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7fe96b813d
* Fix error too deep recursion * Add space * space no 'AND' * update snapshots * fix test * fix test * Remove team_id from prop_clause
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
from typing import List
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from dateutil.relativedelta import relativedelta
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from ee.clickhouse.client import sync_execute
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from ee.clickhouse.models.property import parse_prop_clauses
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from ee.clickhouse.queries.sessions.util import entity_query_conditions
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from ee.clickhouse.queries.util import (
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format_ch_timestamp,
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get_earliest_timestamp,
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get_interval_func_ch,
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get_trunc_func_ch,
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parse_timestamps,
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)
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from ee.clickhouse.sql.events import NULL_SQL
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from ee.clickhouse.sql.sessions.average_all import AVERAGE_SQL
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from ee.clickhouse.sql.sessions.average_per_period import AVERAGE_PER_PERIOD_SQL
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from ee.clickhouse.sql.sessions.no_events import SESSIONS_NO_EVENTS_SQL
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from posthog.models import Filter, Team
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from posthog.queries.sessions.sessions import scale_time_series
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from posthog.utils import append_data, friendly_time
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class ClickhouseSessionsAvg:
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def calculate_avg(self, filter: Filter, team: Team):
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parsed_date_from, parsed_date_to, date_params = parse_timestamps(filter, team.pk)
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filters, params = parse_prop_clauses(
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filter.properties, has_person_id_joined=False, group_properties_joined=False
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)
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trunc_func = get_trunc_func_ch(filter.interval)
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interval_func = get_interval_func_ch(filter.interval)
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entity_conditions, entity_params = entity_query_conditions(filter, team)
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if not entity_conditions:
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entity_conditions = ["event != '$feature_flag_called'"] # default conditino
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params = {**params, **entity_params, **date_params}
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entity_query = " OR ".join(entity_conditions)
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avg_query = SESSIONS_NO_EVENTS_SQL.format(
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team_id=team.pk,
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date_from=parsed_date_from,
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date_to=parsed_date_to,
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filters=filters,
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sessions_limit="",
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entity_filter=f"AND ({entity_query})",
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)
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per_period_query = AVERAGE_PER_PERIOD_SQL.format(sessions=avg_query, interval=trunc_func)
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null_sql = NULL_SQL.format(trunc_func=trunc_func, interval_func=interval_func,)
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final_query = AVERAGE_SQL.format(sessions=per_period_query, null_sql=null_sql)
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params["team_id"] = team.pk
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params["date_from"] = format_ch_timestamp(filter.date_from or get_earliest_timestamp(team.pk), filter)
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params["date_to"] = format_ch_timestamp(filter.date_to, filter)
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params["interval"] = filter.interval
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response = sync_execute(final_query, params)
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values = self.clean_values(filter, response)
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time_series_data = append_data(values, interval=filter.interval, math=None)
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scaled_data, _ = scale_time_series(time_series_data["data"])
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time_series_data.update({"data": scaled_data})
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# calculate average
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total = sum(val[1] for val in values)
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if total == 0:
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return []
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valid_days = sum(1 if val[1] else 0 for val in values)
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overall_average = (total / valid_days) if valid_days else 0
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result = self._format_avg(overall_average)
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time_series_data.update(result)
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return [time_series_data]
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def clean_values(self, filter: Filter, values: List) -> List:
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if filter.interval == "month":
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return [(item[1] + relativedelta(months=1, days=-1), item[0]) for item in values]
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else:
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return [(item[1], item[0]) for item in values]
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def _format_avg(self, avg: float):
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avg_formatted = friendly_time(avg)
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avg_split = avg_formatted.split(" ")
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time_series_data = {}
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time_series_data.update(
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{"label": "Average Session Length ({})".format(avg_split[1]), "count": int(avg_split[0]),}
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)
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time_series_data.update({"chartLabel": "Average Session Length ({})".format(avg_split[1])})
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return time_series_data
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