Hi Community!

Consider I have InterSystems IRIS server in a cloud and want to introduce a DR server for it.

Are there any requirements for DR server for InterSystems IRIS if it is in a cloud?

Should it be the same subnet? One private cloud?

Can DR server be placed on another cloud, say Primary on Google Cloud and DR on Azure? Are there any caveats/concerns?

Last answer 14 November 2018 Last comment 16 November 2018
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This post provides useful links and an overview of best practice configuration for low latency storage IO by creating LVM Physical Extent (PE) stripes for database disks on InterSystems Data Platforms; InterSystems IRIS, Caché, and Ensemble.

Consistent low latency storage is key to getting the best database application performance. For applications running on Linux, Logical Volume Manager (LVM) is often used for database disks, for example because of the ability to grow volumes and filesystems or create snapshots for online backups. For database applications the parallelism of writes using LVM PE striped logical volumes can also help increase performance for large sequential reads and writes by improving the efficiency of the data I/O

Last comment 25 May 2018
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InterSystems Data Platform includes utilities and tools for system monitoring and alerting, however System Administrators new to solutions built on the InterSystems Data Platform (a.k.a Caché) need to know where to start and what to configure.

This guide shows the path to a minimum monitoring and alerting solution using references from online documentation and developer community posts to show you how to enable and configure the following;

  1. Caché Monitor: Scans the console log and sends emails alerts.

  2. System Monitor: Monitors system status and resources, generating notifications (alerts and warnings) based on fixed parameters and also tracks overall system health.

  3. Health Monitor: Samples key system and user-defined metrics and compares them to user-configurable parameters and established normal values, generating notifications when samples exceed applicable or learned thresholds.

  4. History Monitor: Maintains a historical database of performance and system usage metrics.

  5. pButtons: Operating system and Caché metrics collection scheduled daily.

Remember this guide is a minimum configuration, the included tools are flexible and extensible so more functionality is available when needed. This guide skips through the documentation to get you up and going. You will need to dive deeper into the documentation to get the most out of the monitoring tools, in the meantime, think of this as a set of cheat sheets to get up and running.

Last comment 11 March 2019
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A request came from a customer to estimate how long it would take to encrypt a database with cvencrypt utility.

This question is a little bit like how long is a piece of string — it depends. But its an interesting question. The answer primarily depends on the performance of CPU and storage on the target platform the customer is using, so the answer is more about coming up with a simple methodology that can be used to benchmark the CPU and storage while running cvencrypt.

Methodology

  1. Copy a large and representative CACHE.DAT file to target storage
  2. Create a keyfile via System Management Portal (includes a key)
  3. Run the cvencrypt over your sample CACHE.DAT file (as below)

The following shows the process once the test file is in place

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I am often asked by customers, vendors or internal teams to explain CPU capacity planning for large production databases running on VMware vSphere.

In summary there are a few simple best practices to follow for sizing CPU for large production databases:

  • Plan for one vCPU per physical CPU core.
  • Consider NUMA and ideally size VMs to keep CPU and memory local to a NUMA node.
  • Right-size virtual machines. Add vCPUs only when needed.

Generally this leads to a couple of common questions

Last comment 8 November 2017
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Prometheus is one of the monitoring systems adapted for collecting time series data.

Its installation and initial configuration are relatively easy. The system has a built-in graphic subsystem called PromDash for visualizing data, but developers recommend using a free third-party product called Grafana. Prometheus can monitor a lot of things (hardware, containers, various DBMS's), but in this article, I would like to take a look at the monitoring of a Caché instance (to be exact, it will be an Ensemble instance, but the metrics will be from Caché). If you are interested – read along

Last comment 31 August 2017
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I am looking for experience of people running Veeam with Caché databases. 

Tips/Tricks/General questions like; what Veeam features are you using, what your backup cycle looks like, where does your data end up, what recovery/integrity checks you do, what sort of compression/dedupe you get. 

Also what questions _you_ have and what problems you might be trying to solve.

 

Last answer 25 April 2017 Last comment 22 January 2019
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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

Last comment 11 May 2017
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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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