nix: add process monitoring for loadtests

Closes https://github.com/PostgREST/postgrest/issues/4107.

Adds two python scripts:

- monitor_pid.py: monitors the postgrest process each second
  until it exits, then outputs a csv with the results. The nix wrappers
  use the `loadtest` dir for the output.
- merge_monitor_result.py: receives a list of csvs and merges them into
  a single markdown table. The nix wrappers use the `loadtest/*.csv`
  files for the input.

The nix `postgrest-with-pgrst` and `postgrest-loadtest-report` commands
use these scripts to add monitoring for `postgrest-loadtest` and
`postgrest-loadtest-against`.

nix/tools/monitor_pid.py

@@ -0,0 +1,58 @@
+# Monitor a process pid with psutil and emits a CSV.
+import sys
+import time
+import psutil
+import pandas as pd
+
+KEY = "Elapsed seconds"
+SAMPLE_INTERVAL_SECS = 1
+
+if len(sys.argv) != 2 or not sys.argv[1].isdigit():
+    sys.exit(f"Usage: {sys.argv[0]} <PID>")
+
+pid = int(sys.argv[1])
+try:
+    proc = psutil.Process(pid)
+except psutil.NoSuchProcess:
+    sys.exit(f"Error: process {pid} not found.")
+
+print(f"Starting monitoring of {pid} pid", file=sys.stderr)
+
+records = []
+start = time.time()
+# ignore first result as per docs recommendation
+# https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
+proc.cpu_percent(None)
+
+while True:
+    try:
+        if not proc.is_running():
+            break
+        time.sleep(SAMPLE_INTERVAL_SECS)
+
+        elapsed_secs = int(time.time() - start)
+        cpu = proc.cpu_percent(None)
+        mem_pct = proc.memory_percent()
+        meminfo = proc.memory_info()
+        bytes_in_MB = 1024**2
+        rss_mb = meminfo.rss / bytes_in_MB
+
+        records.append(
+            [
+                str(elapsed_secs),
+                f"{cpu:.3f}",
+                f"{mem_pct:.3f}",
+                f"{rss_mb:.3f}",
+            ]
+        )
+
+    except psutil.NoSuchProcess:
+        break
+
+end = time.time()
+total_time = end - start
+print(f"Finished {pid} pid monitoring in {total_time:.3f}", file=sys.stderr)
+
+cols = [KEY, "CPU (%)", "MEM (%)", "Real (MB)"]
+df = pd.DataFrame(records, columns=cols, dtype=str)
+df.to_csv(sys.stdout, index=False)