normalize_sort_by(sort_by)
+        dff: pl.DataFrame = _load_filter_sort_postprocess(
+            filter_query=filter_query, sort_by_key=sort_by_key
+        )
+    else:
+        if isinstance(data, list):
+            lff: pl.LazyFrame = pl.LazyFrame(
+                data, strict=False, infer_schema_length=5000
+            )
+        elif isinstance(data, pl.LazyFrame):
+            lff = data
+        else:
+            lff = query_marches().lazy()
+
+        if filter_query:
+            lff = filter_table_data(lff, filter_query, source_table)
+
+        if sort_by and len(sort_by) > 0:
+            lff = sort_table_data(lff, sort_by)
+
+        dff = lff.collect()
+        dff = dff.cast(pl.String)
+        dff = dff.fill_null("")
+        dff = add_links(dff)
+        if "sourceFile" in dff.columns:
+            dff = add_resource_link(dff)
+        if dff.height > 0:
+            dff = format_values(dff)
 
-    # Matérialisation des filtres
-    dff: pl.DataFrame = lff.collect()
     height = dff.height
 
     if height > 0:
-        nb_rows = f"{format_number(height)} lignes ({format_number(dff.select('uid').unique().height)} marchés)"
+        nb_rows = (
+            f"{format_number(height)} lignes "
+            f"({format_number(dff.select('uid').unique().height)} marchés)"
+        )
     else:
         nb_rows = "0 lignes (0 marchés)"
 
-    # Pagination des données
     start_row = page_current * page_size
-    # end_row = (page_current + 1) * page_size
     dff = dff.slice(start_row, page_size)
 
-    # Tout devient string
-    dff = dff.cast(pl.String)
-
-    # Remplace les strings null par "", mais pas les numeric null
-    dff = dff.fill_null("")
-
-    # Ajout des liens vers les pages de détails
-    dff = add_links(dff)
-
-    # Ajout des liens vers les fichiers Open Data
-    if "sourceFile" in dff.columns:
-        dff = add_resource_link(dff)
-
-    # Formatage des montants
-    if height > 0:
-        dff = format_values(dff)
-
-    # Récupération des colonnes et tooltip
     table_columns, tooltip = setup_table_columns(dff)
 
     dicts = dff.to_dicts()
 
-    # Propriétés du bouton de téléchargement
     download_disabled, download_text, download_title = get_button_properties(height)
 
     return (

tests/test_table.py

@@ -155,3 +155,153 @@ def test_load_filter_sort_postprocess_adds_links(flask_app, monkeypatch, sample_
     assert "<a href" in df["uid"][0]
     assert "<a href" in df["acheteur_nom"][0]
     assert "<a href" in df["titulaire_nom"][0]
+
+
+def test_prepare_table_data_returns_expected_tuple(monkeypatch, flask_app, sample_lff):
+    from src.utils import table
+
+    monkeypatch.setattr(table, "query_marches", lambda: sample_lff.collect())
+
+    with flask_app.app_context():
+        result = table.prepare_table_data(
+            data=None,
+            data_timestamp=5,
+            filter_query=None,
+            page_current=0,
+            page_size=20,
+            sort_by=[],
+            source_table="tableau",
+        )
+
+    # Same arity as before: 9 outputs
+    assert len(result) == 9
+    dicts, columns, tooltip, ts, nb_rows, dl_disabled, dl_text, dl_title, cleanup = (
+        result
+    )
+    assert isinstance(dicts, list)
+    assert ts == 6  # data_timestamp + 1 must still increment
+    assert "1 lignes" in nb_rows
+
+
+def test_prepare_table_data_calls_track_search_on_filter(
+    monkeypatch, flask_app, sample_lff
+):
+    from src.utils import table
+
+    calls = []
+    monkeypatch.setattr(table, "query_marches", lambda: sample_lff.collect())
+    monkeypatch.setattr(table, "track_search", lambda *a, **kw: calls.append(a))
+
+    with flask_app.app_context():
+        table.prepare_table_data(
+            data=None,
+            data_timestamp=0,
+            filter_query="{objet} icontains travaux",
+            page_current=0,
+            page_size=20,
+            sort_by=[],
+            source_table="tableau",
+        )
+
+    assert calls == [("{objet} icontains travaux", "tableau")]
+
+
+def test_prepare_table_data_paginates_without_recomputing(
+    monkeypatch, flask_app, sample_lff
+):
+    """Two calls with same filter+sort but different pages must invoke
+    the inner heavy work only once."""
+    from src.utils import table
+
+    call_count = {"n": 0}
+    real_query = sample_lff.collect()
+
+    def counting_query():
+        call_count["n"] += 1
+        return real_query
+
+    monkeypatch.setattr(table, "query_marches", counting_query)
+
+    with flask_app.app_context():
+        # First call: cache miss
+        table.prepare_table_data(
+            data=None,
+            data_timestamp=0,
+            filter_query=None,
+            page_current=0,
+            page_size=10,
+            sort_by=[],
+            source_table="tableau",
+        )
+        first_count = call_count["n"]
+
+        # Second call, different page: cache hit, query_marches must NOT fire again
+        table.prepare_table_data(
+            data=None,
+            data_timestamp=0,
+            filter_query=None,
+            page_current=1,
+            page_size=10,
+            sort_by=[],
+            source_table="tableau",
+        )
+
+    assert call_count["n"] == first_count, (
+        "query_marches was called again — pagination triggered cache miss"
+    )
+
+
+def test_prepare_table_data_cleanup_trigger_for_non_tableau(
+    monkeypatch, flask_app, sample_lff
+):
+    """Non-tableau pages still get a fresh uuid trigger, not no_update."""
+    from dash import no_update
+
+    from src.utils import table
+
+    monkeypatch.setattr(table, "query_marches", lambda: sample_lff.collect())
+
+    with flask_app.app_context():
+        result = table.prepare_table_data(
+            data=None,
+            data_timestamp=0,
+            filter_query="{objet} icontains travaux",
+            page_current=0,
+            page_size=20,
+            sort_by=[],
+            source_table="acheteur",
+        )
+
+    cleanup = result[8]
+    assert cleanup is not no_update
+    assert isinstance(cleanup, str)
+    assert len(cleanup) >= 32  # uuid4 hex string
+
+
+def test_prepare_table_data_with_external_data_does_not_use_cache(
+    monkeypatch, flask_app, sample_lff
+):
+    """When a caller passes data (acheteur/titulaire/observatoire path),
+    bypass the memoized helper entirely."""
+    from src.utils import table
+
+    sentinel = {"called": False}
+
+    def should_not_be_called(*a, **kw):
+        sentinel["called"] = True
+        raise AssertionError("Memoized helper must not be called when data is provided")
+
+    monkeypatch.setattr(table, "_load_filter_sort_postprocess", should_not_be_called)
+
+    with flask_app.app_context():
+        table.prepare_table_data(
+            data=sample_lff,  # external LazyFrame
+            data_timestamp=0,
+            filter_query=None,
+            page_current=0,
+            page_size=20,
+            sort_by=[],
+            source_table="acheteur",
+        )
+
+    assert sentinel["called"] is False