折叠 window 间隔

Collapsing window intervals

我有如下数据库:

select * from (
  values ('A', 1, 2), ('A', 2, 3), ('A', 3, 4),
         ('B', 4, 5), ('B', 5, 6), ('A', 6, 7),
         ('C', 7, 8), ('C', 8, 9)
) example_table("state", "start", "end")

-- example table: 
-- state start stop
--     A    t1   t2
--     A    t2   t3
--     A    t3   t4
--     B    t4   t5
--     B    t5   t6
--     A    t6   t7
--     C    t7   t8
--     C    t8   t9

我想通过 state 折叠间隔,同时也尊重干预状态切换:

state start stop
    A    t1   t4
    B    t4   t6
    A    t6   t7
    C    t7   t9

仅使用 group by state 是行不通的,因为给定的 state.

的输出中有多行

这似乎是 window functions 的用例,但我不确定按什么对行进行分区。

我想创建一个 group_id 作为中间步骤:

group state start stop
    1     A    t1   t2
    1     A    t2   t3
    1     A    t3   t4
    2     B    t4   t5
    2     B    t5   t6
    3     A    t6   t7
    4     C    t7   t8
    4     C    t8   t9

然后我可以按 group 和 select min(start)max(stop) 分组,但我不知道如何(有效地)创建此变量。在 R 中,我会使用 rle 函数来执行此操作,但我不知道任何 Presto 等价物。

此答案已更新以反映以下成功答案

with example_table("state", "start", "end") as (
  values ('A', 1, 2), ('A', 2, 3), ('A', 3, 4),
         ('B', 4, 5), ('B', 5, 6), ('A', 6, 7),
         ('C', 7, 8), ('C', 8, 9)
), table_with_lags as (
  -- detect state changes by observing the lagged value
  select *, lag(state) over(order by start) as lag_state,
  -- need to track the final value since it may be lost below
  last_value("end") over(order by start rows between 
                         0 preceding and unbounded following)
  as end_period
  from example_table
)
select state, start, 
       -- force-re-establish the start(+1) = end(0) link;
       --   at the end of the period, override this with the
       --   final observed value instead of null
       lead(start, 1, end_period) over(order by start) as "end"
from table_with_lags
-- lag_state will be null for the first row
where state <> lag_state or lag_state is null
order by start

输出:

state start stop
    A     1    4
    B     4    6
    A     6    7
    C     7    9

rows between 0 preceding and unbounded following 位有点冗长,因此您也可以翻转逻辑并执行以下操作:

table_with_leads as (
  select state, start, "end", 
         lead(state) over(order by start) as lead_state,
         first_value(start) over(order by start) as start_period
  from example_table
)
select state, lag("end", 1, start_period) over(order by start) as start, "end"
from table_with_lags
where state <> lead_state or lead_state is null
order by start

原回答

以下有效,但在规模上表现不佳(即使在 10% 的数据子样本上,我也会收到 "exceeded local memory limit" 错误):

with switches as (
  -- coalesce since the first row will be NULL, need it false
  select *, coalesce(state <> lag(state) over(order by start), false) switched
  from (
    values ('A', 1, 2), ('A', 2, 3), ('A', 3, 4),
           ('B', 4, 5), ('B', 5, 6), ('A', 6, 7),
           ('C', 7, 8), ('C', 8, 9)
  ) example_table("state", "start", "stop")
), groups as (
  -- create the group ID as the accumulation of the state switches
  --   since only one state switch can happen per group
  select *, sum(cast(switched as bigint)) over (order by start) group_id
  from switches
)
select min(state) state, min(start) start, max(stop) stop
from groups group by group_id order by start;

-- state start stop
--     A     1    4
--     B     4    6
--     A     6    7
--     C     7    9

目前通过暂时将groups存储为table,然后从groups中单独select找到了成功,这似乎解决了 RAM 问题(令我有些惊讶)。这似乎不太理想,但由于它完成了工作,我现在很满足于坚持使用它。