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Are you stuck in the 90s?

If you are only using the SQL-92 language reference, then you are overlooking so many great features like:

Some test data

In my previous article I imported some CSV Dropwizard metrics into PostgreSQL for further analysis.

Our time series data consists of a total request count and a data recording time stamp:

count t 151 1398778568 169 1398778583 169 1398778598 169 1398778613 169 1398778628 169 1398778643 1587 1398778658 6473 1398778673 11985 1398778688

Arrival velocity

I want to calculate the arrival velocity which can be defined as:

λ = arrival_velocity = Δcount / Δt

For each time event, we need to subtract the current and previous count and timestamp values.

Window functions allow us to aggregate/reference previous/next rows without restricting the SELECT clause to a single result row:

SELECT t as "Current time stamp", prev_t as "Previous time stamp", current_count as "Current total request count", prev_count as "Previous total request count", ROUND( ((current_count - prev_count)::numeric/ (t - prev_t)::numeric), 3 ) as "Velocity [req/sec]" FROM ( SELECT t, lag(t, 1) over () as prev_t, count as current_count, lag(count, 1) over () as prev_count FROM connection_lease_millis WINDOW grouping AS ( ORDER BY t ROWS BETWEEN 1 PRECEDING AND CURRENT ROW ) ) raw_data

Giving us the arrival velocity:

Current time stamp Previous time stamp Current total request count Previous total request count Velocity [req/sec] 1398778568 151 1398778583 1398778568 169 151 1.200 1398778598 1398778583 169 169 0.000 1398778613 1398778598 169 169 0.000 1398778628 1398778613 169 169 0.000 1398778643 1398778628 169 169 0.000 1398778658 1398778643 1587 169 94.533 1398778673 1398778658 6473 1587 325.733 1398778688 1398778673 11985 6473 367.467

Arrival acceleration

But what if we want to calculate the arrival acceleration (e.g. so we can figure out how the arrival rate fluctuates), which is

arrival_acceleration = Δarrival_velocity/ Δt

This is how we can do it:

SELECT t as "Current time stamp", prev_t as "Previous time stamp", velocity "Velocity [Req/sec]", ROUND( (velocity - lag(velocity, 1) over ())::numeric / (t - prev_t)::numeric, 3 ) as "Acceleration [req/sec2]" FROM ( SELECT t, prev_t, current_count, prev_count, ROUND( ((current_count - prev_count)::numeric/ (t - prev_t)::numeric), 3 ) as velocity FROM ( SELECT t, lag(t, 1) over () as prev_t, count as current_count, lag(count, 1) over () as prev_count FROM connection_lease_millis WINDOW grouping AS ( ORDER BY t ROWS BETWEEN 1 PRECEDING AND CURRENT ROW ) ) raw_data ) velocity_data

Giving us:

Current time stamp Previous time stamp Velocity [Req/sec] Acceleration [req/sec2] 1398778568 1398778583 1398778568 1.200 1398778598 1398778583 0.000 -0.080 1398778613 1398778598 0.000 0.000 1398778628 1398778613 0.000 0.000 1398778643 1398778628 0.000 0.000 1398778658 1398778643 94.533 6.302 1398778673 1398778658 325.733 15.413 1398778688 1398778673 367.467 2.782

Giving us a nice overview over the arrival rate distribution:

Conclusion

SQL has more to offer than the standard aggregate functions. The window functions allow you to group rows while still retaining the selection criteria.

How many of you are still using the 1.0 versions of Java, C# or Python? Shouldn’t we benefit from the latest SQL features the same way we do with any other programming language we use on a daily basis?

In case you’re still skeptic, this great article may shatter your doubts.

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