Tutorial by Examples: du

$skuList = array('SKU-1', 'SKU-2',...,'SKU-n); $_productCollection = Mage::getModel('catalog/product') ->getCollection() ->addAttributeToFilter('sku', array('in' => $skuList)); OR $_productCollection = Mage::getResourceModel('catalog/product_collection') ->addAttributeToFilter('s...

Instead of bloating your main js file that contains your navigator with buttons. It's cleaner to just inject buttons on-demand in any page that you need. //In the page "Home", I want to have the right nav button to show //a settings modal that resides in "Home" component. c...

Things are easy when you have to use a C++ library in a Python project. Just you can use Boost. First of all here is a list of components you need: A CMakeList.txt file, because you're going to use CMake. The C++ files of the C++ project. The python file - this is your python project. Let's...

To get products from the database, you need to use Magento 2's repository design pattern. Each module can be bundled with it's own repositories, and the Product Catalog module is not any different. You can use dependency injection in your class to access the repository. A working example would look...

First, import modules and set connection strings. If you need parameters, you can either put them directly in the URL string (an API in this case) or build them as a dict and pass them to the params argument. import requests import json params = {'id': 'blahblah', 'output': 'json'} # You could ...

mongodump --db mydb --gzip --out "mydb.dump.$(date +%F_%R)" This command will dump a bson gzipped archive of your local mongod 'mydb' database to the 'mydb.dump.{timestamp}' directory

mongorestore --db mydb mydb.dump.2016-08-27_12:44/mydb --drop --gzip This command will first drop your current 'mydb' database and then restore your gzipped bson dump from the 'mydb mydb.dump.2016-08-27_12:44/mydb' archive dump file.

ElasticSearch has a well-documented JSON API, but you'll probably want to use some libraries that handle that for you: Elasticsearch - the official low level wrapper for the HTTP API Elasticsearch-rails - the official high level Rails integration that helps you to connect your Rails models...

User defined table functions represented by org.apache.hadoop.hive.ql.udf.generic.GenericUDTF interface. This function allows to output multiple rows and multiple columns for a single input. We have to overwrite below methods : 1.we specify input and output parameters abstract StructObjectInspe...

Plugins are a way for a developer to modify a chart as it is being created. Chart.js calls all plugins at the following chart states: Start of initialization End of initialization Start of update After the chart scales have calculated Start of datasets update End of datasets update End of u...

Mapreduce is a programming model to do processing on (very) large amounts of data. Traditional 'HPC' (High Performance Computing) speeds up large calculations on relatively large amounts of data by creating a set of highly connected computers (using things like extremely quick networking, and quick...

public override void ViewDidLoad() { base.ViewDidLoad(); // Perform any additional setup after loading the view, typically from a nib. //Declare the search bar and add it to the header of the table searchBar = new UISearchBar(); searchBar.SizeToFit(); ...

Dependency installation ( https://www.npmjs.com/package/gulp-imagemin ) $ npm install --save-dev gulp-imagemin Usage /* * Your other dependencies. */ var imagemin = require('gulp-imagemin'); /* * `gulp images` - Run lossless compression on all the images. */ gulp.task('images', f...

// my-feature.module.ts import { CommonModule } from '@angular/common'; import { NgModule } from '@angular/core'; import { MyComponent } from './my.component'; import { MyDirective } from './my.directive'; import { MyPipe } from './my.pipe'; import { MyService } from './my.service...

Generics was introduced in Java in its version (1.)5. These are erased during compilation, so runtime reflection is not possible for them. Generics generate new types parametrized by other types. For example we do not have to create new classes in order to use type safe collection of Strings and Num...

The simplest approach to parallel reduction in CUDA is to assign a single block to perform the task: static const int arraySize = 10000; static const int blockSize = 1024; __global__ void sumCommSingleBlock(const int *a, int *out) { int idx = threadIdx.x; int sum = 0; for (int i ...

Doing parallel reduction for a non-commutative operator is a bit more involved, compared to commutative version. In the example we still use a addition over integers for the simplicity sake. It could be replaced, for example, with matrix multiplication which really is non-commutative. Note, when ...

Multi-block approach to parallel reduction in CUDA poses an additional challenge, compared to single-block approach, because blocks are limited in communication. The idea is to let each block compute a part of the input array, and then have one final block to merge all the partial results. To do t...

Multi-block approach to parallel reduction is very similar to the single-block approach. The global input array must be split into sections, each reduced by a single block. When a partial result from each block is obtained, one final block reduces these to obtain the final result. sumNoncommSin...

Sometimes the reduction has to be performed on a very small scale, as a part of a bigger CUDA kernel. Suppose for example, that the input data has exactly 32 elements - the number of threads in a warp. In such scenario a single warp can be assigned to perform the reduction. Given that warp execut...