优化Sqlite查询：子查询中的分组

Question

我有一个非常简单的Sqlite模式，用于按用户操作记录每日计数，并按天和操作记录各种用户操作延迟百分比：

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
)

这里所有日期都存储为每天午夜的Unix时间戳（如果有帮助，我可以更改）。

现在，这是一个我在苦苦挣扎的查询：显示上周按平均交易量降序排列的操作，包括平均延迟百分位分别为50％，90％和95％。 我提出了一个庞大的查询，说明计划说需要17个步骤，而且非常慢。 有人可以改善吗？

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;

Answer 1

我假设您已经为action_date_count和latency_percentiles时间表中的date列建立了索引。

那么问题是，sqlite无法使用给定您提供的查询的日期索引。 您可以通过调整日期比较来解决此问题。

代替这个：

julianday(md.max_date, 'unixepoch', 'localtime') - julianday(lp.date, 'unixepoch', 'localtime') between 1 and 7

做这个：

lp.date between md.max_date - 7 * 24 * 3600 and md.max_date

通过在latency_percentiles (date, percentile, value)上创建覆盖索引，您也可能会获得良好的结果。 因人而异。