Hi Developers!

Often when we develop some library, tool, package, whatever on InterSystems ObjectScript we have a question, how we deploy this package on the target machine?

Also, we often expect that some other libraries already installed, so our package depends on them, and often on some particular version of it.

When you code on javascript, python, etc the role of packages deployment with dependency management takes package manager.

So, I'm pleased to announce that InterSystems ObjectScript Package Manager available!

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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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Article
Robert Cemper · Feb 8, 2019 2m read
Client for WebSockets based on CSP
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 !

WebSockets as a communication technology wins increasing importance.
In the SAMPLES namespace, you find a nice example for running a WebSocket Server.
There is also a useful example for a Browser Client. But it is still in the browser. 

My point is:
How to consume the output of a WebSocket Server in your application?

 

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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

 

Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

Extracting and plotting pButtons data including timeframes and iostat.

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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!

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Article
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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++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).

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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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or "So you just got yelled at by your boss, for sending him an unformatted Hello World webpage"

Our previous lesson ended with us serving a Message value obtained from a Caché REST service to the client, using Angular as a runtime.  While there is a lot of moving parts involved in this process, the page is not especially exciting at the moment.  Before we can start adding new features, we should take a step back and review our tools.

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Last week saw the launch of the InterSystems IRIS Data Platform in sunny California.

For the engaging eXPerience Labs (XP-Labs) training sessions, my first customer and favourite department (Learning Services), was working hard assisting and supporting us all behind the scene.

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

 

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 !

Background

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.

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This post provides guidelines for configuration, system sizing and capacity planning when deploying Caché 2015 and later on a VMware ESXi 5.5 and later environment.

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Article
Richard Zimmermann · Aug 21, 2019 2m read
ToolBox-4-Iris

Hello everyone,
After some work with IRIS we want to share our ToolBox-4-Iris with you.

What is this about?

The ToolBox-4-Iris is an API for IRIS with a collection of handy and useful tools - features that are not available in IRIS, but greatly simplify application development. To save time and effort on the "typical tools" that every developer needs. This includes additional classes, individual methods or even more efficient macros, which are described in the respective packages.

Content

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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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Over the past year or so, my team (Application Services at InterSystems - tasked with building and maintaining many of our internal applications, and providing tools and best practices for other departmental applications) has embarked on a journey toward building Angular/REST-based user interfaces to existing applications originally built using CSP and/or Zen. This has presented an interesting challenge that may be familiar to many of you - building out new REST APIs to existing data models and business logic.

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Article
Robert Cemper · Apr 25, 2020 2m read
Semi-Persistent Classes and Tables

If you define a Persistent Class / Table the class compiler generates for you an appropriate Storage definition.
A different option is to define a SQL mapping for an already existing Global storage.  This has been excellently
explained already in a different series of articles.   The Art of Mapping Globals to Classes 1 of 3

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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.

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Article
Robert Cemper · Jul 29, 2020 5m read
SQL for ^ERROR Global
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 !

Standard error logs in IRIS / Caché / Ensemble are written global ^ERRORS.
As this piece dates back some decades back to previous millennium its structure
is far from the typical SQL storage structures.

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Article
Mikhail Khomenko · Feb 13, 2017 14m read
Creating custom SNMP OIDs

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.

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"Telegram" is a well-known instant messenger, which provides an API for creating bots. The features of this API allow you to create bots with a wide range of functionality including receiving payments. 
With the help of the telegram bot, I solved a simple task - sending Alerts from Ensemble to Telegram.

Advantages: Alerts come to the mobile phone, a notification appears, so there is no need to install any additional applications (in contrast to the solution https://community.intersystems.com/post/sending-alerts-mobile-phone-using-pushover- httpoutboundadapter).

The bot can be extended by adding new commands, for example, for managing the Production or for solving other tasks in Iris, Ensemble or Cache.

 

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