change min/max to overall_min/overall_max + update comparison results…

… publisher Signed-off-by: Michael Oviedo <[email protected]>
opensearch-project · Nov 12, 2024 · af6e812 · af6e812
1 parent 3ff5f24
commit af6e812
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Showing 2 changed files with 17 additions and 10 deletions.
diff --git a/osbenchmark/aggregator.py b/osbenchmark/aggregator.py
@@ -202,15 +202,22 @@ def calculate_weighted_average(self, task_metrics: Dict[str, List[Any]], iterati
                         weighted_metrics[metric][item_key] = values[0][item_key]
                     else:
                         item_values = [value.get(item_key, 0) for value in values]
-                        weighted_sum = sum(value * iterations for value in item_values)
-                        total_iterations = iterations * len(item_values)
-                        weighted_avg = weighted_sum / total_iterations
-                        weighted_metrics[metric][item_key] = weighted_avg
+                        if item_key == 'min':
+                            weighted_metrics[metric]['overall_min'] = min(item_values)
+                        elif item_key == 'max':
+                            weighted_metrics[metric]['overall_max'] = max(item_values)
+                        elif item_key == 'median':
+                            weighted_sum = sum(value * iterations for value in item_values)
+                            total_iterations = iterations * len(item_values)
+                            weighted_metrics[metric][item_key] = weighted_sum / total_iterations
+                        else:
+                            weighted_sum = sum(value * iterations for value in item_values)
+                            total_iterations = iterations * len(item_values)
+                            weighted_metrics[metric][item_key] = weighted_sum / total_iterations
             else:
                 weighted_sum = sum(value * iterations for value in values)
                 total_iterations = iterations * len(values)
-                weighted_avg = weighted_sum / total_iterations
-                weighted_metrics[metric] = weighted_avg
+                weighted_metrics[metric] = weighted_sum / total_iterations
 
         return weighted_metrics
 

diff --git a/osbenchmark/results_publisher.py b/osbenchmark/results_publisher.py
@@ -464,16 +464,16 @@ def _write_results(self, metrics_table, metrics_table_console):
                             data_plain=metrics_table, data_rich=metrics_table_console)
 
     def _publish_throughput(self, baseline_stats, contender_stats, task):
-        b_min = baseline_stats.metrics(task)["throughput"]["min"]
+        b_min = baseline_stats.metrics(task)["throughput"].get("overall_min") or baseline_stats.metrics(task)["throughput"]["min"]
         b_mean = baseline_stats.metrics(task)["throughput"]["mean"]
         b_median = baseline_stats.metrics(task)["throughput"]["median"]
-        b_max = baseline_stats.metrics(task)["throughput"]["max"]
+        b_max = baseline_stats.metrics(task)["throughput"].get("overall_max") or baseline_stats.metrics(task)["throughput"]["max"]
         b_unit = baseline_stats.metrics(task)["throughput"]["unit"]
 
-        c_min = contender_stats.metrics(task)["throughput"]["min"]
+        c_min = contender_stats.metrics(task)["throughput"].get("overall_min") or contender_stats.metrics(task)["throughput"]["min"]
         c_mean = contender_stats.metrics(task)["throughput"]["mean"]
         c_median = contender_stats.metrics(task)["throughput"]["median"]
-        c_max = contender_stats.metrics(task)["throughput"]["max"]
+        c_max = contender_stats.metrics(task)["throughput"].get("overall_max") or contender_stats.metrics(task)["throughput"]["max"]
 
         return self._join(
             self._line("Min Throughput", b_min, c_min, task, b_unit, treat_increase_as_improvement=True),