perf(exports): retain chart benchmark evidence

.jules/bolt.md

@@ -61,3 +61,7 @@
 ## 2026-07-13 - Array.from mapping optimization
 **Learning:** Using `Array.from({ length: N }).map(...)` creates an intermediate array of `undefined` values which requires memory allocation and garbage collection, adding O(N) unnecessary overhead in frequently re-rendered UI components.
 **Action:** Use `Array.from({ length: N }, (_, index) => ...)` to map elements directly during array creation, avoiding intermediate allocations.
+
+## 2026-09-08 - O(N^2) list-based deduplication replaced with O(1) dict keys
+**Learning:** Checking for element existence in a list using `not in` before appending leads to O(N^2) time complexity. However, for bounded small lists ($N < 10$), standard list traversal in CPython can be marginally faster and use less memory overhead than hashing/allocating dict keys. For unbounded or large cardinalities (e.g., thousands of deduplications across a large song export payload with 1000+ sections and highly duplicated roles), dictionary O(1) insertions preserve insertion order while preventing super-linear CPU bounds.
+**Action:** Replace `if item not in lst: lst.append(item)` patterns with `dct[item] = None` and `list(dct.keys())` for efficient and order-preserving deduplication in high-throughput data exports, provided we can demonstrate concrete wall-clock wins under profiling.

CHANGELOG.md

@@ -10,6 +10,7 @@
 
 ### Changed
 
+- Kept chart-export role, cue, and footer de-duplication expected-linear with insertion-ordered dictionaries, semantic internal names, and exact Unicode/order/blank-value regression coverage.
 - Consolidated Bandit, dependency audits, supplemental secret checks, and Trivy into one trusted-branch security backstop, delegated CodeQL to GitHub default setup, and removed duplicate local PR security and release-preflight runs.
 - Pinned npm `10.9.9` as the approved lockfile generator, activated it through Node-bundled Corepack before dependency consumption, and fail closed unless its bundled `tar` is at least `7.5.19`; primary CI still consumes the committed lock only through frozen `npm ci` validation, rejects mutable npm resolution in the lock gate, requires integrity evidence for public-registry lock entries, and preserves generator-sensitive root `@esbuild/*` peer metadata.
 

services/analysis-engine/src/bandscope_analysis/exports/chart.py

@@ -73,19 +73,19 @@ def _section_roles(section: Mapping[str, object]) -> list[Mapping[str, object]]:
     return [role for role in roles if isinstance(role, Mapping)]
 
 
-def _active_role_ids(section: Mapping[str, object]) -> list[str] | None:
+def _active_role_ids(section_record: Mapping[str, object]) -> list[str] | None:
     """Return active role ids from the part graph, or ``None`` when absent."""
-    part_graph = section.get("partGraph")
-    if not isinstance(part_graph, list):
+    part_graph_nodes = section_record.get("partGraph")
+    if not isinstance(part_graph_nodes, list):
         return None
-    active: list[str] = []
-    for node in part_graph:
-        if not isinstance(node, Mapping) or node.get("is_active") is not True:
+    active_role_ids_by_value: dict[str, None] = {}
+    for part_graph_node in part_graph_nodes:
+        if not isinstance(part_graph_node, Mapping) or part_graph_node.get("is_active") is not True:
             continue
-        role_id = node.get("role_id")
-        if isinstance(role_id, str) and role_id and role_id not in active:
-            active.append(role_id)
-    return active
+        role_identifier = part_graph_node.get("role_id")
+        if isinstance(role_identifier, str) and role_identifier:
+            active_role_ids_by_value[role_identifier] = None
+    return list(active_role_ids_by_value)
 
 
 def _active_roles(section: Mapping[str, object]) -> list[Mapping[str, object]]:
@@ -119,27 +119,27 @@ def _role_display_name(role: Mapping[str, object]) -> str | None:
     return None
 
 
-def _active_role_names(section: Mapping[str, object]) -> list[str]:
+def _active_role_names(section_record: Mapping[str, object]) -> list[str]:
     """Return de-duplicated display names for the section's active roles."""
-    names: list[str] = []
-    for role in _active_roles(section):
-        name = _role_display_name(role)
-        if name is not None and name not in names:
-            names.append(name)
-    return names
+    active_role_names_by_value: dict[str, None] = {}
+    for role_record in _active_roles(section_record):
+        role_display_name = _role_display_name(role_record)
+        if role_display_name is not None:
+            active_role_names_by_value[role_display_name] = None
+    return list(active_role_names_by_value)
 
 
-def _section_cue(section: Mapping[str, object]) -> str:
+def _section_cue(section_record: Mapping[str, object]) -> str:
     """Join the active roles' cue values into a single cue string."""
-    cues: list[str] = []
-    for role in _active_roles(section):
-        cue = role.get("cue")
-        if not isinstance(cue, Mapping):
+    section_cues_by_value: dict[str, None] = {}
+    for role_record in _active_roles(section_record):
+        role_cue_record = role_record.get("cue")
+        if not isinstance(role_cue_record, Mapping):
             continue
-        value = cue.get("value")
-        if isinstance(value, str) and value and value not in cues:
-            cues.append(value)
-    return "; ".join(cues)
+        cue_text = role_cue_record.get("value")
+        if isinstance(cue_text, str) and cue_text:
+            section_cues_by_value[cue_text] = None
+    return "; ".join(section_cues_by_value)
 
 
 def _confidence_level(section: Mapping[str, object]) -> str | None:
@@ -185,28 +185,33 @@ def _section_lines(sections: list[Mapping[str, object]]) -> list[str]:
     return lines
 
 
-def _footer_lines(song: Mapping[str, object], sections: list[Mapping[str, object]]) -> list[str]:
+def _footer_lines(
+    song_record: Mapping[str, object], section_records: list[Mapping[str, object]]
+) -> list[str]:
     """Build the footer: per-role rehearsal priorities and the export focus."""
-    lines: list[str] = []
-    priorities: list[str] = []
-    for section in sections:
-        for role in _section_roles(section):
-            name = _role_display_name(role)
-            priority = role.get("rehearsalPriority")
-            if name is None or not isinstance(priority, str) or not priority:
+    footer_lines: list[str] = []
+    rehearsal_priority_lines_by_value: dict[str, None] = {}
+    for section_record in section_records:
+        for role_record in _section_roles(section_record):
+            role_display_name = _role_display_name(role_record)
+            rehearsal_priority = role_record.get("rehearsalPriority")
+            if (
+                role_display_name is None
+                or not isinstance(rehearsal_priority, str)
+                or not rehearsal_priority
+            ):
                 continue
-            entry = f"  - {name}: {priority}"
-            if entry not in priorities:
-                priorities.append(entry)
-    if priorities:
-        lines.append("Priorities:")
-        lines.extend(priorities)
-    summary = song.get("exportSummary")
-    if isinstance(summary, Mapping):
-        headline = summary.get("headline")
-        if isinstance(headline, str) and headline:
-            lines.append(f"Focus: {headline}")
-    return lines
+            priority_line = f"  - {role_display_name}: {rehearsal_priority}"
+            rehearsal_priority_lines_by_value[priority_line] = None
+    if rehearsal_priority_lines_by_value:
+        footer_lines.append("Priorities:")
+        footer_lines.extend(rehearsal_priority_lines_by_value)
+    export_summary = song_record.get("exportSummary")
+    if isinstance(export_summary, Mapping):
+        focus_headline = export_summary.get("headline")
+        if isinstance(focus_headline, str) and focus_headline:
+            footer_lines.append(f"Focus: {focus_headline}")
+    return footer_lines
 
 
 def build_chart_text(song: Mapping[str, object] | None) -> str:

services/analysis-engine/tests/benchmark_chart_export.py

@@ -0,0 +1,80 @@
+"""Performance benchmarking script for rehearsal chart text and cue exports."""
+
+import statistics
+import time
+import tracemalloc
+
+from bandscope_analysis.exports.chart import build_chart_text, build_cue_sheet_rows
+
+
+def make_large_song_fixture(num_sections_count=96, roles_per_section_count=24):
+    """Realistic large-song export fixture for benchmarking."""
+    song_sections = []
+    for section_index in range(num_sections_count):
+        section_roles = []
+        part_graph_nodes = []
+        for role_index in range(roles_per_section_count):
+            role_identifier = f"role_{role_index % 5}"
+            section_roles.append(
+                {
+                    "id": role_identifier,
+                    "name": f"Role Name {role_identifier}",
+                    "cue": {"value": f"Cue {role_index % 4}"},
+                    "rehearsalPriority": f"Priority {role_index % 2}",
+                }
+            )
+            part_graph_nodes.append({"role_id": role_identifier, "is_active": True})
+
+        song_sections.append(
+            {
+                "label": f"Section {section_index}",
+                "timeRange": {"start": section_index * 10, "end": section_index * 10 + 5},
+                "roles": section_roles,
+                "partGraph": part_graph_nodes,
+                "confidence": {"level": "high"},
+            }
+        )
+
+    return {
+        "title": "Benchmark Large Song",
+        "bpm": 120,
+        "key": "C major",
+        "feel": "Straight",
+        "sections": song_sections,
+        "exportSummary": {"headline": "Benchmark"},
+    }
+
+
+def chart_export_benchmark():
+    """Execute the large-song performance benchmark and report timing overhead."""
+    benchmark_song = make_large_song_fixture()
+
+    # Warmup
+    for _warmup_iteration in range(100):
+        build_chart_text(benchmark_song)
+        build_cue_sheet_rows(benchmark_song)
+
+    print("Running Benchmark...")
+    tracemalloc.start()
+
+    benchmark_iteration_count = 1000
+    export_timings = []
+    total_duration_seconds = 0.0
+    for _benchmark_iteration in range(benchmark_iteration_count):
+        benchmark_started_at = time.perf_counter()
+        build_chart_text(benchmark_song)
+        build_cue_sheet_rows(benchmark_song)
+        export_timings.append(time.perf_counter() - benchmark_started_at)
+        total_duration_seconds += export_timings[-1]
+
+    _current_allocation_bytes, peak_allocation_bytes = tracemalloc.get_traced_memory()
+    tracemalloc.stop()
+
+    print(f"Total time for {benchmark_iteration_count} iterations: {total_duration_seconds:.4f}s")
+    print(f"Median time per iteration: {statistics.median(export_timings) * 1000:.2f}ms")
+    print(f"P95 time per iteration: {statistics.quantiles(export_timings, n=100)[94] * 1000:.2f}ms")
+    print(f"Peak memory overhead: {peak_allocation_bytes / 1024 / 1024:.2f} MB")
+
+
+if __name__ == "__main__":
+    chart_export_benchmark()