feat(crons): Record stats for volume history at clock tick

This adds a function `_evaluate_tick_decision` which looks back at the
last MONITOR_VOLUME_RETENTION days worth of history and compares the
minute we just ticked past to that data.

We record 3 metrics from this comparison

- z_value: This is measured as a ratio of standard deviations from the
  mean value

- pct_deviation: This is the percentage we've deviated from the mean

- count: This is the number of historic data points we're considering

The z_value and pct_deviation will be most helpful in making our
decision as to whether we've entered an "incident" state or not.

src/sentry/monitors/clock_dispatch.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 import logging
+import statistics
 from collections import Counter
 from collections.abc import Sequence
 from datetime import datetime, timedelta, timezone
+from typing import List
 
 from arroyo import Topic as ArroyoTopic
 from arroyo.backends.kafka import KafkaPayload, KafkaProducer, build_kafka_configuration
@@ -27,6 +29,18 @@
 # This key is used to record historical date about the volume of check-ins.
 MONITOR_VOLUME_HISTORY = "sentry.monitors.volume_history:{}"
 
+# When fetching historic volume data to make a decision whether we have lost
+# data this value will determine how many historic volume data-points we fetch
+# of the window of the MONITOR_VOLUME_RETENTION. It is important to consider
+# the expected uniformity of the volume for different steps.
+#
+# For example, since we tend to have much larger volume of check-ins
+# on-the-hour it may not make sense to look at each minute as a data point.
+# This is why this is set to 1 day, since this should closely match the
+# harmonics of how check-ins are sent (people like to check-in on the hour, but
+# there are probably more check-ins at midnight, than at 3pm).
+MONITOR_VOLUME_DECISION_STEP = timedelta(days=1)
+
 # We record 30 days worth of historical data for each minute of check-ins.
 MONITOR_VOLUME_RETENTION = timedelta(days=30)
 
@@ -85,6 +99,91 @@ def _make_reference_ts(ts: datetime):
     return int(ts.replace(second=0, microsecond=0).timestamp())
 
 
+def _evaluate_tick_decision(tick: datetime):
+    """
+    When the clock is ticking, we may decide this tick is invalid and should
+    result in unknown misses and marking all in-progress check-ins as having an
+    unknown result.
+
+    We do this by looking at the historic volume of check-ins for the
+    particular minute boundary we just crossed.
+
+    XXX(epurkhiser): This is currently in development and no decision is made
+    to mark unknowns, instead we are only recording metrics for each clock tick
+    """
+    redis_client = redis.redis_clusters.get(settings.SENTRY_MONITORS_REDIS_CLUSTER)
+
+    # The clock has just ticked to the next minute. Look at the previous minute
+    # volume across the last with the values
+    past_ts = tick - timedelta(minutes=1)
+    start_ts = past_ts - MONITOR_VOLUME_RETENTION
+
+    # Generate previous timestamps to fetch. The first past_ts timestamp is
+    # also included in this query
+    historic_timestamps: List[datetime] = [past_ts]
+    historic_ts = past_ts
+
+    while historic_ts > start_ts:
+        historic_ts = historic_ts - MONITOR_VOLUME_DECISION_STEP
+        historic_timestamps.append(historic_ts)
+
+    # Bulk fetch volume counts
+    volumes = redis_client.mget(
+        MONITOR_VOLUME_HISTORY.format(_make_reference_ts(ts)) for ts in historic_timestamps
+    )
+
+    past_minute_volume = _int_or_none(volumes.pop(0))
+    historic_volume: List[int] = [int(v) for v in volumes if v is not None]
+
+    # Can't make any decisions if we didn't have data for the past minute
+    if past_minute_volume is None:
+        return
+
+    # Can't make any decisions if we don't have historic data
+    if len(historic_volume) == 0:
+        return
+
+    # Record some statistics about the past_minute_volume volume in comparison
+    # to the historic_volume data
+
+    historic_mean = statistics.mean(historic_volume)
+    historic_stdev = statistics.stdev(historic_volume)
+
+    historic_stdev_pct = (historic_stdev / historic_mean) * 100
+
+    # Calculate the z-score of our past minutes volume in comparison to the
+    # historic volume data. The z-score is measured in terms of standard
+    # deviations from the mean
+    z_score = (past_minute_volume - historic_mean) / historic_stdev
+
+    # Percentage deviation from the mean for our past minutes volume
+    pct_deviation = (abs(past_minute_volume - historic_mean) / historic_mean) * 100
+
+    metrics.gauge(
+        "monitors.task.clock_tick.historic_volume_stdev_pct",
+        historic_stdev_pct,
+        sample_rate=1.0,
+    )
+    metrics.gauge("monitors.task.volume_history.count", len(historic_volume), sample_rate=1.0)
+    metrics.gauge("monitors.task.volume_history.z_score", z_score, sample_rate=1.0)
+    metrics.gauge("monitors.task.volume_history.pct_deviation", pct_deviation, sample_rate=1.0)
+
+    # XXX(epurkhiser): We're not actually making any decisions with this data
+    # just yet.
+    logger.info(
+        "monitors.clock_dispatch.volume_history",
+        extra={
+            "reference_datetime": str(tick),
+            "evaluation_minute": past_ts.strftime("%H:%M"),
+            "history_count": len(historic_volume),
+            "z_score": z_score,
+            "pct_deviation": pct_deviation,
+            "historic_mean": historic_mean,
+            "historic_stdev": historic_stdev,
+        },
+    )
+
+
 def update_check_in_volume(ts_list: Sequence[datetime]):
     """
     Increment counters for a list of check-in timestamps. Each timestamp will be
@@ -180,7 +279,9 @@ def try_monitor_clock_tick(ts: datetime, partition: int):
             extra = {"reference_datetime": str(backfill_tick)}
             logger.info("monitors.consumer.clock_tick_backfill", extra=extra)
 
+            _evaluate_tick_decision(backfill_tick)
             _dispatch_tick(backfill_tick)
             backfill_tick = backfill_tick + timedelta(minutes=1)
 
+    _evaluate_tick_decision(tick)
     _dispatch_tick(tick)

tests/sentry/monitors/test_clock_dispatch.py

@@ -1,4 +1,6 @@
-from datetime import timedelta
+import itertools
+from datetime import datetime, timedelta
+from typing import List
 from unittest import mock
 
 from arroyo import Topic
@@ -8,8 +10,11 @@
 from django.utils import timezone
 
 from sentry.monitors.clock_dispatch import (
+    MONITOR_VOLUME_DECISION_STEP,
     MONITOR_VOLUME_HISTORY,
+    MONITOR_VOLUME_RETENTION,
     _dispatch_tick,
+    _evaluate_tick_decision,
     try_monitor_clock_tick,
     update_check_in_volume,
 )
@@ -177,3 +182,134 @@ def make_key(offset: timedelta) -> str:
     assert minute_1 == "1"
     assert minute_2 is None
     assert minute_3 == "1"
+
+
+def fill_historic_volume(start: datetime, length: timedelta, step: timedelta, counts: List[int]):
+    """
+    Creates a volume history starting at the `start` and going back `length`,
+    where each bucket is spaced by `step`s apart.
+
+    `count` Is a list of counts for each step. This value is a list that will
+    be cycled through, it must be a division of the number of steps between the
+    start and length.
+    """
+    aligned_start = start.replace(second=0, microsecond=0)
+
+    # The length of counts should be divisible into the number of steps
+    steps = length // step
+    assert steps % len(counts) == 0
+
+    counts_cycle = itertools.cycle(counts)
+    ts = aligned_start
+    end = aligned_start - length
+
+    ts_list = []
+    while ts >= end:
+        count = next(counts_cycle)
+        ts_list.extend([ts] * count)
+        ts = ts - step
+
+    update_check_in_volume(ts_list)
+
+
+@mock.patch("sentry.monitors.clock_dispatch.logger")
+@mock.patch("sentry.monitors.clock_dispatch.metrics")
+def test_evaluate_tick_decision_simple(metrics, logger):
+    tick = timezone.now().replace(second=0, microsecond=0)
+
+    # This is the timestamp we're looking at just after the tick
+    past_ts = tick - timedelta(minutes=1)
+
+    # Fill histroic volume data for earlier minutes.
+    fill_historic_volume(
+        start=past_ts - MONITOR_VOLUME_DECISION_STEP,
+        length=MONITOR_VOLUME_RETENTION,
+        step=MONITOR_VOLUME_DECISION_STEP,
+        counts=[170, 150, 130, 210, 154],
+    )
+
+    # Record a volume of 200 for the timestamp we are considerng
+    update_check_in_volume([past_ts] * 165)
+
+    _evaluate_tick_decision(tick)
+
+    logger.info.assert_called_with(
+        "monitors.clock_dispatch.volume_history",
+        extra={
+            "reference_datetime": str(tick),
+            "evaluation_minute": past_ts.strftime("%H:%M"),
+            "history_count": 30,
+            "z_score": 0.08064694302168258,
+            "pct_deviation": 1.3513513513513442,
+            "historic_mean": 162.8,
+            "historic_stdev": 27.279397303484902,
+        },
+    )
+
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.count",
+        30,
+        sample_rate=1.0,
+    )
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.z_score",
+        0.08064694302168258,
+        sample_rate=1.0,
+    )
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.pct_deviation",
+        1.3513513513513442,
+        sample_rate=1.0,
+    )
+
+
+@mock.patch("sentry.monitors.clock_dispatch.logger")
+@mock.patch("sentry.monitors.clock_dispatch.metrics")
+def test_evaluate_tick_decision_volume_drop(metrics, logger):
+    tick = timezone.now().replace(second=0, microsecond=0)
+
+    # This is the timestamp we're looking at just after the tick
+    past_ts = tick - timedelta(minutes=1)
+
+    # Fill histroic volume data for earlier minutes.
+    fill_historic_volume(
+        start=past_ts - MONITOR_VOLUME_DECISION_STEP,
+        length=MONITOR_VOLUME_RETENTION,
+        step=MONITOR_VOLUME_DECISION_STEP,
+        counts=[13_000, 12_000, 12_500, 12_400, 12_600],
+    )
+
+    # Record a volume much lower than what we had been recording previously
+    update_check_in_volume([past_ts] * 6_000)
+
+    _evaluate_tick_decision(tick)
+
+    # Note that the pct_deviation and z_score are extremes
+    logger.info.assert_called_with(
+        "monitors.clock_dispatch.volume_history",
+        extra={
+            "reference_datetime": str(tick),
+            "evaluation_minute": past_ts.strftime("%H:%M"),
+            "history_count": 30,
+            "z_score": -19.816869917656856,
+            "pct_deviation": 52.0,
+            "historic_mean": 12500,
+            "historic_stdev": 328.0033641543204,
+        },
+    )
+
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.count",
+        30,
+        sample_rate=1.0,
+    )
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.z_score",
+        -19.816869917656856,
+        sample_rate=1.0,
+    )
+    metrics.gauge.assert_any_call(
+        "monitors.task.volume_history.pct_deviation",
+        52.0,
+        sample_rate=1.0,
+    )