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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Suppose you have developed your own app with InterSystems technologies stack and now want to perform multiple deployments on the customers' side. During the development process you've composed a detailed installation guide for your application, because you need to not only import classes, but also fine-tune the environment according to your needs.
To address this specific task, InterSystems has created a special tool called %Installer. Read on to find out how to use it.

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Enterprises need to grow and manage their global computing infrastructures rapidly and efficiently while simultaneously optimizing and managing capital costs and expenses. Amazon Web Services (AWS) and Elastic Compute Cloud (EC2) computing and storage services meet the needs of the most demanding Caché based application by providing
 a highly robust global computing infrastructure.

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++ Update: August 1, 2018

The use of the InterSystems Virtual IP (VIP) address built-in to Caché database mirroring has certain limitations. In particular, it can only be used when mirror members reside the same network subnet. When multiple data centers are used, network subnets are not often “stretched” beyond the physical data center due to added network complexity (more detailed discussion here). For similar reasons, Virtual IP is often not usable when the database is hosted in the cloud.

Network traffic management appliances such as load balancers (physical or virtual) can be used to achieve the same level of transparency, presenting a single address to the client applications or devices. The network traffic manager automatically redirects clients to the current mirror primary’s real IP address. The automation is intended to meet the needs of both HA failover and DR promotion following a disaster.

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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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Importance and Collection of Exact Version Information ($zv / $zversion)
The explanation of:
- why collecting $zv is important (The WHY),
- what the components of $zv mean (The WHAT),
- and how to collect $zv (The HOW).
(The Ultimate $zv Guide to the ISC Galaxy in large, friendly letters)
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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 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 ^pButtons command, you can replace it with ^SystemPerformance.


While some posts are updated to preserve links, others will be marked as strikethrough to indicate that the post is legacy. Generally, I will say, "See: some other post" if it is appropriate.


Capacity Planning and Performance Series

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


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Ansible helped me solve the problem of quickly deploying Caché and application components for Data Platforms benchmarks. You can use the same tools and methodology for standing up your test labs, training systems, development or other environments. If you deploy applications at customer sites you could automate much of the deployment and ensure that system, Caché and your application are configured to your applications best practice standards.

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Article
· Sep 22, 2016 3m read
Windows write caching

(This article was reviewed in February 2021. It is still relevant to Caché-based installations and similarly applies to IRIS-based installations.)

This article discusses the Windows write caching setting which can leave systems vulnerable to data loss or corruption in the event of power loss or operating system crash. The setting is on by default in some Windows configurations.

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Article
· Aug 26, 2016 8m read
Enterprise Monitor and HealthShare

Enterprise Monitor is a component of Ensemble and can help organizations monitor multiple productions running on different namespaces within the same instance or namespaces running on multiple instances.

Documentation can be found at:

http://docs.intersystems.com/ens20161/csp/docbook/DocBook.UI.Page.cls?KEY=EMONITOR_all#EMONITOR_enterprise

In Ensemble 2016.1 there were changes made to make this utility work with HealthShare environments.

This article will:

  • Show how to set up Enterprise Monitor for HealthShare sites
  • Show some features of Enterprise Monitor
  • Show some features of Enterprise Message Viewer

For this article, I used the following version of HealthShare:

Cache for Windows (x86-64) 2016.1 (Build 656U) Fri Mar 11 2016 17:42:42 EST [HealthShare Modules:Core:14.02.2415 + Linkage Engine:14.02.2415 + Patient Index:14.02.2415 + Clinical Viewer:14.02.2415 + Active Analytics:14.02.2415]

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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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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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This text is a continuation of my article where I explained the structure a Caché database. In this article, I described the types of blocks, connections between them and their relation to globals. The article was purely theoretical. I made a project that helps visualize the block tree - and this article will explain how it works in great detail.

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When running the built in Ensemble Purge task (Ens.Util.Tasks.Purge) there are three parameters DaysToKeep, BodiesToo, and KeepIntegrity. This article focuses on the KeepIntegrity boolean parameter, but more information about running this task can be found here:

http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=...

KeepIntegrity determines which Ensemble Messages are marked for deletion within the time specified by DaysToKeep.

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Article
· Nov 9, 2016 5m read
How the Ensemble Scheduler Works

The Ensemble Scheduler is used for automatically turning on and off business hosts at certain dates and times. You could use it if, for example, you wanted to only run a business host from 9am to 5pm every day. Conversely, if you want do to trigger an event to occurr at a specific time, for example, a job running at 1am to batch up and send off all the previous day's transactions in one file, we recommend other methods such as the Task Manager.

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Here are a few examples of conversions and operations you might need, along with links to documentation where you can learn more.

At the time I wrote this, Eastern Daylight Time was in effect for my Caché system.

How Caché keeps the time and date

Caché has a simple time format, with a longer range of recognized dates compared to some other technologies.

The current time is maintained in a special variable $HOROLOG ($H):

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After reading Stephen Wilson's article "Windows 7 performs shutdown too fast for Cache to close and so it gets forced down" I've recalled another solution that was based on Local Group Policy, which allows to control extra actions that should occur on computer startup or shutdown. How to add a computer shutdown script is well documented in MS Technet article.

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A short post for now to answer a question that came up. In post two of this series I included graphs of performance data extracted from pButtons. I was asked off-line if there is a quicker way than cut/paste to extract metrics for mgstat etc from a pButtons .html file for easy charting in Excel.

See: - Part 2 - Looking at the metrics we collected

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Article
· Nov 8, 2016 4m read
Returning Disk Space to File Systems

This is the first article in a series discussing how to regain disk space from Caché databases at the operating system level. This introductory article discusses Caché database growth and gives an overview of various methods you can use to return unused disk space that is allocated to database files back to the file system. But before we talk about returning space to the file system, let’s first review how does it get allocated in the first place.

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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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