PostgreSQL select count with dynamic date range

If you have an index on LastLoggedIn, deleting a few tens of thousands of records should be fast without needing to partition. I ran a small test on MS SQL:

CREATE TABLE Temp.Temp
(
UserID  INT NOT NULL PRIMARY KEY,
LastLoggedIn    DATETIME2(0) NOT NULL INDEX,
Dummy1  VARCHAR(30),
Dummy2  INT
)

I insert ~57 M dummy records, then selected a LastLoggedIn date such that 35,000 records were at least that old. Deleting these records took less than a second. This is on a four-core machine with 128 GB of memory, under light load; your mileage may vary.

Of course, if you run this purge only every few weeks, you may have millions of records to delete rather than thousands (300 K records took seven seconds for me; 1.3 M records took 30 seconds). But in that scenario, it's part of your monthly maintenance window rather than daily upkeep, and a brief delay should be acceptable.

If you get poor performance with just an index, then sure, consider partitioning. As others have pointed out, there will be a significant cost to UPDATE operations, a greater overhead than maintaining an index. You will have many more updates than purges, and the latter can happen during relatively idle periods, so I would worry about the updates more.

If you need always-on uptime, and the delete locks the table for an unacceptable length of time, that might justify using partitioning, but even there I'd look for alternatives. You could SELECT a list of old users and then delete them one at a time (confirming their LastLoggedIn dates first of course) with a cursor, for example.

I'm an MS SQL guy, so my apologies if any of this is incorrect for PostgreSQL.

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:

• Referencing current row in FILTER clause of window function

The frame definition of a window cannot depend on values of the current row.

And rather call generate_series() with timestamp input:

• Generating time series between two dates in PostgreSQL

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.