Functions that depend on a local timezone setting inside a continuous aggregate 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.

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

  1. At the psqlprompt, create the view and declare the timezone:

    WITH (timescaledb.continuous)
    time_bucket('1 hour', observation_time) AS bucket,
    min(observation_time AT TIME ZONE 'EST') AS min_time,
    avg(metric) AS metric_avg,
    max(metric) - min(metric) AS metric_spread
    GROUP BY bucket, device_id;
  2. Alternatively, you can cast to a timestamp after the view using SELECT:

    SELECT min_time::timestamp FROM device_summary;

Date and time is usually expressed as year-month-day and hours:minutes:seconds. Most Timescale 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 hypertable called devices that contains CPU and disk usage information. The devices measure time using the Unix epoch.

To create a hypertable 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. At the psql prompt, create a table and define the integer-based time column:

    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)
  2. Define the chunk time interval:

    SELECT create_hypertable('devices', by_range('time', 10));

To define a continuous aggregate on a hypertable 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 hypertable. You can do this with the set_integer_now_func function. It can be defined as a regular PostgreSQL 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 continuous aggregate.

  1. At the psql prompt, set up a function to convert the time to the Unix epoch:

    SELECT set_integer_now_func('devices', 'current_epoch');
  2. Create the continuous aggregate 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)


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