partitioning

Let's say I have a list of [1,2,3,4] I want to produce all subsets of this set which covers all members once, the result should has 15 lists which the order isn't important, instead t provides all possible subgroups: >>>>[[1,2,3,4]] [[1][2][3][4]] [[1,2],[3][4]] [[1,2],[3,4]] [[1][2],[3,4]] [[1,3],[2][4]] [[1,3],[2,4]] [[1][3],[2,4]] [[1],[2,3][4]] [[1,4],[2,3]] [[1][2,3,4]] [[2][1,3,4]] [[3][1,2,4]] [[4][1,2,3]] This is a set partitioning problem or partitions of a set which is discussed here , but the response made me confused as it just suggests recalling permutations, but I don't know how!

1- I'm trying to delete multiple partitions at once, but struggling to do it with either Impala or Hive. I tried the following query, with and without ' : ALTER TABLE cz_prd_corrti_st.s1mme_transstats_info DROP IF EXISTS PARTITION (pr_load_time='20170701000317') PARTITION (pr_load_time='20170701000831') The error I'm getting is as follow: AnalysisException: Syntax error in line 3: PARTITION (pr_load_time='20170701000831') ^ Encountered: PARTITION Expected: CACHED, LOCATION, PURGE, SET, UNCACHED CAUSED BY: Exception: Syntax error The partition column is bigint type, query for deleting only one

I generate KML files which may have 50,000 placemarks or more, arranged in Folders based on a domain-specific grouping. The KML file uses custom images which are packed in to a KMZ file. I'm looking to breakup the single KML file in to multiple files, partitioned based on the grouping, so rather than having 1 large document with folders, i'd have a root/index KML file with folders linking to the smaller KML files. Is this possible though? I think that a KMZ file can contain only 1 KML file, regardless of where it's located or its name, in the zip. Furthermore, I'm not exactly sure how a KML

I have a table with huge data( say millions of records, its just a case study though!) of 5 years, with a partition for each year. Now i would want to retain the last 2 years data, and transfer the rest of the 3 year data to a new table called archive? What would be the Ideal method, with minimal down time and high performance? alter table exchange partition is the answer. This command exange the segment of a partition with the segment of a table. It is at light speed because it does only some reference interchages. So, you need some temp tables, because AFAIK you can't exchange them directly.

I was reading the Dutch national flag problem , but couldn't understand what the low and high arguments are in the threeWayPartition function in the C++ implementation. If I assume them as min and max elements of the array to be sorted, then the if and else if statements doesn't makes any sense since (data[i] < low) and (data[i] > high) always returns zero. Where am I wrong? low and high are the values you have defined to do the three-way partition i.e. to do a three-way partition you only need two values: [bottom] <= low < [middle] < high <= [top] In the C++ program what you are moving are

I'm wondering if there is an "optimal" solution for this problem: I have a n x m (pixel) sized space with p preexisting rectangled - objects in various sizes on it. Now I want to place q (same sized) new objects in this space without any overlapping. The algorithm I came up with: Create array A[][] with the size [(n)/(size_of_object_from_q)]x[(n)/(size_of_object_from_q)] Iterate all Elements from p and for each: mark all fields in A[][] as occupied, where the element "lies" Place all elements from q in the according places where the fields in A[][] are not marked (Boy, I hope I could make that

From the question " Is partitioning easier than sorting? ": Suppose I have a list of items and an equivalence relation on them, and comparing two items takes constant time. I want to return a partition of the items, e.g. a list of linked lists, each containing all equivalent items. One way of doing this is to extend the equivalence to an ordering on the items and order them (with a sorting algorithm); then all equivalent items will be adjacent. (Keep in mind the distinction between equality and equivalence .) Clearly the equivalence relation must be considered when designing the ordering

I have a spark-streaming app which looks like this: val message = KafkaUtils.createStream(...).map(_._2) message.foreachRDD( rdd => { if (!rdd.isEmpty){ val kafkaDF = sqlContext.read.json(rdd) kafkaDF.foreachPartition( i =>{ createConnection() i.foreach( row =>{ connection.sendToTable() } ) closeConnection() } ) And, I run it on a yarn cluster using spark-submit --master yarn-cluster --num-executors 3 --driver-memory 2g --executor-memory 2g --executor-cores 5.... When I try to log kafkaDF.rdd.partitions.size , the result turns out be '1' or '5' mostly. I am confused, is it possible to control

have an application in which many companies post information. the data from each company is self contained - there is no data overlap. performance-wise, is it better to: keep the company ID on each row of each table and have each index use it? partition each table according to the company ID partition and create a user to access each company to ensure security create multiple databases, one for each company web-based application with persistent connections. my thoughts: new pg connections are expensive, so a single database creates less new connections having only one copy of the dictionary