nix: adjacent process monitoring report columns

Author: steve-chavez

nix/tools/loadtest.nix

@@ -240,7 +240,7 @@ let
         echo -e 'If a branch finishes its loadtest in less seconds than another branch, it will have blank cells for the missing seconds.\n'
 
         find loadtest -type f -iname '*.csv' \
-          | sort -nr \
+          | sort -m \
           | ${mergeMonitorResults}
       '';
 

nix/tools/merge_monitor_result.py

@@ -3,26 +3,42 @@
 import pandas as pd
 
 KEY = "Elapsed seconds"
-
+BASE_METRICS = ["CPU (%)", "MEM (%)", "Real (MB)"]
+branch_order = []
 merged = None
 
 paths = [p.strip() for p in sys.stdin.read().split() if p.strip()]
 
 for csv_path in paths:
     # br is branch (variable shortened to pass linter)
     br = os.path.splitext(os.path.basename(csv_path))[0]
+    branch_order.append(br)
 
     df = pd.read_csv(csv_path)
 
     if KEY not in df.columns:
         sys.exit(f"{csv_path} is missing the {KEY} column")
 
+    for m in BASE_METRICS:
+        if m not in df.columns:
+            sys.exit(f"Error: '{csv_path}' missing required column '{m}'.")
+
     # add branch marker to every metric column
     df = df.rename(columns={c: f"{c} [{br}]" for c in df.columns if c != KEY})
 
     # outer join so missing rows appear
     merged = df if merged is None else merged.merge(df, on=KEY, how="outer")
 
+# Re-order columns so related metrics are adjacent
+ordered_cols = [KEY]
+for metric in BASE_METRICS:
+    for br in branch_order:
+        col_name = f"{metric} [{br}]"
+        if col_name in merged.columns:
+            ordered_cols.append(col_name)
+
+merged = merged[ordered_cols]
+
 # replace nan with empty string
 merged = merged.fillna("")
 merged.to_markdown(sys.stdout, index=False, tablefmt="github")

nix/tools/monitor_pid.py

@@ -5,6 +5,7 @@
 import pandas as pd
 
 KEY = "Elapsed seconds"
+BASE_METRICS = ["CPU (%)", "MEM (%)", "Real (MB)"]
 SAMPLE_INTERVAL_SECS = 1
 
 if len(sys.argv) != 2 or not sys.argv[1].isdigit():
@@ -53,6 +54,6 @@
 total_time = end - start
 print(f"Finished {pid} pid monitoring in {total_time:.3f}", file=sys.stderr)
 
-cols = [KEY, "CPU (%)", "MEM (%)", "Real (MB)"]
+cols = [KEY] + BASE_METRICS
 df = pd.DataFrame(records, columns=cols, dtype=str)
 df.to_csv(sys.stdout, index=False)