Question

我有一个非常简单的Sqlite架构，用于记录用户操作的每日计数以及按日和行动划分的各种用户操作延迟百分位数：

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
)

此处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）。

现在这里是一个我正在努力解决的问题：show actions按上周的平均音量降序排序，包括50％，90％，95％水平的平均潜伏百分位数。我提出了一个巨大的查询，解释计划说需要17步，而且速度很慢。任何人都可以改进吗？

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

我假设您已将date和action_date_count表格上的latency_percentiles列编入索引。

问题是sqlite不能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。

而不是：

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

执行此操作：

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，您也可以获得良好的效果。 YMMV。

优化Sqlite查询：在子查询中进行分组

1 个答案: