perf: add flight coalesce regression case with high-cardinality aggregations (#9214)

* perf: add flight coalesce regression case with high-cardinality aggregations

Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>

* perf: add flight coalesce aggregations bench case

Add a direct_readable_sst case exercising grouped aggregation over
coalesced batches: 16 hosts x 4096 instances, 32 SSTs of 32768 rows,
timestamp-major series layout, three SQL queries (aggregation, topk,
count_by_host) each with a 10% max candidate latency regression
threshold.

Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>

---------

Signed-off-by: discord9 <55937128+discord9@users.noreply.github.com>

tests/perf/query_cases/flight_coalesce_aggregations/case.toml

@@ -0,0 +1,101 @@
+# Qualification case for the Flight record-batch coalescing on the
+# datanode-to-frontend path (#9167). mito2 emits small (2k-row) batches for
+# timestamp-major layouts; the coalescer should merge them before they cross
+# the internal Flight connection. High-cardinality tags make the aggregate and
+# topk outputs wide, so the extra batches are visible end to end.
+
+[case]
+name = "flight_coalesce_aggregations"
+description = "Aggregate and topk queries over a high-cardinality direct-SST fixture exercising Flight batch coalescing"
+
+[scenario]
+kind = "direct_readable_sst"
+seed = 9167
+
+[[scenario.tables]]
+database = "public"
+name = "flight_coalesce_metrics"
+engine = "mito"
+append_mode = true
+sst_format = "flat"
+primary_key = ["host", "instance"]
+time_index = "ts"
+
+[[scenario.tables.columns]]
+name = "host"
+type = "STRING"
+semantic = "tag"
+distribution = { kind = "cardinality", values = 16, prefix = "host" }
+
+[[scenario.tables.columns]]
+name = "instance"
+type = "STRING"
+semantic = "tag"
+distribution = { kind = "cardinality", values = 4096, prefix = "instance" }
+
+[[scenario.tables.columns]]
+name = "value"
+type = "DOUBLE"
+semantic = "field"
+distribution = { kind = "deterministic_wave", min = 0.0, max = 1000.0 }
+
+[[scenario.tables.columns]]
+name = "ts"
+type = "TIMESTAMP(3)"
+semantic = "timestamp"
+
+[scenario.layout]
+regions = 1
+sst_count = 32
+rows_per_sst = 32768
+row_group_size = 8192
+series_count = 4096
+start_unix_nanos = 1_704_067_200_000_000_000  # 2024-01-01T00:00:00Z
+step_nanos = 15_000_000_000
+time_range_layout = "non_overlapping_per_sst"
+series_layout = "timestamp_major"
+
+[[scenario.queries]]
+name = "aggregation"
+kind = "sql"
+query = '''
+SELECT host, instance, avg(value), max(value), count(*)
+FROM flight_coalesce_metrics
+GROUP BY host, instance
+ORDER BY host, instance
+'''
+warmup = 3
+iterations = 9
+
+[scenario.queries.thresholds]
+max_candidate_latency_regression_pct = 10
+
+[[scenario.queries]]
+name = "topk"
+kind = "sql"
+query = '''
+SELECT instance, max(value)
+FROM flight_coalesce_metrics
+GROUP BY instance
+ORDER BY max(value) DESC
+LIMIT 100
+'''
+warmup = 3
+iterations = 9
+
+[scenario.queries.thresholds]
+max_candidate_latency_regression_pct = 10
+
+[[scenario.queries]]
+name = "count_by_host"
+kind = "sql"
+query = '''
+SELECT host, count(*)
+FROM flight_coalesce_metrics
+GROUP BY host
+'''
+warmup = 3
+iterations = 9
+
+[scenario.queries.thresholds]
+max_candidate_latency_regression_pct = 10