partitioning

问题 What I have been trying to get my head around is to perform some kind of partitioning(split by predicate) in a mongo query. My current query looks like: db.posts.aggregate([ {"$match": { $and:[ {$or:[{"toggled":false},{"toggled":true, "status":"INACTIVE"}]} , {"updatedAt":{$gte:1549786260000}} ] }}, {"$unwind" :"$interests"}, {"$group" : {"_id": {"iid": "$interests", "pid":"$publisher"}, "count": {"$sum" : 1}}}, {"$project":{ _id: 0, "iid": "$_id.iid", "pid": "$_id.pid", "count": 1 }} ]) This

问题 I've created table with number like this: How to find gaps of data and insert NULL data points instead having gap ;WITH Pass0 as (select 1 as C union all select 1), --2 rows Pass1 as (select 1 as C from Pass0 as A, Pass0 as B),--4 rows Pass2 as (select 1 as C from Pass1 as A, Pass1 as B),--16 rows Pass3 as (select 1 as C from Pass2 as A, Pass2 as B),--256 rows Pass4 as (select 1 as C from Pass3 as A, Pass3 as B),--65536 rows Pass5 as (select 1 as C from Pass4 as A, Pass4 as B),--4,294,967,296

问题 I've created table with number like this: How to find gaps of data and insert NULL data points instead having gap ;WITH Pass0 as (select 1 as C union all select 1), --2 rows Pass1 as (select 1 as C from Pass0 as A, Pass0 as B),--4 rows Pass2 as (select 1 as C from Pass1 as A, Pass1 as B),--16 rows Pass3 as (select 1 as C from Pass2 as A, Pass2 as B),--256 rows Pass4 as (select 1 as C from Pass3 as A, Pass3 as B),--65536 rows Pass5 as (select 1 as C from Pass4 as A, Pass4 as B),--4,294,967,296

问题 I would like to get all the possible partitions (disjoint subsets of a set which union is the original set) of a multiset (some elements are equal and non-distinguishable from each other). Simpler case when one would like to yield the partitions of a simple set, in which there are no elements with multiplicity, in other words all elements are different. For this scenario I found this Ruby code on StackOwerflow which is very efficient, as not storing all the possible partitions, but yielding

问题 I have a MySQL database with over 20 tables, but one of them is significantly large because it collects measurement data from different sensors. It's size is around 145 GB on disk and it contains over 1 billion records. All this data is also being replicated to another MySQL server. I'd like to separate the data to smaller "shards", so my question is which of the below solutions would be better. I'd use the record's "timestamp" for dividing the data by years. Almost all SELECT queries that

问题 I have a MySQL database with over 20 tables, but one of them is significantly large because it collects measurement data from different sensors. It's size is around 145 GB on disk and it contains over 1 billion records. All this data is also being replicated to another MySQL server. I'd like to separate the data to smaller "shards", so my question is which of the below solutions would be better. I'd use the record's "timestamp" for dividing the data by years. Almost all SELECT queries that

问题 I am looking towards a set of numbers and aiming to split them into subsets via set partitioning. The deciding factor on how these subsets will be generated will be ensuring that the sum of all the elements in the subset is as close as possible to a number generated by a pre-determined distribution. The subsets need not be the same size and each element can only be in one subset. I had previously been given guidance on this problem via the greedy algorithm (Link here), but I have found that

问题 I need to find all the different intersections between two partitions of the same set. For example, if we have the following two partitions of the same set x = [[1, 2], [3, 4, 5], [6, 7, 8, 9, 10]] y = [[1, 3, 6, 7], [2, 4, 5, 8, 9, 10]] the required result is [[1], [2], [3], [4, 5], [6, 7], [8, 9, 10]]. In detail, we calculate the cartesian product between every subset of x and y, and for each of these products, we classify the elements in new subsets accordingly if they belong to the

问题 I would like to implement in Apache Flink the following scenario: Given a Kafka topic having 4 partitions, I would like to process the intra-partition data independently in Flink using different logics, depending on the event's type. In particular, suppose the input Kafka topic contains the events depicted in the previous images. Each event have a different structure: partition 1 has the field " a " as key, partition 2 has the field " b " as key, etc. In Flink I would like to apply different

问题 The problem statement is - "You are given a set of N points, where N is even and N <= 1000. You have to find the number of pairs of points, such that if you draw a line through that pair, each side of the line will contains equal number of points(N/2-1)." I can't figure out, how to solve this problem in O(n^2) or less time? Here is my brute-force solution- class Point{ public: int x, y; Point(){x = y = 0;} void make_point(int X, int Y){ x = X; y = Y; } int Point:: orientation (Point &p0,