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interpolate()
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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.

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 bucketed period 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 otherwise contain valid values. For example, the interpolation would require looking before this first time bucket period, yet the query's outer time predicate WHERE time > ... normally restricts the function to only evaluate values within this time range. Thus, the prev and next expression tell the function how to look for values outside of the range specified by the time predicate. These expressions will only be evaluated when no suitable value is returned by the outer query (i.e., the first and/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 datatype of time needs to be the same as the time datatype in the time_bucket_gapfill call. The datatype of value needs to be the same as the value datatype 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 7 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)