Now a much better way to do this is to use the rdd.aggregateByKey() method. Because this method is so poorly documented in the Apache Spark with Python documentation — and is why I wrote this Q&A — until recently I had been using the above code sequence. But again, it’s less efficient, so avoid doing … Read more
One option is to put the subquery in a LEFT JOIN: select sum ( t.graduates ) – t1.summedGraduates from table as t left join ( select sum ( graduates ) summedGraduates, id from table where group_code not in (‘total’, ‘others’ ) group by id ) t1 on t.id = t1.id where t.group_code=”total” group by t1.summedGraduates … Read more
Am I misinterpreting the intent of the Repository pattern? I’m going to say “yeah”, but know that me and every person I’ve worked with has asked the same thing for the same reason… “You’re not thinking 4th dimensionally, Marty”. Let’s simplify it a little and stick with constructors instead of Create methods first: Editor e … Read more
By the looks of it, your tags is not nested. For this aggregation to work, you need it nested so that there is an association between an id and a name. Without nested the list of ids is just an array and the list of names is another array: “item”: { “properties”: { “meta”: { … Read more
As @Brian Riley already said you should either remove 1 column in your select select countrylanguage.language ,sum(country.population*countrylanguage.percentage/100) from countrylanguage join country on countrylanguage.countrycode = country.code group by countrylanguage.language order by sum(country.population*countrylanguage.percentage) desc ; or add it to your grouping select countrylanguage.language, country.code, sum(country.population*countrylanguage.percentage/100) from countrylanguage join country on countrylanguage.countrycode = country.code group by countrylanguage.language, country.code … Read more
I would focus on the example queries rather than your quote from the documentation. Aggregate calculates values for the entire queryset. Annotate calculates summary values for each item in the queryset. Aggregation >>> Book.objects.aggregate(average_price=Avg(‘price’)) {‘average_price’: 34.35} Returns a dictionary containing the average price of all books in the queryset. Annotation >>> q = Book.objects.annotate(num_authors=Count(‘authors’)) >>> … Read more
Usually you can plug a Query’s result (which is basically a table) as the FROM clause source of another query, so something like this will be written: SELECT COUNT(*), SUM(SUBQUERY.AGE) from ( SELECT availables.bookdate AS Date, DATEDIFF(now(),availables.updated_at) as Age FROM availables INNER JOIN rooms ON availables.room_id=rooms.id WHERE availables.bookdate BETWEEN ‘2009-06-25’ AND date_add(‘2009-06-25’, INTERVAL 4 DAY) … Read more