MQE: improve runtime of mixed metrics tests (#10678)

* Make gauntlet tests ~3x faster by not using `t.Run()`

* Don't bother running functions that operate over single series at a time over multiple series at a time

* Don't run `sum` over functions that operate over range vector selectors, and only run with two series rather than up to four

* Restore one `sum` over range vector function test case

pkg/streamingpromql/engine_test.go

@@ -2864,20 +2864,20 @@ func runMixedMetricsTests(t *testing.T, expressions []string, pointsPerSeries in
 		t.Cleanup(func() { require.NoError(t, storage.Close()) })
 
 		for _, expr := range expressions {
-			testName := fmt.Sprintf("Expr: %s, Start: %d, End: %d, Interval: %s", expr, start.Unix(), end.Unix(), tr.interval)
-			t.Run(testName, func(t *testing.T) {
-				q, err := prometheusEngine.NewRangeQuery(context.Background(), storage, nil, expr, start, end, tr.interval)
-				require.NoError(t, err)
-				defer q.Close()
-				prometheusResults := q.Exec(context.Background())
+			// We run so many combinations that calling t.Run() for each of them has a noticeable performance impact.
+			// So we instead just log the test case before we run it.
+			t.Logf("Expr: %s, Start: %d, End: %d, Interval: %s", expr, start.Unix(), end.Unix(), tr.interval)
+			q, err := prometheusEngine.NewRangeQuery(context.Background(), storage, nil, expr, start, end, tr.interval)
+			require.NoError(t, err)
+			defer q.Close()
+			prometheusResults := q.Exec(context.Background())
 
-				q, err = mimirEngine.NewRangeQuery(context.Background(), storage, nil, expr, start, end, tr.interval)
-				require.NoError(t, err)
-				defer q.Close()
-				mimirResults := q.Exec(context.Background())
+			q, err = mimirEngine.NewRangeQuery(context.Background(), storage, nil, expr, start, end, tr.interval)
+			require.NoError(t, err)
+			defer q.Close()
+			mimirResults := q.Exec(context.Background())
 
-				testutils.RequireEqualResults(t, expr, prometheusResults, mimirResults, skipAnnotationComparison)
-			})
+			testutils.RequireEqualResults(t, expr, prometheusResults, mimirResults, skipAnnotationComparison)
 		}
 	}
 }
@@ -2889,10 +2889,8 @@ func TestCompareVariousMixedMetricsFunctions(t *testing.T) {
 
 	// Test each label individually to catch edge cases in with single series
 	labelCombinations := testutils.Combinations(labelsToUse, 1)
-	// Generate combinations of 2, 3, and 4 labels. (e.g., "a,b", "e,f", "c,d,e", "a,b,c,d", "c,d,e,f" etc)
+	// Generate combinations of 2 labels. (e.g., "a,b", "e,f" etc)
 	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 2)...)
-	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 3)...)
-	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 4)...)
 
 	expressions := []string{}
 
@@ -3011,10 +3009,8 @@ func TestCompareVariousMixedMetricsVectorSelectors(t *testing.T) {
 
 	// Test each label individually to catch edge cases in with single series
 	labelCombinations := testutils.Combinations(labelsToUse, 1)
-	// Generate combinations of 2, 3, and 4 labels. (e.g., "a,b", "e,f", "c,d,e", "a,b,c,d", "c,d,e,f" etc)
+	// Generate combinations of 2 labels. (e.g., "a,b", "e,f" etc)
 	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 2)...)
-	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 3)...)
-	labelCombinations = append(labelCombinations, testutils.Combinations(labelsToUse, 4)...)
 
 	expressions := []string{}
 
@@ -3024,7 +3020,6 @@ func TestCompareVariousMixedMetricsVectorSelectors(t *testing.T) {
 			expressions = append(expressions, fmt.Sprintf(`%s(series{label=~"(%s)"}[45s])`, function, labelRegex))
 			expressions = append(expressions, fmt.Sprintf(`%s(series{label=~"(%s)"}[1m])`, function, labelRegex))
 			expressions = append(expressions, fmt.Sprintf(`sum(%s(series{label=~"(%s)"}[2m15s]))`, function, labelRegex))
-			expressions = append(expressions, fmt.Sprintf(`sum(%s(series{label=~"(%s)"}[5m]))`, function, labelRegex))
 		}
 
 		expressions = append(expressions, fmt.Sprintf(`predict_linear(series{label=~"(%s)"}[1m], 30)`, labelRegex))