InterSystems IRIS

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InterSystems Official
Steve Brunner · Jan 31, 2018
InterSystems IRIS Data Platform 2018.1.0 Release

InterSystems is pleased to announce that InterSystems IRIS Data Platform 2018.1.0 is now released.

This press release was issued this morning.

InterSystems IRIS is a complete, unified data platform that makes it faster and easier to build real-time, data-rich applications.  It mirrors our design philosophy that software should be interoperable, reliable, intuitive, and scalable.

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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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In the last post we scheduled 24-hour collections of performance metrics using pButtons. In this post we are going to be looking at a few of the key metrics that are being collected and how they relate to the underlying system hardware. We will also start to explore the relationship between Caché (or any of the InterSystems Data Platforms) metrics and system metrics. And how you can use these metrics to understand the daily beat rate of your systems and diagnose performance problems.

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Container Images

In this second post on containers fundamentals, we take a look at what container images are.

What is a container image?

A container image is merely a binary representation of a container.

A running container or simply a container is the runtime state of the related container image.

Please see the first post that explains what a container is.

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Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.

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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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Recently viewed a demo on the new Dynamic Objects in InterSystems (we are still running 2016.1); together with the upcoming IRIS data platform I started thinking about possibilities of building dynamic databases. That is, I can imagine some projects were it might be really nice to store dynamic objects and then run sql queries on them without ever defining the fields of the dynamic objects (i.e. not at storage, but only if you run your sql query).

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I am often asked by customers, vendors or internal teams to explain CPU capacity planning for large production databases running on VMware vSphere.

In summary there are a few simple best practices to follow for sizing CPU for large production databases:

  • Plan for one vCPU per physical CPU core.
  • Consider NUMA and ideally size VMs to keep CPU and memory local to a NUMA node.
  • Right-size virtual machines. Add vCPUs only when needed.

Generally this leads to a couple of common questions:

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Learning Services Live Webinars are back! 

At this year’s Global Summit, InterSystems debuted InterSystems IRIS Data Platform™, a single, comprehensive product that provides capabilities spanning data management, interoperability, transaction processing, and analytics. InterSystems IRIS sets a new level of performance for the rapid development and deployment of data-rich and mission-critical applications. Now is your chance to learn more! 

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This summer the Database Platforms department here at InterSystems tried out a new approach to our internship program.  We hired 10 bright students from some of the top colleges in the US and gave them the autonomy to create their own projects which would show off some of the new features of the InterSystems IRIS Data Platform.  The team consisting of Ruchi Asthana, Nathaniel Brennan, and Zhe “Lily” Wang used this opportunity to develop a smart review analysis engine, which they named Lumière.   As they explain:

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Hello.

The idea of this post is to introduce Frontier: An abstraction layer that allows Rapid REST development.

REQUIREMENTS:

Why?

Have you ever found yourself dealing with repetitive tasks like mounting objects, serializing them and eventually handling multiple errors for multiple cases? Frontier can boost your development by making you focus on what really matters: your application.
 

Frontier is made to stop you from WRITE'ing by instead forcing your methods to return values.
It's designed to make you code clean, and you'll see the why pretty soon.

This is the Part 1, where you'll learn he basics about how to work with Frontier. That means at the end of this part you should be capable of 

creating GET requests without difficulties. Since this also serves as a way to introduce the framework, I'll be calling this part: Core concepts.

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As IRIS was announced yesterday by Paul Grabscheid I would be highly interested to get in touch with it.

- How can we get qualified for a closer view ? 
- Where can we get some (preliminary) description + documentation.
- Is there any FT available ?

 I'm especially interested in the areas around included/embedded/attached JVM and SPARK .

 

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This post will show you an approach to size shared memory requirements for database applications running on InterSystems data platforms including global and routine buffers, gmheap, and locksize as well as some performance tips you should consider when configuring servers and when virtualizing Caché applications. As ever when I talk about Caché I mean all the data platform (Ensemble, HealthShare, iKnow and Caché).


A list of other posts in this series is here

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Hyper-Converged Infrastructure (HCI) solutions have been gaining traction for the last few years with the number of deployments now increasing rapidly. IT decision makers are considering HCI when scoping new deployments or hardware refreshes especially for applications already virtualised on VMware. Reasons for choosing HCI include; dealing with a single vendor, validated interoperability between all hardware and software components, high performance especially IO, simple scalability by addition of hosts, simplified deployment and simplified management.

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Globals, these magic swords for storing data, have been around for a while, but not many people can use them efficiently or know about this super-weapon altogether.

If you use globals for tasks where they truly shine, the results may be amazing, either in terms of increased performance or dramatic simplification of the overall solution (1, 2).

Globals offer a special way of storing and processing data, which is completely different from SQL tables. They were first introduced in 1966 in the M(UMPS) programming language, which was initially used in medical databases. It is still used in the same way, but has also been adopted by some other industries where reliability and high performance are top priorities: finance, trading, etc.

Later M(UMPS) evolved into Caché ObjectScript (COS). COS was developed by InterSystems as a superset of M. The original language is still accepted by developers' community and alive in a few implementations. There are several signs of activity around the web: MUMPS Google group, Mumps User's group), effective ISO Standard, etc.

Modern global based DBMS supports transactions, journaling, replication, partitioning. It means that they can be used for building modern, reliable and fast distributed systems.

Globals do not restrict you to the boundaries of the relational model. They give you the freedom of creating data structures optimized for particular tasks. For many applications reasonable use of globals can be a real silver bullet offering speeds that developers of conventional relational applications can only dream of.

Globals as a method of storing data can be used in many modern programming languages, both high- and low-level. Therefore, this article will focus specifically on globals and not the language they once came from.

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Beginning - see Part 1.

 

3. Variants of structures when using globals

 

A structure, such as an ordered tree, has various special cases. Let's take a look at those that have practical value for working with globals.

 

 

 

 

3.1 Special case 1. One node without branches

 

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Created by Daniel Kutac, Sales Engineer, InterSystems

 

Part 3. Appendix

InterSystems IRIS OAUTH classes explained

In the previous part of our series we have learned about configuring InterSystems IRIS to act as an OAUTH client as well as authorization and authentication server (by means of OpenID Connect). In this final part of our series we are going to describe classes implementing InterSystems IRIS OAuth 2.0 framework. We will also discuss use cases for selected methods of API classes.

The API classes implementing OAuth 2.0 can be separated into three different groups according to their purpose. All classes are implemented in %SYS namespace. Some of them are public (via % package), some not and should not be called by developers directly.

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In this post, I am going to detail how to set up a mirror using SSL, including generating the certificates and keys via the Public Key Infrastructure built in to Caché. The goal of this is to take you from new installations to a working mirror with SSL, including a primary, backup, and DR async member, along with a mirrored database. I will not go into security recommendations or restricting access to the files. This is meant to just simply get a mirror up and running.

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Article
Peter Steiwer · Mar 10, 2017 1m read
DeepSee Tips & Tricks: Auto-execute

By default, Analyzer executes queries as components are added. Often, there are times where you may know exactly what you are looking to do, and you do not want Analyzer to execute 5 queries as you drag and drop items onto rows/columns and select your filters. This is where toggling Auto-execute off can help.

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** Revised Feb-12, 2018

While this article is about InterSystems IRIS, it also applies to Caché, Ensemble, and HealthShare distributions.

Introduction

Memory is managed in pages.  The default page size is 4KB on Linux systems.  Red Hat Enterprise Linux 6, SUSE Linux Enterprise Server 11, and Oracle Linux 6 introduced a method to provide an increased page size in 2MB or 1GB sizes depending on system configuration know as HugePages.

At first HugePages required to be assigned at boot time, and if not managed or calculated appropriately could result in wasted resources.  As a result various Linux distributions introduced Transparent HugePages with the 2.6.38 kernel as enabled by default.  This was meant as a means to automate creating, managing, and using HugePages.  Prior kernel versions may have this feature as well however may not be marked as [always] and potentially set to [madvise].  

Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages.  However in current Linux releases THP can only map individual process heap and stack space.

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One of the great availability and scaling features of Caché is Enterprise Cache Protocol (ECP). With consideration during application development distributed processing using ECP allows a scale out architecture for Caché applications. Application processing can scale to very high rates from a single application server to the processing power of up to 255 application servers with no application changes.

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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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