A blog for my learnings
Application icons are prominently displayed throughout the Qlik Sense hub, and they are usually either the default blue, or some horrendously stretched icon that has no business being on an enterprise system.
This simple tool (packaged as an extension and accessible as a mashup) helps users generate consistent, appropriate app icons for use in their apps.
Forewarning – loading data directly from the repository is not recommended. Most requirements can be met through the QRS APIs.
This script loads all QRS data from the repository into an app. The code is below, or the latest is available on GitHub.
Continue reading “Qlik Sense Repository Explorer (PostgreSQL extractor)”If you’re doing anything but vanilla Qlik Sense development, it’s likely you’ll need to get to the object IDs at some point. You can do this by appending /options/developer to the end of the page URL and clicking on each object in turn, or using dev tools – but that’s slow.
This bookmarklet makes getting the object IDs fast in Chrome (and, begrudgingly, IE11).
I’m often asked how to create mashups with Qlik Sense, and I strongly believe that it’s both easy and intuitive to leverage Qlik Sense APIs to build mashups…when you understand the options available to you.
To help new developers, I’ve put together a basic mashup using the Material Design Lite template. This example connects to a provided app and demonstrates several different ways of embedding Qlik Sense into a HTML site using just a little Javascript.
Previously, I tested the performance of a load script while using RecNo() and RowNo() functions. This conveniently gave me a script which consumes up to 25GB of RAM, along with considerable CPU power.
So, what about testing it on two cloud boxes? I’ve chosen a machine from both AWS and Azure, loaded them with Qlik Sense September 2018 and run the load script.
The summary: The AWS box was approx 8% faster than the Azure box.
There’s often a discussion about what each of these autonumber/hash functions does in Qlik. We commonly see these functions used for creating integer key fields, anonymising data (for example, names in demo apps), and maintaining long string fields for later comparison (as the hashes are shorter than the strings they replace).
To do this, I’m using the script below. I’m also keen to show outputs from QlikView vs Qlik Sense, and results of running the same script on another machine.
My observations are the following:
– AutoNumber/AutoNumberHash128/256 – different output per load as the value is typically based on the load order of the source data
– Hash128/160/256 – the same output, across every load. Stays the same between Qlik Sense and QlikView, and also between different machines
Using RecNo() or RowNo() will impart a performance impact on your load script. I discussed these functions in a previous post where I looked at the output of RecNo vs RowNo.
I recently spotted an unexpected slow-down in a load script, which was caused by using one of these functions. In summary:
– Using RowNo() in a simple load script is considerably slower than RecNo()
– If you must use RecNo(), it may be faster to do this in a direct load
– If you must use RowNo(), it may be faster to do this in a resident load
Continue reading “Qlik load performance with RecNo() and RowNo()”
In this post I explore the outputs of RecNo() and RowNo() to demonstrate the difference visually.
These two fields are often used interchangeably, but they provide different output. In summary:
– RecNo() is a record number based on source table(s)
– RowNo() is a record number based on the resulting table
As a result, RowNo will always provide a unique integer per row in an output table, while RecNo is only guaranteed to be unique when a single source is loaded (RecNo is based on each single source table, interpreted individually rather than collectively).
Continue reading “Qlik Counter Functions and their outputs – RecNo() and RowNo()”
Any environment with more than on developer will quickly lose consistency of attributes across the environment. Agreeing standards as part of developer onboarding, and validating these before app acceptance is very important.
An example set of naming conventions is discussed below. This assumes a common directory structure similar to a Qlik Deployment Framework (QDF) model.
The example below follows a concept of one common container (for data and files which aren’t app specific), and a hierarchy of product (one or more applications developed for a specific purpose) followed by customer (a standalone version of those applications, loaded with different data).
The resulting directory structure is therefore Root > Product > Customer.
On Demand App Generation (ODAG) was an approach introduced in Qlik Sense 3.0 to use the Qlik Analytic Platform (QAP) APIs to create Qlik Sense applications for very large or frequently changing data sets.