Vertica - Is there LATERAL VIEW functionality?

Question

Need to rotate a matrix to do TIMESERIES interpolation / gap filling, and would like to avoid the messy & inefficient UNION ALL approach. Is there anything like Hive's LATERAL VIEW EXPLODE functionality available in Vertica?

EDIT: @marcothesane -- thanks for your interesting scenario -- I like your approach for interpolation. I will play around with it more and see how it goes. Looks promising.

FYI -- here is the solution that I came up with -- My scenario is that I am trying to view memory usage over time by query (and user / resource pool, etc. basically trying to get a cost metric). I need to do interpolation so that I can see the total usage at any point in time. So here is my query which does timeseries slicing by second, then aggregates to give a metric of "Megabyte_Seconds" by minute.

with qry_cte as
(
select 
session_id
, request_id
, date_trunc('second',start_timestamp) as dat_str
, timestampadd('ss'
    , ceiling(request_duration_ms/1000)::int
    , date_trunc('second',start_timestamp)
    ) as dat_end
, ceiling(request_duration_ms/1000)::int as secs
, memory_acquired_mb
from query_requests
where request_type = 'QUERY'
and request_duration_ms > 0
and memory_acquired_mb > 0
)

select date_trunc('minute',slice_time) as dat_minute
, count(distinct session_id ||  request_id::varchar) as queries
, sum(memory_acquired_mb) as mb_seconds
from (
select session_id, request_id, slice_time, ts_first_value(memory_acquired_mb) as memory_acquired_mb
from (
select session_id, request_id, dat_str as dat, memory_acquired_mb from qry_cte
union all
select session_id, request_id, dat_end as dat, memory_acquired_mb from qry_cte
) x
timeseries slice_time as '1 second' over (partition by session_id, request_id order by dat)
) x
group by 1 order by 1 desc
;

Answer 1

I actually have a scenario handy that could match your requirements:

Out of this:

id|day_strt           |sales_01 |sales_02 |sales_03 |sales_04 |sales_05 |sales_06
 1|2016-01-19 08:00:00| 1,842.25| 5,449.40|-        |39,776.86|-        | 9,424.10
 2|2016-01-19 08:00:00|73,810.66|-        | 9,867.70|-        |76,723.91|95,605.14

Make this:

id|day_strt           |sales_01 |sales_02 |sales_03 |sales_04 |sales_05 |sales_06
 1|2016-01-19 08:00:00| 1,842.25| 5,449.40|22,613.13|39,776.86|24,600.48| 9,424.10
 2|2016-01-19 08:00:00|73,810.66|41,839.18| 9,867.70|43,295.81|76,723.91|95,605.14

01 through 06 refers to the n-th hour of the day when sales were recorded, starting from 08:00.

Below is the whole scenario, including the initial input data.

1. the input data as a SELECT .. UNION ALL SELECT ... .
2. A table consisting of 6 integers to CROSS JOIN to the table of 1.
3. The vertical pivot: Cross join the input with the 6 integers, and depending on the index, output just the n-th sales column in a CASE expression. Finally, filter out wherever the same CASE expression evaluates to NULL.
4. Fill the gaps using the TIMESERIES clause and linear interpolation: The sales figures and also the indexing column.
5. Horizontal pivot everything again in the final query.

More performant than a UNION ALL over all columns of the table, I can guarantee you that.

Here goes:

WITH
-- input 
input(id,day_strt,sales_01,sales_02,sales_03,sales_04,sales_05,sales_06) AS (
          SELECT 1,'2016-01-19 08:00:00'::TIMESTAMP(0), 1842.25, 5449.40 ,NULL::INT,39776.86 ,NULL::INT, 9424.10
UNION ALL SELECT 2,'2016-01-19 08:00:00'::TIMESTAMP(0),73810.66 ,NULL::INT, 9867.70 ,NULL::INT,76723.91 ,95605.14
)
-- debug
-- SELECT * FROM input;
,
-- 6 months to pivot vertically -> 6 integers
six_idxs(idx) AS (
          SELECT 1
UNION ALL SELECT 2
UNION ALL SELECT 3
UNION ALL SELECT 4
UNION ALL SELECT 5
UNION ALL SELECT 6
)
,
-- pivot input vertically and remove rows with null measures
-- (could probably add the TIMESERIES clause here directly,
-- but less readable and maintainable)
vert_pivot AS (
SELECT
  id
, idx 
, TIMESTAMPADD(HOUR,idx-1,day_strt)::TIMESTAMP(0) AS sales_ts
, CASE idx
    WHEN 1 THEN  sales_01
    WHEN 2 THEN  sales_02
    WHEN 3 THEN  sales_03
    WHEN 4 THEN  sales_04
    WHEN 5 THEN  sales_05
    WHEN 6 THEN  sales_06
  END AS sales
FROM input
CROSS JOIN six_idxs
WHERE (
    CASE idx
      WHEN 1 THEN  sales_01
      WHEN 2 THEN  sales_02
      WHEN 3 THEN  sales_03
      WHEN 4 THEN  sales_04
      WHEN 5 THEN  sales_05
      WHEN 6 THEN  sales_06
    END
  ) IS NOT NULL
)
-- debug:
-- SELECT * FROM vert_pivot;
,
-- gap filling and interpolation
gaps_filled AS (
SELECT
  id
, TS_FIRST_VALUE(idx,'LINEAR')   AS idx
, tm_sales_ts::TIMESTAMP(0) AS sales_ts
, TS_FIRST_VALUE(sales,'LINEAR') AS sales
FROM vert_pivot
TIMESERIES tm_sales_ts AS '1 HOUR' OVER(
  PARTITION BY id ORDER BY sales_ts
  )
)
-- debug
-- SELECT * FROM gaps_filled ORDER BY 1,2;
-- pivot horizontally; final query
SELECT
  id
, MIN(sales_ts) AS day_strt
, SUM(CASE idx WHEN 1 THEN sales END)::NUMERIC(7,2) AS sales_01
, SUM(CASE idx WHEN 2 THEN sales END)::NUMERIC(7,2) AS sales_02
, SUM(CASE idx WHEN 3 THEN sales END)::NUMERIC(7,2) AS sales_03
, SUM(CASE idx WHEN 4 THEN sales END)::NUMERIC(7,2) AS sales_04
, SUM(CASE idx WHEN 5 THEN sales END)::NUMERIC(7,2) AS sales_05
, SUM(CASE idx WHEN 6 THEN sales END)::NUMERIC(7,2) AS sales_06
FROM gaps_filled
GROUP BY id
ORDER BY id
;

happy playing -

Marco the Sane