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.


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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Here in Community I use the WYSIWYG Text format control to answer questions and other quick text entries.

But for longer posts when I want formatting or if I am building incrementally over several days I use the Plain text (supports markdown) control because it's quicker and easier to post an article I have written offline. In this post I share my workflow and a set of tools to publish long read posts.

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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.

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. For larger deployments ECP was a way to scale out processing on commodity servers rather than a single large and expensive big iron enterprise server. Even the high core count enterprise servers had limits so ECP was also used to scale deployments on them as well

Last comment 8 December 2016
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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

To start

  • There are three globals on disk ^A, ^B and ^C.
  • Global ^B equals "B"
  • There is one Data server and two or more Application servers.
  • The diagrams show the cache (global buffers) on each server.

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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. 



A list of other posts in this series is here
 


It is vital to have well set up storage such as a storage array to provide predictable disk IO performance, support high availability features and provide storage redundancy, scalability, and reliability for your applications

Last comment 13 November 2016
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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.

Specifically anti-virus software must exclude Caché files including the following:
* Caché databases (CACHE.DAT).
* Caché executable in the Ensemble/bin or cache/bin directory.
* Write Image Journal (WIJ).
* Journal files in the journal directory.

More details are available in the online documentation

Last comment 27 May 2016
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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. One of the most common methods of data collection is to use SNMP (Simple Network Management Protocol). SNMP a standard way for Caché to provide management and monitoring information to a wide variety of management tools. The Caché online documentation includes details of the interface between Caché and SNMP. While SNMP should 'just work' with Caché there are some configuration tricks and traps.

Last comment 17 March 2017
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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

Last comment 22 August 2017
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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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