As we all well know, InterSystems IRIS has an extensive range of tools for improving the scalability of application systems. In particular, much has been done to facilitate the parallel processing of data, including the use of parallelism in SQL query processing and the most attention-grabbing feature of IRIS: sharding. However, many mature developments that started back in Caché and have been carried over into IRIS actively use the multi-model features of this DBMS, which are understood as allowing the coexistence of different data models within a single database. For example, the HIS qMS database contains both semantic relational (electronic medical records) as well as traditional relational (interaction with PACS) and hierarchical data models (laboratory data and integration with other systems). Most of the listed models are implemented using SP.ARM's qWORD tool (a mini-DBMS that is based on direct access to globals). Therefore, unfortunately, it is not possible to use the new capabilities of parallel query processing for scaling, since these queries do not use IRIS SQL access.

Meanwhile, as the size of the database grows, most of the problems inherent to large relational databases become right for non-relational ones. So, this is a major reason why we are interested in parallel data processing as one of the tools that can be used for scaling.

In this article, I would like to discuss those aspects of parallel data processing that I have been dealing with over the years when solving tasks that are rarely mentioned in discussions of Big Data. I am going to be focusing on the technological transformation of databases, or, rather, technologies for transforming databases.

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Developing a Full-Stack JavaScript web app with Caché requires you to bring together the right building blocks. Previously, I outlined the basic steps to install and connect Node.js to Caché and make it's powerful multi-model database capabilites available for use with Node.js. You can use Caché as a NoSQL-, document- (with unique key-level access!), SQL- and object-database with Node.js. When developing JavaScript applications, you'll see how powerful this combination is and makes Caché a perfect fit for Node.js.

In the first part of this article series I will show how to get started with the React framework, one of the most popular frameworks currently taking over front-end development. In the next parts you'll learn how to connect a basic web app to a Caché back-end.

You'll see, it's very easy to get started with this technology - you can even compare the amount of basic knowledge you need to COS because you only need to know a few basic concepts to start!

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In my previous article, we reviewed possible use-cases for macros, so let’s now proceed to a more comprehensive example of macros usability. In this article we will design and build a logging system.

Logging system

Logging system is a useful tool for monitoring the work of an application that saves a lot of time during debugging and monitoring. Our system would consist of two parts:

  • Storage class (for log records)
  • Set of macros that automatically add a new record to the log

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Evgeny Shvarov · May 7, 2020 4m read
Unit Testing with ZPM

Hi Developers!

I want to share with you the approach you can use working with Unit Testing which I personally find very convenient and robust.

Will not talk much here about Unit Testing, we have documentation, enough good articles on the topic not only on the Internet but here on Developers Community too,  e.g. this one.

How can you manage the Unit Testing of your ZPM module?

This is pretty handy with ZPM. It has a special command "test" which runs all the tests you mentioned in the module. You can execute it as shown below:

zpm:USER>module-name test

And this command runs all the tests you mentioned in module.xml.

Let's see how it works.

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We are in the age of the multiplatform economy and APIs are the "glue" in this digital scenario. Since they are so important, they are seen by developers as a service or product to be consumed. Therefore, usage experience is a crucial factor for its success.

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Several resources tell us how to run IRIS in a Kubernetes cluster, such as Deploying an InterSystems IRIS Solution on EKS using GitHub Actions and Deploying InterSystems IRIS solution on GKE Using GitHub Actions. These methods work but they require that you create Kubernetes manifests and Helm charts, which might be rather time-consuming.
To simplify IRIS deployment, InterSystems developed an amazing tool called InterSystems Kubernetes Operator (IKO). A number of official resources explain IKO usage in details, such as  New Video: Intersystems IRIS Kubernetes Operator and InterSystems Kubernetes Operator.

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Robert Cemper · Nov 21, 2020 3m read
ObjectScript over ODBC
This is a coding example working on IRIS 2020.1 and on Caché 2018.1.3 
It will not be kept in sync with new versions      
It is also NOT serviced by InterSystems Support !   
Every now and then you may encounter a situation where for various reasons
ODBC is the only option to access a remote system. Which is sufficient as long as you need to examine or change tables.
But you can't directly execute some commands or change some Global.

Special thanks @Anna Golitsyna for inspiring me to publish this.

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Earlier this year, the AppS.REST package was released. AppS.REST is a framework for easily exposing existing persistent classes in IRIS as REST resources. AppS.REST-enabled classes support CRUD operations with little effort from the developer, bridging the gap between persistent data in IRIS and data consumers, such as an Angular front end application.

But IRIS classes are much more than just a definition for loading and saving individual records! This article aims to highlight a few ways to leverage the power of IRIS in your REST applications.  Using the Phone.Contact sample app, we'll look at out-of-the-box query support, use of class queries and finally ObjectScript methods.

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According IDC, 80% of all data produced are NoSQL. See:

There are digital documents, scanned documents, online and offline texts, blob content into SQL, images, videos and audio. Imagine a Corporate Analytics initiative without all these data to analyze and support decisions?

In all the world, many projects are using techonologies to transform these NoSQL data into textual content, to allows analyze it. See:

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Keywords: Python, JDBC, SQL, IRIS, Jupyter Notebook, Pandas, Numpy, and Machine Learning 

1. Purpose

This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data  into an IRIS database instance via SQL syntax, for demo purpose. 

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Hi Community,
OpenAPI-Client Gen has just released, this is an application to create an IRIS Interoperability Production client from Swagger 2.0 specification.
Instead of the existing tool ^%REST that creates a server-side REST application, OpenAPI-Client Gen creates a complete REST Interoperability Production client template.


Install by ZPM:

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In this article, we will run an InterSystems IRIS cluster using docker and Merge CPF files - a new feature allowing you to configure servers with ease.

On UNIX® and Linux, you can modify the default iris.cpf using a declarative CPF merge file. A merge file is a partial CPF that sets the desired values for any number of parameters upon instance startup. The CPF merge operation works only once for each instance.

Our cluster architecture is very simple, it would consist of one Node1 (master node) and two Data Nodes (check all available roles). Unfortunately, docker-compose cannot deploy to several servers (although it can deploy to remote hosts), so this is useful for local development of sharding-aware data models,  tests, and such. For a productive InterSystems IRIS Cluster deployment, you should use either ICM or IKO.

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According to IDC, more than 80% of information it is NoSQL, especially text into documents. When the digital services or applications not process all this information, the business lose. To face this challenge, it is possible use OCR technology. OCR uses machine learning and/or trained image patterns to transform image pixels into text. This is important, because many documents are scanned into images inside PDF, or many documents contains images with text inside. So OCR are an important step to get all possible data from a document.

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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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YURI MARX GOMES · Nov 18, 2020 7m read
Creating a PEX Business Operation

PEX is a new InterSystems IRIS feature to allows extends IRIS with existent features from Java or .NET.

It is possible create interoperability inbound and outbound adapters, business services (integrate from external to internal) and operations (integrate internal to external).

To create a PEX component it is necessary import .NET (InterSystems.EnsLib.PEX.*) or Java (com.intersystems.enslib.pex.*) packages and extends or implements the properly class.

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How can you allow computers to trust one another in your absence while maintaining security and privacy?

“A Dry Martini”, he said. “One. In a deep champagne goblet.”
“Oui, monsieur.”
“Just a moment. Three measures of Gordons, one of vodka, half a measure of Kina Lillet. Shake it very well until it’s ice-cold, then add a large thin slice of lemon peel. Got it?”
"Certainly, monsieur." The barman seemed pleased with the idea.
Casino Royale, Ian Fleming, 1953

OAuth helps to separate services with user credentials from “working” databases, both physically and geographically. It thereby strengthens the protection of identification data and, if necessary, helps you comply with the requirements of countries' data protection laws.

With OAuth, you can provide the user with the ability to work safely from multiple devices at once, while "exposing" personal data to various services and applications as little as possible. You can also avoid taking on "excess" data about users of your services (i.e. you can process data in a depersonalized form).

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Hello developers!

I submitted for the contest a solution that would allow organizing message notifications and providing information to users of the messenger Telegam. This is achieved by creating two bots: an administrator and an informant. The administrator manages the list of users connected to the bot informator and their access rights.

The Solution based on project

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Oliver Wilms · Oct 5, 2020 2m read
File Passthrough Feeder

IRIS Interoperability Productions formerly known as Ensemble are fun to work with. Yes, I really think my work is fun. I have seen File Passthrough Services and File Passthrough Operations come in handy. At one point we placed test messages in files, then we utilized a File Passthrough Service with Inbound File Adapter to send the contents of the file as a Stream to a File Passthrough Operation with Outbound TCP Adapter.

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My team is working on redesigning an Interoperability solution that currently runs on a HealthShare server that is part of a mirror. Most of the messages are delimited records processed using Complex Record Mapping. We were told to utilize cloud services available in AWS, use containers, autoscaling…


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The productions are components developed with InterSystems IRIS Interoperability module to promote integrations between systems, data sources/targets, web services, API, message channels, etc. Productions are composed by:

1) Business services to get/ingest/get data events or requests;

2) Business operations to send or persist data to repositories, systems, API, web services, etc; and

3) BPL - BPEL flows to orchestrate, mediate, compose and route data from business services to business operations.

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Tony Pepper · May 25, 2016 5m read
Random Read IO Storage Performance Tool


This tool is used to generate random read Input/Output (IO) from within the database. The goal of this tool is to drive as many jobs as possible to achieve target IOPS and ensure acceptable disk response times are sustained. Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis.

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