Performance tag groups posts regarding software performance issues and the best practices on solving and monitoring performance issues.
When looking at system performance and capacity planning I need to know what processors a server is running.
In ^SystemPerformance Linux systems report Intel processors explicitly, for example:
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 79
model name : Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz
stepping : 1
microcode : 0xffffffff
cpu MHz : 2294.Hi,
I have a large XML zen report file that I want to convert to a CSV, what is the most efficient way to do it.
XML file:
<Details> <Col1> aa1 </Col1> <Col2> bb1</Col2> <Col3> cc1 </Col3> </Details> <Details> <Col1> aa2 </Col1> <Col2> bb2</Col2> <Col3> cc3 </Col3> </Details>
Expected Output:
Col1,Col2,col3 aa1,bb1,bb1 aa2,bb2,bb2
Thank you.
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.
This post will guide you through the process of sizing shared memory requirements for database applications running on InterSystems data platforms. It will cover key aspects such as global and routine buffers, gmheap, and locksize, providing you with a comprehensive understanding. Additionally, it will offer performance tips for configuring servers and virtualizing IRIS applications. Please note that when I refer to IRIS, I include all the data platforms (Ensemble, HealthShare, iKnow, Caché, and IRIS).
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.
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 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 the
^pButtonscommand, you can replace it with^SystemPerformance.
Could anyone please explain why Caché has a %STARTSWITH function while it supports ANSI SQL "LIKE"?
thank you very much.
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.
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?
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.
In the first article in this series, we’ll take a look at the entity–attribute–value (EAV) model in relational databases to see how it’s used and what it’s good for. Then we'll compare the EAV model concepts to globals.
Sometimes you have objects with an unknown number of fields, or perhaps hierarchically nested fields, for which, as a rule, you need to search.
Take, for example, an online store with diverse groups of products. Each product group has its own unique set of properties and has common properties as well.
Do you ever have the desire to optimize your code as much as possible?
Do you have any cases where every nanosecond matters?
Do you use If statements?
This quick tip can help your code run slightly faster with no additional code.
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.
Please see PerfTools IO Test Suite for a later version of the Random Read IO tool.
This tool is used to generate random read Input/Output (IO) from within the database. The goal of this tool is to drive as many jobs as possible to achieve target IOPS and ensure acceptable disk response times are sustained. Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis.
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.
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.
ECP was used widely for many years in TrakCare deployments I was involved in. A decade ago a 'big' x86 server from one of the major vendors might only have a total of eight cores.
Often InterSystems technology architect team is asked about recommended storage arrays or storage technologies. To provide this information to a wider audience as reference, a new series is started to provide some of the results we have encountered with various storage technologies. As a general recommendation, all-flash storage is highly recommended with all InterSystems products to provide the lowest latency and predictable IOPS capabilities.
The first in the series was the most recently tested Netapp AFF A300 storage array. This is middle-tier type storage array with several higher models above it. This specific A300 model is capable of supporting a minimal configuration of only a few drives to hundreds of drives per HA pair, and also capable of being clustered with multiple controller pairs for tens of PB's of disk capacity and hundreds of thousands of IOPS or higher.
AWS launched their first generation of Amazon EC2 A1 instances last year, powered by Arm-based AWS Graviton processors. At AWS re:Invent 2019, Amazon announced the second-generation AWS Graviton2 processors and associated Amazon EC2 M6g instance type, boasting up to 40% better price performance over current generation Intel Xeon based M5 instances.
The AWS Graviton2-based M6g instances are currently in preview, and InterSystems jumped on the opportunity to measure their performance with the InterSystems IRIS Data Platform.
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.
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!
Date range queries going too slow for you? SQL Performance got you down? I have ANOTHER weird trick that SQL Developers don't want you to know!*
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.
This is the first in a pair of articles on SQL indices.
Picture the last time you went to a library. Typically they have books sorted by subject matter (and then author and title), and each shelf has an end-plate with a code describing the subject of its books. If you wanted to collect books of a certain subject, instead of walking across every aisle and reading the inside cover of every book, you could head straight for the bookshelf labelled with your desired subject matter and choose your books.
This document describes considerations around SDDC and HCI solutions for InterSystems Clients.
Software Defined Data Centers (SDDC) and Hyper-Converged Infrastructure (HCI) – Important Considerations for InterSystems Clients
A growing number of IT organizations are exploring the potential use of SDDC and HCI solutions. These solutions appear attractive and are marketed as simplification of IT management and potential cost reductions across heterogeneous data centers and cloud infrastructure options.
Note (October 2022): yape has been deprecated and replaced by YASPE, there is no more development on yape.
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.
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.
Hello Community,
I recently encountered a issue with Caché and I can't figure out where the problem is coming from.
I noticed that the license limit (200) was reached whenever I was opening my Studio (so it seems). When this occurs, I restart Caché (with the Cube in the Taskbar), and the number of license used is back to 1%, but grows back after. The time taken before the number of license grows back again looks pretty random.
Here is a couple of screenshots :
Does anybody know where the problem might come from ?
APM normally focuses on the activity of the application but gathering information about system usage gives you important background information that helps understand and manage the performance of your application so I am including the IRIS History Monitor in this series.
In this article I will briefly describe how you start the IRIS or Caché History Monitor to build a record of the system level activity to go with the application activity and performance information you gather. I will also give examples of SQL to access the information.
See Part 1 here.
Now you have a good idea of what kind of indices you need for your class and how to define them. Next, how do you handle them?
(REMEMBER: Like any modifications to a class, adding indices in a live system has its risks – if users are accessing or updating data while an index is populated, they may encounter empty or incorrect query results, or even corrupt the indices that are being built.
It has been noticed that some customers running JAVA programs (for example, FOP) on AIX would see the server eventually running low then out of memory. Customer would notice the system pages heavily and user experience becomes bad. And the server would crash when out of memory.
When the problem happens, we can see in ipcs a lot of shared memory segment marked for deletion (Capital D at the beginning of MODE section). This means they will not disappear until the last process attached to the segment detaches it.