--- title: Time and continuous aggregates | Tiger Data Docs description: Learn to work with timezones and continuous aggregates --- 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. ## 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 a timestamp after the view** Alternatively, you can cast to a timestamp 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 TimescaleDB databases use a [PostgreSQL date/time-type](https://www.postgresql.org/docs/current/datatype-datetime.html) 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. 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 ( tsdb.hypertable, tsdb.partition_column='time', tsdb.chunk_interval='10' ); ``` For TimescaleDB [v2.23.0](https://github.com/timescale/timescaledb/releases/tag/2.23.0) 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, TimescaleDB throws an error and asks for an explicit definition. For earlier versions, set `partition_column` to a time column. If you are self-hosting TimescaleDB [v2.20.0](https://github.com/timescale/timescaledb/releases/tag/2.23.0) to [v2.22.1](https://github.com/timescale/timescaledb/releases/tag/2.23.0), to convert your data to the columnstore after a specific time interval, you have to call [add\_columnstore\_policy](/docs/reference/timescaledb/hypercore/add_columnstore_policy/index.md) after you call [CREATE TABLE](/docs/reference/timescaledb/hypertables/create_table/index.md) If you are self-hosting TimescaleDB [v2.19.3](https://github.com/timescale/timescaledb/releases/tag/2.19.3) and below, create a [PostgreSQL relational table](https://www.postgresql.org/docs/current/sql-createtable.html), then convert it using [create\_hypertable](/docs/reference/timescaledb/hypertables/create_hypertable/index.md). You then enable hypercore with a call to [ALTER TABLE](/docs/reference/timescaledb/hypercore/alter_table/index.md). 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`](/docs/reference/timescaledb/hypertables/set_integer_now_func/index.md) function. It can be defined as a regular PostgreSQL function, but needs to be [`STABLE`](https://www.postgresql.org/docs/current/sql-createfunction.html), 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. **Set up the time conversion function** ``` 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 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** ``` 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 the view 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) ``` ## Learn more - [Understand continuous aggregates](/docs/learn/continuous-aggregates/index.md): How continuous aggregates work, JOINs, and function support. - [Create a continuous aggregate](/docs/build/continuous-aggregates/create-a-continuous-aggregate/index.md): Step-by-step guide to creating and querying a continuous aggregate. - [Refresh continuous aggregates](/docs/build/continuous-aggregates/refresh-policies/index.md): Set up automatic refresh policies. - [Understand time buckets](/docs/learn/data-lifecycle/time-buckets/about-time-buckets/index.md): How time bucket origins and timezones work. - [`CREATE MATERIALIZED VIEW` reference](/docs/reference/timescaledb/continuous-aggregates/create_materialized_view/index.md): Full API reference.