Time and continuous aggregates

Work with timezones and integer-based time in continuous aggregates

Functions that depend on a local timezone setting inside a are not supported. You cannot adjust to a local time because the timezone setting changes from user to user.

To manage this, you can use explicit timezones in the view definition. Alternatively, you can create your own custom aggregation scheme for tables that use an integer time column.

Declare an explicit timezone

The most common method of working with timezones is to declare an explicit timezone in the view query.

1. Create the view with an explicit timezone

   CREATE MATERIALIZED VIEW device_summary
   WITH (timescaledb.continuous)
   AS
   SELECT
     time_bucket('1 hour', observation_time) AS bucket,
     min(observation_time AT TIME ZONE 'EST') AS min_time,
     device_id,
     avg(metric) AS metric_avg,
     max(metric) - min(metric) AS metric_spread
   FROM
     device_readings
   GROUP BY bucket, device_id;
   

2. Cast to timestamp at query time

   Alternatively, you can cast to a timestamp after the view using SELECT:

   SELECT min_time::timestamp FROM device_summary;
   

Integer-based time

Date and time is usually expressed as year-month-day and hours:minutes:seconds. Most databases use a date/time-type column to express the date and time. However, in some cases, you might need to convert these common time and date formats to a format that uses an integer. The most common integer time is Unix epoch time, which is the number of seconds since the Unix epoch of 1970-01-01, but other types of integer-based time formats are possible.

These examples use a called devices that contains CPU and disk usage information. The devices measure time using the Unix epoch.

To create a that uses an integer-based column as time, you need to provide the chunk time interval. In this case, each chunk is 10 minutes.

1. Create a with an integer time column

   Create a and define the integer-based time column and chunk time interval:

   CREATE TABLE devices(
     time BIGINT,        -- Time in minutes since epoch
     cpu_usage INTEGER,  -- Total CPU usage
     disk_usage INTEGER, -- Total disk usage
     PRIMARY KEY (time)
   ) WITH (
     timescaledb.hypertable,
     timescaledb.partition_column='time',
     timescaledb.chunk_interval='10'
   );
   

   For v2.23.0 and higher, the table is automatically partitioned on the first column in the table with a timestamp data type. If multiple columns are suitable candidates as a partitioning column, throws an error and asks for an explicit definition. For earlier versions, set partition_column to a time column.

   If you are self-hosting v2.20.0 to v2.22.1, to convert your data to the after a specific time interval, you have to call add_columnstore_policy after you call CREATE TABLE

   If you are self-hosting v2.19.3 and below, create a relational table, then convert it using create_hypertable. You then enable with a call to ALTER TABLE.

To define a on a that uses integer-based time, you need to have a function to get the current time in the correct format, and set it for the . You can do this with the set_integer_now_func function. It can be defined as a regular function, but needs to be STABLE, take no arguments, and return an integer value of the same type as the time column in the table. When you have set up the time-handling, you can create the .

1. Set up a function to convert the time to the Unix epoch

   CREATE FUNCTION current_epoch() RETURNS BIGINT
   LANGUAGE SQL STABLE AS $$
   SELECT EXTRACT(EPOCH FROM CURRENT_TIMESTAMP)::bigint;$$;
   
   SELECT set_integer_now_func('devices', 'current_epoch');
   

2. Create the for the devices table

   CREATE MATERIALIZED VIEW devices_summary
   WITH (timescaledb.continuous) AS
   SELECT time_bucket('500', time) AS bucket,
      avg(cpu_usage) AS avg_cpu,
      avg(disk_usage) AS avg_disk
   FROM devices
   GROUP BY bucket;
   

3. Insert some rows into the table

   CREATE EXTENSION tablefunc;
   
   INSERT INTO devices(time, cpu_usage, disk_usage)
   SELECT time,
      normal_rand(1,70,10) AS cpu_usage,
     normal_rand(1,2,1) * (row_number() over()) AS disk_usage
   FROM generate_series(1,10000) AS time;
   

   This command uses the tablefunc extension to generate a normal distribution, and uses the row_number function to turn it into a cumulative sequence.

4. Check that the view contains the correct data

   postgres=# SELECT * FROM devices_summary ORDER BY bucket LIMIT 10;
   bucket |       avg_cpu       |       avg_disk
   --------+---------------------+----------------------
        0 | 63.0000000000000000 |   6.0000000000000000
        5 | 69.8000000000000000 |   9.6000000000000000
       10 | 70.8000000000000000 |  24.0000000000000000
       15 | 75.8000000000000000 |  37.6000000000000000
       20 | 71.6000000000000000 |  26.8000000000000000
       25 | 67.6000000000000000 |  56.0000000000000000
       30 | 68.8000000000000000 |  90.2000000000000000
       35 | 71.6000000000000000 |  88.8000000000000000
       40 | 66.4000000000000000 |  81.2000000000000000
       45 | 68.2000000000000000 | 106.0000000000000000
   (10 rows)