Calculating average with biginteger time intervals using TimescaleDB - sql

Question

I have a schema with the following fields:
Name of row | Type
--------------------------+--------
name | string
value1 | numeric
timestamp | bigint
The rows contain entries with a name, a numeric value and a bigint value storing the unix timestamp in nanoseconds. Using TimescaleDB and I would like to use time_buckets_gapfill to retrieve the data. Given the timestamps are stored in bigint, this is quite cumbersome.
I would like to get aggregated data for these intervals: 5min, hour, day, week, month, quarter, year. I have managed to make it work using normal time_buckets, but now I would like to fill the gaps as well. I am using the following query now:
SELECT COALESCE(COUNT(*), 0), COALESCE(SUM(value1), 0), time_bucket_gapfill('5 min', date_trunc('quarter', to_timestamp(timestamp/1000000000)), to_timestamp(1599100000), to_timestamp(1599300000)) AS bucket
FROM playground
WHERE name = 'test' AND timestamp >= 1599100000000000000 AND timestamp <= 1599300000000000000
GROUP BY bucket
ORDER BY bucket ASC
This returns the values correctly, but does not fill the empty spaces. If I modified my query to
time_bucket_gapfill('5 min',
date_trunc('quarter',
to_timestamp(timestamp/1000000000),
to_timestamp(1599100000),
to_timestamp(1599200000))
I would get the first entry correctly and then empty rows every 5 minutes. How could I make it work? Thanks!
Here is a DB fiddle, but it doesn't work as it doesn't support TimeScaleDB. The query above returns the following:
coalesce | coalesce | avg_val
------------------------+-------------------------
3 | 300 | 2020-07-01 00:00:00+00
0 | 0 | 2020-09-03 02:25:00+00
0 | 0 | 2020-09-03 02:30:00+00

Answer 1

You should use datatypes in your time_bucket_gapfill that matches the datatypes in your table. The following query should get you what you are looking for:
SELECT
COALESCE(count(*), 0),
COALESCE(SUM(value1), 0),
time_bucket_gapfill(300E9::BIGINT, timestamp) AS bucket
FROM
t
WHERE
name = 'example'
AND timestamp >= 1599100000000000000
AND timestamp < 1599200000000000000
GROUP BY
bucket;

Answer 2

I have managed to solve it by building on Sven's answer. It first uses his function to fill out the gaps and then date_trunc is called eliminating the extra rows.
WITH gapfill AS (
SELECT
COALESCE(count(*), 0) as count,
COALESCE(SUM(value1), 0) as sum,
time_bucket_gapfill(300E9::BIGINT, timestamp) as bucket
FROM
playground
WHERE
name = 'test'
AND timestamp >= 1599100000000000000
AND timestamp < 1599300000000000000
GROUP BY
bucket
)
SELECT
SUM(count),
SUM(sum),
date_trunc('quarter', to_timestamp(bucket/1000000000)) as truncated
FROM
gapfill
GROUP BY truncated
ORDER BY truncated ASC