def tickets_assigned_in_interval(sw: SnowflakeWrapper, developer_id: str, interval: Interval) -> pd.DataFrame:
sql = "SELECT " \
" i.KEY, " \
" i.FIELDS:priority:name::string IssuePriority, " \
" i.FIELDS:issuetype:name::string IssueType, " \
" i.FIELDS:status: name::string status, " \
" SUM(1) DAYS_ASSIGNED " \
"FROM ( "
days = []
for i in range((interval.toDate(raw=True) - interval.fromDate(raw=True)).days + 1):
day = interval.fromDate(raw=True) + timedelta(days=i)
sql_date = Interval.strdate(day)
days.append(
f" SELECT "
f" DISTINCT KEY "
f" FROM TIMELINES "
f" WHERE "
f" ASSIGNEE IN ('{developer_id}') "
f" AND ( "
f" {sql_date} BETWEEN DATEFROM AND DATETO "
f" OR ( "
f" DATEDIFF('day', {sql_date}, DATEFROM) = 0 "
f" AND DATEDIFF('day', {sql_date}, DATETO) > 1 "
f" ) "
f" ) "
)
sql += " UNION ALL ".join(days) + ") t INNER JOIN ISSUES i ON t.KEY = i.KEY GROUP BY 1, 2, 3, 4 ORDER BY 5, 1;"
# print(sql)
return sw.fetch_df(sql)
def get_authored_activity(sw: SnowflakeWrapper, interval: Interval, user_id: Union[None, list] = None):
"""
includes comments, changelogs where the [optional] userId has performed the changes
:param sw: SnowflakeWrapper
:param interval: [start, end)
:param user_id: ID of a user [if not given gets all users]
:return:
"""
ids = "" if user_id is None else f" USERID IN ({mask_in(user_id)}) AND "
return sw.fetch_df(
f"SELECT "
f" USERID, "
f" ARRAY_AGG( "
f" OBJECT_CONSTRUCT( "
f" 'key', KEY, "
f" 'dateCreated', DATECREATED, "
f" 'changelogItem', CHANGELOGITEM "
f" ) "
f" ) CHANGELOGITEMS "
f"FROM CHANGELOGS "
f"WHERE "
f" {ids} "
f" DATECREATED >= {interval.fromDate()} AND "
f" DATECREATED < {interval.toDate()} "
f"GROUP BY USERID;"
)
def tickets_assigned_per_day(sw: SnowflakeWrapper, developer_id: str, interval: Interval) -> pd.DataFrame:
sql = "SELECT " \
" DAY_ASSIGNED, "\
" KEY " \
"FROM ( "
days = []
for i in range((interval.toDate(raw=True) - interval.fromDate(raw=True)).days + 1):
day = interval.fromDate(raw=True) + timedelta(days=i)
sql_date = Interval.strdate(day)
days.append(
f" SELECT "
f" TO_DATE({sql_date}) AS DAY_ASSIGNED,"
f" KEY "
f" FROM TIMELINES "
f" WHERE "
f" ASSIGNEE IN ('{developer_id}') "
f" AND ( "
f" {sql_date} BETWEEN DATEFROM AND DATETO "
f" OR ( "
f" DATEDIFF('day', {sql_date}, DATEFROM) = 0 "
f" AND DATEDIFF('day', {sql_date}, DATETO) > 1 "
f" ) "
f" ) "
f" GROUP BY 1, 2"
)
sql += " UNION ALL ".join(days) + ") GROUP BY 1, 2 ORDER BY 1;"
# print(sql)
return sw.fetch_df(sql)
def get_unresolved_ticket_counts(
sw: SnowflakeWrapper,
interval: Interval,
all_unresolved_until: bool = False, # disregards the interval.fromDate()
breakdowns: Union[list, None] = None,
) -> DataFrame:
if breakdowns is None:
breakdowns = ["issueType", "issuePriority"]
breakedown = {
"issueType": "fields:issuetype:name::string issueType",
"issuePriority": "fields:priority:name::string issuePriority",
"status": "FIELDS:status:name::string status",
}
dimensions = [breakedown[by] for by in breakdowns]
date_from = "" if all_unresolved_until else f"AND DateCreated >= TO_DATE({interval.fromDate()})"
sql = (
f" SELECT KEY, "
f" {','.join(dimensions)}, "
f" convert_timezone('UTC', to_timestamp_tz(FIELDS:created::string, "
f' \'YYYY-MM-DD"T"HH24:MI:SS.FF TZHTZM\')) DateCreated, '
f" DATEDIFF('day', DateCreated, {interval.toDate()}) daysUnresolved "
f" FROM ISSUES "
f" WHERE "
f" FIELDS:resolution IS NULL "
f" AND FIELDS:status:name::string NOT IN ('Done', 'Cancelled', 'Live', 'Reverted') "
f" {date_from} "
f" AND DateCreated < TO_DATE({interval.toDate()})"
f" ORDER BY {len(dimensions) + 3}; ")
# print(sql)
return sw.fetch_df(sql)
def get_developer_ids(sw: SnowflakeWrapper) -> list:
return sw.fetch_df(
"SELECT USERKEY KEY "
"FROM USERS "
"WHERE ACTIVE = TRUE AND USERKEY NOT ILIKE '%addon%' AND USERKEY NOT ILIKE 'ID%' "
"ORDER BY 1;"
)['KEY'].tolist()
def get_ticket_counts(sw: SnowflakeWrapper,
breakdowns: Union[list, None] = None) -> DataFrame:
if breakdowns is None:
breakdowns = ["issueType", "issuePriority", "resolved"]
breakedown = {
"issueType":
"i.fields:issuetype:name::string issueType",
"issuePriority":
"i.fields:priority:name::string issuePriority",
# "resolved": "IFF(i.fields:resolutiondate IS NULL, 'No', 'Yes') Resolved"
"resolved":
"IFF(i.FIELDS:resolution IS NULL AND i.FIELDS:status:name NOT IN ('Done', 'Cancelled', 'Live', 'Reverted'), 'No', 'Yes') Resolved"
}
dimensions = [breakedown[by] for by in breakdowns]
group_by = ','.join([str(i + 1) for i, _ in enumerate(dimensions)])
sql = (f"SELECT "
f" {','.join(dimensions)}, "
f" count(*) Count "
f"FROM "
f" ISSUES i "
f"GROUP BY {group_by} "
f"ORDER BY {len(dimensions) + 1}, {group_by} DESC; ")
# print(sql)
return sw.fetch_df(sql)
def tickets_active_on_interval(sw: SnowflakeWrapper,
interval: Interval,
cols=None) -> Union[list, pd.DataFrame]:
get_keys_sql = (f"SELECT "
f" KEY "
f"FROM "
f" CHANGELOGS "
f"WHERE "
f" DATECREATED >= {interval.fromDate()} AND "
f" DATECREATED < {interval.toDate()} "
f"GROUP BY KEY")
if cols is None:
return sw.fetch_df(get_keys_sql)["KEY"].tolist()
else:
return sw.fetch_df(f"SELECT "
f" {utils.mask_props(cols)} "
f"FROM "
f" ISSUES "
f" WHERE "
f" KEY IN ({get_keys_sql});")
def persist_issue_timelines(sw: SnowflakeWrapper,
interval: Interval,
keys: Union[None, list] = None) -> None:
try:
timelines = build_issue_timelines(sw, interval, keys)
SnowflakeWrapper.execute_df_query(timelines,
"timelines_temp",
ifexists='replace')
sw.execute(f"CREATE OR REPLACE TABLE TIMELINES ( "
f" KEY VARCHAR(32), "
f" STATUS VARCHAR(256), "
f" ASSIGNEE VARCHAR(128), "
f" DATEFROM TIMESTAMP_NTZ(9), "
f" DATETO TIMESTAMP_NTZ(9), "
f" TIMEDELTA NUMBER(38,0) "
f"); ")
sw.execute(f"INSERT ALL INTO TIMELINES "
f" SELECT "
f" c.KEY key, "
f" t.VALUE:status::string status, "
f" t.VALUE:assignee::string assignee, "
f" TO_TIMESTAMP_NTZ(t.VALUE:date_from) datefrom, "
f" TO_TIMESTAMP_NTZ(t.VALUE:date_to) dateto, "
f" t.VALUE:tdelta::NUMBER timedelta "
f" FROM "
f" timelines_temp c, "
f" LATERAL FLATTEN(PARSE_JSON(TIMELINES)) t; ")
except Exception as e:
print(f"Failed persisting timlines to Snowflake: {e}")
raise e
def get_tickets(sw: SnowflakeWrapper,
interval: Interval,
resolved: bool = True,
max_days: int = 10) -> DataFrame:
resolution_date = f"{convert_date('i.FIELDS:resolutiondate')}"
where_resolution = f"{resolution_date} >= {interval.fromDate()} AND {resolution_date} < {interval.toDate()}" if resolved else ""
max_duration = f" AVG_DAYS <= {min(30, max_days)} " if max_days > 1 else " AVG_HOUR < 10 "
sql = (
f'SELECT TICKET_KEY, '
f' STATUS, '
f' "IssueType", '
f' "IssuePriority", '
f' "States", '
f' "Transitions", '
f' "DegreeOfCycling", '
f' AVG_DAYS, '
f' MAX_DAYS, '
f' MIN_DAYS, '
f' AVG_HOUR, '
f' MAX_HOURS, '
f' MIN_HOURS '
f'FROM (SELECT t.KEY TICKET_KEY, '
f' STATUS, '
f' i.FIELDS:issuetype: name::string "IssueType", '
f' i.FIELDS:priority: name::string "IssuePriority", '
f' COUNT(DISTINCT t.STATUS) "States", '
f' COUNT(*) "Transitions", '
f' ("Transitions" / "States") - 1 "DegreeOfCycling", '
f' AVG(TIMEDELTA) / (60 * 60 * 24) AVG_DAYS, '
f' MAX(TIMEDELTA) / (60 * 60 * 24) MAX_DAYS, '
f' MIN(TIMEDELTA) / (60 * 60 * 24) MIN_DAYS, '
f' AVG(TIMEDELTA) / (60 * 60) AVG_HOUR, '
f' MAX(TIMEDELTA) / (60 * 60) MAX_HOURS, '
f' MIN(TIMEDELTA) / (60 * 60) MIN_HOURS '
f' FROM TIMELINES t '
f' INNER JOIN ISSUES i ON t.KEY = i.KEY '
f' WHERE t.KEY IN '
f' (SELECT DISTINCT KEY FROM CHANGELOGS WHERE DATECREATED >= {interval.fromDate()} AND DATECREATED < {interval.toDate()}) '
f' AND {where_resolution}'
f' GROUP BY 1, 2, 3, 4) '
f'WHERE '
f' AVG_HOUR > 2 '
f' AND {max_duration} '
f'ORDER BY 1; ')
# print(sql)
df = sw.fetch_df(sql)
df["AVG_HOUR"] = df["AVG_HOUR"].map(lambda x: np.nan
if x is None else float(x))
df["AVG_DAYS"] = df["AVG_DAYS"].map(lambda x: np.nan
if x is None else float(x))
df.insert(2, "MERGED_STATUS", map_statuses(df["STATUS"]))
return df
def transition_frequency(sw: SnowflakeWrapper,
interval: Interval,
limit=10,
order="DESC",
by_status=True,
by_week=False,
by_issue_type=False,
by_issue_priority=False,
get_sql=False) -> Union[pd.DataFrame, str]:
limit_query = f"LIMIT {limit}" if 0 < limit < 100 else ""
breakdown_by_issue_type = f'I.FIELDS:issuetype:name::string "IssueType",' if by_issue_type else ""
breakdown_by_issue_priority = f'I.FIELDS:priority:name::string "IssuePriority",' if by_issue_priority else ""
breakdown_by_week = f'YEAR "Year", IFF(MONTH = 12 AND WEEKOFYEAR = 1, WEEKOFYEAR + 52, WEEKOFYEAR) "WeekOfYear",' if by_week else ""
breakdown_by_status = f'CHANGELOGITEM:fromString::string || \' -> \' || CHANGELOGITEM:toString::string "Transition",' if by_status else ""
join_issues = "INNER JOIN ISSUES I on C.KEY = I.KEY " if breakdown_by_issue_type or by_issue_priority else ""
query_group_by_props = 0 + (2 if by_week else 0) + (
1 if by_status else 0) + (1 if by_issue_type else
0) + (1 if by_issue_priority else 0)
group_by = f"GROUP BY {','.join([str(i + 1) for i in range(query_group_by_props)])}" if query_group_by_props > 0 else ""
query_order = order if order.upper() in ["ASC", "DESC"] else "DESC"
if not by_week:
order_by = f"ORDER BY {query_group_by_props + 1} {query_order}"
else:
order_by = f"ORDER BY {','.join([str(i + 1) for i in range(query_group_by_props + 1)])} {query_order}"
# TODO: add join to weeks table which will yield NULLs wherever there are missing values
# later on, the NULLs will be converted to zeros (0).
sql = (
f'SELECT '
f' {breakdown_by_week} '
f' {breakdown_by_issue_type} '
f' {breakdown_by_issue_priority} '
f' {breakdown_by_status} '
f' COUNT(*) "TotalTransitions" '
f'FROM CHANGELOGS C '
f' {join_issues} '
f'WHERE '
f' C.CHANGELOGITEM:field::string = \'status\' '
f' AND C.DATECREATED >= {interval.fromDate()} AND C.DATECREATED < {interval.toDate()} '
f'{group_by} '
f'{order_by} '
f'{limit_query}; ')
# print(sql)
if get_sql:
return sql
return sw.fetch_df(sql)
def get_avg_timeline(sw: SnowflakeWrapper, interval: Interval) -> pd.DataFrame:
query = (f'SELECT '
f' STATUS "Status", '
f' COUNT(DISTINCT KEY) "UniqueIssues", '
f' COUNT(*) "Issues", '
f' "Issues" - "UniqueIssues" "Reassignments", '
f' AVG(TIMEDELTA) / (60 * 60 * 24) "AvgDays", '
f' MAX(TIMEDELTA) / (60 * 60 * 24) "MaxDays", '
f' MIN(TIMEDELTA) / (60 * 60 * 24) "MinDays" '
f'FROM TIMELINES t '
f'WHERE '
f' t.DATEFROM >= {interval.fromDate()} '
f' AND t.DATETO < {interval.toDate()} '
f'GROUP BY 1 '
f'ORDER BY 1, 4 DESC; ')
print(query)
return sw.fetch_df(query)
def work_activity_on_interval(
sw: SnowflakeWrapper,
interval: Interval,
ticket_keys: Union[None, list] = None) -> pd.DataFrame:
"""
Returns the work activity (changes of assignees and statuses) on a given interval.
To be able to infer the beginning state of the issue at intervalStartDate, the activity is provided on the interval:
[issueCreationDate, intervalEndDate)
:param sw: SnowflakeWrapper
:param interval: (from:date, to:date)
:param ticket_keys: custom list of ticket keys
:return:
"""
if ticket_keys is None:
ticket_keys = tickets_active_on_interval(sw, interval)
ids = f" c.KEY IN ({utils.mask_in(ticket_keys)}) AND "
if interval or ids:
print(
"WARNING: Given interval and/or ids are being ignored at the moment."
)
sql = (
f"SELECT "
f" c.KEY, "
f" i.{utils.decode_user('fields', 'reporter')} reporter, "
f" TO_TIMESTAMP_NTZ(convert_timezone('UTC', to_timestamp_tz(i.{utils.decode_field('fields', 'created')}::string, 'YYYY-MM-DD\"T\"HH24:MI:SS.FF TZHTZM'))) dateCreated, "
f" ARRAY_AGG( "
f" OBJECT_CONSTRUCT( "
f" 'author', USERID, "
f" 'dateCreated', c.DATECREATED, "
f" 'changelogItems', ARRAY_CONSTRUCT(CHANGELOGITEM) "
f" ) "
f" ) CHANGELOGITEMS "
f"FROM CHANGELOGS c INNER JOIN ISSUES i ON c.KEY = i.KEY "
f"WHERE "
f" c.changelogItem:field IN ('status', 'assignee') AND "
f" c.DATECREATED < {interval.toDate()} "
f"GROUP BY 1, 2, 3 ")
print(f"Executing: {sql}")
changelog = sw.fetch_df(sql)
changelog['CHANGELOGITEMS'] = changelog['CHANGELOGITEMS'].apply(
lambda x: sort_and_merge(
json.loads(x, object_pairs_hook=load_with_datetime)))
return changelog
def get_aggregated_authored_activity(sw: SnowflakeWrapper, interval: Interval, user_id: Union[None, list] = None):
ids = "" if user_id is None else f" USERID IN ({mask_in(user_id)}) AND"
result = sw.fetch_df(
f"SELECT "
f" USERID, "
f" ARRAY_AGG( "
f" OBJECT_CONSTRUCT( "
f" 'userId', USERID, "
f" 'field', FIELD, "
f" 'count', CNT "
f" ) "
f" ) ACTIVITY "
f"FROM ( "
f" SELECT "
f" activity.USERID, "
f" item.VALUE:field::string FIELD, "
f" COUNT(*) CNT "
f" FROM "
f" (SELECT "
f" USERID, "
f" ARRAY_AGG(CHANGELOGITEM) CHANGELOGITEMS "
f" FROM CHANGELOGS "
f" WHERE "
f" {ids} "
f" DATECREATED >= {interval.fromDate()} "
f" AND DATECREATED < {interval.toDate()} "
f" GROUP BY USERID) activity, "
f" lateral flatten(activity.CHANGELOGITEMS) item "
f" GROUP BY 1, 2 "
f") "
f"GROUP BY 1 "
f"ORDER BY 1; "
)
return pd.concat(
result['ACTIVITY'].apply(
lambda x:
pd.DataFrame(json.loads(x))
.pivot(index='userId', columns='field', values='count')).tolist(), # TODO Add breakdown by issue type
sort=True)
f" AND ( "
f" {sql_date} BETWEEN DATEFROM AND DATETO "
f" OR ( "
f" DATEDIFF('day', {sql_date}, DATEFROM) = 0 "
f" AND DATEDIFF('day', {sql_date}, DATETO) > 1 "
f" ) "
f" ) "
f" GROUP BY 1, 2"
)
sql += " UNION ALL ".join(days) + ") GROUP BY 1, 2 ORDER BY 1;"
# print(sql)
return sw.fetch_df(sql)
if __name__ == '__main__':
with SnowflakeWrapper.create_snowflake_connection() as connection:
sw = SnowflakeWrapper(connection)
interval = Interval(date(2019, 10, 1), date(2020, 1, 1))
# result = get_authored_activity(sw, interval, ['marko.prelevikj'])
# plt.figure()
# result.hist('status', bins=40)
# plt.show()
# dev = get_developer(sw, Interval(date(2019, 10, 1), date(2020, 1, 1)), 'marko.prelevikj')
# avg_dev = get_avg_developer(sw, interval, include_nans=False)
# avg_dev_nan = get_avg_developer(sw, include_nans=True)
# assigned_interval = Interval(date(2019, 10, 1), date(2019, 11, 6))
assigned_interval = Interval(date(2019, 10, 1), date(2020, 1, 1))
# assigned = tickets_assigned_in_interval(sw, 'marko.prelevikj', assigned_interval)
assigned_per_day = tickets_assigned_per_day(sw, 'marko.prelevikj', assigned_interval)
# data = get_developers(sw, Interval(date(2019, 10, 1), date(2020, 1, 1)), break_by=[], user_filters={})
def get_ticket_types(sw: SnowflakeWrapper) -> list:
return sw.fetch_df(
'SELECT DISTINCT i.FIELDS:issuetype: name::string "IssueType" FROM ISSUES i;'
)["IssueType"].to_list()
def get_ticket_priorities(sw: SnowflakeWrapper) -> list:
return sw.fetch_df(
'SELECT DISTINCT i.FIELDS:priority: name::string "IssuePriority" FROM ISSUES i;'
)["IssuePriority"].to_list()
def get_developers(
sw: SnowflakeWrapper,
interval: Interval,
user_filters: Union[dict, None] = None,
break_by: Union[list, None] = None,
max_duration: int = 30,
debug: bool = False,
) -> DataFrame:
if user_filters is None:
user_filters = {"status": "dev"}
if break_by is None:
break_by = ["assignee"]
else:
break_by = list(set(["assignee"] + break_by))
dimensions = {
"type": ' i.FIELDS:issuetype:name::string "IssueType" ',
"priority": ' i.FIELDS:priority:name::string "IssuePriority" ',
"status": " STATUS ",
"assignee": " ASSIGNEE ",
}
statuses = ','.join([f"'{s}'" for s in expand_statuses(user_filters["status"])]) if "status" in user_filters and \
user_filters["status"] else None
filters = {
"status": f"STATUS IN ({statuses})" if statuses else " 1 = 1 ",
"priority": f'i.FIELDS:priority:name::string IN (\'{user_filters["priority"]}\')' if "priority" in user_filters and
user_filters[
"priority"] else " 1 = 1 ",
"type": f'i.FIELDS:issuetype:name::string IN (\'{user_filters["type"]}\')' if "type" in user_filters and
user_filters["type"] else " 1 = 1"
}
sql_filters = ' AND '.join(filters.values())
dims = ','.join([dimensions[dim] for dim in break_by])
group_by = ','.join([str(i + 1) for i, _ in enumerate(break_by)])
max_duration = f" AVG_DAYS <= {min(30, max_duration)} " if max_duration > 1 else " AVG_HOURS < 10 "
sql = (
f' SELECT * '
f' FROM ( '
f' SELECT '
f' {dims}, '
f' COUNT(DISTINCT t.KEY) "UniqueIssues", '
f' COUNT(t.KEY) "Issues", '
f' ("Issues" / "UniqueIssues") - 1 "DegreeOfCycling", '
f' AVG(TIMEDELTA) / (60 * 60 * 24) AVG_DAYS, '
f' MAX(TIMEDELTA) / (60 * 60 * 24) MAX_DAYS, '
f' MIN(TIMEDELTA) / (60 * 60 * 24) MIN_DAYS, '
f' AVG(TIMEDELTA) / (60 * 60) AVG_HOURS, '
f' MAX(TIMEDELTA) / (60 * 60) MAX_HOURS, '
f' MIN(TIMEDELTA) / (60 * 60) MIN_HOURS '
f' FROM TIMELINES t INNER JOIN ISSUES i ON t.KEY = i.KEY '
f' WHERE DATEFROM >= {interval.fromDate()} '
f' AND (DATETO < {interval.toDate()} OR DATETO IS NULL) '
f' AND ASSIGNEE IS NOT NULL '
f' AND {sql_filters}'
f' GROUP BY {group_by} '
f' ) '
f' WHERE AVG_HOURS > 2 '
f' AND {max_duration} '
f' ORDER BY 1 '
)
if debug:
print(sql)
df = sw.fetch_df(sql)
df["AVG_DAYS"] = df["AVG_DAYS"].map(lambda x: np.nan if x is None else float(x))
df["AVG_HOURS"] = df["AVG_HOURS"].map(lambda x: np.nan if x is None else float(x))
if "status" in break_by:
df.insert(2, "MERGED_STATUS", map_statuses(df["STATUS"]))
return df
def get_distinct_statuses(sw: SnowflakeWrapper) -> list:
return sw.fetch_df("SELECT ID "
"FROM STATUSES "
"ORDER BY 1;")['ID'].tolist()
