If you're grouping by time in BigQuery and don't want gaps in your data, you need to generate a series of time values. In the example below, we're generating a series of timestamps that increment by the hour. In these examples, the data type of the schema is a
First we generate our series with no gaps:
select * from UNNEST(GENERATE_TIMESTAMP_ARRAY('2015-10-01', '2015-10-03', INTERVAL 1 HOUR)) AS hour
| hour | |-------------------------| | 2015-10-01 00:00:00 UTC | | 2015-10-01 01:00:00 UTC | | 2015-10-01 02:00:00 UTC | | 2015-10-01 03:00:00 UTC | | 2015-10-01 04:00:00 UTC | | 2015-10-01 05:00:00 UTC | ...
Note: BigQuery allows you to generate ~1 million intervals with this method. If your schema is
datetime, simply use
Next use a common table expression to create a table that contains the gapless series we created above. We'll call it
hours 🧐. We can now
left join any table with “gappy” time series data to our gapless
with hours as ( select * from UNNEST(GENERATE_TIMESTAMP_ARRAY('2015-10-01', '2015-10-03', INTERVAL 1 HOUR)) AS hour ) select hours.hour, COUNT(id) from hours left join `bigquery-public-data.hacker_news.comments` on timestamp_trunc(`bigquery-public-data.hacker_news.comments`.time_ts,hour) = hours.hour group by 1 order by 1;
We're using a BigQuery public dataset on Hacker News in our example above, so you can follow along. It only takes 5 minutes to get started with BigQuery in PopSQL.
Not only does PopSQL allow you to share queries with your teammates, but it autogenerates charts to save you even more time. Here's the results from the query above:
Isn't exploring BigQuery exhilarating? 🎢