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#Beginner

8

Beginner tag unites articles and questions for those who are getting started with InterSystems Data Platform

Article
· Aug 1, 2019 3m read
Nested set model for ObjectScript

In many projects I was faced with storing hierarchical data (tree) in classes.
By tree, I mean such data, where each node has a parent node — an object of the same class.
Many examples of such data can be given. For example, a catalog in the online store. Suppose that this online store sells books, in this case, the category tree might look like this:

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Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

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In the previous parts (1, 2) we talked about globals as trees. In this article, we will look at them as sparse arrays.

A sparse array - is a type of array where most values assume an identical value.

In practice, you will often see sparse arrays so huge that there is no point in occupying memory with identical elements. Therefore, it makes sense to organize sparse arrays in such a way that memory is not wasted on storing duplicate values.

In some programming languages, sparse arrays are part of the language - for example, in J, MATLAB. In other languages, there are special libraries that let you use them. For C++, those would be Eigen and the like.

Globals are good candidates for implementing sparse arrays for the following reasons:

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Article
· Jul 27, 2018 4m read
Load a ML model into InterSystems IRIS

Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools.

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The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform, see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked.

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Terminal scripts can be used to run pre-designed commands on the terminal, like a batch file. You can write anything that can be executed on terminal, like for loop, if else and so on, inside Terminal scripts. In this article, I will show you how to call Terminal scripts, how to use parameters in Terminal scripts and how to avoid session disconnected when running Terminal scripts. If you have any information about how to use Terminal scripts or you have any feedback, please feel free to leave a comment.

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I needed to know programmatically if last ran failed or not.

After some exploring, here's the code:

ClassMethod isLastTestOk() As %Boolean
{
  set in = ##class(%UnitTest.Result.TestInstance).%OpenId(^UnitTest.Result)
  for i=1:1:in.TestSuites.Count() {
    #dim suite As %UnitTest.Result.TestSuite
    set suite = in.TestSuites.GetAt(i)
    return:suite.Status=0 $$$NO
  }
  quit $$$YES
}

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Article
· Mar 31, 2019 20m read
How to write the home address right?

How Tax Service, OpenStreetMap, and InterSystems IRIS
could help developers get clean addresses

Pieter Brueghel the Younger, Paying the Tax (The Tax Collector), 1640

In my previous article, we just skimmed the surface of objects. Let's continue our reconnaissance. Today's topic is a tough one. It's not quite BIG DATA, but it's still the data not easy to work with: we're talking about fairly large amounts of data. It won't all fit into RAM at once, and some of it won't even fit on the drive (not due to lack of space, but because there's a lot of junk). The name of our subject is FIAS DB: the Federal Information Address System database - the databases of addresses in Russia. The archive is 5.5 GB. And it's a compressed XML file. After extraction, it will be a full 53 GB (set aside 110 GB for extraction). And when you start to parse and convert it, that 110 GB won't be enough. There won't be enough RAM either.

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InterSystems Data Platform includes utilities and tools for system monitoring and alerting, however System Administrators new to solutions built on the InterSystems Data Platform (a.k.a Caché) need to know where to start and what to configure.

This guide shows the path to a minimum monitoring and alerting solution using references from online documentation and developer community posts to show you how to enable and configure the following;

  1. Caché Monitor: Scans the console log and sends emails alerts.

  2. System Monitor: Monitors system status and resources, generating notifications (alerts and warnings) based on fixed parameters and also tracks overall system health.

  3. Health Monitor: Samples key system and user-defined metrics and compares them to user-configurable parameters and established normal values, generating notifications when samples exceed applicable or learned thresholds.

  4. History Monitor: Maintains a historical database of performance and system usage metrics.

  5. pButtons: Operating system and Caché metrics collection scheduled daily.

Remember this guide is a minimum configuration, the included tools are flexible and extensible so more functionality is available when needed. This guide skips through the documentation to get you up and going. You will need to dive deeper into the documentation to get the most out of the monitoring tools, in the meantime, think of this as a set of cheat sheets to get up and running.

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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

In the previous 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. Still, our focus was on the implementation part of software development, but this part presents:

  • GitLab Workflow - a complete software life cycle process - from idea to user feedback
  • Continuous Delivery - software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time. It aims at building, testing, and releasing software faster and more frequently.

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Article
· Apr 26, 2017 2m read
How to install and use TFS in Atelier

Hi,

This is a quick tutorial how to install and use TFS in Atelier. It is based on my self experience and some tricks that I 've noted.

If you are used to using visual studio maybe you feel that is a bit slow and heavy, but you have the same TFS panel as you have in Visual Studio, so don't need any special "training" to use it smiley

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Recently I needed a classmethod that returns annotation value based on a name of a activity.

As doing it at runtime seemed inefficient, I wrote compile-time utility that iterates over all business process activities and generates relevant code.

This code could be used in a variety of situations when you need to iterate over business process activities, just add it as a secondary superclass to your BPL processes.

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The following post outlines an architectural design of intermediate complexity for DeepSee. As in the previous example, this implementation includes separate databases for storing the DeepSee cache, DeepSee implementation and settings. This post introduces two new databases: the first to store the globals needed for synchronization, the second to store fact tables and indices.

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InterSystems products (IRIS, Caché, Ensemble) already include a built-in Apache web server. But the built-in server is designed for the development and administration tasks and thus has certain limitations. Though you may find some useful workarounds for these limitations, the more common approach is to deploy a full-scale web server for your production environment. This article describes how to set up Apache to work with InterSystems products and how to provide HTTPS access. We will be using Ubuntu, but the configuration process is almost the same for all Linux distributions.

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This article was written as an attempt to share the experience of installing the InterSystems Caché DBMS for production environment.
We all know that the development configuration of a DBMS is very different from real-life conditions.
As a rule, development is carried out in “hothouse conditions” with a bare minimum of security measures, but when we publish our project online, we must ensure its reliable and uninterrupted operation in a very aggressive environment.

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Everybody has a testing environment.

Some people are lucky enough to have a totally separate environment to run production in.

-- Unknown

.

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
  • GitLab CI/CD
  • CI/CD with containers

This first part deals with the cornerstone of modern software development - Git version control system and various Git flows.

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Here I’ll walk you through the process of creating a simple Node/Express API and connect it to a InterSystems IRIS instance.

I won't go into much detail about how to work with any of the technologies I will mention in this tutorial but I will leave links, in case you want to learn more.

The objective here is to give you a practical guide on how to set up and connect a node.js back-end API to IRIS.

Before we get our hands dirty, make sure you have Node.js running on your machine. So I'll check:

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InterSystems IRIS supports publish and subscribe message delivery. Publish and subscribe refers to the technique of routing a message to one or more subscribers based on the fact that those subscribers have previously registered to be notified about messages on a specific topic.

This article demonstrates how several InterSystems IRIS capabilities can work together:

In this article we would send emails about:

  • New workflow tasks
  • Unassigned workflow tasks
  • Uncompleted workflow tasks
  • Ensemble alerts

Email recipients would be determined using Publish/Subscribe operation and each user would receive only digest email whenever possible.

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Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following:

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The following post outlines a more flexible architectural design for DeepSee. As in the previous example, this implementation includes separate databases for storing the DeepSee cache, DeepSee implementation and settings, and synchronization globals. This example introduces one new databases to store the DeepSee indices. We will redefine the global mappings so that the DeepSee indices are not mapped together with the fact and dimension tables.

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Introduction

Any election is a highly mysterious process, and when you look at its results, the overall picture is not quite clear. I decided to put them, region by region, on the map of Moscow using InterSystems technologies that offer both storage and data analysis functionality. In this particular case, I used InterSystems Ensemble, a platform for application development and integration, but you can also build this solution using the multi-model InterSystems Caché DBMS, as well as InterSystems’ new product called IRIS Data Platform.

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Generally speaking, InterSystems products supported dynamic objects and JSON for a long while, but version 2016.2 came with a completely new implementation of these features, and the corresponding code was moved from the ObjectScript level to the kernel/C level, which made for a substantial performance boost in these areas. This article is about innovations in the new version and the migration process (including the ways of preserving backward compatibility).

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