In this article I would like to present the RESTForms project - generic REST API backend for modern web applications.

The idea behind the project is simple -after I wrote several REST APIs I realized that generally, REST API consists of two parts:

  • Work with persistent classes
  • Custom business logic

And, while you'll have to write your own custom business logic, RESTForms provides all things related to working with persistent classes right out of the box.
Use cases

  • You already have a data model in Caché and you want to expose some (or all) of the information in a form of REST API
  • You are developing a new Caché application and you want to provide a REST API
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Article
Robert Cemper · Mar 5, 2021 3m read
Using ECP across IRIS and Caché

Migration from Caché to IRIS can be quite a challenge if your code is grown over many years
and probably not so clean structured as you may like it. So you face the need to check your
migrated code against some reference data. A few samples might not be a problem,
but some hundred GB of data for testing might be.

A possible step could be to have your fresh code in IRIS but leave your huge datastore on Caché and connect both environments over ECP. I have created a demo project that gives you the opportunity to try this based on 2 Docker images with IRIS and with Caché connected over ECP.

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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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I bet that not everyone familiar with InterSystems Caché knows about Studio extensions for working with the source code. You can actually use the Studio to create your own type of source code, compile it into interpretable (INT) and object code, and sometimes even add code completion support. That is, theoretically, you can make the Studio support any programming language that will be executed by the DBMS just as well as Caché ObjectScript. In this article, I will give you a simple example of writing programs in Caché Studio using a language that resembles JavaScript. If you are interested, please read along.

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Article
Peter Cooper · Mar 18, 2018 1m read
Replacing ZEN - Index to articles

Hi All
This is the index to a series of articles I hope to create over the coming months.

ZEN and ZEN Mojo are no longer being actively developed by Intesystems - this is a great shame as it is a fine product that works so well for business applications.
However ZEN is a 15 year old product and I need a path forward to replace the ZEN UI with a supported development framework.

This article is an index of the other articles I have, or plan to write. - the articles will be subject to change as I develop my thoughts and climb the learning curve.

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Are you all ready for something you wish you knew ages ago (or, in my case, a DECADE ago)? Open up a portal in your favorite instance and go to:

System Administration->Configuration->Additional Settings->Startup

Scroll down to "Terminal Prompt" and click 'Edit'. This allows you to edit what you see on your terminal prompt. You can change that to my current setting: 8,3,2

What does this do? It adds your instance name for your prompt. So now your prompt can look like:

DEVELOPMENT:USER>

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Article
Robert Cemper · Apr 26, 2020 5m read
Materialized Views

A VIEW in SQL is basically a prepared SQL statement.
It has to be executed and assembled like any other SQL query.
MATERIALIZED VIEW means that the content is collected before hands and can be retrieved rather fast.
I saw the concept first with my favorite competitor named O* and they made a lot of noise about it.

{ favorite: because I could win every benchmark against them devil }

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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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In my earlier article on Adopting Bitmaps I described the technique already.
Now you can find a code example also on Open Exchange.

This is a coding example working on Caché 2018.1.3 and IRIS 2020.2 
It will not be kept in sync with new versions 
It is also NOT serviced by InterSystems Support !

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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.

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Healthcare interoperability is instrumental in improving patient care, decreasing healthcare provider costs, and providing a more accurate picture to providers. However, with so many different systems, data is formatted in many different ways. There are many standards that have been created to try to solve this problem, including HL7v2, HL7v3, and CDA but each one has its drawbacks.

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Article
Fabian Haupt · Sep 2, 2016 2m read
Advanced URL mapping for REST

By now it's a commonplace how to implement a basic REST API in Caché and there is good documentation about it here: REST in Caché

A question that comes up from time to time is:

How can I make a parameter in my REST url optional?

Simply put, is it possible to create a URL map in Caché that maps a URL like this:

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Article
Vicky Li · Nov 14, 2016 14m read
Mastering the JDBC SQL Gateway

As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.

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Article
Robert Cemper · Feb 23, 2018 2m read
M:N Relationship

If you have worked with Caché Objects,
You know already all about Relationships (one:many , parent:child) ...
But you will not find a word on many:many relationships in the docs.

But I met the question quite often from new adopters of Caché objects:
"Is it possible to implement many:many relationships ?" YES - of course !

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Article
Robert Cemper · Jul 20, 2020 2m read
SPOOL as SQL Table

Device #2 named SPOOL dates back to the predecessors of Caché and IRIS
It was the first "%Stream" like option to buffer output before printing.
It is also the first and still today the most simple way of output redirection.

Solution

This is also an example of a mapped Global. USE 2 write "actual date: ",$ZDT($h,3),!
redirects the output into the Global ^SPOOL

You can read the global manually or with some ancient utilities
or use this mapping to access it as class or a table with SQL.

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Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.

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