interpolate()

The interpolate function does linear interpolation for missing values. It can only be used in an aggregation query with time_bucket_gapfill. The interpolate function call cannot be nested inside other function calls.

For more information about gapfilling and interpolation functions, see the hyperfunctions documentation.

Required arguments

NameTypeDescription
valueANY VALUESThe value to interpolate (int2/int4/int8/float4/float8)

Optional arguments

NameTypeDescription
prevEXPRESSIONThe lookup expression for values before the gapfill time range (record)
nextEXPRESSIONThe lookup expression for values after the gapfill time range (record)

Because the interpolation function relies on having values before and after each time bucket to compute the interpolated value, it might not have enough data to calculate the interpolation for the first and last time bucket if those buckets do not contain valid values. For example, the interpolation requires looking before the first time bucket period, but the query's outer time predicate WHERE time > ... restricts the function to only evaluate values within this time range. You can use the prev and next expressions to tell the function how to look for values outside of the range specified by the time predicate. These expressions are only evaluated when no suitable value is returned by the outer query, such as when the first or last bucket in the queried time range is empty. The returned record for prev and next needs to be a time,value tuple. The data type of time needs to be the same as the time data type in the time_bucket_gapfill call. The data type of value needs to be the same as the value data type of the interpolate call.

Sample usage

Get the temperature every day for each device over the last week, interpolating for missing readings:

SELECT
time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
device_id,
avg(temperature) AS value,
interpolate(avg(temperature))
FROM metrics
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
day | device_id | value | interpolate
------------------------+-----------+-------+-------------
2019-01-10 01:00:00+01 | 1 | |
2019-01-11 01:00:00+01 | 1 | 5.0 | 5.0
2019-01-12 01:00:00+01 | 1 | | 6.0
2019-01-13 01:00:00+01 | 1 | 7.0 | 7.0
2019-01-14 01:00:00+01 | 1 | | 7.5
2019-01-15 01:00:00+01 | 1 | 8.0 | 8.0
2019-01-16 01:00:00+01 | 1 | 9.0 | 9.0
(7 row)

Get the average temperature every day for each device over the last seven days, interpolating for missing readings, with lookup queries for values before and after the gapfill time range:

SELECT
time_bucket_gapfill('1 day', time, now() - INTERVAL '1 week', now()) AS day,
device_id,
avg(value) AS value,
interpolate(avg(temperature),
(SELECT (time,temperature) FROM metrics m2 WHERE m2.time < now() - INTERVAL '1 week' AND m.device_id = m2.device_id ORDER BY time DESC LIMIT 1),
(SELECT (time,temperature) FROM metrics m2 WHERE m2.time > now() AND m.device_id = m2.device_id ORDER BY time DESC LIMIT 1)
) AS interpolate
FROM metrics m
WHERE time > now () - INTERVAL '1 week'
GROUP BY day, device_id
ORDER BY day;
day | device_id | value | interpolate
------------------------+-----------+-------+-------------
2019-01-10 01:00:00+01 | 1 | | 3.0
2019-01-11 01:00:00+01 | 1 | 5.0 | 5.0
2019-01-12 01:00:00+01 | 1 | | 6.0
2019-01-13 01:00:00+01 | 1 | 7.0 | 7.0
2019-01-14 01:00:00+01 | 1 | | 7.5
2019-01-15 01:00:00+01 | 1 | 8.0 | 8.0
2019-01-16 01:00:00+01 | 1 | 9.0 | 9.0
(7 row)

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