Best way to count rows by arbitrary time intervals - sql

Question

My app has a Events table with time-stamped events.
I need to report the count of events during each of the most recent N time intervals. For different reports, the interval could be "each week" or "each day" or "each hour" or "each 15-minute interval".
For example, a user can display how many orders they received each week, day, or hour, or quarter-hour.
1) My preference is to dynamically do a single SQL query (I'm using Postgres) that groups by an arbitrary time interval. Is there a way to do that?
2) An easy but ugly brute force way is to do a single query for all records within the start/end timeframe sorted by timestamp, then have a method manually build a tally by whatever interval.
3) Another approach would be add separate fields to the event table for each interval and statically store an the_week the_day, the_hour, and the_quarter_hour field so I take the 'hit' at the time the record is created (once) instead of every time I report on that field.
What's best practice here, given I could modify the model and pre-store interval data if required (although at the modest expense of doubling the table width)?

Answer 1

Luckily, you are using PostgreSQL. The window function generate_series() is your friend.
Test case
Given the following test table (which you should have provided):
CREATE TABLE event(event_id serial, ts timestamp);
INSERT INTO event (ts)
SELECT generate_series(timestamp '2018-05-01'
, timestamp '2018-05-08'
, interval '7 min') + random() * interval '7 min';
One event for every 7 minutes (plus 0 to 7 minutes, randomly).
Basic solution
This query counts events for any arbitrary time interval. 17 minutes in the example:
WITH grid AS (
SELECT start_time
, lead(start_time, 1, 'infinity') OVER (ORDER BY start_time) AS end_time
FROM (
SELECT generate_series(min(ts), max(ts), interval '17 min') AS start_time
FROM event
) sub
)
SELECT start_time, count(e.ts) AS events
FROM grid g
LEFT JOIN event e ON e.ts >= g.start_time
AND e.ts < g.end_time
GROUP BY start_time
ORDER BY start_time;
The query retrieves minimum and maximum ts from the base table to cover the complete time range. You can use an arbitrary time range instead.
Provide any time interval as needed.
Produces one row for every time slot. If no event happened during that interval, the count is 0.
Be sure to handle upper and lower bound correctly. See:
Unexpected results from SQL query with BETWEEN timestamps
The window function lead() has an often overlooked feature: it can provide a default for when no leading row exists. Providing 'infinity' in the example. Else the last interval would be cut off with an upper bound NULL.
Minimal equivalent
The above query uses a CTE and lead() and verbose syntax. Elegant and maybe easier to understand, but a bit more expensive. Here is a shorter, faster, minimal version:
SELECT start_time, count(e.ts) AS events
FROM (SELECT generate_series(min(ts), max(ts), interval '17 min') FROM event) g(start_time)
LEFT JOIN event e ON e.ts >= g.start_time
AND e.ts < g.start_time + interval '17 min'
GROUP BY 1
ORDER BY 1;
Example for "every 15 minutes in the past week"`
Formatted with to_char().
SELECT to_char(start_time, 'YYYY-MM-DD HH24:MI'), count(e.ts) AS events
FROM generate_series(date_trunc('day', localtimestamp - interval '7 days')
, localtimestamp
, interval '15 min') g(start_time)
LEFT JOIN event e ON e.ts >= g.start_time
AND e.ts < g.start_time + interval '15 min'
GROUP BY start_time
ORDER BY start_time;
Still ORDER BY and GROUP BY on the underlying timestamp value, not on the formatted string. That's faster and more reliable.
db<>fiddle here
Related answer producing a running count over the time frame:
PostgreSQL: running count of rows for a query 'by minute'