RPOPC-1307: Add multi-metric support for Passmark benchmark

src/chronicler/processors/passmark_processor.py

@@ -16,7 +16,7 @@
 
 from .base_processor import BaseProcessor, ProcessorError
 from .run_utils import run_data_timeseries_to_objects, timeseries_summary_from_metric
-from ..schema import Run, create_run_key, create_sequence_key
+from ..schema import Run, PrimaryMetric, StatisticalSummary, create_run_key, create_sequence_key
 from ..utils.parser_utils import read_file_content
 
 logger = logging.getLogger(__name__)
@@ -270,3 +270,59 @@ def _build_run_object(self, run_data: Dict[str, Any]) -> Run:
             timeseries=timeseries if timeseries else None,
             timeseries_summary=ts_summary
         )
+
+    def _extract_primary_metrics(
+        self, runs: Dict[str, Any],
+        overall_stats: Optional[StatisticalSummary]
+    ) -> Optional[List[PrimaryMetric]]:
+        """
+        Extract CPU Mark and Memory Mark as coequal primary metrics.
+
+        Passmark is a multi-metric benchmark reporting two key performance scores:
+        - CPU Mark: SUMM_CPU_mean from aggregated CPU benchmark results
+        - Memory Mark: SUMM_ME_mean from aggregated memory benchmark results
+
+        Both metrics are important for system characterization.
+
+        Returns list of PrimaryMetric objects for both metrics.
+        """
+        if not runs:
+            return None
+
+        # Get first run to extract metrics
+        first_run = list(runs.values())[0]
+
+        # Handle both dict and Run dataclass objects
+        metrics = None
+        if isinstance(first_run, dict) and 'metrics' in first_run:
+            metrics = first_run['metrics']
+        elif hasattr(first_run, 'metrics') and first_run.metrics:
+            metrics = first_run.metrics
+
+        if not metrics:
+            return None
+
+        # Build list of primary metrics
+        primary_metrics = []
+
+        # CPU Mark from SUMM_CPU_mean
+        if 'SUMM_CPU_mean' in metrics and metrics['SUMM_CPU_mean'] is not None:
+            primary_metrics.append(
+                PrimaryMetric(
+                    name='CPU Mark',
+                    value=float(metrics['SUMM_CPU_mean']),
+                    unit='score'
+                )
+            )
+
+        # Memory Mark from SUMM_ME_mean
+        if 'SUMM_ME_mean' in metrics and metrics['SUMM_ME_mean'] is not None:
+            primary_metrics.append(
+                PrimaryMetric(
+                    name='Memory Mark',
+                    value=float(metrics['SUMM_ME_mean']),
+                    unit='score'
+                )
+            )
+
+        return primary_metrics if primary_metrics else None

tests/test_passmark_integration.py

@@ -0,0 +1,157 @@
+"""
+Integration test for Passmark multi-metric processing.
+
+Demonstrates end-to-end processing of Passmark results with both CPU and Memory marks.
+"""
+
+import pytest
+from pathlib import Path
+from unittest.mock import patch
+
+from chronicler.processors.passmark_processor import PassmarkProcessor
+
+pytestmark = pytest.mark.integration
+
+
+def test_passmark_processor_multi_metric_extraction(result_dir):
+    """
+    Processor-level integration test: Passmark YML → parsed runs → Results with 2 primary_metrics.
+
+    This demonstrates the complete processing flow for RPOPC-1307.
+    Passmark reports CPU Mark (SUMM_CPU) and Memory Mark (SUMM_ME).
+    """
+    # Create realistic Passmark YML with CPU and Memory summary scores
+    yml_content = """BaselineInfo:
+  WebDBID: -1
+  TimeStamp: 20260502203522
+Version:
+  Major: 11
+  Minor: 0
+  Build: 1002
+Results:
+  NumTestProcesses: 16
+  CPU_INTEGER_MATH: 73090.879000000001
+  CPU_FLOATINGPOINT_MATH: 52092.075564261577
+  CPU_ENCRYPTION: 16095.081709056702
+  ME_ALLOC_S: 7821.9859399764991
+  ME_READ_S: 26985.984375
+  ME_WRITE: 11951.0166015625
+  SUMM_CPU: 24685.407296119931
+  SUMM_ME: 2106.6563041301797
+SystemInformation:
+  OSName: Red Hat Enterprise Linux 9.8 (Plow)
+  Processor: Intel Xeon Platinum 8488C
+"""
+
+    yml_path = result_dir / "results_all_1.yml"
+    yml_path.write_text(yml_content)
+
+    # Create dummy zip
+    dummy_zip = result_dir / "results_passmark.zip"
+    dummy_zip.write_bytes(b"")
+
+    # Process with Passmark processor
+    processor = PassmarkProcessor(str(result_dir))
+    extracted_result = {"files": {}, "extracted_path": str(result_dir)}
+
+    with patch.object(processor.archive_handler, "extract_result_archive") as mock_extract:
+        mock_extract.return_value = extracted_result
+        results = processor.build_results()
+
+    # Verify structure
+    assert results is not None
+    assert results.status == "PASS"
+    assert results.total_runs == 1
+    assert results.runs is not None
+    assert "run_0" in results.runs
+
+    # Verify primary_metrics
+    assert results.primary_metrics is not None
+    assert len(results.primary_metrics) == 2, "Should have exactly 2 primary metrics"
+
+    metrics_by_name = {m.name: m for m in results.primary_metrics}
+
+    # Verify both metrics present
+    assert "CPU Mark" in metrics_by_name, "Missing CPU Mark metric"
+    assert "Memory Mark" in metrics_by_name, "Missing Memory Mark metric"
+
+    # Verify units
+    assert metrics_by_name["CPU Mark"].unit == "score"
+    assert metrics_by_name["Memory Mark"].unit == "score"
+
+    # Verify values (from SUMM_CPU and SUMM_ME in yml)
+    # CPU Mark = SUMM_CPU = 24685.407296119931
+    assert metrics_by_name["CPU Mark"].value == pytest.approx(24685.407296119931)
+
+    # Memory Mark = SUMM_ME = 2106.6563041301797
+    assert metrics_by_name["Memory Mark"].value == pytest.approx(2106.6563041301797)
+
+
+def test_passmark_processor_multi_metric_with_multiple_iterations(result_dir):
+    """
+    Test primary_metrics extraction with multiple iterations.
+
+    Should extract mean values from aggregated metrics (SUMM_CPU_mean, SUMM_ME_mean).
+    """
+    # Create multiple YML files (simulating multiple iterations)
+    yml_content_1 = """BaselineInfo:
+  WebDBID: -1
+  TimeStamp: 20260502203522
+Version:
+  Major: 11
+Results:
+  NumTestProcesses: 16
+  CPU_INTEGER_MATH: 70000.0
+  SUMM_CPU: 24000.0
+  SUMM_ME: 2000.0
+SystemInformation:
+  OSName: Red Hat Enterprise Linux 9.8
+"""
+
+    yml_content_2 = """BaselineInfo:
+  WebDBID: -1
+  TimeStamp: 20260502204522
+Version:
+  Major: 11
+Results:
+  NumTestProcesses: 16
+  CPU_INTEGER_MATH: 75000.0
+  SUMM_CPU: 25000.0
+  SUMM_ME: 2200.0
+SystemInformation:
+  OSName: Red Hat Enterprise Linux 9.8
+"""
+
+    yml_path_1 = result_dir / "results_all_1.yml"
+    yml_path_1.write_text(yml_content_1)
+    yml_path_2 = result_dir / "results_all_2.yml"
+    yml_path_2.write_text(yml_content_2)
+
+    # Create dummy zip
+    dummy_zip = result_dir / "results_passmark.zip"
+    dummy_zip.write_bytes(b"")
+
+    # Process with Passmark processor
+    processor = PassmarkProcessor(str(result_dir))
+    extracted_result = {"files": {}, "extracted_path": str(result_dir)}
+
+    with patch.object(processor.archive_handler, "extract_result_archive") as mock_extract:
+        mock_extract.return_value = extracted_result
+        results = processor.build_results()
+
+    # Verify primary_metrics
+    assert results.primary_metrics is not None
+    assert len(results.primary_metrics) == 2, "Should have exactly 2 primary metrics"
+
+    metrics_by_name = {m.name: m for m in results.primary_metrics}
+
+    # Verify both metrics present
+    assert "CPU Mark" in metrics_by_name
+    assert "Memory Mark" in metrics_by_name
+
+    # Verify mean values
+    # CPU Mark mean = (24000 + 25000) / 2 = 24500
+    assert metrics_by_name["CPU Mark"].value == pytest.approx(24500.0)
+
+    # Memory Mark mean = (2000 + 2200) / 2 = 2100
+    assert metrics_by_name["Memory Mark"].value == pytest.approx(2100.0)