Since Continuous Views are continuously and incrementally updated over time, PipelineDB has the capability to consider the current time when updating the result of a continuous view. Queries that include a
WHERE clause with a temporal component relating to the current time are called sliding-window queries. The set of events that a sliding
WHERE clause filters or accepts perpetually changes over time.
There are two important components of a sliding
clock_timestamp ( )
A built-in function that always returns the current timestamp.
A special attribute of all incoming events containing the time at which PipelineDB received them, as described in Arrival Ordering.
However, it is not necessary to explicitly add a
WHERE clause referencing these values. PipelineDB does this internally and it is only necessary to specify
sw storage parameter in a continuous view’s definition.
These concepts are probably best illustrated by an example.
Even though sliding windows are a new concept for a SQL database, PipelineDB does not use any sort of new or proprietary windowing syntax. Instead, PipelineDB uses standard PostgreSQL 9.5 syntax. Here’s a simple example:
What users have I seen in the last minute?
CREATE VIEW recent_users WITH (sw = '1 minute') AS SELECT user_id::integer FROM stream;
Internally, PipelineDB will rewrite this query to the following:
CREATE VIEW recent_users AS SELECT user_id::integer FROM stream WHERE (arrival_timestamp > clock_timestamp() - interval '1 minute');
PipelineDB allows users to manually construct a sliding window
WHERE clause when defining sliding-window continuous views, although it is recommended that
sw be used in order to avoid tedium.
The result of a
SELECT on this continuous view would only contain the specific users seen within the last minute. That is, repeated
SELECT s would contain different rows, even if the continuous view wasn’t explicitly updated.
Let’s break down what’s going on with the
(arrival_timestamp > clock_timestamp() - interval '1 minute') predicate.
clock_timestamp() - interval '1 minute' is evaluated, it will return a timestamp corresponding to 1 minute in the past. Adding in
> means that this predicate will evaluate to
true if the
arrival_timestamp for a given event is greater than 1 minute in the past. Since the predicate is evaluated every time a new event is read, this effectively gives us a sliding window that is 1 minute width.
PipelineDB exposes the
current_timestamp values to use within queries, but by design these don’t work with sliding-window queries because they remain constant within a transaction and thus don’t necessarily represent the current moment in time.
Sliding-window queries also work with aggregate functions. Sliding aggregates work by aggregating their inputs as much as possible, but without losing the granularity needed to know how to remove information from the window as time progresses. This partial aggregatation is all transparent to the user–only fully aggregated results will be visible within sliding-window aggregates.
Let’s look at a few examples:
How many users have I seen in the last minute?
CREATE VIEW count_recent_users WITH (sw = '1 minute') AS SELECT COUNT(*) FROM stream;
Each time a
SELECT is run on this continuous view, the count it returns will be the count of only the events seen within the last minute. For example, if events stopped coming in, the count would decrease each time a
SELECT was run on the continuous view. This behavior works for all of the Continuous Aggregates that PipelineDB supports:
What is the 5-minute moving average temperature of my sensors?
CREATE VIEW sensor_temps WITH (sw = '5 minutes') AS SELECT sensor::integer, AVG(temp::numeric) FROM sensor_stream GROUP BY sensor;
How many unique users have we seen over the last 30 days?
CREATE VIEW uniques WITH (sw = '30 days') AS SELECT COUNT(DISTINCT user::integer) FROM user_stream;
What is my server’s 99th precentile response latency over the last 5 minutes?
CREATE VIEW latency WITH (sw = '5 minutes') AS SELECT server_id::integer, percentile_cont(0.99) WITHIN GROUP (ORDER BY latency::numeric) FROM server_stream GROUP BY server_id;
Obviously, sliding-window rows in continuous views become invalid after a certain amount of time because they’ve become too old to ever be included in a continuous view’s result. Such rows must thus be garbage collected, which can happen in two ways:
A background process similar to PostgreSQL’s autovacuumer periodically runs and physically removes any expired rows from sliding-window continuous views.
When a continuous view is read with a
SELECT, any data that are too old to be included in the result are discarded on the fly while generating the result. This ensures that even if invalid rows still exist, they aren’t actually included in any query results.
Internally, the materialization tables backing sliding-window queries are aggregated as much as possible. However, rows can’t be aggregated down to the same level of granularity as the query’s final output because data must be removed from aggregate results when it goes out of window.
For example, a sliding-window query that aggregates by hour may actually have minute-level aggregate data on disk so that only the last 60 minutes are included in the final aggregate result returned to readers. These internal, more granular aggregate levels for sliding-window queries are called “steps”. An “overlay” view is placed over these step aggregates in order to perform the final aggregation at read time.
You have probably noticed at this point that step aggregates can be a significant factor in determining sliding-window query read performance, because each final sliding-window aggregate group will internally be composed of a number of steps. The number of steps that each sliding-window aggregate group will have is tunable via the step_factor parameter:
An integer between 1 and 50 that specifices the size of a sliding-window step as a percentage of window size given by sw. A smaller step_factor will provide more granularity in terms of when data goes out of window, at the cost of larger on-disk materialization table size. A larger step_factor will reduce on-disk materialization table size at the expense of less out-of-window granularity.
Here’s an example of using step_factor in conjunction with sw to aggregate over an hour with a step size of 30 minutes:
CREATE VIEW hourly (WITH sw = '1 hour', step_factor = 50) AS SELECT COUNT(*) FROM stream;
Now that you know how sliding-window queries work, it’s probably a good time to learn about Continuous JOINs.