compact_state_agg() functions

ToolkitTimescaleDB Toolkit functions are available under Timescale Community Edition. They are automatically included with Timescale, but must be installed separately for self-hosted TimescaleDB. Click to learn more.

Given a system or value that switches between discrete states, aggregate the amount of time spent in each state. For example, you can use the compact_state_agg functions to track how much time a system spends in error, running, or starting states.

compact_state_agg is designed to work with a relatively small number of states. It might not perform well on datasets where states are mostly distinct between rows.

If you need to track when each state is entered and exited, use the state_agg functions. If you need to track the liveness of a system based on a heartbeat signal, consider using the heartbeat_agg functions.

Related hyperfunction groups

This group of functions uses the two-step aggregation pattern.

Rather than calculating the final result in one step, you first create an intermediate aggregate by using the aggregate function.

Then, use any of the accessors on the intermediate aggregate to calculate a final result. You can also roll up multiple intermediate aggregates with the rollup functions.

The two-step aggregation pattern has several advantages:

More efficient because multiple accessors can reuse the same aggregate
Easier to reason about performance, because aggregation is separate from final computation
Easier to understand when calculations can be rolled up into larger intervals, especially in window functions and continuous aggregates
Can perform retrospective analysis even when underlying data is dropped, because the intermediate aggregate stores extra information not available in the final result

To learn more, see the blog post on two-step aggregates.

Functions in this group

Warning

This function group includes some experimental functions. Experimental functions might change or be removed in future releases. We do not recommend using them in production. Experimental functions are marked with an Experimental tag.

compact_state_agg: ExperimentalAggregate state data into a state aggregate for further analysis

duration_in: ExperimentalCalculate the total time spent in a given state from a state aggregate
interpolated_duration_in: ExperimentalCalculate the total time spent in a given state from a state aggregate, interpolating values at time bucket boundaries
into_values: ExperimentalExpand a state aggregate into a set of rows displaying the duration of each state

rollup: ExperimentalCombine multiple state aggregates

Function details

compact_state_agg(
  ts TIMESTAMPTZ,
  value {TEXT | BIGINT}
) RETURNS StateAgg

Aggregate a dataset containing state data into a state aggregate to track the time spent in each state.

Required arguments

Name	Type	Description
`ts`	`TIMESTAMPTZ`	Timestamps associated with each state reading
`value`	`TEXT`, `BIGINT`	The state at that time

Returns

Column	Type	Description
`agg`	`StateAgg`	An object storing the total time spent in each state

Examples

Create a state aggregate to track the status of some devices:


SELECT toolkit_experimental.compact_state_agg(time, status) FROM devices;

duration_in(
  agg StateAgg,
  state {TEXT | BIGINT}
  [, start TIMESTAMPTZ]
  [, interval INTERVAL]
) RETURNS INTERVAL

Given a state aggregate, calculate the total time spent in the given state. If you need to interpolate missing values across time bucket boundaries, use interpolated_duration_in.

Required arguments

Name	Type	Description
`agg`	`StateAgg`	A state aggregate created with `compact_state_agg`
`state`	`TEXT`, `BIGINT`	The state to query

Optional arguments

Name	Type	Description
`start`	`TIMESTAMPTZ`	If specified, only the time in the state after this time is returned.
`interval`	`INTERVAL`	If specified, only the time in the state from the start time to the end of the interval is returned.

Returns

Column	Type	Description
`duration_in`	`INTERVAL`	The time spent in the given state. Displayed in `days`, `hh:mm:ss`, or a combination of the two.

Examples

Create a test table that tracks when a system switches between starting, running, and error states. Query the table for the time spent in the running state.

If you prefer to see the result in seconds, EXTRACT the epoch from the returned result:


SET timezone TO 'UTC';
CREATE TABLE states(time TIMESTAMPTZ, state TEXT);
INSERT INTO states VALUES
  ('1-1-2020 10:00', 'starting'),
  ('1-1-2020 10:30', 'running'),
  ('1-3-2020 16:00', 'error'),
  ('1-3-2020 18:30', 'starting'),
  ('1-3-2020 19:30', 'running'),
  ('1-5-2020 12:00', 'stopping');

SELECT toolkit_experimental.duration_in(
  toolkit_experimental.compact_state_agg(time, state),
  'running'
) FROM states;


duration_in  
---------------
3 days 22:00:00

interpolated_duration_in(
  agg StateAgg,
  state {TEXT | BIGINT},
  start TIMESTAMPTZ,
  interval INTERVAL
  [, prev StateAgg]
) RETURNS DOUBLE PRECISION

Calculate the total duration in the given state. Unlike duration_in, you can use this function across multiple state aggregates that cover multiple time buckets. Any missing values at the time bucket boundaries are interpolated from adjacent state aggregates.

Required arguments

Name	Type	Description
`agg`	`StateAgg`	A state aggregate created with `compact_state_agg`
`state`	`TEXT`, `BIGINT`	The state to query
`start`	`TIMESTAMPTZ`	The start of the interval to be calculated
`interval`	`INTERVAL`	The length of the interval to be calculated

Optional arguments

Name	Type	Description
`prev`	`StateAgg`	The state aggregate from the prior interval, used to interpolate the value at `start`. If `NULL`, the first timestamp in `aggregate` is used as the start of the interval.

Returns

Column	Type	Description
`interpolated_duration_in`	`INTERVAL`	The total time spent in the queried state. Displayed as `days`, `hh:mm:ss`, or a combination of the two.

Examples

Create a test table that tracks when a system switches between starting, running, and error states. Query the table for the time spent in the running state. Use LAG and LEAD to get the neighboring aggregates for interpolation. If you prefer to see the result in seconds, EXTRACT the epoch from the returned result:


SELECT 
  time,
  toolkit_experimental.interpolated_duration_in(
    agg,
    'running',
    time,
    '1 day',
    LAG(agg) OVER (ORDER BY time)
) FROM (
  SELECT
    time_bucket('1 day', time) as time,
    toolkit_experimental.compact_state_agg(time, state) as agg
  FROM
    states
  GROUP BY time_bucket('1 day', time)
) s;


time                    | interpolated_duration_in 
------------------------+--------------------------
2020-01-01 00:00:00+00  | 13:30:00
2020-01-02 00:00:00+00  | 16:00:00
2020-01-03 00:00:00+00  | 04:30:00
2020-01-04 00:00:00+00  | 12:00:00

into_values(
  agg StateAgg
) RETURNS (TEXT, INTERVAL)

into_int_values(
  agg StateAgg
) RETURNS (INT, INTERVAL)

Unpack the state aggregate into a set of rows with two columns, displaying the duration of each state. By default, the columns are named state and duration. You can rename them using the same method as renaming a table.

Required arguments

Name	Type	Description
`agg`	`StateAgg`	A state aggregate created with `compact_state_agg`

Returns

Column	Type	Description
`state`	`TEXT`, `BIGINT`	A state found in the state aggregate
`duration`	`INTERVAL`	The total time spent in that state

Examples

Create a state aggregate from the table states_test. The time column is named time, and the state column contains text values corresponding to different states of a system. Use into_values to display the data from the state aggregate:


SELECT state, duration FROM toolkit_experimental.into_values(
  (SELECT toolkit_experimental.compact_state_agg(time, state) FROM states_test)
);


state | duration
------+----------
ERROR | 00:00:03
OK    | 00:01:46
START | 00:00:11

rollup(
    agg StateAgg
) RETURNS StateAgg

Combine multiple state aggregates into a single state aggregate. For example, you can use rollup to combine state aggregates from 15-minute buckets into daily buckets.

Required arguments

Name	Type	Description
`agg`	`StateAgg`	State aggregates created using `compact_state_agg`

Returns

Column	Type	Description
`agg`	`StateAgg`	A new state aggregate that combines the input state aggregates

Examples

Combine multiple state aggregates and calculate the duration spent in the START state:


WITH buckets AS (SELECT
    time_bucket('1 minute', ts) as dt,
    toolkit_experimental.compact_state_agg(ts, state) AS sa
FROM states_test
GROUP BY time_bucket('1 minute', ts))
SELECT toolkit_experimental.duration_in(
    'START',
    toolkit_experimental.rollup(buckets.sa)
)
FROM buckets;

compact_state_agg() functions

Related hyperfunction groups

Two-step aggregation

Functions in this group

Warning

Aggregate

Accessor

Rollup

Function details