Optimizing a Vertica SQL query to do running totals

I have a table S with time series data like this:

key   day   delta

For a given key, it's possible but unlikely that days will be missing.

I'd like to construct a cumulative column from the delta values (positive INTs), for the purposes of inserting this cumulative data into another table. This is what I've got so far:

SELECT key, day,
   SUM(delta) OVER (PARTITION BY key ORDER BY day asc RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW),
   delta
FROM S

In my SQL flavor, default window clause is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, but I left that in there to be explicit.

This query is really slow, like order of magnitude slower than the old broken query, which filled in 0s for the cumulative count. Any suggestions for other methods to generate the cumulative numbers?

I did look at the solutions here: R开发者_JAVA百科unning total by grouped records in table

The RDBMs I'm using is Vertica. Vertica SQL precludes the first subselect solution there, and its query planner predicts that the 2nd left outer join solution is about 100 times more costly than the analytic form I show above.

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I think you're essentially there. You may just need to update the syntax a bit:

SELECT s_qty, 
   Sum(s_price) 
     OVER( 
       partition BY NULL 
       ORDER BY s_qty ASC rows UNBOUNDED PRECEDING ) "Cumulative Sum" 
FROM   sample_sales;

Output:

S_QTY | Cumulative Sum 
------+----------------
1     | 1000
100   | 11000
150   | 26000
200   | 28000
250   | 53000
300   | 83000
2000  | 103000
(7 rows)

reference link:

https://dwgeek.com/vertica-cumulative-sum-average-and-example.html/

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Sometimes it's faster to just use a correlated subquery:

SELECT 
    [key]
    , [day]
    , delta
    , (SELECT SUM(delta) FROM S WHERE [key] < t1.[key]) AS DeltaSum
FROM S t1