Finding the gaps in sensor-telemetry

Finding the gaps in sensor-telemetry comes up often enough to be worth a note. sensor-telemetry is a convenient thing to try it on — 8,000 rows, one row per sensor reading.

with numbered as (
  select site, reading_at,
         row_number() over (partition by site order by reading_at) as rn
  from sensor_telemetry
)
select site,
       min(reading_at) as island_start,
       max(reading_at) as island_end,
       count(*)   as readings
from (
  select *, date_trunc('day', reading_at) - (rn * interval '1 day') as grp_key
  from numbered
) t
group by site, grp_key
order by site, island_start;

The trick is that subtracting a dense row number from a dense date gives a constant inside a run and a different constant across a gap. Once you have seen it the query is obvious; before that it looks like a magic trick.

Run it against the real file in the SQL playground — the engine there is enough of a SQL implementation to execute this as written, and the dataset is already loaded. The pattern page has the version with the failure modes spelled out.

Window functions are the difference between SQL that describes rows and SQL that describes sequences. Almost every "we exported it to pandas to do this bit" turns out to be one of these five.