At the heart of IRIS and Cache is a very interesting database architecture that we, at M/Gateway Developments, refer to as "Global Storage".  If you ever wanted to know more about the fundamentals and capabilities of this underlying database, you might want to read a major analysis we've put together:

https://github.com/robtweed/global_storage

Amongst other things you'll discover that:

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Article
Eduardo Anglada · May 11 8m read
IRIS in Astronomy

In this article we are going to show the results of the comparision between IRIS and Postgress when handling Astronomy data.

Introduction

Since the earliest days of human civilization we have been fascinated by the sky at night. There are so many stars! Everybody has dreamed about them and fantasized about life in other planets.

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Article
Tony Pepper · May 25, 2016 5m read
Random Read IO Storage Performance Tool

Purpose

This tool is used to generate random read Input/Output (IO) from within the database. The goal of this tool is to drive as many jobs as possible to achieve target IOPS and ensure acceptable disk response times are sustained. Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis.

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About regulations

Personal data privacy regulations have become an indispensable requirement for projects dealing with personal data. The compliance with these laws is based on 4 principles:

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Article
Oleh Dontsov · Jun 4, 2020 1m read
Easy data import into IRIS

Sometimes you need quickly and easily import data into IRIS. For this, an IRIS import manager has been developed.

This application allows you to import JSON data and also provides a really simple interface for transferring data from MongoDB collections to IRIS globals. It has never been so easy.

Let's look at examples.

Import JSON

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In this article I'd like to share with you a phenomena that is best you avoid - something you should be aware of when designing your data model (or building your Business Processes) in Caché or in Ensemble (or older HealthShare Health Connect Ensemble-based versions).

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A More Industrial-Looking Global Storage Scheme

In the first article in this series, we looked at the entity–attribute–value (EAV) model in relational databases, and took a look at the pros and cons of storing those entities, attributes and values in tables. We learned that, despite the benefits of this approach in terms of flexibility, there are some real disadvantages, in particular a basic mismatch between the logical structure of the data and its physical storage, which causes various difficulties.

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Introduction

In the first article in this series, we’ll take a look at the entity–attribute–value (EAV) model in relational databases to see how it’s used and what it’s good for. Then we'll compare the EAV model concepts to globals.

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Article
Guillaume Rongier · Apr 9, 2019 3m read
IRIS/Ensemble as an ETL

IRIS and Ensemble are designed to act as an ESB/EAI. This mean they are build to process lots of small messages.

But some times, in real life we have to use them as ETL. The down side is not that they can't do so, but it can take a long time to process millions of row at once.

To improve performance, I have created a new SQLOutboundAdaptor who only works with JDBC.

BatchSqlOutboundAdapter

Extend EnsLib.SQL.OutboundAdapter to add batch batch and fetch support on JDBC connection.

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Article
Dmitry Maslennikov · Mar 24, 2020 2m read
BlocksExplorer map generator

I hope you already know one of my projects Cache Blocks Explorer. I've recently renamed it to Blocks Explorer.

For the recent contest, I've added a new feature, the ability to generate a static picture of any Cache or IRIS database. Like below. Where unique globals have a unique color. This is how looks like inside 9.5GB database. Where 1 pixel represents one block. By link on image you will get even bigger image, with more detalization.


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Hello Community,

Thank you all for your continued feedback and support of our ad hoc reporting platform, VDM.  There's been some questions around setting up a non-ODBC connection for InterSystems platforms.  We published a new YouTube video showing the steps necessary to connect to InterSystems Caché and InterSystem IRIS with BridgeWorks VDM. 

 

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Article
Sergey Kamenev · Nov 11, 2019 11m read
Transactions in Global InterSystems IRIS

InterSystems IRIS supports a unique data structure, called globals, for information storage. Essentially, globals are persistent arrays with multi-level indices, having several extra capabilities—transactions, quick traversal of tree structures, and a programming language known as ObjectScript.

I'd note that for the remainder of the article, or at least the code samples, we'll assume you have familiarised yourself with the basics of globals:

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Article
Nikolay Soloviev · Aug 1, 2019 3m read
Nested set model for ObjectScript

In many projects I was faced with storing hierarchical data (tree) in classes.
By tree, I mean such data, where each node has a parent node — an object of the same class.
Many examples of such data can be given. For example, a catalog in the online store. Suppose that this online store sells books, in this case, the category tree might look like this:

 

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Article
Robert Cemper · Mar 26, 2019 2m read
Synchronize Data with DSTIME

 For Data Synchronization inside Caché you have a range of ways to synchronize objects and tables.
At DB level you can use Shadowing  or Mirroring 

This works excellent and if you need just a part of your data to be synchronized you may split your
data into smaller pieces using Global mapping 
Or if you need bi-directional synchronization on Class/Table level you can use the Object Synchronization Feature 


The limit of all these excellent features:
They just work from Caché/IRIS to Caché/IRIS.

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Article
David Underhill · Jul 12, 2019 2m read
Basic Database Metrics example

This is a self contained class that can be run from the Intersystems Task Scheduler which records peak usage details for databases and licenses built up throughout the day and retaining 30 days history.

To schedule the task to run every hour:  

d ##class(Metrics.Task).Schedule()

You can also specify your own start time, stop time, and run interval:

d ##class(Metrics.Task).Schedule(startTime, stopTime, intervalMins)

Metrics are stored in ^Metrics in the namespace that the class resides in/is run from.

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Article
Robert Cemper · May 30, 2019 2m read
Background Jobs over ECP
This is a coding example working on Caché 2018.1.3 and IRIS 2020.2 
It will not be kept in sync with new versions 
It is also NOT serviced by InterSystems Support !

Running a Background Job using JOB command is a well-known feature.
Using ECP to distribute databases to several servers is also well know.
But using the combination of both to run a process on a different server
seems to be a rare case.

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Astronomers’ tools

5 years ago, on December 19, 2013, the ESA launched an orbital telescope called Gaia. Learn more about the Gaia mission on the official website of the European Space Agency or in the article by Vitaly Egorov (Billion pixels for a billion stars).

However, few people know what technology the agency chose for storing and processing the data collected by Gaia. Two years before the launch, in 2011, the developers were considering a number of candidates (see “Astrostatistics and Data Mining” by Luis Manuel Sarro, Laurent Eyer, William O’Mullane, Joris De Ridder, pp. 111-112):

Comparing the technologies side-by-side produced the following results (source):

Technology Time
DB2 13min55s
PostgreSQL 8 14min50s
PostgreSQL 9 6min50s
Hadoop 3min37s
Cassandra 3min37s
Caché 2min25s

The first four will probably sound familiar even to schoolchildren. But what is Caché XEP?

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The following post outlines an architectural design of intermediate complexity for DeepSee. As in the previous example, this implementation includes separate databases for storing the DeepSee cache, DeepSee implementation and settings. This post introduces two new databases: the first to store the globals needed for synchronization, the second to store fact tables and indices.

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Mirroring 101

Caché mirroring is a reliable, inexpensive, and easy to implement high availability and disaster recovery solution for Caché and Ensemble-based applications. Mirroring provides automatic failover under a broad range of planned and unplanned outage scenarios, with application recovery time typically limited to seconds. Logical data replication eliminates storage as a single point of failure and a source of data corruption. Upgrades can be executed with little or no downtime.

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