Best Practices recommendations on how to develop, test, deploy and manage solutions on InterSystems Data Platforms better.
I am planning to implement Business Intelligence based on the data in my instances. What is the best way to set up my databases and environment to use DeepSee?
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;
Caché Monitor: Scans the console log and sends emails alerts.
System Monitor: Monitors system status and resources, generating notifications (alerts and warnings) based on fixed parameters and also tracks overall system health.
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.
History Monitor: Maintains a historical database of performance and system usage metrics.
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.
In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:
In the previous article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software. Still, our focus was on the implementation part of software development, but this part presents:
I have a few cubes and numerous dashboards and I am ready to deploy them to our end users and administrators. How to configure DeepSee so that users don’t disrupt each other’s areas and are restricted from using functionalities specific to developers?
Hi,
This is a quick tutorial how to install and use TFS in Atelier. It is based on my self experience and some tricks that I 've noted.
If you are used to using visual studio maybe you feel that is a bit slow and heavy, but you have the same TFS panel as you have in Visual Studio, so don't need any special "training" to use it 
It's important don't store the file .buildpath because it has the server definition that you are working in a team, each one have the personal configuration, so the name of the server could be different.
To prevent it, create a file called
This is a posting about a particular feature of Caché which I find useful but is probably not well known or used. I am referring to the feature of Language Extensions.
This feature allows you to extend the commands, special variables and functions available in Caché Object Script with commands, special variables and functions of your own. This functionality also applies to other languages the Caché supports at the server, including Caché Basic and Multivalue Basic.
Why would I need or want to add new commands ?
There are a number of use cases.
When working at the Caché command prompt I sometimes want to run an operating system command on the server host. By prefixing my command line with ! or $ I can do this with ease. The following examples are from 2017.1 on Windows, but the feature is available on all versions and platforms:
USER>!dir
Volume in drive C has no label.
Volume Serial Number is 2EF9-50B3
Directory of c:\intersystems\ens171\mgr\user
12/06/2017 12:41 <DIR> .
12/06/2017 12:41 <DIR> ..
02/02/2017 18:05 11,534,336 CACHE.DAT
The goal of this “DeepSee Troubleshooting Guide” is to help you track down and fix problems in your DeepSee project.
If the problem can’t be fixed by following the guidelines, you will at least have enough information to submit a WRC issue with DeepSee Support and provide all the evidence to us, so we can continue the investigation together and resolve it faster!
Please NOTE: If you are unfamiliar with the consequences of a given action or command, please don’t run it as it might have an effect on your production system. In this case contact DeepSee Support for further assistance.
Everybody has a testing environment.
Some people are lucky enough to have a totally separate environment to run production in.
-- Unknown
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In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:
This first part deals with the cornerstone of modern software development - Git version control system and various Git flows.
With the launch of InterSystems IRIS Data Platform, we provide our product even in a Docker container. But what is a container?
The fundamental container definition is that of a sandbox for a process.
Containers are software-defined packages that have some similarities to virtual machines (VM) like for example they can be executed.
Containers provide isolation without a full OS emulation. Containers are therefore much lighter than a VM.
Continuing on with providing some examples of various storage technologies and their performance profiles, this time we looked at the growing trend of leveraging internal commodity-based server storage, specifically the new HPE Cloudline 3150 Gen10 AMD processor-based single socket servers with two 3.2TB Samsung PM1725a NVMe drives.
Google Cloud Platform (GCP) provides a feature rich environment for Infrastructure-as-a-Service (IaaS) as a cloud offering fully capable of supporting all of InterSystems products including the latest InterSystems IRIS Data Platform. Care must be taken, as with any platform or deployment model, to ensure all aspects of an environment are considered such as performance, availability, operations, and management procedures. Specifics of each of those areas will be covered in this article.
++ 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.
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.
Container images consist of a basic OS substratum and all the software we need to run our service. I use the term service for a given "container solution" as a generic description of a software solution wrapped in a container.
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
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 this article, we'll explore two approaches for modelling DeepSee-like synchronization from table data locations using callbacks and other features of the iKnow domain definition infrastructure.
In this post, I am going to detail how to set up a mirror using SSL, including generating the certificates and keys via the Public Key Infrastructure built in to Caché. The goal of this is to take you from new installations to a working mirror with SSL, including a primary, backup, and DR async member, along with a mirrored database.
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 important ones to get started.
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.
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.
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.
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.
See: - Part 2 - Looking at the metrics we collected
pButtons compiles data it collects into a single html file to make it easier to send to WRC and review the collated data.