++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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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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In this post I show strategies for backing up Caché using External Backup with examples of integrating with snapshot based solutions. The majority of solutions I see today are deployed on Linux on VMware so a lot of the post shows how solutions integrate VMware snapshot technology as examples.

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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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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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Index

This is a list of all the posts in the data platforms capacity planning and performance series in order. Also a general list of my other posts. I will update as new posts in the series are added.


You will notice that I wrote some of the posts before IRIS was released and refer to Caché. I will revisit the posts over time, but in the meantime; Generally, the advice for configuration is the same for Caché and IRIS. Some command names may have changed; the most obvious example is that anywhere you see ^pButtons command, you can replace it with ^SystemPerformance.

Capacity Planning and Performance Series

Generally posts build on previous, but you can also just dive in to subjects that look interesting.


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This week I am going to look at CPU, one of the primary hardware food groups :) A customer asked me to advise on the following scenario; Their production servers are approaching end of life and its time for a hardware refresh. They are also thinking of consolidating servers by virtualising and want to right-size capacity either bare-metal or virtualized. Today we will look at CPU, in later posts I will explain the approach for right-sizing other key food groups - memory and IO.

So the questions are:

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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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The Amazon Web Services (AWS) Cloud provides a broad set of infrastructure services, such as compute resources, storage options, and networking that are delivered as a utility: on-demand, available in seconds, with pay-as-you-go pricing. New services can be provisioned quickly, without upfront capital expense. This allows enterprises, start-ups, small and medium-sized businesses, and customers in the public sector to access the building blocks they need to respond quickly to changing business requirements.

 

Updated: 2-Apr, 2021 

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Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?

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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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Database systems have very specific backup requirements that in enterprise deployments require forethought and planning. For database systems, the operational goal of a backup solution is to create a copy of the data in a state that is equivalent to when application is shut down gracefully.  Application consistent backups meet these requirements and Caché provides a set of APIs that facilitate the integration with external solutions to achieve this level of backup consistency.

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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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Myself and the other Technology Architects often have to explain to customers and vendors Caché IO requirements and the way that Caché applications will use storage systems. The following tables are useful when explaining typical Caché IO profile and requirements for a transactional database application with customers and vendors.  The original tables were created by Mark Bolinsky.

In future posts I will be discussing more about storage IO so am also posting these tables now as a reference for those articles. 

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Note (June 2019): A lot has changed, for the latest details go here

Note (Sept 2018): There have been big changes since this post first appeared, I suggest using the Docker Container version, the project and details for running as a container are still in the same place  published on GitHub so you can download, run - and modify if you need to.

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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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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
Timothy Leavitt · Jul 8, 2020 7m read
Tips for debugging with %Status

Introduction

If you're solving complex problems in ObjectScript, you probably have a lot of code that works with %Status values. If you have interacted with persistent classes from an object perspective (%Save, %OpenId, etc.), you have almost certainly seen them. A %Status provides a wrapper around a localizable error message in InterSystems' platforms. An OK status ($$$OK) is just equal to 1, whereas a bad status ($$$ERROR(errorcode,arguments...)) is represented as a 0 followed by a space followed by a $ListBuild list with structured information about the error. $System.Status (see class reference) provides several handy APIs for working with %Status values; the class reference is helpful and I won't bother duplicating it here. There have been a few other useful articles/questions on the topic as well (see links at the end). My focus in this article will be on a few debugging tricks techniques rather than coding best practices (again, if you're looking for those, see links at the end).

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Article
Guillaume Rongier · Apr 9, 2019 3m read
IRIS/Ensemble as an ETL

IRIS and Ensemble are designed to act as an ESB/EAI. This mean they are build to process lots of small messages.

But some times, in real life we have to use them as ETL. The down side is not that they can't do so, but it can take a long time to process millions of row at once.

To improve performance, I have created a new SQLOutboundAdaptor who only works with JDBC.

BatchSqlOutboundAdapter

Extend EnsLib.SQL.OutboundAdapter to add batch batch and fetch support on JDBC connection.

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Here is an ObjectScript snippet which lets to create database, namespace and a web application for InterSystems IRIS:

    set currentNS = $namespace

    zn "%SYS"

    write "Create DB ...",!
    set dbName="testDB"
    set dbProperties("Directory") = "/InterSystems/IRIS/mgr/testDB"
    set status=##Class(Config.Databases).Create(dbName,.dbProperties)
    write:'status $system.Status.DisplayError(status)
    write "DB """_dbName_""" was created!",!!


    write "Create namespace ...",!
    set nsName="testNS"
    //DB for globals
    set nsProperties("Globals") = dbName
    //DB for routines
    set nsProperties("Routines") = dbName
    set status=##Class(Config.Namespaces).Create(nsName,.nsProperties)
    write:'status $system.Status.DisplayError(status)
    write "Namespace """_nsName_""" was created!",!!


    write "Create web application ...",!
    set webName = "/csp/testApplication"
    set webProperties("NameSpace") = nsName
    set webProperties("Enabled") = $$$YES
    set webProperties("IsNameSpaceDefault") = $$$YES
    set webProperties("CSPZENEnabled") = $$$YES
    set webProperties("DeepSeeEnabled") = $$$YES
    set webProperties("AutheEnabled") = $$$AutheCache
    set status = ##class(Security.Applications).Create(webName, .webProperties)
    write:'status $system.Status.DisplayError(status)
    write "Web application """webName""" was created!",!

    zn currentNS

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Introduction

InterSystems has recently completed a performance and scalability benchmark of IRIS for Health 2020.1, focusing on HL7 version 2 interoperability. This article describes the observed throughput for various workloads, and also provides general configuration and sizing guidelines for systems where IRIS for Health is used as an interoperability engine for HL7v2 messaging.

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Keywords: Deep Learning, Grad-CAM, X-Ray, Covid-19, HealthShare, IRIS

Purpose

Over the Easter Weekend I touched on some deep learning classifier for Covid-19 Lungs.  The demo result seems fine, seemingly matching some academic research publications around that time on this topic. But is it really "fine "? 

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The following steps show you how to display a sample list of metrics available from the /api/monitor service.

In the last post, I gave an overview of the service that exposes IRIS metrics in Prometheus format. The post shows how to set up and run IRIS preview release 2019.4 in a container and then list the metrics.


This post assumes you have Docker installed. If not, go and do that now for your platform :)

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Hi all,

 

I want to share a quick little method you can use to enable ssl with a self signed certificate on your local development instance of IRIS/HealthShare. This enables you to test https-specific features such as OAuth without a huge lift.

 

 

1. Install OpenSSL

Windows: https://slproweb.com/download/Win64OpenSSL_Light-1_1_1g.exe

Debian Linux: $ sudo apt-get -y install openssl

RHEL: $ sudo yum install openssl

 

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The fhir-react project is a React UI framework based on Google Material Design, which covers almost all FHIR resources for versions DSTU2, STU3 and R4.

It design it's really friendly - there's just one component! As FHIR resource types are standards, the framework resolves internally what rendering class must be used.

To display your FHIR resource just write this component:

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This is more for my memory that anything else but I thought I'd share it because it often comes up in comments, but is not in the InterSystems documentation. 

There is a wonderful utility called ^REDEBUG that increases the level of logging going into mgr\cconsole.log. 

You activate it by

a) start terminal/login

b) zn "%SYS"

c) do ^REDEBUG

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