#Best Practices

In this article, I would like to talk about the spec-first approach to REST API development.

While traditional code-first REST API development goes like this:

• Writing code
• REST-enabling it
• Documenting it (as a REST API)

Spec-first follows the same steps but reverse. We start with a spec, also doubling as documentation, generate a boilerplate REST app from that and finally write some business logic.

This is advantageous because:

• You always have relevant and useful documentation for external or frontend developers who want to use your REST API
• Specification created in OAS (Swagger) can be imported into a variety of tools allowing editing, client generation, API Management, Unit Testing and automation or simplification of many other tasks
• Improved API architecture. In code-first approach, API is developed method by method so a developer can easily lose track of the overall API architecture, however with the spec-first developer is forced to interact with an API from the position if API consumer which usually helps with designing cleaner API architecture
• Faster development - as all boilerplate code is automatically generated you won't have to write it, all that's left is developing business logic.
• Faster feedback loops - consumers can get a view of the API immediately and they can easier offer suggestions simply by modifying the spec

Let's develop our API in a spec-first approach!

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.

In this article I'll show you how you can easily convert between many different document formats, these to be precise:

• in: html, odt, doc, docx, rtf, sdw, txt...
• out: html, odt, docx, rtf, pdf, txt, tex, xhtml...

and many more, check out this table. We'll use LibreOffice as a conversion engine.

Over the years, I have found myself needing to create multiple HL7 messages based on a single inbound message. Usually these take the form of an order or result from a lab. Each time I have approached it, I have tried to start from scratch under the belief that the previous attempt could have been done better.

Artificial Intelligence (AI) is getting a lot of attention lately because it can change many areas of our lives. Better computer power and more data have helped AI do amazing things, like improving medical tests and making self-driving cars. AI can also help businesses make better decisions and work more efficiently, which is why it's becoming more popular and widely used. How can one integrate the OpenAI API calls into an existing IRIS Interoperability application?

One of the pain points for maintaining HL7 interfaces is the need to run a reliable regression test upon deployment to new environments and after upgrades. The %UnitTest class allows unit tests to be created and packaged alongside interface code. Test data can also be maintained within the unit test class, allowing for quick and easily repeatable smoke-testing and regression testing.

Resources:

• Standard %UnitTest class

In this tutorial, I’d like to talk about Class Queries. To be more precise, about the Queries based on user-written code:

++Update: August 2, 2018

This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.

Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).

In the vast and varied SQL database market, InterSystems IRIS stands out as a platform that goes way beyond just SQL, offering a seamless multimodel experience and supporting a rich set of development paradigms. Especially the advanced Object-Relational engine has helped organizations use the best-fit development approach for each facet of their data-intensive workloads, for example ingesting data through Objects and simultaneously querying it through SQL. Persistent Classes correspond to SQL tables, their properties to table columns and business logic is easily accessed using User-Defined Functions or Stored Procedures. In this article, we'll zoom in on a little bit of the magic just below the surface, and discuss how it may affect your development and deployment practices. This is an area of the product where we have plans to evolve and improve, so please don't hesitate to share your views and experiences using the comments section below.

The %Net.SSH.Session class lets you connect to servers using SSH. It's most commonly used with SFTP, especially in the FTP inbound and outbound adaptors.

In this article, I'm going to give a quick example of how to connect to an SSH server using the class, describe your options for authenticating, and how to debug when things go wrong.

Here's an example of making the connection:

This post is dedicated to the task of monitoring a Caché instance using SNMP. Some users of Caché are probably doing it already in some way or another. Monitoring via SNMP has been supported by the standard Caché package for a long time now, but not all the necessary parameters are available “out of the box”. For example, it would be nice to monitor the number of CSP sessions, get detailed information about the use of the license, particular KPI’s of the system being used and such. After reading this article, you will know how to add your parameters to Caché monitoring using SNMP.

This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

The object and relational data models of the Caché database support three types of indexes, which are standard, bitmap, and bitslice. In addition to these three native types, developers can declare their own custom types of indexes and use them in any classes since version 2013.1. For example, iFind text indexes use that mechanism.

[Background]

InterSystems IRIS family has a nice utility ^SystemPerformance (as known as ^pButtons in Caché and Ensemble) which outputs the database performance information into a readable HTML file. When you run ^SystemPerformance on IRIS for Windows, a HTML file is created where both our own performance log mgstat and Windows performance log are included.