Searching by time
The underlying data has a 15 minute granularity. Aggregating the data into higher time intervals is achieved using Time Functions. Here we are using
toYear(datetime) and the
count() function to count the number of events by year.
SELECT toYear(timestamp) AS year, count() FROM sample.gdelt WHERE (timestamp BETWEEN '2015-01-01 00:00:00' AND '2020-01-01 00:00:00') GROUP BY year ORDER BY year ASC
This returns about 344 million events with 61 million in 2018 and about 51 million in 2019
2015 59,063,790 2016 97,228,713 2017 75,609,229 2018 61,544,481 2019 50,877,104
We could have partitioned by
toMonth(), toWeek(), toHour(), toQuarter() and hence derived all the extra unnecessary time fields in the GDELT data schema.
This query used absolute times, but relative time and date math is also valid. We could as easily query the last 10 years(
subtractYears(date, num)) from
SELECT count() FROM sample.gdelt WHERE (timestamp BETWEEN subtractYears(now(), 10) AND now())
Time Functions can be used in SQL
Count distinct values
Figure out how many distinct primary actors there are.
SELECT uniq(actor1_name) FROM sample.gdelt WHERE (timestamp BETWEEN '2015-01-01 00:00:00' AND '2020-01-01 00:00:00')
During this time, there were 16,955 unique identities in
Find the most frequently occuring values
Find the 10 most occuring values of
SELECT topK(10)(actor1_name) FROM sample.gdelt WHERE (timestamp BETWEEN '2015-01-01 00:00:00' AND '2020-01-01 00:00:00') ;
This query returns this list:
['UNITED STATES','\0','POLICE','UNITED KINGDOM','PRESIDENT','GOVERNMENT','CHINA','SCHOOL','RUSSIA','CANADA']
One of the most occurring values is ‘\0’ where no primary actor exists.
Find the last article classified SCHOOL & avg_tone > 1
We can easily find the last value in a filtered data set.
argMax(field, datetime) is much simpler than grouping and ordering by time desc. It also works with field tuples.
SELECT argMax((timestamp, avg_tone, source_url), timestamp) FROM sample.gdelt WHERE (timestamp BETWEEN '2018-01-01 00:00:00' AND '2020-01-01 00:00:00') AND actor1_name='SCHOOL' AND avg_tone > 1
The latest article classified as
SCHOOL and with a positive
avg_tone was an Edison State Community College conference
'2019-11-20 21:45:00', 3.6649214659685896, 'https://www.dailycall.com/news/69658/edison-state-holds-guidance-counselor-conference'
Find the average value
Calculate the average tone of events for a particular primary actor by month.
SELECT toYear(timestamp) AS year, round(avg(avg_tone),3) AS tone FROM sample.gdelt WHERE (timestamp between '2018-01-01 00:00:00' AND '2020-01-01 00:00:00') AND actor1_name='SCHOOL' GROUP BY year ORDER BY year ASC;
2018 -0.651 2019 -0.443
avg_tone has values ranging from -100 (very negative) to +100 (very positive). Even though average sentiment has gone up in events where
SCHOOL is the primary actor, these articles very slightly negative in tone. The value is greater than -1, but less than zero.
Count event types
Protests (event_root_code=‘14’) are an interesting event type to monitor over time.
SELECT toStartOfMonth(timestamp) month_year, sum(num_articles) articles FROM sample.gdelt WHERE (timestamp BETWEEN '2016-09-01 00:00:00' AND '2017-01-31 00:00:00') AND event_root_code='14' GROUP BY month_year
Take for example the 2016 US election time frame. There is a marked jump in the number of protest around election time.
2016-09-01 295,535 2016-10-01 282,903 2016-11-01 369,609 2016-12-01 730,917 2017-01-01 309,717
Find the top source_url domains by year
Each event has a source_url. We can use url functions to easily extract root domains
SELECT toYear(timestamp) as year, topK(10)(domain(source_url)) FROM sample.gdelt WHERE (timestamp BETWEEN '2018-01-01 00:00:00' AND '2020-01-01 00:00:00') GROUP BY year
We have extracted the root domain from the full url at query time, allowing us to trend news outlets over time.
2018 ['www.msn.com','www.dailymail.co.uk','www.business-standard.com','allafrica.com','www.xinhuanet.com','www.thenews.com.pk','www.yahoo.com','www.4-traders.com','www.nbcnews.com','www.sfgate.com'] 2019 ['www.dailymail.co.uk','www.msn.com','www.business-standard.com','news.yahoo.com','allafrica.com','menafn.com','www.thenews.com.pk','www.nbcnews.com','www.reuters.com','www.xinhuanet.com']