partitioning

I have posted a bit here related to a project I have been trying to work on and I keep hitting design problems and have to design from scratch. So I'm wondering if I can post what I'm trying to do and someone can help me understand how I can get the result I want. BackGround: I'm new to programming and trying to learn. So I took a project that interested me which involves basically taking list and breaking down each number using only numbers from the list. I know I could easily brute force this(which I did) but I wanted to also learn Hbase, Hadoop, and parallel processing so I wanted do it in

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 have to print all the distinct partitions, only the number of them. So I'm a bit lost with this. I've

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 the REST API) to have the SDK to execute a query across partitions . This makes sense with non-key

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); } static int cmpDouble(const void *i, const void *j) { if (*(double *)i < *(double *)j) return 1; else if (*

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 JFFS2 from mtd1 3. Increase the starting offset + reduce size of mtd1 by X bytes (or delete mtd1 and

Is there any relationship between the number of elements an RDD contained and its ideal number of partitions ? I have a RDD that has thousand of partitions (because I load it from a source file composed by multiple small files, that's a constraint I can't fix so I have to deal with it). I would like to repartition it (or use the coalesce method). But I don't know in advance the exact number of events the RDD will contain. So I would like to do it in an automated way. Something that will look like: val numberOfElements = rdd.count() val magicNumber = 100000 rdd.coalesce( numberOfElements /

I am having a lot trouble finding the answer to this question. Let's say I write a dataframe to parquet and I use repartition combined with partitionBy to get a nicely partitioned parquet file. See Below: df.repartition(col("DATE")).write.partitionBy("DATE").parquet("/path/to/parquet/file") Now later on I would like to read the parquet file so I do something like this: val df = spark.read.parquet("/path/to/parquet/file") Is the dataframe partitioned by "DATE" ? In other words if a parquet file is partitioned does spark maintain that partitioning when reading it into a spark dataframe. Or is it