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Published at Mar 20, 2024

Understanding the Postgres extract() Function

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A female developer looking at her computer screen. There's an elephant on it.

Written by Team Timescale

The PostgreSQL extract() function allows you to extract a specific unit from a date. If you only want the year, month, or day, you can use extract() to access those values.

Syntax:

extract(<field> FROM <timestamp | interval>) Here is a list of the fields you can extract:

  • century

  • day

  • decade

  • dow (day of the week with Sunday as 0 and Saturday as 6)

  • doy (day of the year)

  • epoch

  • hour

  • isodow (day of the week with Monday as 1 and Sunday as 7)

  • isoyear

  • julian

  • microseconds

  • millennium

  • milliseconds

  • minute

  • month

  • quarter

  • second

  • timezone

  • timezone_hour

  • timezone_minute

  • week

  • year

Examples

For the first set of examples, we will use this weather data collected from two cities over a period of five days.

measured

city

temperature

precipitation

2021-09-01

Miami

65.30

0.28

2021-09-01

Atlanta

63.14

0.20

2021-09-02

Miami

64.40

0.79

2021-09-02

Atlanta

62.60

0.59

2021-09-03

Atlanta

62.60

0.39

2021-09-03

Miami

71.60

0.47

2021-09-04

Miami

68.36

0.00

2021-09-04

Atlanta

67.28

0.00

2021-09-05

Miami

72.50

0.00

2021-09-05

Atlanta

70.80

0.00

Extracting Month, Year, and Date

Using extract() is pretty simple. Here is a query that breaks up the day, month, and year of the date column into their own columns in the result set. Notice that we set aliases for each of the extracted fields and then can order the results by one of those aliases.

SELECT EXTRACT(DAY FROM measured) "day", EXTRACT(MONTH FROM measured) "month", EXTRACT(YEAR FROM measured) "year", city, temperature FROM city_data ORDER BY "day"; And here are the results:

day

month

year

city

temperature

1

9

2021

Miami

65.30

1

9

2021

Atlanta

63.14

2

9

2021

Atlanta

62.60

2

9

2021

Miami

64.40

3

9

2021

Atlanta

62.60

3

9

2021

Miami

71.60

4

9

2021

Miami

68.36

4

9

2021

Atlanta

67.28

5

9

2021

Miami

72.50

5

9

2021

Atlanta

70.80

Grouping by an Extracted Value

The last example showed a simple use of extract() to extract various units from the results being returned by the query; we can sort by those extracted values. You can also group by those values to do aggregate queries based on them.

Here is one way we could get the average value of the temperature between both cities for each day. While we could just group by the date itself, this demonstrates how you can do more advanced queries on tables that contain dates using extract().

SELECT EXTRACT(DAY FROM measured) "day", avg(temperature) FROM city_data GROUP BY "day" ORDER BY "day"; Here are the results:

day

avg

1

64.22

2

63.50

3

67.10

4

67.82

5

71.65

Advanced Examples

For the next example, we are going to use data that looks like this from a table called conditions. It contains temperature and humidity information from 200 separate sensors taken every two minutes.

time

device_id

temperature

humidity

2016-11-16 16:18:00.000 -0600

weather-pro-000279

42

48

2016-11-16 16:18:00.000 -0600

weather-pro-000000

42

54.6

2016-11-16 16:18:00.000 -0600

weather-pro-000001

42

54.4

2016-11-16 16:18:00.000 -0600

weather-pro-000002

42

55.2

2016-11-16 16:18:00.000 -0600

weather-pro-000003

42

52.7

2016-11-16 16:18:00.000 -0600

weather-pro-000004

70

49

In this query, we want to get the average temperature for each device for each hour of the day, so we extract the hour from the time column and group the results by the hour and the device_id.

SELECT   device_id,   EXTRACT(hour from time) as hours,   avg(temperature) FROM conditions GROUP BY hours, device_id;

Here is an example of some of the results:

device_id

hours

avg

weather-pro-000000

0

38.0599999999999446

weather-pro-000001

0

37.9699999999999428

weather-pro-000002

0

36.9233333333332606

weather-pro-000003

0

36.8799999999999271

weather-pro-000004

0

61.9766666666666389

weather-pro-000005

0

62.7766666666666493

weather-pro-000006

0

37.8733333333332746

Here is another example using the same set of data that counts how many readings were taken in each 10-minute interval. We can use the trunc() function to aggregate the readings by 10-minute intervals.

SELECT   EXTRACT(hour from time) as hours,   trunc(EXTRACT(minute from time) / 10)*10 AS ten_mins,   COUNT(*) FROM conditions GROUP BY hours, ten_mins; While the sensors in this table were pretty consistent in their readings, if one of the counts were off, it would show how a sensor could be malfunctioning or that there are network issues.

Here is the first part of the results:

hours

ten_mins

count

0

0

5000

0

10

5000

0

20

5000

0

30

5000

0

40

5000

Next Steps

To learn more about using extract() in PostgreSQL, you can read our article on date/time functions. For more examples on how to use extract() in your own TimescaleDB SQL queries, see these Timescale documentation sections:

  • Perform Advanced Analytic Queries

  • Time and Continuous Aggregates

  • How to Write Better Queries for Time-Series Data Analysis With Custom SQL Functions

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