Best Practices recommendations on how to develop, test, deploy and manage solutions on InterSystems Data Platforms better.
++ 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.
In this article I would like to present the RESTForms project - generic REST API backend for modern web applications.
The idea behind the project is simple -after I wrote several REST APIs I realized that generally, REST API consists of two parts:
And, while you'll have to write your own custom business logic, RESTForms provides all things related to working with persistent classes right out of the box.
Use cases
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.
In this second post on containers fundamentals, we take a look at what container images are.
What is a container image?
A container image is merely a binary representation of a container.
A running container or simply a container is the runtime state of the related container image.
Please see the first post that explains what a container is.
Hi, Community!
Please find the digest of the best articles you posted on DC in 2017 regarding InterSystems Data platform.
We had 280 articles in 2017 and split them into 3 categories: posts gathered most of the views, most voted posts and most commented posts.
Here we go!
TOP 20 Most viewed
Vue.js: getting started with a basic HTML/REST/JSON example, by Ward De Backer 1936
InterSystems Data Platforms and performance – VM Backups and Caché freeze/thaw scripts, by Murray Oldfield 1752
Node.js: create a basic web app with React - part 1, by Ward De Backer 1670
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.
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).
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.
In this article I would like to tell you about macros in InterSystems Caché. A macro is a symbolic name that is replaced with a set of instructions during compilation. A macro can “unfold” in various instruction sets each time it is called, depending on the parameters passed to it and activated scenarios. This can be both static code and the result of ObjectScript execution. Let's take a look at how you can use them in your application.
If you've worked with iKnow domain definitions, you know they allow you to easily define multiple data locations iKnow needs to fetch its data from when building a domain. If you've worked with DeepSee cube definitions, you'll know how they tie your cube to a source table and allow you to not just build your cube, but also synchronize it, only updating the facts that actually changed since the last time you built or synced the cube. As iKnow also supports loading from non-table data sources like files, globals and RSS feeds, the same tight synchronization link doesn't come out of the box. In
After a five-part series on sample iKnow applications (parts 1, 2, 3, 4, 5), let's turn to a new feature coming up in 2017.1: the iKnow REST APIs, allowing you to develop rich web and mobile applications. Where iKnow's core COS APIs already had 1:1 projections in SQL and SOAP, we're now making them available through a RESTful service as well, in which we're trying to offer more functionality and richer results with fewer buttons and less method calls. This article will take you through the API in detail, explaining the basic principles we used when defining them and exploring the most
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.
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.
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.
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.
While some posts are updated to preserve links, others will be marked as
strikethroughto indicate that the post is legacy. Generally, I will say, "See: some other post" if it is appropriate.
Generally, posts build on previous ones, but you can also just dive into subjects that look interesting.
In this post I would like to talk about the syslog table. I will cover what it is, how you look at it, what the entries really are, and why it may be important to you. The syslog table can contain important diagnostic information. If your system is having any problems, it is important to understand how to look at this table and what information is contained there.
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.
This article contains the tutorial document for a Global Summit academy session on Text Categorization and provides a helpful starting point to learn about Text Categorization and how iKnow can help you to implement Text Categorization models. This document was originally prepared by Kerry Kirkham and Max Vershinin and should work based on the sample data provided in the SAMPLES namespace.
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
In a conference call earlier this week, a customer described how they built an iKnow domain with clinical notes and now wanted to filter the contents of that domain based on the patient's diagnosis codes. With such filters, they wanted to explore the corellations between iKnow entities and certain diagnosis codes, first through the Knowledge Portal to get a good sense of the sort of entities and then through more analytical means with the aim of eventually building smart early warning systems.
In previous articles on iKnow, we described a number of demo applications (iKnow demo apps parts 1, 2, 3, 4 & 5) that are either part of the regular kit or can be easily installed from GitHub. All of those applications assumed you already had your iKnow domain ready, with your data of interest loaded and ready for exploration. In this article, we'll shed more light on how exactly you can get to that stage: how you define and then build a domain.
++Update: August 2, 2018
This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.
Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).
Recently I have been posting some updates to our JSON capabilities and I am very glad that so many of you provided feedback. Today I would like to focus on another facet: Producing JSON with a SQL query.
During recent large scale benchmarking activities, we were seeing excessive %sys CPU time that negatively impacted scaling of the application.
Problem
We have found that a lot of the time was spent in the localtime() system call due to the TZ environment variable not being set. A simple test routine was created to confirm the observation, and the elapse time differences and CPU resources needed with TZ set versus TZ not set were astonishing. It was discovered that the inherit use of stat() system calls to /etc/local_time from localtime() are very expensive when TZ is not set.
Recommendation
The Update Checklist for v2015.1 recommends setting the TZ environment variable on Linux platforms and points to the manpage for tzset. This is recommended to improve the performance of Cache’s time-related functions. You can find out more about this here:
https://community.intersystems.com/post/linux-tz-environment-variable-not-being-set-and-impact-caché
The manpage on my CentOS 7 test system (RHEL 6 says the same) has this to say:
Order is a necessity for everyone, but not everyone understands it in the same way (Fausto Cercignani)
Disclaimer: This article uses Russian language and Cyrillic alphabet as examples, but is relevant for anyone who uses Caché in a non-English locale.Please note that this article refers mostly to NLS collations, which are different than SQL collations. SQL collations (such as SQLUPPER, SQLSTRING, EXACT which means no collation, TRUNCATE, etc.) are actual functions that are explicitly applied to some values, and whose results are sometimes explicitly stored in the global subscripts. When stored in subscripts, these values would naturally follow the NLS collation in effect (“SQL and NLS Collations”).
The object and relational data models of the Caché database support three types of indexes, which are standard, bitmap, and bitslice. In addition to these three native types, developers can declare their own custom types of indexes and use them in any classes since version 2013.1. For example, iFind text indexes use that mechanism.