#API

As you might have heard, we just introduced the InterSystems API Manager (IAM); a new feature of the InterSystems IRIS Data Platform™, enabling you to monitor, control and govern traffic to and from web-based APIs within your IT infrastructure. In case you missed it, here is the link to the announcement.

In this article, I will show you how to set up IAM and highlight some of the many capabilities IAM allows you to leverage.

Intro

For many in today's interoperability landscape, REST reigns supreme. With the overabundance of tools and approaches to REST API development, what tools do you choose and what do you need to plan for before writing any code?
This article focuses on design patterns and considerations that allow you to build highly robust, adaptive, and consistent REST APIs. Viable approaches to challenges of CORS support and authentication management will be discussed, along with various tips and tricks and best tools for all stages of REST API development. Learn about the open-source REST APIs available for InterSystems IRIS Data Platform and how they tackle the challenge of ever-increasing API complexity.
The article is a write-up for a recent webinar on the same topic.

GraphQL is a standard for declaring data structures and methods of data access that serves as a middleware layer between the client and the server. If you’ve never heard about GraphQL, here is a couple of useful online resources: here, here and here.

In this article, I will tell you how you can use GraphQL in your projects based on InterSystems technologies.

Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

Recently I needed to generate a Swagger spec from persistent and serial classes, so I'm publishing my code (it's not complete - you still need to hash out the application specifics, but it's a start). It's available here.

Let's say you have these classes:

What about having your IRIS REST APIs scanned every push you did and being reported on possible vulnerabilities? This is what I am going to show you in this article.

1. Scope and Objective:

Recently we supported a few NHS cases that required TIE (Trust Integration Engine) integration with the PKB service. Hence this article is meant to be a 10-minute quick guide to describe a demo solution (simple configurations and end-2-end implementation steps) for Health Connect (Ensemble) Integration with PKB (Patient-Knows-Best) service.

Introduction

Nowadays, there is a lot of applications that are using Open Authorization framework (OAuth) to access resources from all kinds of services in a secure, reliable and efficient manner. InterSystems IRIS is already compatible with OAuth 2.0 framework, in fact, there is a great article in the community regarding OAuth 2.0 and InterSystems IRIS in the following link here.

I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.

Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.

• Feedback : Using embedded python daily for 2 years
• Introduction
• Starting with Python
  • Python is not ObjectScript
    • Pep8
    • Modules
    • Dunders
  • Conclusion
• Embedded Python
  • What is Embedded Python ?
  • How to use Embedded Python ?
  • How I use Embedded Python
    • Use Python libraries and code as they were ObjectScript classes
      • With the language tag
      • Without the language tag
      • Conclusion
    • Use a python first approach
      • Example : iris-fhir-python-strategy
      • Remarks
      • Where to find the code
      • How to implement a Strategy
      • Implementation of InteractionsStrategy
      • Implementation of Interactions
      • Interactions in Python
      • Implementation of the abstract python class
      • Too long, do a summary

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 -

I already talked about GraphQL and the ways of using it in this article. Now I am going to tell you about the tasks I was facing and the results that I managed to achieve in the process of implementing GraphQL for InterSystems platforms.

What this article is about

• Generation of an AST for a GraphQL request and its validation
• Generation of documentation
• Generation of a response in the JSON format

In this 3-part series of articles, is shown how you can use IAM to simply add security, according to OAuth 2.0 standards, to a previously unauthenticated service deployed in IRIS.

In the first part, was provided some OAuth 2.0 background together with some IRIS and IAM initial definitions and configurations in order to facilitate the understanding of the whole process of securing your services.

Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.

To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.

You can try it out here.

Here's a sneak peek of the results: