InterSystems supports use of the InterSystems IRIS Docker images it provides on Linux only. Rather than executing containers as native processes, as on Linux platforms, Docker for Windows creates a Linux VM running under Hyper-V, the Windows virtualizer, to host containers. These additional layers add complexity that prevents InterSystems from supporting Docker for Windows at this time.

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Created by Daniel Kutac, Sales Engineer, InterSystems

Warning: if you get confused by URLs used: the original series used screens from machine called dk-gs2016. The new screenshots are taken from a different machine. You can safely treat url WIN-U9J96QBJSAG as if it was dk-gs2016.

Part 2. Authorization server, OpenID Connect server

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What is Web Scraping:

In simple terms, Web scraping, web harvesting, or web data extraction is an automated process of collecting large data(unstructured) from websites. The user can extract all the data on particular sites or the specific data as per the requirement. The data collected can be stored in a structured format for further analysis.

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++ Update: August 1, 2018

The use of the InterSystems Virtual IP (VIP) address built-in to Caché database mirroring has certain limitations. In particular, it can only be used when mirror members reside the same network subnet. When multiple data centers are used, network subnets are not often “stretched” beyond the physical data center due to added network complexity (more detailed discussion here). For similar reasons, Virtual IP is often not usable when the database is hosted in the cloud.

Network traffic management appliances such as load balancers (physical or virtual) can be used to achieve the same level of transparency, presenting a single address to the client applications or devices. The network traffic manager automatically redirects clients to the current mirror primary’s real IP address. The automation is intended to meet the needs of both HA failover and DR promotion following a disaster.

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Article
· Jul 22, 2016 16m read
Using Regular Expressions in Caché

1.About this article

Just like Caché pattern matching, Regular Expressions can be used in Caché to identify patterns in text data – only with a much higher expressive power. This article provides a brief introduction into Regular Expressions and what you can do with it in Caché. The information provided herein is based on various sources, most notably the book “Mastering Regular Expressions” by Jeffrey Friedl and of course the Caché online documentation. The article is not intended to discuss all the possibilities and details of regular expressions. Please refer to the information sources listed in chapter 5 if you would like to learn more. If you prefer to read off-line you can also download the PDF version of this article.

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Have some free text fields in your application that you wish you could search efficiently? Tried using some methods before but found out that they just cannot match the performance needs of your customers? Do I have one weird trick that will solve all your problems? Don’t you already know!? All I do is bring great solutions to your performance pitfalls!

As usual, if you want the TL;DR (too long; didn’t read) version, skip to the end. Just know you are hurting my feelings.

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So, one day you're working away at WidgetsDirect, the leading supplier of widget and widget accessories, when your boss asks you to develop the new customer facing portal to allow the client base to access the next generation of Widgets..... and he wants you to use Angular 1.x to read into the department's Caché server.

There's only one problem: You've never used Angular, and don't know how to make it talk to Caché.

This guide is going to walk through the process of setting up a full Angular stack which communicates with a Caché backend using JSON over REST.

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This time I want to talk about something not specific to InterSystems IRIS, but that I think is important if you want to work with Docker and your server at work is a PC or laptop with Windows 10 Pro or Enterprise.

As you likely know, containers technology comes basically from Linux world and, nowadays, is on Linux hosts were it shows maximum potential. Those who use Windows on a normal basis see that both, Microsoft and Docker, have done important efforts during these last years that allow us to run containers based on Linux images on our Windows system in a really easy way... but it's something not supported for production systems and, this is the big problem, is not reliable if we want to keep persistent data outside of containers, in the host system,... mostly due to the big differences between Windows and Linux file systems. In the end, Docker for Windows itself uses a small linux virtual machine (MobiLinux) to run the containers... it does it transparently for the windows user... and it works perfectly well if, as I said, you don't require that your databases survive longer than the container...

Well,...let's get to the point,... the point is that many times, to avoid issues and simplify, we need a full Linux system and, if our server is based on Windows, the only way of having it is through a virtual machine. At least till WSL2 in Windows is released, but that will be another story and sure it'll take a bit of time to become robust enough.

In this article, I'll tell you, step by step, how to install an environment where you'll be able to work, if you need it, with Docker containers on an Ubuntu system in your Windows server. Let's go...

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In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:

  • Git 101
  • Git flow (development process)
  • GitLab installation
  • GitLab Workflow
  • Continuous Delivery
  • GitLab installation and configuration
  • GitLab CI/CD
  • Why containers?
  • Containers infrastructure
  • CD using containers

In the first article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software.

In the second article, we covered GitLab Workflow - a complete software life cycle process and Continuous Delivery.

In the third article, we covered GitLab installation and configuration and connecting your environments to GitLab

In the fourth article, we wrote a CD configuration.

In the fifth article, we talked about containers and how (and why) they can be used.

In the sixth article let's discuss main components you'll need to run a continuous delivery pipeline with containers and how they all work together.

In this article, we'll build Continuous Delivery configuration discussed in the previous articles.

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Hi Community!

I think everyone keeps the source code of the project in the repository nowadays: Github, GitLab, bitbucket, etc. Same for InterSystems IRIS projects check any on Open Exchange.

What do we do every time when start or continue working with a certain repository with InterSystems Data Platform?

We need a local InterSystems IRIS machine, have the environment for the project set up and the source code imported.

So every developer performs the following:

  1. Check out the code from repo
  2. Install/Run local IRIS installation
  3. Create a new namespace/database for a project
  4. Import the code into this new namespace
  5. Setup all the rest environment
  6. Start/continue coding the project

If you dockerize your repository this steps line could be shortened to this 3 steps:

  1. Check out the code from repo
  2. Run docker-compose build
  3. Start/continue coding the project

Profit - no any hands-on for 3-4-5 steps which could take minutes and bring head ache sometime.

You can dockerize (almost) any your InterSystems repo with a few following steps. Let’s go!

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In the last post we scheduled 24-hour collections of performance metrics using pButtons. In this post we are going to be looking at a few of the key metrics that are being collected and how they relate to the underlying system hardware. We will also start to explore the relationship between Caché (or any of the InterSystems Data Platforms) metrics and system metrics. And how you can use these metrics to understand the daily beat rate of your systems and diagnose performance problems.

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The following steps show you how to display a sample list of metrics available from the /api/monitor service.

In the last post, I gave an overview of the service that exposes IRIS metrics in Prometheus format. The post shows how to set up and run IRIS preview release 2019.4 in a container and then list the metrics.


This post assumes you have Docker installed. If not, go and do that now for your platform :)

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Article
· Mar 17, 2021 3m read
Making the most of $Query

I ran into an interesting ObjectScript use case today with a general solution that I wanted to share.

Use case:

I have a JSON array (specifically, in my case, an array of issues from Jira) that I want to aggregate over a few fields - say, category, priority, and issue type. I then want to flatten the aggregates into a simple list with the total for each of the groups. Of course, for the aggregation, it makes sense to use a local array in the form:

agg(category, priority, type) = total

Such that for each record in the input array I can just:

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This week I am going to look at CPU, one of the primary hardware food groups :) A customer asked me to advise on the following scenario; Their production servers are approaching end of life and its time for a hardware refresh. They are also thinking of consolidating servers by virtualising and want to right-size capacity either bare-metal or virtualized. Today we will look at CPU, in later posts I will explain the approach for right-sizing other key food groups - memory and IO.

So the questions are:

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Article
· Oct 11, 2022 2m read
ZPM Simple Implementation Cookbook

ZPM is designed to work with applications and modules for InterSystems IRIS Data Platform. It consists of two components, the ZPN Client which is a CLI to manage modules, and The Registry which is a database of modules and meta-information. We can use ZPM to search, install, upgrade, remove and publish modules. With ZPM you can install ObjectScript classes, Frontend applications, Interoperability productions, IRIS BI solutions, IRIS Datasets or any files such as Embedded Python wheels.

Today this cookbook will go through 3 sections:

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** Revised Feb-12, 2018

While this article is about InterSystems IRIS, it also applies to Caché, Ensemble, and HealthShare distributions.

Introduction

Memory is managed in pages. The default page size is 4KB on Linux systems. Red Hat Enterprise Linux 6, SUSE Linux Enterprise Server 11, and Oracle Linux 6 introduced a method to provide an increased page size in 2MB or 1GB sizes depending on system configuration know as HugePages.

At first HugePages required to be assigned at boot time, and if not managed or calculated appropriately could result in wasted resources. As a result various Linux distributions introduced Transparent HugePages with the 2.6.38 kernel as enabled by default. This was meant as a means to automate creating, managing, and using HugePages. Prior kernel versions may have this feature as well however may not be marked as [always] and potentially set to [madvise].

Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages. However in current Linux releases THP can only map individual process heap and stack space.

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In an ever-changing world, companies must innovate to stay competitive. This ensures that they’ll make decisions with agility and safety, aiming for future results with greater accuracy.
Business Intelligence (BI) tools help companies make intelligent decisions instead of relying on trial and error. These intelligent decisions can make the difference between success and failure in the marketplace.
Microsoft Power BI is one of the industry’s leading business intelligence tools. With just a few clicks, Power BI makes it easy for managers and analysts to explore a company’s data. This is important because when data is easy to access and visualize, it’s much more like it’ll be used to make business decisions.


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Here you'll find a simple program that uses Python in an IRIS environment and another simple program that uses ObjectScript in a Python environment. Also, I'd like to share a few of the troubles I went trough while learning to implement this.

Python in IRIS environment

Let's say, for example, you're in an IRIS environment and you want to solve a problem that you find easy, or more efficient with Python.

You can simply change the environment: create your method as any other, and in the end of it's name and specifications, you add [ Language = python ]:

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