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I have a time series table where measurements are recorded into "wide" rows. Rows may contain all measurements or only some. The other columns are then set to NULL.

I would like to use timebucket_gapfill() to "clean" this table and make sure that every row in the output has data in all columns, even if the underlying dataset has some null values for some of the columns.

This is how I prepare the table with some data (schema from the getting started guide):

CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,
  temperature DOUBLE PRECISION  NULL,
  humidity    DOUBLE PRECISION  NULL
);
SELECT create_hypertable('conditions', 'time');
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:14-07', 'office', 70.0, 50.0);
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:15-07', 'office', 71.0, null);
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:16-07', 'office', 72.0, 48.0);
-- gap at 2019-07-10 05:02:17-07
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:18-07', 'office', 72.0, 48.0);
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:18.8-07', 'office', 72.1, NULL);
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:19.2-07', 'office', NULL, 46.0);
INSERT INTO conditions(time, location, temperature, humidity)
  VALUES ('2019-07-10 05:02:20-07', 'office', 73.0, 45.0);

And this is how I query it:

SELECT
    time_bucket_gapfill('1000ms', time,
      start => '2019-07-10 05:02:13',
      finish => '2019-07-10 05:02:21'
    ) as ival,
    count(*) as samplesUsed,
    interpolate(avg(temperature)) as lineartemperature,
    interpolate(avg(humidity)) as linearhumidity
 FROM conditions
 GROUP BY ival
 ORDER BY ival;

The output is:

          ival          | samplesused | lineartemperature | linearhumidity 
------------------------+-------------+-------------------+----------------
 2019-07-10 05:02:13-07 |             |                   |               
 2019-07-10 05:02:14-07 |           1 |                70 |             50
 2019-07-10 05:02:15-07 |           1 |                71 |               
 2019-07-10 05:02:16-07 |           1 |                72 |             48
 2019-07-10 05:02:17-07 |             |            72.025 |             48
 2019-07-10 05:02:18-07 |           2 |             72.05 |             48
 2019-07-10 05:02:19-07 |           1 |                   |             46
 2019-07-10 05:02:20-07 |           1 |                73 |             45
  • I understand why the first row is empty - no data in the dataset.
  • At 5:02:17, interpolation is working fine when there are no rows in the dataset.
  • However, at 5:02:15 and 5:02:19, where the underlying rows are "partial", the database did not use values from the previous and next rows to interpolate a result for respectively humidity and temperature.

How do I write the query to return an interpolated value for all measurement columns?

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1 回答 1

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Timescaledb 不将 NULL 视为缺失值。我必须重写查询以避免具有 NULL 值的行，这意味着使用多个查询timebucket_gapfill并将结果连接在一起。

这有效并且做我想要的：

SELECT
    condh.ival, humidity, temperature
from
(
    select
    time_bucket_gapfill('1000ms', time,
      start => '2019-07-10 05:02:13',
      finish => '2019-07-10 05:02:21'
    ) as ival,
    count(*) as samplesUsed,
    interpolate(avg(humidity)) as humidity
    FROM conditions
    WHERE humidity is not NULL
    GROUP BY ival
) condh 
INNER JOIN 
(
     SELECT
    time_bucket_gapfill('1000ms', time,
      start => '2019-07-10 05:02:13',
      finish => '2019-07-10 05:02:21'
    ) as ival,
    count(*) as samplesUsed,
    interpolate(avg(temperature)) as temperature
    FROM conditions
    WHERE temperature is not NULL
    GROUP BY ival
) condt
on (condt.ival = condh.ival)
ORDER BY ival;

输出：

          ival          | humidity | temperature 
------------------------+----------+-------------
 2019-07-10 05:02:13-07 |          |            
 2019-07-10 05:02:14-07 |       50 |          70
 2019-07-10 05:02:15-07 |       49 |          71
 2019-07-10 05:02:16-07 |       48 |          72
 2019-07-10 05:02:17-07 |       48 |      72.025
 2019-07-10 05:02:18-07 |       48 |       72.05
 2019-07-10 05:02:19-07 |       46 |      72.525
 2019-07-10 05:02:20-07 |       45 |          73
(8 rows)

在 timescaledb 松弛方面得到了一些帮助 - 谢谢 gayathri。

于 2019-09-18T20:06:33.170 回答