There are often questions surrounding the ideal Apache HTTPD Web Server configuration for HealthShare.  The contents of this article will outline the initial recommended web server configuration for any HealthShare product. 

As a starting point, Apache HTTPD version 2.4.x (64-bit) is recommended.  Earlier versions such as 2.2.x are available, however version 2.2 is not recommended for performance and scalability of HealthShare.

Hello everyone,

Recently, I've been working on a Business Process that processes a large JSON FHIR message containing up to 50k requests in an array within the JSON.

Currently, the code imports the JSON as a dynamic object from the original message stream, obtains an iterator from it, and processes each request one at a time in a loop.

The performance meets the requirements, even with much larger requests than the one exposed above.

We are experimenting with IIS, as the PWS will be gone in newer versions.

The code which is executed, takes 15ms to run. If we execute it through PWS (REST), there is some overhead and the total execution time is 40ms, which is acceptable. However, if we go through IIS, it takes 150ms or sometimes even more.

Both PWS and IIS are running on the same server as IRIS in this case. No optimisations have been done on IIS.

Any suggestions on where to look/what to optimize on IIS?

[Background]

InterSystems IRIS family has a nice utility ^SystemPerformance (as known as ^pButtons in Caché and Ensemble) which outputs the database performance information into a readable HTML file. When you run ^SystemPerformance on IRIS for Windows, a HTML file is created where both our own performance log mgstat and Windows performance log are included.

You may have heard about our mg-dbx-napi interface for IRIS which provides insanely fast access from Node.js.  If you've been following recent developments in the server-side JavaScript world, you'll be excited to know that mg-dbx-napi also works with Bun.js, the latter proving to be significantly faster than Node.js for many/most purposes.

Of course, if you're a Node.js user, you'll probably wonder how mg-dbx-napi compares with the Native API for Node.js that is included with IRIS.

Online document says: TUNE TABLE updates the SQL table definition (and therefore requires privileges to alter the table definition). Commonly, TUNE TABLE also updates the corresponding persistent class definition. This allows the gathered statistics to be used by the query optimizer without requiring a class compilation.

I don't get the point. If I run tune table once with  class update and next time no class update,  does this cause inconsistce of sql definition and class definition? What happen if I recompile the class ?

It seems like yesterday when we did a small project in Java to test the performance of IRIS, PostgreSQL and MySQL (you can review the article we wrote back in June at the end of this article). If you remember, IRIS was superior to PostgreSQL and clearly superior to MySQL in insertions, with no big difference in queries.

Well, shortly after @Dmitry Maslennikov told me "Why don't you test it from a Python project?" Well, here is the Python version of the tests we previously performed using the JDBC connections.

Windows Subsystem for Linux (WSL) is a feature of Windows that allows you to run a Linux environment on your Windows machine, without the need for a separate virtual machine or dual booting. 

WSL is designed to provide a seamless and productive experience for developers who want to use both Windows and Linux at the same time**.

WSL 2 is the default distro type when installing a Linux distribution. WSL 2 uses virtualization technology to run a Linux kernel inside of a lightweight utility virtual machine (VM). Linux distributions run as isolated containers inside of the WSL 2 managed VM.

Most transactional applications have a 70:30 RW profile. However, some special cases have extremely high write IO profiles.

I ran storage IO tests in the ap-southeast-2 (Sydney) AWS region to simulate IRIS database IO patterns and throughput similar to a very high write rate application.

The test aimed to determine whether the EC2 instance types and EBS volume types available in the AWS Australian regions will support the high IO rates and throughput required.

Minimal tuning was done in the operating system or IRIS (see Operating System and IRIS configuration below).

Hi Community,

Watch this video to learn how InterSystems software enables both horizontal and vertical scaling:

⏯ Scaling for User and Data Volume in InterSystems IRIS

InterSystems IRIS offers various ways how to profile your code, in most cases it produces enough information to find the places where the most time is spent or where the most global sets. But sometimes it's difficult to understand the execution flow and how it ended at that point. 

To solve this, I've decided to implement a way to build a report in a way, so, it's possible to dive by stack down

Hello,

I would like to get a list of all globals that have been read or written during a given context. In Portal, there are counters in dashboard that give the number of read/write to globals in general.

What I am looking for : 

- some handler (eg: like $ZTRAP) that will be called everytime something is read/written to a global.

- to activate a "global log mode" in Portal that will dump some information to a file (like ^ISCSOAP for SOAP requests).

I understand this is something that can considerably slow down IRIS, but it's intended to be used only for debbuging and under no load.

I am often asked to review customers' IRIS application performance data to understand if system resources are under or over-provisioned.

This recent example is interesting because it involves an application that has done a "lift and shift" migration of a large IRIS database application to the Cloud. AWS, in this case.

A key takeaway is that once you move to the Cloud, resources can be right-sized over time as needed. You do not have to buy and provision on-premises infrastructure for many years in the future that you expect to grow into.

Continuous monitoring is required. Your application transaction rate will change as your business changes, the application use or the application itself changes. This will change the system resource requirements. Planners should also consider seasonal peaks in activity. Of course, an advantage of the Cloud is resources can be scaled up or down as needed.

For more background information, there are several in-depth posts on AWS and IRIS in the community. A search for "AWS reference" is an excellent place to start. I have also added some helpful links at the end of this post.

AWS services are like Lego blocks, different sizes and shapes can be combined. I have ignored networking, security, and standing up a VPC for this post. I have focused on two of the Lego block components;

• Compute requirements.
• Storage requirements.

I deployed the IRIS container on my Mac M1 Docker Desktop Kubernetes cluster:

I limited the container 1.5Gb memory:

In the "merge.cpf" file I constrained IRIS memory usage aspects:

Now I load-test the container by multiple installing  and uninstalling the %ZPM package:

• install ZPM (zpm-installer.routine and execution):

Hey Developers,

Enjoy watching the new video on InterSystems Developers YouTube:

⏯ System Performance Review for InterSystems IRIS Applications @ Global Summit 2022

Hi guys,

I'm working on a project with my client.
They have a visit table which has about 7,000,000 records. The table is used in a random search page witch holds 20+ conditions to be combined.
The table is defined as below:

CREATETABLEVARCHAR

A good writer is supposed to draw you in with the title and bury the answer somewhere in the article.  I suppose this makes me a bad writer – don’t think less of me, my whole feeling of self-worth comes from the opinions of internet strangers! 

In terms of general through-put design and long term support, I'm considering what would be a "best approach" for needing to create multiple batch files in a few different layouts from the same data-sets.

I need to iterate over a few very large tables to generate consistent batch outputs for different partner applications that need to be working on the same "version" of the source data.

YASPE is the successor to YAPE (Yet Another pButtons Extractor). YASPE has been written from the ground up with many internal changes to allow easier maintenance and enhancements.

YASPE functions:

• Parse and chart InterSystems Caché pButtons and InterSystems IRIS SystemPerformance files for quick performance analysis of Operating System and IRIS metrics.
• Allow a deeper dive by creating ad-hoc charts and by creating charts combining the Operating System and IRIS metrics with the "Pretty Performance" option.
• The "System Overview" option saves you from searching your SystemPerformance files for system details or common configuration options.

YASPE is written in Python and is available on GitHub as source code or for Docker containers at:

• https://github.com/murrayo/yaspe

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?

InterSystems Data Platforms and performance - Part 5 Monitoring with SNMP

In previous posts I have shown how it is possible to collect historical performance metrics using pButtons. I go to pButtons first because I know it is installed with every Data Platforms instance (Ensemble, Caché, …). However there are other ways to collect, process and display Caché performance metrics in real time either for simple monitoring or more importantly for much more sophisticated operational analytics and capacity planning.

While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".

After this session I thought it would be a nice exercise to try and provide some easier human interface for this.

The first step in this was to wrap a class-based API to the existing pButtons routine.

I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.

The next step was to add on top of this API, a REST API class.

With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.

For example -

Hello IRIS Community,

InterSystems Certification is developing a certification exam for IRIS system administrators and, if you match the exam candidate description given below, we would like you to beta test the exam. The exam will be available for beta testing on June 20-23, 2022 at InterSystems Global Summit 2022, but only for Summit registrants (visit this page to learn more about Certification at GS22) . Beta testing will open for all other interested beta testers on July 18, 2022. However, interested beta testers should sign up now by emailing certification@intersystems.com

Could anyone please explain why Caché has a %STARTSWITH function while it supports ANSI SQL "LIKE"?

thank you very much.

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.

Since Caché 2017 the SQL engine has included new set of statistics. These record the number of times a query is executed and the time it takes to run.

This is a gold mine for anyone monitoring and trying to optimize the performance of an application that includes many SQL statements but it isn’t as easy to access the data as some people want.

This article and the associated sample code explains how to use this information and how to routinely extract a summary of daily statistics and keep a historic record of the SQL performance of your application.

What is recorded?

Hello,

I'm using the InterSystems Iris odbc driver (DriverODBCVer=02.10) to get data from Iris table but it is slow.

Is there any options to improve performance ? Increase the packet size ?

Change to do in odbcinst.ini / odbc.ini to make it quicker ?

Thanks

This post provides guidelines for configuration, system sizing and capacity planning when deploying IRIS and IRIS on a VMware ESXi. This post is based on and replaces the earlier IRIS-era guidance and reflects current VMware and InterSystems recommendations.

Last update Jan 2026. These guidelines are a best effort, remember requirements and capabilities of VMware and IRIS can change.

I jump right in with recommendations assuming you already have an understanding of VMware vSphere virtualization platform.

I am surprised with the performance (poor) of Native API for .NET

       Following code (where ^testGlobal,node1 has 50 records) takes 5 seconds to run.  Anyone else think there is room for the improvement? 

    string global = "^testGlobal";
            object[] Subs = new object[1];
            Subs[0] = "node1";

            try
            {
                IRISIterator iter = iris.GetIRISIterator(global, Subs); 
               foreach (var item in iter)
                {        
                    Console.WriteLine((string)(iter.CurrentSubscript));
                }               

            }
            catch (Exception ex)
            {

            }

One of my colleagues had developed an interface in Health Connect (HealthShare 2019.1) to add large amounts of data to an external SQL Server database. The data comes from many text files with delimited rows and data for one table per file. There is a business process to read a file line by line and send an Insert Request to an operation. The request contains an Insert statement like ‘Insert into TABLE columns (col1, col2, … colZ) values (val1, val2, … valZ).’ The Health Connect operation utilizes Outbound SQL Adapter to insert one row into a table per request.