Temporal filters

Temporal filters will enable you to filter data based on a particular time, such as the current time, a specific date, or a range of dates. By using temporal filters, you can ensure that your queries only return data relevant to the period you are interested in, making your data analysis more accurate and efficient. The records that become out of the time range defined by the temporal filter will be deleted from the storage and the storage space will be reclaimed.

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Syntax

A temporal filter is a filter condition with a NOW() function call. It can only be used in the WHERE and HAVING clauses in the query.

A valid temporal filter comprises the following components:

• A comparison operator, among <, >, <=, >=, = and BETWEEN.
• A time expression of the columns in the base relation as one comparing side.
• A non-decreasing time expression with exactly one NOW() function call as the other comparing side.

-- Valid
t > NOW() - INTERVAL '1 hour'
t <= NOW() - INTERVAL '1 hour' -- To delay event handling by 1 hour
t < DATE_TRUNC('hour', NOW()) - INTERVAL '1 hour'

There could be multiple temporal filters and other expressions in the WHERE clause conjoined with the AND operator.

-- Valid
t > NOW() - INTERVAL '1 hour' AND t < NOW() + INTERVAL '1 hour' AND a < 1

A temporal filter condition cannot be disjoined with another temporal filter using the OR operator. But it is allowed to be disjoined with another normal filter. See the examples below:

-- Valid
t > NOW() - INTERVAL '1 hour' OR t IS NULL OR a < 1

-- Invalid
t > NOW() - INTERVAL '1 hour' OR t < NOW() - INTERVAL '1 hour'

-- Invalid
(a < 1) OR (t > NOW() - INTERVAL '1 hour' AND t < NOW() - INTERVAL '1')

Also, in the WHERE clause, each expression conjoined by the AND operator should have only one temporal filter disjoined with the OR operator.

-- Invalid
(t < NOW() - INTERVAL '1 hour' OR t > NOW() OR a < 1)
AND (t < NOW() - INTERVAL '1 hour' OR a < 1)

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Usage 1: Delete and clean expired data

When the time expression with NOW() is the lower bound condition of the base relation, such as t > NOW() - INTERVAL '1 hour', it can filter records with event times that are too old. In RisingWave, the source will pull data from upstream only after some materialized views (MVs) are created and their definitions include this source. The source itself does not store any records. Therefore, with the temporal filter, we can easily limit the total storage space.

The following query returns all rows from the sales_source sources where the sale_date column plus one week is greater than the current date and time. In other words, it will return all sales records within the past week.

CREATE SOURCE sales_source(...) with (connector = 'kafka', ...) FORMAT PLAIN ENCODE JSON;

CREATE MATERIALIZED VIEW sales AS
SELECT *
FROM sales_source
WHERE sale_date > NOW() - INTERVAL '1 week';

The temporal filter in this query is sale_date > NOW() - INTERVAL '1 week'. It filters the rows based on the sale_date column and checks if it is within one week of the current time or NOW().

The following query returns all rows from the user_sessions table where the sum of the last_active timestamp and double the session_timeout duration is greater than the current timestamp, indicating active user sessions. This query could be used to clean up old user sessions from the database by deleting any rows that no longer satisfy the condition.

CREATE SOURCE user_sessions_source(...) with (connector = 'kafka', ...) FORMAT PLAIN ENCODE JSON;

CREATE MATERIALIZED VIEW user_sessions AS
SELECT *
FROM user_sessions_source
WHERE last_active + session_timeout * 2 > NOW();

The temporal filter in this query is in the WHERE clause. It checks whether the timestamp of the last activity plus twice the session timeout is greater than the current time or NOW(). This indicates that the session is still active.

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Usage 2: Delay table changes

When the time expression with NOW() is the upper bound condition of the base relation such as ts + interval '1 hour' < now(), it can “delay” the table’s changes of the input relation. It could be useful when used with the Temporal Join.

Here is a typical example of the temporal join used to widen a fact table.

  CREATE SOURCE fact(id1 INT, a1 INT, p_time TIMESTAMPTZ AS proctime()) WITH (connector = 'kafka', ...);
  CREATE TABLE dimension(id2 INT, a2 INT, PRIMARY KEY (id2)) WITH (connector = 'jdbc', ...);
  CREATE MATERIALIZED VIEW mv AS SELECT id1, a1, a2 FROM fact LEFT JOIN dimension FOR SYSTEM_TIME AS OF PROCTIME() ON id1 = id2;

However, due to delays caused by the network or other phases, it is not guaranteed that when the record of the fact arrives, the corresponding record in the dimension table has arrived. Therefore, a temporal filter can be set on the fact source to introduce a delay and wait for the dimension table’s changes.

  CREATE MATERIALIZED VIEW mv AS
  SELECT
    id1, a1, a2
  FROM (
    -- Delay the source for 5 seconds
    SELECT * FROM fact WHERE fact.p_time + INTERVAL '5' SECOND < NOW()
  ) fact
  LEFT JOIN dimension FOR SYSTEM_TIME AS OF PROCTIME() ON id1 = id2;