How to convert the subsequent MySQL schema into CouchDB
Messi boots f50 if each shoe is a contract, Employing a date_in and date_out, Then an reduce function will +1 if the date_out is null, And also +0(No convert) If date_out just isn’t null. That gives the total count of shoes in the warehouse.
To calculate the average time, Each shoe, You know the time in the factory. So the reduce function simply gathers the average. Since reduce will work must be commutative and associative, You use unique average algorithm. The simplest way is to reduce to a[Payment, Count up] Wide variety, Where sum is an accumulator in messi boots f50 them all messi boots f50 for all shoes, And count is a counter for function shoes counted. Then your customer simply divides sum / adidas f50 adizero count to compute the final average.
I think you could combine both of these into one big messi boots f50 reduce in order for you, Perhaps accumulating a dakuohaozuo”Shoes adidas f50 adizero in manufacturing facility, 1, “Average time in facility, [253, 15]dakuohaoyou dakuohaozuotype ofshugangsort ofshugangform ofshugangstyle ofshugangtypes ofshugangsorts ofshugangtypeshugangparticularshugangsomewhatshugangvariety ofshugangquiteshugangkindshugangkindashugangsortshugangmore or lessshugangmethod ofshugangformshugangreasonablyshugangrathershugangversion ofshugangvarietyshugangmore than a littleshugangprettyshugangjust a littleshugangkinds ofshugangalmostshugangamount ofshugangmodel ofshugangway ofshugangfairlyshugangstyleshuganga bitshugangroughlyshugangverydakuohaoyou object.
Suffice to say, Whenever you can accept two different views for this data, Then there might be a shortcut for the average. To the map, Produce(Zero, Season) Where time it’s time spent in the warehouse. In the decline, Set the overall reduce value to _stats(See Builtin reduce elements). The view output will be an item with the sum and count already computed messi boots f50 adidas f50 adizero.