fix(ab): only reduce dimension set for printing

By reducing the dimension set to eliminate all dimensions that only
every take on a single value straight during parsing, we broke the
ignore list, which does require all dimensions to be present. Fix this
by moving the "dimension reduction" to only happen during printing of
failure messages.

Fixes: fcb39a6 ("test(ab): do not print dimension that are the same across all metrics")
Signed-off-by: Patrick Roy <roypat@amazon.co.uk>

Author: roypat

tools/ab_test.py

@@ -119,8 +119,6 @@ def load_data_series(report_path: Path, tag=None, *, reemit: bool = False):
     # Dictionary mapping EMF dimensions to A/B-testable metrics/properties
     processed_emf = {}
 
-    distinct_values_per_dimenson = defaultdict(set)
-
     report = json.loads(report_path.read_text("UTF-8"))
     for test in report["tests"]:
         for line in test["teardown"]["stdout"].splitlines():
@@ -140,9 +138,6 @@ def load_data_series(report_path: Path, tag=None, *, reemit: bool = False):
                 if not dimensions:
                     continue
 
-                for dimension, value in dimensions.items():
-                    distinct_values_per_dimenson[dimension].add(value)
-
                 dimension_set = frozenset(dimensions.items())
 
                 if dimension_set not in processed_emf:
@@ -159,24 +154,27 @@ def load_data_series(report_path: Path, tag=None, *, reemit: bool = False):
 
                         values.extend(result[metric][0])
 
-    irrelevant_dimensions = set()
+    return processed_emf
 
-    for dimension, distinct_values in distinct_values_per_dimenson.items():
-        if len(distinct_values) == 1:
-            irrelevant_dimensions.add(dimension)
 
-    post_processed_emf = {}
+def uninteresting_dimensions(processed_emf):
+    """
+    Computes the set of cloudwatch dimensions that only ever take on a
+    single value across the entire dataset.
+    """
+    values_per_dimension = defaultdict(set)
+
+    for dimension_set in processed_emf:
+        for dimension, value in dimension_set:
+            values_per_dimension[dimension].add(value)
 
-    for dimension_set, metrics in processed_emf.items():
-        processed_key = frozenset(
-            (dim, value)
-            for (dim, value) in dimension_set
-            if dim not in irrelevant_dimensions
-        )
+    uninteresting = set()
 
-        post_processed_emf[processed_key] = metrics
+    for dimension, distinct_values in values_per_dimension.items():
+        if len(distinct_values) == 1:
+            uninteresting.add(dimension)
 
-    return post_processed_emf
+    return uninteresting
 
 
 def collect_data(binary_dir: Path, pytest_opts: str):
@@ -304,6 +302,7 @@ def analyze_data(
             )
 
     messages = []
+    do_not_print_list = uninteresting_dimensions(processed_emf_a)
     for dimension_set, metric, result, unit in failures:
         # Sanity check as described above
         if abs(statistics.mean(relative_changes_by_metric[metric])) <= noise_threshold:
@@ -325,7 +324,7 @@ def analyze_data(
                 f"for metric \033[1m{metric}\033[0m with \033[0;31m\033[1mp={result.pvalue}\033[0m. "
                 f"This means that observing a change of this magnitude or worse, assuming that performance "
                 f"characteristics did not change across the tested commits, has a probability of {result.pvalue:.2%}. "
-                f"Tested Dimensions:\n{json.dumps(dict(dimension_set), indent=2, sort_keys=True)}"
+                f"Tested Dimensions:\n{json.dumps(dict({k: v for k,v in dimension_set.items() if k not in do_not_print_list}), indent=2, sort_keys=True)}"
             )
             messages.append(msg)