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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Article
· Feb 25, 2019 4m read
Using Grafana directly from IRIS

There have been some very helpful articles in the community that show how to use Grafana with IRIS (or Cache/Ensemble) by using an intermediate database.

But I wanted to get at IRIS structures directly. In particular, i wanted to access the Cache History monitor data that is accessible by SQL as described here

https://community.intersystems.com/post/apm-using-cach%C3%A9-history-mon...

and didn't want anything between me and the data.

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In this post, I am going to detail how to set up a mirror using SSL, including generating the certificates and keys via the Public Key Infrastructure built in to InterSystems IRIS Data Platform. I did a similar post in the past for Caché, so feel free to check that out here if you are not running InterSystems IRIS. Much like the original, the goal of this is to take you from new installations to a working mirror with SSL, including a primary, backup, and DR async member, along with a mirrored database. I will not go into security recommendations or restricting access to the files. This is meant to just simply get a mirror up and running. Example screenshots are taken on a 2018.1.1 version of IRIS, so yours may look slightly different.

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There's an easy new way to add certificate authority (CA) certificates to your SSL/TLS configurations on InterSystems IRIS 2019.1 (and 2018.1.2) on Windows and Mac. You can ask IRIS to use the operating system's certificate store by entering:

%OSCertificateStore

in the field for "File containing Trusted Certificate Authority X.509 certificate(s)". Here's an image of how to do this in the portal:

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If a picture is worth a thousand words, what's a video worth? Certainly more than typing a post.

Please check out my "Coding talks" on InterSystems Developers YouTube:

1. Analysing InterSystems IRIS System Performance with Yape. Part 1: Installing Yape

https://www.youtube.com/embed/3KClL5zT6MY
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Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

https://www.youtube.com/embed/cuMLSO9NQCM
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Extracting and plotting pButtons data including timeframes and iostat.

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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 31, 2023 3m read
Using JSON in IRIS

Saw the other day an article with the usage of the %ZEN package when working with JSON and decided to write an article describing a more modern approach. At some recent point, there was a big switch from using %ZEN.Auxiliary.* to dedicated JSON classes. This allowed to work with JSONs more organically.

Thus, at this point there are basically 3 main classes to work with JSON:

  • %Library.DynamicObject - provides a simple and efficient way to encapsulate and work with standard JSON documents. Also, there is a possibility instead of writing the usual code for creating an instance of a class like
set obj = ##class(%Library.DynamicObject).%New()

it is possible to use the following syntax

set obj = {}
  • %Library.DynamicArray - provides a simple yet efficient way to encapsulate and work with standard JSON arrays. With arrays you can use the same approach as with objects, meaning that yu can either create an instance of the class
set array = ##class(%DynamicArray).%New()

or you can do it by using brackets []

set array = []
  • %JSON.Adaptor is a means for mapping ObjectScript objects (registered, serial or persistent) to JSON text or dynamic entities.
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Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).

We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.

Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.

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A short post for now to answer a question that came up. In post two of this series I included graphs of performance data extracted from pButtons. I was asked off-line if there is a quicker way than cut/paste to extract metrics for mgstat etc from a pButtons .html file for easy charting in Excel.

See: - Part 2 - Looking at the metrics we collected

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Article
· Jul 4, 2016 8m read
Introduction to the iKnow REST APIs

After a five-part series on sample iKnow applications (parts 1, 2, 3, 4, 5), let's turn to a new feature coming up in 2017.1: the iKnow REST APIs, allowing you to develop rich web and mobile applications. Where iKnow's core COS APIs already had 1:1 projections in SQL and SOAP, we're now making them available through a RESTful service as well, in which we're trying to offer more functionality and richer results with fewer buttons and less method calls. This article will take you through the API in detail, explaining the basic principles we used when defining them and exploring the most important ones to get started.

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Article
· Jun 6, 2016 7m read
Language Extensions

This is a posting about a particular feature of Caché which I find useful but is probably not well known or used. I am referring to the feature of Language Extensions.

This feature allows you to extend the commands, special variables and functions available in Caché Object Script with commands, special variables and functions of your own. This functionality also applies to other languages the Caché supports at the server, including Caché Basic and Multivalue Basic.


Why would I need or want to add new commands ?

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Article
· Aug 26, 2016 8m read
Enterprise Monitor and HealthShare

Enterprise Monitor is a component of Ensemble and can help organizations monitor multiple productions running on different namespaces within the same instance or namespaces running on multiple instances.

Documentation can be found at:

http://docs.intersystems.com/ens20161/csp/docbook/DocBook.UI.Page.cls?KEY=EMONITOR_all#EMONITOR_enterprise

In Ensemble 2016.1 there were changes made to make this utility work with HealthShare environments.

This article will:

  • Show how to set up Enterprise Monitor for HealthShare sites
  • Show some features of Enterprise Monitor
  • Show some features of Enterprise Message Viewer

For this article, I used the following version of HealthShare:

Cache for Windows (x86-64) 2016.1 (Build 656U) Fri Mar 11 2016 17:42:42 EST [HealthShare Modules:Core:14.02.2415 + Linkage Engine:14.02.2415 + Patient Index:14.02.2415 + Clinical Viewer:14.02.2415 + Active Analytics:14.02.2415]

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Last time we launched an IRIS application in the Google Cloud using its GKE service.

And, although creating a cluster manually (or through gcloud) is easy, the modern Infrastructure-as-Code (IaC) approach advises that the description of the Kubernetes cluster should be stored in the repository as code as well. How to write this code is determined by the tool that’s used for IaC.

In the case of Google Cloud, there are several options, among them Deployment Manager and Terraform. Opinions are divided as to which is better: if you want to learn more, read this Reddit thread Opinions on Terraform vs. Deployment Manager? and the Medium article Comparing GCP Deployment Manager and Terraform.

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While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".

After this session I thought it would be a nice exercise to try and provide some easier human interface for this.

The first step in this was to wrap a class-based API to the existing pButtons routine.

I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.

The next step was to add on top of this API, a REST API class.

With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.

For example -

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cAdvisor (short for container Advisor) analyzes and exposes resource usage and performance data from running containers. cAdvisor exposes Prometheus metrics out of the box.

https://prometheus.io/docs/guides/cadvisor/

Prometheus is integrated in SAM. This makes it possible to leverage the cAdvisor metrics and expose them via Prometheus and Grafana.

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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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Introduction

Data analytics is a crucial aspect of business decision-making in today's fast-paced world. Organizations rely heavily on data analysis to make informed decisions and stay ahead of the competition. In this article, we will explore how data analytics can be performed using Pandas and Intersystems Embedded Python. We will discuss the basics of Pandas, the benefits of using Intersystems Embedded Python, and how they can be used together to perform efficient data analytics.

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Continuing on with providing some examples of various storage technologies and their performance profiles, this time we looked at the growing trend of leveraging internal commodity-based server storage, specifically the new HPE Cloudline 3150 Gen10 AMD processor-based single socket servers with two 3.2TB Samsung PM1725a NVMe drives.

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