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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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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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 have attached a document that describes the product I have developed called NiPaRobotica Pharmacy. This is an interface I developed that accepts Pharmacy Dispense Requests and converts the line items on the order into dispense dialogues which it sends to pharmacy robots. I deployed the interface into 3 Hospital pharmacies two of which had 6 robots that were arranged in such a way that the dispense chutes channelled medications to desks by the pharmacists sitting in windows serving 1200 patients a day. The robots cut the average waiting time from 2 hours down to one hour.

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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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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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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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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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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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I'd like to share with you some storage features that also exist in Caché but are almost 
unknown and mostly not used. They are of course available in IRIS and gain importance
with large and distributed storage architectures.

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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 4, 2020 2m read
WebSocket Client IRIS internal
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 !

IRIS 2020.1 brought us a new feature: %Net.WebSocket.Client

As a continuation of my series of WS Clients I just couldn't resist trying it.
Well, this is the result and it was rather simple in the end.

After I succeeded in my personal fight against Windows Firewall ;-)

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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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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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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 · Feb 8, 2021 3m read
WebSocket Client with embedded Python

This is a demo to make use of a simple WebSocket Client with Embedded Python in IRIS.

To continue my series of WebSocket Client I have added an example written in Python.
The most impressive experience was how easy the writing and testing of the client was
which happened total offline from IRIS.
Embedding, running and feeding the client with data from IRIS was also incredibly simple.

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Over the last couple of weeks the Solution Architecture team has been working to finish off our 2019 workload: this included open-sourcing the Readmission Demo that was brought to HIMSS last year, so we could make it available to anyone looking for an interactive-way of exploring the tooling provided by IRIS.

 

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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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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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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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I have just created a new Global Master Topic, "IRIS Cheatsheets". IRIS has introduced a lot of new functionality, especially in scripting languages, FHIR R4 support, enhanced Interoperability Tools, and IRIS Analytics. Having spent 35 years working on Windows-based PC's and Laptops, I have surprisingly little knowledge of Linux, Docker and Git. Furthermore, I have written almost every application and Interface in ObjectScript with splatterings of SQL, .Net, and Java Gateways and the most basic knowledge of WinSCP, Putty, SSH. All that changed when I received my first Raspberry Pi.

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