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This leads to significant speedups. In a test, with 100 random unique event classes, the old code processed a batch of 100 rows (on average 66-ish unique in the batch) in 0.45 seconds. Doing this in a single query processes the same batch in 0.0076 seconds.
35 lines
1.5 KiB
Python
35 lines
1.5 KiB
Python
from datetime import datetime
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from zerver.lib.queue import queue_json_publish
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from zerver.lib.timestamp import datetime_to_timestamp
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from zerver.models import UserActivityInterval, UserProfile
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def do_update_user_activity_interval(user_profile: UserProfile, log_time: datetime) -> None:
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effective_end = log_time + UserActivityInterval.MIN_INTERVAL_LENGTH
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# This code isn't perfect, because with various races we might end
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# up creating two overlapping intervals, but that shouldn't happen
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# often, and can be corrected for in post-processing
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try:
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last = UserActivityInterval.objects.filter(user_profile=user_profile).order_by("-end")[0]
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# Two intervals overlap iff each interval ends after the other
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# begins. In this case, we just extend the old interval to
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# include the new interval.
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if log_time <= last.end and effective_end >= last.start:
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last.end = max(last.end, effective_end)
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last.start = min(last.start, log_time)
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last.save(update_fields=["start", "end"])
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return
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except IndexError:
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pass
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# Otherwise, the intervals don't overlap, so we should make a new one
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UserActivityInterval.objects.create(
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user_profile=user_profile, start=log_time, end=effective_end
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)
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def update_user_activity_interval(user_profile: UserProfile, log_time: datetime) -> None:
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event = {"user_profile_id": user_profile.id, "time": datetime_to_timestamp(log_time)}
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queue_json_publish("user_activity_interval", event)
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