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ich ein ziemlich einfaches SQLite-Schema für durch Benutzeraktionen und verschiedene Benutzeraktion Latenz Perzentile bei Tag und Aktion täglich zählt die Aufnahme:Optimierung SQLite Abfrage: Gruppierung in Subqueries
create table user_actions (
id integer primary key,
name text not null
)
create table action_date_count (
action_id integer not null
references user_actions(id) on delete restrict on update restrict,
date integer not null,
count integer not null,
unique (action_id, date) on conflict fail
)
create table latency_percentiles (
action_id integer not null
references user_actions(id) on delete restrict on update restrict,
date integer not null,
percentile integer not null,
value real not null,
unique (action_id, date, percentile) on conflict fail
)
Hier wird alle Daten werden als Unix-Zeitstempel gespeichert von Mitternacht eines jeden Tages (ich kann das ändern, wenn es hilft).
Jetzt ist hier eine Abfrage, mit der ich kämpfe: zeigen Aktionen nach dem durchschnittlichen Volumen in der letzten Woche absteigend sortiert, enthalten durchschnittliche Latenz Perzentile bei 50%, 90%, 95% Ebenen. Ich habe eine riesige Frage gestellt, die erklärt, dass der Plan sagt, dass es 17 Schritte dauert, und es ist ziemlich langsam. Kann jemand es verbessern?
select ua.id, ua.name, ac.avg_count, al50.avg_lat_50, al90.avg_lat_90, al95.avg_lat_95
from
user_actions as ua,
(
select adc.action_id as action_id, avg(adc.count) as avg_count
from
action_date_count as adc,
(select max(date) as max_date from action_date_count) as md
where
julianday(md.max_date, 'unixepoch', 'localtime') - julianday(adc.date, 'unixepoch', 'localtime') between 1 and 7
group by action_id
) as ac,
(
select lp.action_id as action_id, avg(lp.value) as avg_lat_50
from
latency_percentiles as lp,
(select max(date) as max_date from action_date_count) as md
where
lp.percentile = 50 and
julianday(md.max_date, 'unixepoch', 'localtime') - julianday(lp.date, 'unixepoch', 'localtime') between 1 and 7
group by action_id
) as al50,
(
select lp.action_id as action_id, avg(lp.value) as avg_lat_90
from
latency_percentiles as lp,
(select max(date) as max_date from action_date_count) as md
where
lp.percentile = 90 and
julianday(md.max_date, 'unixepoch', 'localtime') - julianday(lp.date, 'unixepoch', 'localtime') between 1 and 7
group by action_id
) as al90,
(
select lp.action_id as action_id, avg(lp.value) as avg_lat_95
from
latency_percentiles as lp,
(select max(date) as max_date from action_date_count) as md
where
lp.percentile = 95 and
julianday(md.max_date, 'unixepoch', 'localtime') - julianday(lp.date, 'unixepoch', 'localtime') between 1 and 7
group by action_id
) as al95
where ua.id = ac.action_id and ua.id = al50.action_id and ua.id = al90.action_id and ua.id = al95.action_id
order by ac.avg_count desc;