17.10 - Option 3: Replace Missing Values with an Estimated Value - Advanced SQL Engine - Teradata Database

Teradata Vantageā„¢ - Time Series Tables and Operations

Product
Advanced SQL Engine
Teradata Database
Release Number
17.10
Release Date
July 2021
Content Type
Programming Reference
Publication ID
B035-1208-171K
Language
English (United States)

You can estimate a value to update the rows with a missing value.

The value loaded can be the result of any function, including other aggregates.

The example shows this for the TEMPERATURE column in Table and Data Definition for Time Series Aggregates Examples. The example updates all missing values in each timebucket with the average of the present values:

Create table ocean_buoys2 as ocean_buoys with no data;
INSERT INTO OCEAN_BUOYS2 VALUES(TIMESTAMP '2014-01-06 08:00:00.000000', 0, 55, );
INSERT INTO OCEAN_BUOYS2 VALUES(TIMESTAMP '2014-01-06 08:09:59.999999', 0, 55, );
INSERT INTO OCEAN_BUOYS2 VALUES(TIMESTAMP '2014-01-06 09:01:25.122200', 1, 55, );
INSERT INTO OCEAN_BUOYS2 VALUES(TIMESTAMP '2014-01-06 09:02:25.122200', 1, 55, );
 
MERGE INTO OCEAN_BUOYS2
USING (SELECT TD_TIMECODE, BUOYID, Avg(TEMPERATURE) FROM OCEAN_BUOYS GROUP BY (TD_TIMECODE, BUOYID) WHERE TEMPERATURE IS NOT NULL) AS S(a,b,c)
ON TD_TIMECODE=S.a AND BUOYID=S.b AND TEMPERATURE IS NULL
WHEN MATCHED THEN UPDATE SET TEMPERATURE=S.c;
select * from ocean_buoys2 order by 1;

Result:

TIMECODE BUOYID SALINITY TEMPERATURE
2014-01-06 08:00:00.000000 0 55 10
2014-01-06 08:09:59.999999 0 55 99
2014-01-06 09:01:25.122200 1 55 73
2014-01-06 09:02:25.122200 1 55 74
SELECT $TD_TIMECODE_RANGE, $TD_GROUP_BY_TIME, BUOYID, AVG(TEMPERATURE), COUNT(*)
FROM OCEAN_BUOYS
WHERE TD_TIMECODE BETWEEN TIMESTAMP '2014-01-06 08:00:00' AND TIMESTAMP '2014-01-06 10:30:00'
AND BUOYID=0
GROUP BY TIME (MINUTES(10) AND BUOYID)
ORDER BY 2,3;

Result: Note the effect on the Average column.

TIMECODE_RANGE GROUP BY TIME(MINUTES(10)) BUOYID Average(TEMPERATURE) COUNT(*)
('2014-01-06 08:00:00.000000+00:00', '2014-01-06 08:10:00.000000+00:00') 1 0 54 3
('2014-01-06 08:10:00.000000+00:00', '2014-01-06 08:20:00.000000+00:00') 2 0 55 2