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Article
· Sep 30, 2016 1m read
ECP Magic

I saw someone recently refer to ECP as magic. It certainly seems so, and there is a lot of very clever engineering to make it work. But the following sequence of diagrams is a simple view of how data is retrieved and used across a distributed architecture.

For more more on ECP including capacity planning follow this link: Data Platforms and Performance - Part 7 ECP for performance, scalability and availability

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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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Article
· May 26, 2016 1m read
Windows, Caché and virus scanners

I have seen a customer problem recently where the use of a virus scanner running over Caché databases was causing intermittent application slow downs and bad user response times.

This is a surprisingly common problem, so this short post is just a reminder to exclude key Caché components from your virus scanning.

Generally virus scanning must exclude the CACHE.DAT database files and Caché binaries. If an anti-virus is scanning CACHE.DATs and InterSystems files then system performance will be significantly impacted.

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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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Presenter: Murray Oldfield
Task: Avoid bottlenecks caused by scaling up, before they become a problem
Approach: Discuss what operating system and InterSystems metrics you should look at and how to interpret them

Systems behave differently when database activity scales up. In the worst case, bottlenecks appear and users are impacted. This session shows you which operating system and InterSystems metrics you should be looking at and how to interpret them so you can head off bottlenecks before they impact users. This sessions also shows strategies for planning infrastructure taking into consideration InterSystems' data platforms requirements.

Content related to this session, including slides, video and additional learning content can be found here.

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Presenter: Murray Oldfield
Task: Deploy applications based on InterSystems’ technology using VMware.
Approach: Provide a checklist of factors to consider, particularly when deploying a production database application that requires high availability

Are you ready to deploy your applications on a virtualized architecture? This talk will highlight what you need to plan and do when deploying applications built on ISC data platforms using VMware. Special focus on what you need to know when planning for highly available (HA) production database applications.

Content related to this session, including slides, video and additional learning content can be found here.

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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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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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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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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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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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