Early version of profiler harness

.github/workflows/profiler.yml

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+name: TileDB-SOMA Profiler run
+
+on:
+  pull_request:
+
+  push:
+    branches:
+      - main
+      - 'release-*'
+  workflow_dispatch:
+
+jobs:
+
+  check-links:
+
+  name: Setup profiler
+
+  run: |
+    python -m venv profiler_env
+    source profiler_env/bin/activate
+    pip install -e ./profiler
+
+  run: |
+    ./perf_checker.sh

tools/perf_checker/benchmark1.py

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+from time import perf_counter
+
+import cellxgene_census
+
+import tiledbsoma as soma
+
+census_S3_latest = dict(census_version="2023-10-23")
+
+
+def main():
+    t1 = perf_counter()
+    with cellxgene_census.open_soma(**census_S3_latest) as census:
+        with census["census_data"]["homo_sapiens"].axis_query(
+            measurement_name="RNA",
+            obs_query=soma.AxisQuery(value_filter="""tissue_general == 'eye'"""),
+        ) as query:
+            query.to_anndata(X_name="raw")
+    t2 = perf_counter()
+    print(f"End to end time {t2 - t1}")
+
+
+main()

tools/perf_checker/profile_report.py

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+import data
+
+# Processes the set of previously written logs
+
+threshold = 1.10  # Percent difference
+
+db = data.FileBasedProfileDB()
+actual_max_ts = 0
+dt = db.find("python ann_data.py")
+last_two = dt[-2:]
+c = 0
+
+for s in last_two:
+    new_db = sorted(dt, key=lambda ProfileData: ProfileData.timestamp)
+
+    L = []
+    L[0] = dt[0].user_time_sec + dt[0].elapsed_time
+    L[1] = dt[1].user_time_sec + dt[1].elapsed_time
+    for i in range(0, len(dt)):
+        print(f"{i} dt[{i}].user_time_sec = {dt[i].user_time_sec} ts {dt[i].timestamp}")
+    print(f"Prev = {L[0]} Curr = {L[1]}")
+
+    if threshold * float(L[1]) < float(L[0]) or float(L[1]) > threshold * float(L[0]):
+        raise SystemExit(f"Potential performance degradation detected {L[0]} va {L[1]}")
+    print("No recent performance degradation detected")
+    print(
+        f"Prev TBD version = {dt[0].tiledbsoma_version} Curr TBD version = {dt[1].tiledbsoma_version}"
+    )