partitioning | 易学教程

3 way quicksort (C implementation)

阅读更多关于 3 way quicksort (C implementation)

问题 I try to implement some of the algorithms pure generic using C. I stick with the 3-way quicksort but somehow the implementation does not give correct output. The output nearly sorted but some keys aren't where it should be. The code is below. Thanks in advance. #include <stdio.h> #include <stdlib.h> #include <string.h> #include <time.h> static void swap(void *x, void *y, size_t size) { void *tmp = malloc(size); memcpy(tmp, x, size); memcpy(x, y, size); memcpy(y, tmp, size); free(tmp); }

Number of distinct prime partitions [duplicate]

阅读更多关于 Number of distinct prime partitions [duplicate]

问题 This question already has answers here : Closed 6 years ago . Possible Duplicate: A number as it’s prime number parts I have this homework assignment of mine, hard as hell, where I have to get all the distinct prime partitions of a given number. For example, number 7 has five different prime partitions (or five different ways of representing the 2 prime partitions it has): 5 + 2 2 + 5 3 + 2 + 2 2 + 3 + 2 2 + 2 + 3 As you can see, the number itself is excluded in the case it's a prime. I don't

Resize MTD partitions at runtime

阅读更多关于 Resize MTD partitions at runtime

问题 I am working with embedded devices and would like to enable them to resize their MTD partitions via Linux without rebooting. The problem is that my Linux image size has increased and the current MTD partition (mtd0) in which it resides is now too small. However, the partition right after it (mtd1) is a JFFS2 section used for storing config information, so resizing with a reboot is not an option because the config could be lost. My goal is this: 1. Copy contents of JFFS2 into /tmp/ 2. Unmount

Is the partition key required when retrieving by the document ID

阅读更多关于 Is the partition key required when retrieving by the document ID

问题 Is it possible to retrieve a document by its ID without specifying the partition key? My understanding from reading the documentation is that the query will fan out across all partitions when the partition key is not specified: The following query does not have a filter on the partition key (DeviceId) and is fanned out to all partitions where it is executed against the partition's index. Note that you have to specify the EnableCrossPartitionQuery (x-ms-documentdb-query-enablecrosspartition in

PostgreSQL从继承到分区（一）

阅读更多关于 PostgreSQL从继承到分区（一）

PostgreSQL从继承到分区 (pg_partman) PostgreSQL从继承到分区（一） http://my.oschina.net/lianshunke/blog/205296 PostgreSQL从继承到分区（二） http://my.oschina.net/lianshunke/blog/205296 PostgreSQL从继承到分区（三） http://my.oschina.net/lianshunke/blog/205316 一、Inheritance http://www.postgresql.org/docs/9.3/static/ddl-inherit.html 通过下面的简单例子说明什么是表继承： 1.1 单表继承 postgres=# CREATE DATABASE lsk; CREATE DATABASE postgres=# \c lsk You are now connected to database "lsk" as user "postgres". lsk=# CREATE TABLE t1(id int,name varchar(20)); CREATE TABLE lsk=# CREATE TABLE t2(age int) inherits(t1); CREATE TABLE {在子表中新添的字段会汇同父表的所有字段一起作为子表字段}

Postgresql Table Partitioning Django Project

阅读更多关于 Postgresql Table Partitioning Django Project

问题 I have a Django 1.7 project that uses Postgres 9.3. I have a table that will have rather high volume. The table will have anywhere from 13million to 40million new rows a month. I would like to know what the best way to incorporate Postgres table partitioning with Django? 回答1: As long as you use inheritance, and then only connect the parent table to your Django model, the partitions should be entirely transparent to Django . That is, a SELECT on the parent table will cascade down to the

Filtering a spark partitioned table is not working in Pyspark

阅读更多关于 Filtering a spark partitioned table is not working in Pyspark

I am using spark 2.3 and have written one dataframe to create hive partitioned table using dataframe writer class method in pyspark. newdf.coalesce(1).write.format('orc').partitionBy('veh_country').mode("overwrite").saveAsTable('emp.partition_Load_table') Here is my table structure and partitions information. hive> desc emp.partition_Load_table; OK veh_code varchar(17) veh_flag varchar(1) veh_model smallint veh_country varchar(3) # Partition Information # col_name data_type comment veh_country varchar(3) hive> show partitions partition_Load_table; OK veh_country=CHN veh_country=USA veh_country

Obtaining nice cuts in Hmisc with cut2 (without the [ ) signs )

阅读更多关于 Obtaining nice cuts in Hmisc with cut2 (without the [ ) signs )

I'm currently trying to neatly cut data with use of the Hmisc package, as in the example below: dummy <- data.frame(important_variable=seq(1:1000)) require(Hmisc) dummy$cuts <- cut2(dummy$important_variable, g = 4) The produced cuts are correct with respect to the values: important_variable cuts 1 1 [ 1, 251) 2 2 [ 1, 251) 3 3 [ 1, 251) 4 4 [ 1, 251) 5 5 [ 1, 251) 6 6 [ 1, 251) > table(dummy$cuts) [ 1, 251) [251, 501) [501, 751) [751,1000] 250 250 250 250 However, I would like for the data to be presented slightly differently. For instance instead of [ 1, 251 ) [ 251, 501 ) I would prefer the

Explanation of Hoare Partitioning algorithm

阅读更多关于 Explanation of Hoare Partitioning algorithm

As per the pseudo-code given in many websites, I have written this Hoare partitioning algorithm, which takes an array, the start and end indexes of the sub-array to be partitioned based on the pivot given. It works fine, but can somebody explain the logic, how it does what it does? Here' the code: def hoare(arr,start,end): pivot = 4 i,j = start,end while i < j: while i < j and arr[i] <= pivot: i += 1 while j >= i and arr[j] > pivot: j -= 1 if i < j: arr[i],arr[j] = arr[j],arr[i] return j There's another variant of the partitioning, the Lomuto algorithm. It does something similar, although

How to efficiently cluster voxel space into the fewest number of similar, contiguous blocks possible?

阅读更多关于 How to efficiently cluster voxel space into the fewest number of similar, contiguous blocks possible?

I am doing some research into how feasible it is to use voxels to represent largish (256x256x256 voxels) battlegrounds with destructible terrain for server-hosted multiplayer games. Only one battleground will exist for any game at a time. However, to be able to broadcast rooms and changes to their terrain, I am trying to find an algorithm that can group the voxels into the fewest rectangular blocks as possible. As a simplistic example, if the bottom half of the level was completely filled with voxels of one type and the top half with voxels of another type, the level should be divided into two