The query you have
You could simplify your query using a WINDOW
clause, but that's just shortening the syntax, not changing the query plan.
SELECT id, trans_ref_no, amount, trans_date, entity_id
, SUM(amount) OVER w AS trans_total
, COUNT(*) OVER w AS trans_count
FROM transactiondb
WINDOW w AS (PARTITION BY entity_id, date_trunc('month',trans_date)
ORDER BY trans_date
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING);
- Also using the slightly faster
count(*)
, since id
is certainly defined NOT NULL
?
- And you don't need to
ORDER BY entity_id
since you already PARTITION BY entity_id
You can simplify further, though:
Don't add ORDER BY
to the window definition at all, it's not relevant to your query. Then you don't need to define a custom window frame, either:
SELECT id, trans_ref_no, amount, trans_date, entity_id
, SUM(amount) OVER w AS trans_total
, COUNT(*) OVER w AS trans_count
FROM transactiondb
WINDOW w AS (PARTITION BY entity_id, date_trunc('month',trans_date);
Simpler, faster, but still just a better version of what you have, with static months.
The query you might want
... is not clearly defined, so I'll build on these assumptions:
Count transactions and amount for every 30-day period within the first and last transaction of any entity_id
. Exclude leading and trailing periods without activity, but include all possible 30-day periods within those outer bounds.
SELECT entity_id, trans_date
, COALESCE(sum(daily_amount) OVER w, 0) AS trans_total
, COALESCE(sum(daily_count) OVER w, 0) AS trans_count
FROM (
SELECT entity_id
, generate_series (min(trans_date)::timestamp
, GREATEST(min(trans_date), max(trans_date) - 29)::timestamp
, interval '1 day')::date AS trans_date
FROM transactiondb
GROUP BY 1
) x
LEFT JOIN (
SELECT entity_id, trans_date
, sum(amount) AS daily_amount, count(*) AS daily_count
FROM transactiondb
GROUP BY 1, 2
) t USING (entity_id, trans_date)
WINDOW w AS (PARTITION BY entity_id ORDER BY trans_date
ROWS BETWEEN CURRENT ROW AND 29 FOLLOWING);
This lists all 30-day periods for each entity_id
with your aggregates and with trans_date
being the first day (incl.) of the period. To get values for each individual row join to the base table once more ...
The basic difficulty is the same as discussed here:
The frame definition of a window cannot depend on values of the current row.
And rather call generate_series()
with timestamp
input:
The query you actually want
After question update and discussion:
Accumulate rows of the same entity_id
in a 30-day window starting at each actual transaction.
Since your data is distributed sparsely, it should be more efficient to run a self-join with a range condition, all the more since Postgres 9.1 does not have LATERAL
joins, yet:
SELECT t0.id, t0.amount, t0.trans_date, t0.entity_id
, sum(t1.amount) AS trans_total, count(*) AS trans_count
FROM transactiondb t0
JOIN transactiondb t1 USING (entity_id)
WHERE t1.trans_date >= t0.trans_date
AND t1.trans_date < t0.trans_date + 30 -- exclude upper bound
-- AND t0.entity_id = 114284 -- or pick a single entity ...
GROUP BY t0.id -- is PK!
ORDER BY t0.trans_date, t0.id
SQL Fiddle.
A rolling window could only make sense (with respect to performance) with data for most days.
This does not aggregate duplicates on (trans_date, entity_id)
per day, but all rows of the same day are always included in the 30-day window.
For a big table, a covering index like this could help quite a bit:
CREATE INDEX transactiondb_foo_idx
ON transactiondb (entity_id, trans_date, amount);
The last column amount
is only useful if you get index-only scans out of it. Else drop it.
But it's not going to be used while you select the whole table anyway. It would support queries for a small subset.
The standard deviation can be calculated knowing the number of values, the sum of the values, the sum of the square of the values. Each of these can be updated incrementally as new values arrive and stored in a work table. The work table will look something like
DailyTotals (
OrderDate,
NumberOfValues,
SumOfValues,
SumOfSquareOfValues);
Since the work table is keyed by date, your desired fourteen day sliding window can be achieved. Since each value is a sum, summing again for each date's value is not a mathematical problem. Yes, there is a calculation at runtime. It is much lighter than the full standard deviation one, however.
When new values arrive the work table can updated synchronously (it's a 1-row update), or asynchronously or in batch depending on the application's appetite for stale data.
Best Answer
First count orders grouped by day of last 7 days, and then calculate averages
Using first sample data:
dbfiddle here
Using second sample data:
dbfiddle here