Api v1.15.0 (#1521)
* feat(chalice): upgraded dependencies * feat(chalice): changed path analysis schema * feat(DB): click coordinate support * feat(chalice): changed path analysis issues schema feat(chalice): upgraded dependencies * fix(chalice): fixed pydantic issue * refactor(chalice): refresh token validator * feat(chalice): role restrictions * feat(chalice): EE path analysis changes * refactor(DB): changed creation queries refactor(DB): changed delte queries feat(DB): support new path analysis payload * feat(chalice): save path analysis card * feat(chalice): restrict access * feat(chalice): restrict access * feat(chalice): EE save new path analysis card * refactor(chalice): path analysis * feat(chalice): path analysis new query * fix(chalice): configurable CH config * fix(chalice): assist autocomplete * refactor(chalice): refactored permissions * refactor(chalice): changed log level * refactor(chalice): upgraded dependencies * refactor(chalice): changed path analysis query * refactor(chalice): changed path analysis query * refactor(chalice): upgraded dependencies refactor(alerts): upgraded dependencies refactor(crons): upgraded dependencies * feat(chalice): path analysis ignore start point * feat(chalice): path analysis in progress * refactor(chalice): path analysis changed link sort * refactor(chalice): path analysis changed link sort * refactor(chalice): path analysis changed link sort * refactor(chalice): path analysis new query refactor(chalice): authorizers * refactor(chalice): refactored authorizer * fix(chalice): fixed create card of PathAnalysis * refactor(chalice): compute link-percentage for Path Analysis * refactor(chalice): remove null starting point from Path Analysis * feat(chalice): path analysis CH query * refactor(chalice): changed Path Analysis links-value fix(chalice): fixed search notes for EE * feat(chalice): path analysis enhanced query results * feat(chalice): include timezone in search sessions response * refactor(chalice): refactored logs * refactor(chalice): refactored logs feat(chalice): get path analysis issues * fix(chalice): fixed path analysis issues pagination * fix(chalice): sessions-search handle null values * feat(chalice): PathAnalysis start event support middle-event matching * feat(chalice): PathAnalysis start event support middle-event matching * feat(chalice): PathAnalysis support mixed events with start-point * fix(chalice): PathAnalysis fixed eventType value when metricValue is missing * fix(chalice): PathAnalysis fixed wrong super-class model for update card * fix(chalice): PathAnalysis fixed search issues refactor(chalice): upgraded dependencies
This commit is contained in:
Kraiem Taha Yassine
GitHub
parent
69ee313ce6
commit
5aabf1873b
12 changed files with 407 additions and 64 deletions
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@ -5,7 +5,7 @@ name = "pypi"
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[packages]
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requests = "==2.31.0"
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boto3 = "==1.28.63"
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boto3 = "==1.28.64"
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pyjwt = "==2.8.0"
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psycopg2-binary = "==2.9.9"
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elasticsearch = "==8.10.1"
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@ -101,7 +101,7 @@ def __get_click_map_chart(project_id, user_id, data: schemas.CardClickMap, inclu
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def __get_path_analysis_chart(project_id: int, user_id: int, data: schemas.CardPathAnalysis):
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if len(data.series) == 0:
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data.series.append(
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schemas.CardPathAnalysisSchema(startTimestamp=data.startTimestamp, endTimestamp=data.endTimestamp))
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schemas.CardPathAnalysisSeriesSchema(startTimestamp=data.startTimestamp, endTimestamp=data.endTimestamp))
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elif not isinstance(data.series[0].filter, schemas.PathAnalysisSchema):
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data.series[0].filter = schemas.PathAnalysisSchema()
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@ -294,33 +294,28 @@ def __get_funnel_issues(project_id: int, user_id: int, data: schemas.CardFunnel)
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def __get_path_analysis_issues(project_id: int, user_id: int, data: schemas.CardPathAnalysis):
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if len(data.series) == 0:
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return {"data": {}}
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card_table = schemas.CardTable(
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search_data = schemas.SessionsSearchPayloadSchema(
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startTimestamp=data.startTimestamp,
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endTimestamp=data.endTimestamp,
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metricType=schemas.MetricType.table,
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metricOf=schemas.MetricOfTable.issues,
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viewType=schemas.MetricTableViewType.table,
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series=data.model_dump()["series"],
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limit=data.limit,
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page=data.page
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page=data.page,
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filters=data.series[0].filter.model_dump(by_alias=True)["filters"]
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)
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for s in data.start_point:
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if data.start_type == "end":
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card_table.series[0].filter.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
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operator=s.operator,
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value=s.value))
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search_data.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
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operator=s.operator,
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value=s.value))
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else:
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card_table.series[0].filter.filters.insert(0, schemas.SessionSearchEventSchema2(type=s.type,
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operator=s.operator,
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value=s.value))
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search_data.filters.insert(0, schemas.SessionSearchEventSchema2(type=s.type,
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operator=s.operator,
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value=s.value))
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for s in data.excludes:
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card_table.series[0].filter.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
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operator=schemas.SearchEventOperator._not_on,
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value=s.value))
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# result = __get_table_of_issues(project_id=project_id, user_id=user_id, data=card_table)
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result = sessions.search_table_of_individual_issues(project_id=project_id,
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metric_value=card_table.metric_value,
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data=card_table)
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search_data.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
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operator=schemas.SearchEventOperator._not_on,
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value=s.value))
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result = sessions.search_table_of_individual_issues(project_id=project_id, data=search_data)
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return result
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@ -68,7 +68,7 @@ JOURNEY_TYPES = {
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# compute avg_time_from_previous at the same level as sessions_count
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# sort by top 5 according to sessions_count at the CTE level
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# final part project data without grouping
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def path_analysis(project_id: int, data: schemas.CardPathAnalysis):
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def path_analysis_deprecated(project_id: int, data: schemas.CardPathAnalysis):
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sub_events = []
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start_points_from = "pre_ranked_events"
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start_points_conditions = []
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@ -388,6 +388,344 @@ WITH sub_sessions AS (SELECT session_id
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return __transform_journey(rows=rows, reverse_path=reverse)
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# query: Q5, the result is correct,
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# startPoints are computed before ranked_events to reduce the number of window functions over rows
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# replaced time_to_target by time_from_previous
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# compute avg_time_from_previous at the same level as sessions_count
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# sort by top 5 according to sessions_count at the CTE level
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# final part project data without grouping
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# if start-point is selected, the selected event is ranked n°1
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def path_analysis(project_id: int, data: schemas.CardPathAnalysis):
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sub_events = []
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start_points_from = "pre_ranked_events"
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sub_sessions_extra_projection = ""
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start_points_conditions = []
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sessions_conditions = ["start_ts>=%(startTimestamp)s", "start_ts<%(endTimestamp)s",
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"project_id=%(project_id)s", "events_count > 1", "duration>0"]
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if len(data.metric_value) == 0:
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data.metric_value.append(schemas.ProductAnalyticsSelectedEventType.location)
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sub_events.append({"table": JOURNEY_TYPES[schemas.ProductAnalyticsSelectedEventType.location]["table"],
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"column": JOURNEY_TYPES[schemas.ProductAnalyticsSelectedEventType.location]["column"],
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"eventType": schemas.ProductAnalyticsSelectedEventType.location.value})
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else:
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for v in data.metric_value:
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if JOURNEY_TYPES.get(v):
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sub_events.append({"table": JOURNEY_TYPES[v]["table"],
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"column": JOURNEY_TYPES[v]["column"],
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"eventType": v})
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extra_values = {}
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start_join = []
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reverse = data.start_type == "end"
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for i, sf in enumerate(data.start_point):
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f_k = f"start_point_{i}"
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op = sh.get_sql_operator(sf.operator)
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is_not = sh.is_negation_operator(sf.operator)
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extra_values = {**extra_values, **sh.multi_values(sf.value, value_key=f_k)}
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start_points_conditions.append(f"(event_type='{sf.type}' AND " +
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sh.multi_conditions(f'e_value {op} %({f_k})s', sf.value, is_not=is_not,
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value_key=f_k)
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+ ")")
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main_column = JOURNEY_TYPES[sf.type]["column"]
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sessions_conditions.append(sh.multi_conditions(f'{main_column} {op} %({f_k})s', sf.value, is_not=is_not,
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value_key=f_k))
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sessions_conditions += ["timestamp>=%(startTimestamp)s", "timestamp<%(endTimestamp)s"]
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start_join.append(f"INNER JOIN {JOURNEY_TYPES[sf.type]['table']} USING (session_id)")
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exclusions = {}
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for i, ef in enumerate(data.excludes):
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if ef.type in data.metric_value:
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f_k = f"exclude_{i}"
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extra_values = {**extra_values, **sh.multi_values(ef.value, value_key=f_k)}
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exclusions[ef.type] = [
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sh.multi_conditions(f'{JOURNEY_TYPES[ef.type]["column"]} != %({f_k})s', ef.value, is_not=True,
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value_key=f_k)]
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meta_keys = None
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for i, f in enumerate(data.series[0].filter.filters):
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op = sh.get_sql_operator(f.operator)
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is_any = sh.isAny_opreator(f.operator)
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is_not = sh.is_negation_operator(f.operator)
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is_undefined = sh.isUndefined_operator(f.operator)
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f_k = f"f_value_{i}"
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extra_values = {**extra_values, **sh.multi_values(f.value, value_key=f_k)}
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if not is_any and len(f.value) == 0:
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continue
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# ---- meta-filters
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if f.type == schemas.FilterType.user_browser:
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if is_any:
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sessions_conditions.append('user_browser IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_browser {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type in [schemas.FilterType.user_os]:
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if is_any:
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sessions_conditions.append('user_os IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_os {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type in [schemas.FilterType.user_device]:
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if is_any:
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sessions_conditions.append('user_device IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_device {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type in [schemas.FilterType.user_country]:
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if is_any:
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sessions_conditions.append('user_country IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_country {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type == schemas.FilterType.user_city:
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if is_any:
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sessions_conditions.append('user_city IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_city {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type == schemas.FilterType.user_state:
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if is_any:
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sessions_conditions.append('user_state IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'user_state {op} %({f_k})s', f.value, is_not=is_not, value_key=f_k))
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elif f.type in [schemas.FilterType.utm_source]:
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if is_any:
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sessions_conditions.append('utm_source IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('utm_source IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'utm_source {op} %({f_k})s::text', f.value, is_not=is_not,
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value_key=f_k))
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elif f.type in [schemas.FilterType.utm_medium]:
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if is_any:
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sessions_conditions.append('utm_medium IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('utm_medium IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'utm_medium {op} %({f_k})s::text', f.value, is_not=is_not,
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value_key=f_k))
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elif f.type in [schemas.FilterType.utm_campaign]:
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if is_any:
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sessions_conditions.append('utm_campaign IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('utm_campaign IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f'utm_campaign {op} %({f_k})s::text', f.value, is_not=is_not,
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value_key=f_k))
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elif f.type == schemas.FilterType.duration:
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if len(f.value) > 0 and f.value[0] is not None:
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sessions_conditions.append("duration >= %(minDuration)s")
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extra_values["minDuration"] = f.value[0]
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if len(f.value) > 1 and f.value[1] is not None and int(f.value[1]) > 0:
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sessions_conditions.append("duration <= %(maxDuration)s")
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extra_values["maxDuration"] = f.value[1]
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elif f.type == schemas.FilterType.referrer:
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# extra_from += f"INNER JOIN {events.event_type.LOCATION.table} AS p USING(session_id)"
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if is_any:
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sessions_conditions.append('base_referrer IS NOT NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f"base_referrer {op} %({f_k})s", f.value, is_not=is_not,
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value_key=f_k))
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elif f.type == schemas.FilterType.metadata:
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# get metadata list only if you need it
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if meta_keys is None:
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meta_keys = metadata.get(project_id=project_id)
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meta_keys = {m["key"]: m["index"] for m in meta_keys}
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if f.source in meta_keys.keys():
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if is_any:
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sessions_conditions.append(f"{metadata.index_to_colname(meta_keys[f.source])} IS NOT NULL")
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elif is_undefined:
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sessions_conditions.append(f"{metadata.index_to_colname(meta_keys[f.source])} IS NULL")
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else:
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sessions_conditions.append(
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sh.multi_conditions(
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f"{metadata.index_to_colname(meta_keys[f.source])} {op} %({f_k})s::text",
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f.value, is_not=is_not, value_key=f_k))
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elif f.type in [schemas.FilterType.user_id, schemas.FilterType.user_id_ios]:
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if is_any:
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sessions_conditions.append('user_id IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('user_id IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f"s.user_id {op} %({f_k})s::text", f.value, is_not=is_not,
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value_key=f_k))
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elif f.type in [schemas.FilterType.user_anonymous_id,
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schemas.FilterType.user_anonymous_id_ios]:
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if is_any:
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sessions_conditions.append('user_anonymous_id IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('user_anonymous_id IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f"user_anonymous_id {op} %({f_k})s::text", f.value, is_not=is_not,
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value_key=f_k))
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elif f.type in [schemas.FilterType.rev_id, schemas.FilterType.rev_id_ios]:
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if is_any:
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sessions_conditions.append('rev_id IS NOT NULL')
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elif is_undefined:
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sessions_conditions.append('rev_id IS NULL')
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else:
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sessions_conditions.append(
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sh.multi_conditions(f"rev_id {op} %({f_k})s::text", f.value, is_not=is_not, value_key=f_k))
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elif f.type == schemas.FilterType.platform:
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# op = __ sh.get_sql_operator(f.operator)
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sessions_conditions.append(
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sh.multi_conditions(f"user_device_type {op} %({f_k})s", f.value, is_not=is_not,
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value_key=f_k))
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elif f.type == schemas.FilterType.issue:
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if is_any:
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sessions_conditions.append("array_length(issue_types, 1) > 0")
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else:
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sessions_conditions.append(
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sh.multi_conditions(f"%({f_k})s {op} ANY (issue_types)", f.value, is_not=is_not,
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value_key=f_k))
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elif f.type == schemas.FilterType.events_count:
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sessions_conditions.append(
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sh.multi_conditions(f"events_count {op} %({f_k})s", f.value, is_not=is_not,
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value_key=f_k))
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events_subquery = []
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for t in sub_events:
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sub_events_conditions = ["e.timestamp >= %(startTimestamp)s",
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"e.timestamp < %(endTimestamp)s"] + exclusions.get(t["eventType"], [])
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if len(start_points_conditions) > 0:
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sub_events_conditions.append("e.timestamp >= sub_sessions.start_event_timestamp")
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events_subquery.append(f"""\
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SELECT session_id, {t["column"]} AS e_value, timestamp, '{t["eventType"]}' AS event_type
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FROM {t["table"]} AS e INNER JOIN sub_sessions USING (session_id)
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WHERE {" AND ".join(sub_events_conditions)}""")
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events_subquery = "\n UNION ALL \n".join(events_subquery)
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if reverse:
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path_direction = "DESC"
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else:
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path_direction = ""
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if len(start_points_conditions) == 0:
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start_points_from = """(SELECT event_type, e_value
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FROM pre_ranked_events
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WHERE event_number_in_session = 1
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GROUP BY event_type, e_value
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ORDER BY count(1) DESC
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LIMIT 1) AS top_start_events
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INNER JOIN pre_ranked_events
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USING (event_type, e_value)"""
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else:
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sub_sessions_extra_projection = ", MIN(timestamp) AS start_event_timestamp"
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start_points_conditions = ["(" + " OR ".join(start_points_conditions) + ")"]
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start_points_conditions.append("event_number_in_session = 1")
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steps_query = ["""n1 AS (SELECT event_number_in_session,
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event_type,
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e_value,
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next_type,
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next_value,
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AVG(time_from_previous) AS avg_time_from_previous,
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COUNT(1) AS sessions_count
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FROM ranked_events INNER JOIN start_points USING (session_id)
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WHERE event_number_in_session = 1
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AND next_value IS NOT NULL
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GROUP BY event_number_in_session, event_type, e_value, next_type, next_value
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ORDER BY sessions_count DESC
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LIMIT %(eventThresholdNumberInGroup)s)"""]
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projection_query = ["""(SELECT event_number_in_session,
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event_type,
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e_value,
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next_type,
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next_value,
|
||||
sessions_count,
|
||||
avg_time_from_previous
|
||||
FROM n1)"""]
|
||||
for i in range(2, data.density):
|
||||
steps_query.append(f"""n{i} AS (SELECT *
|
||||
FROM (SELECT re.event_number_in_session,
|
||||
re.event_type,
|
||||
re.e_value,
|
||||
re.next_type,
|
||||
re.next_value,
|
||||
AVG(re.time_from_previous) AS avg_time_from_previous,
|
||||
COUNT(1) AS sessions_count
|
||||
FROM ranked_events AS re
|
||||
INNER JOIN n{i - 1} ON (n{i - 1}.next_value = re.e_value)
|
||||
WHERE re.event_number_in_session = {i}
|
||||
GROUP BY re.event_number_in_session, re.event_type, re.e_value, re.next_type, re.next_value) AS sub_level
|
||||
ORDER BY sessions_count DESC
|
||||
LIMIT %(eventThresholdNumberInGroup)s)""")
|
||||
projection_query.append(f"""(SELECT event_number_in_session,
|
||||
event_type,
|
||||
e_value,
|
||||
next_type,
|
||||
next_value,
|
||||
sessions_count,
|
||||
avg_time_from_previous
|
||||
FROM n{i})""")
|
||||
|
||||
with pg_client.PostgresClient() as cur:
|
||||
pg_query = f"""\
|
||||
WITH sub_sessions AS (SELECT session_id {sub_sessions_extra_projection}
|
||||
FROM public.sessions {" ".join(start_join)}
|
||||
WHERE {" AND ".join(sessions_conditions)}
|
||||
{"GROUP BY session_id" if len(start_points_conditions) > 0 else ""}),
|
||||
sub_events AS ({events_subquery}),
|
||||
pre_ranked_events AS (SELECT *
|
||||
FROM (SELECT session_id,
|
||||
event_type,
|
||||
e_value,
|
||||
timestamp,
|
||||
row_number() OVER (PARTITION BY session_id ORDER BY timestamp {path_direction}) AS event_number_in_session
|
||||
FROM sub_events
|
||||
ORDER BY session_id) AS full_ranked_events
|
||||
WHERE event_number_in_session < %(density)s),
|
||||
start_points AS (SELECT session_id
|
||||
FROM {start_points_from}
|
||||
WHERE {" AND ".join(start_points_conditions)}),
|
||||
ranked_events AS (SELECT *,
|
||||
LEAD(e_value, 1) OVER (PARTITION BY session_id ORDER BY timestamp {path_direction}) AS next_value,
|
||||
LEAD(event_type, 1) OVER (PARTITION BY session_id ORDER BY timestamp {path_direction}) AS next_type,
|
||||
abs(LAG(timestamp, 1) OVER (PARTITION BY session_id ORDER BY timestamp {path_direction}) -
|
||||
timestamp) AS time_from_previous
|
||||
FROM pre_ranked_events INNER JOIN start_points USING (session_id)),
|
||||
{",".join(steps_query)}
|
||||
{"UNION ALL".join(projection_query)};"""
|
||||
params = {"project_id": project_id, "startTimestamp": data.startTimestamp,
|
||||
"endTimestamp": data.endTimestamp, "density": data.density,
|
||||
"eventThresholdNumberInGroup": 4 if data.hide_excess else 8,
|
||||
**extra_values}
|
||||
query = cur.mogrify(pg_query, params)
|
||||
_now = time()
|
||||
|
||||
cur.execute(query)
|
||||
if time() - _now > 2:
|
||||
logger.info(f">>>>>>>>>PathAnalysis long query ({int(time() - _now)}s)<<<<<<<<<")
|
||||
logger.info("----------------------")
|
||||
logger.info(query)
|
||||
logger.info("----------------------")
|
||||
rows = cur.fetchall()
|
||||
|
||||
return __transform_journey(rows=rows, reverse_path=reverse)
|
||||
|
||||
#
|
||||
# def __compute_weekly_percentage(rows):
|
||||
# if rows is None or len(rows) == 0:
|
||||
|
|
|
|||
|
|
@ -344,11 +344,7 @@ def search2_table(data: schemas.SessionsSearchPayloadSchema, project_id: int, de
|
|||
return sessions
|
||||
|
||||
|
||||
def search_table_of_individual_issues(data: schemas.SessionsSearchPayloadSchema, project_id: int,
|
||||
metric_value: List):
|
||||
if len(metric_value) > 0:
|
||||
data.filters.append(schemas.SessionSearchFilterSchema(value=metric_value, type=schemas.FilterType.issue,
|
||||
operator=schemas.SearchEventOperator._is))
|
||||
def search_table_of_individual_issues(data: schemas.SessionsSearchPayloadSchema, project_id: int):
|
||||
full_args, query_part = search_query_parts(data=data, error_status=None, errors_only=False,
|
||||
favorite_only=False, issue=None, project_id=project_id,
|
||||
user_id=None)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
# Keep this version to not have conflicts between requests and boto3
|
||||
urllib3==1.26.16
|
||||
requests==2.31.0
|
||||
boto3==1.28.63
|
||||
boto3==1.28.64
|
||||
pyjwt==2.8.0
|
||||
psycopg2-binary==2.9.9
|
||||
elasticsearch==8.10.1
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
# Keep this version to not have conflicts between requests and boto3
|
||||
urllib3==1.26.16
|
||||
requests==2.31.0
|
||||
boto3==1.28.63
|
||||
boto3==1.28.64
|
||||
pyjwt==2.8.0
|
||||
psycopg2-binary==2.9.9
|
||||
elasticsearch==8.10.1
|
||||
|
|
|
|||
|
|
@ -790,7 +790,8 @@ class PathAnalysisSubFilterSchema(BaseModel):
|
|||
_remove_duplicate_values = field_validator('value', mode='before')(remove_duplicate_values)
|
||||
|
||||
|
||||
class ProductAnalyticsFilter(BaseModel):
|
||||
class _ProductAnalyticsFilter(BaseModel):
|
||||
is_event: Literal[False] = False
|
||||
type: FilterType
|
||||
operator: Union[SearchEventOperator, ClickEventExtraOperator, MathOperator] = Field(...)
|
||||
# TODO: support session metadata filters
|
||||
|
|
@ -799,6 +800,21 @@ class ProductAnalyticsFilter(BaseModel):
|
|||
_remove_duplicate_values = field_validator('value', mode='before')(remove_duplicate_values)
|
||||
|
||||
|
||||
class _ProductAnalyticsEventFilter(BaseModel):
|
||||
is_event: Literal[True] = True
|
||||
type: ProductAnalyticsSelectedEventType
|
||||
operator: Union[SearchEventOperator, ClickEventExtraOperator, MathOperator] = Field(...)
|
||||
# TODO: support session metadata filters
|
||||
value: List[Union[IssueType, PlatformType, int, str]] = Field(...)
|
||||
|
||||
_remove_duplicate_values = field_validator('value', mode='before')(remove_duplicate_values)
|
||||
|
||||
|
||||
# this type is created to allow mixing events&filters and specifying a discriminator for PathAnalysis series filter
|
||||
ProductAnalyticsFilter = Annotated[Union[_ProductAnalyticsFilter, _ProductAnalyticsEventFilter], \
|
||||
Field(discriminator='is_event')]
|
||||
|
||||
|
||||
class PathAnalysisSchema(_TimedSchema, _PaginatedSchema):
|
||||
density: int = Field(default=7)
|
||||
filters: List[ProductAnalyticsFilter] = Field(default=[])
|
||||
|
|
@ -1164,7 +1180,7 @@ class CardInsights(__CardSchema):
|
|||
raise ValueError(f"metricType:{MetricType.insights} not supported yet.")
|
||||
|
||||
|
||||
class CardPathAnalysisSchema(CardSessionsSchema):
|
||||
class CardPathAnalysisSeriesSchema(CardSeriesSchema):
|
||||
name: Optional[str] = Field(default=None)
|
||||
filter: PathAnalysisSchema = Field(...)
|
||||
density: int = Field(default=4, ge=2, le=10)
|
||||
|
|
@ -1182,14 +1198,14 @@ class CardPathAnalysis(__CardSchema):
|
|||
metric_type: Literal[MetricType.pathAnalysis]
|
||||
metric_of: MetricOfPathAnalysis = Field(default=MetricOfPathAnalysis.session_count)
|
||||
view_type: MetricOtherViewType = Field(...)
|
||||
metric_value: List[ProductAnalyticsSelectedEventType] = Field(default=[ProductAnalyticsSelectedEventType.location])
|
||||
metric_value: List[ProductAnalyticsSelectedEventType] = Field(default=[])
|
||||
density: int = Field(default=4, ge=2, le=10)
|
||||
|
||||
start_type: Literal["start", "end"] = Field(default="start")
|
||||
start_point: List[PathAnalysisSubFilterSchema] = Field(default=[])
|
||||
excludes: List[PathAnalysisSubFilterSchema] = Field(default=[])
|
||||
|
||||
series: List[CardPathAnalysisSchema] = Field(default=[])
|
||||
series: List[CardPathAnalysisSeriesSchema] = Field(default=[])
|
||||
|
||||
@model_validator(mode="before")
|
||||
def __enforce_default(cls, values):
|
||||
|
|
@ -1201,17 +1217,14 @@ class CardPathAnalysis(__CardSchema):
|
|||
@model_validator(mode="after")
|
||||
def __clean_start_point_and_enforce_metric_value(cls, values):
|
||||
start_point = []
|
||||
metric_value = []
|
||||
for s in values.start_point:
|
||||
if len(s.value) == 0:
|
||||
continue
|
||||
start_point.append(s)
|
||||
metric_value.append(s.type)
|
||||
values.metric_value.append(s.type)
|
||||
|
||||
values.start_point = start_point
|
||||
if len(metric_value) > 0:
|
||||
metric_value = remove_duplicate_values(metric_value)
|
||||
values.metric_value = metric_value
|
||||
values.metric_value = remove_duplicate_values(values.metric_value)
|
||||
|
||||
return values
|
||||
|
||||
|
|
|
|||
|
|
@ -6,10 +6,10 @@ name = "pypi"
|
|||
[packages]
|
||||
urllib3 = "==1.26.16"
|
||||
requests = "==2.31.0"
|
||||
boto3 = "==1.28.62"
|
||||
boto3 = "==1.28.64"
|
||||
pyjwt = "==2.8.0"
|
||||
psycopg2-binary = "==2.9.9"
|
||||
elasticsearch = "==8.10.0"
|
||||
elasticsearch = "==8.10.1"
|
||||
jira = "==3.5.2"
|
||||
fastapi = "==0.103.2"
|
||||
gunicorn = "==21.2.0"
|
||||
|
|
@ -18,7 +18,7 @@ apscheduler = "==3.10.4"
|
|||
python3-saml = "==1.15.0"
|
||||
python-multipart = "==0.0.6"
|
||||
redis = "==5.0.1"
|
||||
azure-storage-blob = "==12.18.2"
|
||||
azure-storage-blob = "==12.18.3"
|
||||
uvicorn = {extras = ["standard"], version = "==0.23.2"}
|
||||
pydantic = {extras = ["email"], version = "==2.3.0"}
|
||||
clickhouse-driver = {extras = ["lz4"], version = "==0.2.6"}
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@ def __get_insights_chart(project_id: int, data: schemas.CardInsights, user_id: i
|
|||
def __get_path_analysis_chart(project_id: int, user_id: int, data: schemas.CardPathAnalysis):
|
||||
if len(data.series) == 0:
|
||||
data.series.append(
|
||||
schemas.CardPathAnalysisSchema(startTimestamp=data.startTimestamp, endTimestamp=data.endTimestamp))
|
||||
schemas.CardPathAnalysisSeriesSchema(startTimestamp=data.startTimestamp, endTimestamp=data.endTimestamp))
|
||||
elif not isinstance(data.series[0].filter, schemas.PathAnalysisSchema):
|
||||
data.series[0].filter = schemas.PathAnalysisSchema()
|
||||
|
||||
|
|
@ -315,28 +315,29 @@ def __get_funnel_issues(project_id: int, user_id: int, data: schemas.CardFunnel)
|
|||
def __get_path_analysis_issues(project_id: int, user_id: int, data: schemas.CardPathAnalysis):
|
||||
if len(data.series) == 0:
|
||||
return {"data": {}}
|
||||
card_table = schemas.CardTable(
|
||||
search_data = schemas.SessionsSearchPayloadSchema(
|
||||
startTimestamp=data.startTimestamp,
|
||||
endTimestamp=data.endTimestamp,
|
||||
metricType=schemas.MetricType.table,
|
||||
metricOf=schemas.MetricOfTable.issues,
|
||||
viewType=schemas.MetricTableViewType.table,
|
||||
series=data.model_dump()["series"])
|
||||
limit=data.limit,
|
||||
page=data.page,
|
||||
filters=data.series[0].filter.model_dump(by_alias=True)["filters"]
|
||||
)
|
||||
|
||||
for s in data.start_point:
|
||||
if data.start_type == "end":
|
||||
card_table.series[0].filter.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=s.operator,
|
||||
value=s.value))
|
||||
search_data.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=s.operator,
|
||||
value=s.value))
|
||||
else:
|
||||
card_table.series[0].filter.filters.insert(0, schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=s.operator,
|
||||
value=s.value))
|
||||
search_data.filters.insert(0, schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=s.operator,
|
||||
value=s.value))
|
||||
for s in data.excludes:
|
||||
card_table.series[0].filter.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=schemas.SearchEventOperator._not_on,
|
||||
value=s.value))
|
||||
result = __get_table_of_issues(project_id=project_id, user_id=user_id, data=card_table)
|
||||
return result[0] if len(result) > 0 else {}
|
||||
search_data.filters.append(schemas.SessionSearchEventSchema2(type=s.type,
|
||||
operator=schemas.SearchEventOperator._not_on,
|
||||
value=s.value))
|
||||
result = sessions.search_table_of_individual_issues(project_id=project_id, data=search_data)
|
||||
return result
|
||||
|
||||
|
||||
def get_issues(project_id: int, user_id: int, data: schemas.CardSchema):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
# Keep this version to not have conflicts between requests and boto3
|
||||
urllib3==1.26.16
|
||||
requests==2.31.0
|
||||
boto3==1.28.63
|
||||
boto3==1.28.64
|
||||
pyjwt==2.8.0
|
||||
psycopg2-binary==2.9.9
|
||||
elasticsearch==8.10.1
|
||||
|
|
@ -17,4 +17,4 @@ apscheduler==3.10.4
|
|||
|
||||
clickhouse-driver[lz4]==0.2.6
|
||||
python-multipart==0.0.6
|
||||
azure-storage-blob==12.18.2
|
||||
azure-storage-blob==12.18.3
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
# Keep this version to not have conflicts between requests and boto3
|
||||
urllib3==1.26.16
|
||||
requests==2.31.0
|
||||
boto3==1.28.63
|
||||
boto3==1.28.64
|
||||
pyjwt==2.8.0
|
||||
psycopg2-binary==2.9.9
|
||||
elasticsearch==8.10.1
|
||||
|
|
@ -16,4 +16,4 @@ apscheduler==3.10.4
|
|||
|
||||
clickhouse-driver[lz4]==0.2.6
|
||||
redis==5.0.1
|
||||
azure-storage-blob==12.18.2
|
||||
azure-storage-blob==12.18.3
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
# Keep this version to not have conflicts between requests and boto3
|
||||
urllib3==1.26.16
|
||||
requests==2.31.0
|
||||
boto3==1.28.63
|
||||
boto3==1.28.64
|
||||
pyjwt==2.8.0
|
||||
psycopg2-binary==2.9.9
|
||||
elasticsearch==8.10.1
|
||||
|
|
@ -25,4 +25,4 @@ python-multipart==0.0.6
|
|||
|
||||
redis==5.0.1
|
||||
#confluent-kafka==2.1.0
|
||||
azure-storage-blob==12.18.2
|
||||
azure-storage-blob==12.18.3
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue