Improve memory requirements of mempool_monitor.py

Because the demo script was never intended to be run for long periods of
time, the script simply collected *all* event timestamps in lists for
the calculation of the event count and rate metrics. This results in
increasing memory and CPU usage over time.

The problem is resolved by explicitly keeping count of events older than
then minutes and removing the corresponding timestamps from lists.

contrib/tracing/mempool_monitor.py

@@ -182,6 +182,7 @@ def __init__(self, screen):
         self._screen = screen
         self._time_started = datetime.now(timezone.utc)
         self._timestamps = {"added": [], "removed": [], "rejected": [], "replaced": []}
+        self._event_history = {"added": 0, "removed": 0, "rejected": 0, "replaced": 0}
         self._init_windows()
 
     def _init_windows(self):
@@ -263,10 +264,18 @@ def calculate_metrics(self, events):
         for event_ts, event_type, event_data in events:
             self._timestamps[event_type].append(event_ts)
         for event_type, ts in self._timestamps.items():
+            # remove timestamps older than ten minutes but keep track of their
+            # count for the 'total' metric
+            #
+            self._event_history[event_type] += len(
+                [t for t in ts if Dashboard.timestamp_age(t) >= 600]
+            )
+            ts = [t for t in ts if Dashboard.timestamp_age(t) < 600]
+            self._timestamps[event_type] = ts
             # count metric
             count_1m = len([t for t in ts if Dashboard.timestamp_age(t) < 60])
-            count_10m = len([t for t in ts if Dashboard.timestamp_age(t) < 600])
-            count_total = len(ts)
+            count_10m = len(ts)
+            count_total = self._event_history[event_type] + len(ts)
             count[event_type] = (count_total, count_1m, count_10m)
             # rate metric
             runtime = Dashboard.timestamp_age(self._time_started)