Globals are multidimensional sparse arrays which are stored in InterSystems Data Platform. Everything in InterSystems Products is stored in Globals: Classes, Tables, Documents, Code.
M:N Relationship is a recurring object of my interest for a long time. So the subject turns up with me repeatedly. For this article, I found a nice example with TWO separate M:N relationships. Some M:N².
It is not pure fiction but taken from a closer view of OEX Contests that we run on a regular base.
Globals are the core of the InterSystems IRIS for data persistence. It is flexible, allowing store JSON documents, relational data, object oriented data, OLAP cubes and custom data models, like Mindmaps. To see how to store, delete and get mindmap data using globals, follow these steps:
1. Clone/git pull the repo into any local directory
I have created a package that offers a utility to load a Global into JSON object and reverse to create a Global from this type of JSON object. Compact refers to the structure created. Globals nodes are included with data for a fast data load. But also the related code is quite compact.
This project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.
My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.
So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.
This package offers a utility to export an XLarge Global into a JSON object file and to show or import it again. In a previous example, this all was processed in memory. But if this is a large Global you may either experience <MAXSTRING> or an <STORE> error if the generated JSON structure exceeds available memory.
I have created a package that offers a utility to load a Global into JSON object and reverse to create a Global from this type of JSON object. Efficient refers to the structure created. Only Globals nodes containing data are presented in the generated JSON object.
This example demonstrates the difference you may experience when you write to Gllobals directly from Embedded Python compared to native ObjectScript.
To make this demo useful I start 2 background jobs that simply write sequentially to a dedicated global. A common control method signals for a synchronous start. Similar a common stop & view interrupts data feeding.
You may have seen my series of articles on GlobalToJSON closing with Embedded Python. And to me, Embedded means inside a Class and not PEX or other external variants. The first variants showed that ePy is not an ultimate requirement. ISOS/COS may do it as well. Here I try to position ePy inside IRIS.
In my article I described the work using iris.gref . As the official documetation is rather slim on the subject it was necessary to dig into it. Using the power of Python I was able to detect what I needed but was hidden. I decided to share this with you. pydoc did the magic.
I have created a package to export a Global into JSON object file and to re-create it by reloading from this file embeddedPython refers to the new available technologies. It should be understood as a learning exercise of how to handle the language interfaces. Only Global nodes containing data are presented in the generated JSON file. Differently from the previous example, this one is using embedded Python only, no ObjectScript. Therefore PURE
I have created a package to export a Global into JSON object file and to re-create it by reloading from this file embeddedPython refers to the new available technologies. It should be understood as a learning exercise of how to handle the language interfaces. Only Global nodes containing data are presented in the generated JSON file.
I will give you some additional information on my first embedded Python package. it is written as a mix of python and ObjectScript to take the best of both worlds.
I have created a package that offers a utility to load a Global into JSON object and reverse to create a Global from this type of JSON object. Academic refers to the structure created. Each logical node of the Global is presented separately with all its descendants. Even if they don't contain any stored data.
For some years I missed being able to offer, to everybody interested in ObjectScript, a tutorial more or less complete, to start with ObjectScript. Something that could help more and make things easier to those new developers that come to our technology... something intermediate, halfway between the common "Hello World!", that doesn't really get you further, and the "Advanced Training", that is unaffordable because of lack of time,etc.
If there were something truly helpful not only as an introduction to the ecosystem, but as a starting point, as a boost, to really start to walk into ObjectScript and move forward by yourself... wouldn't that be awesome?
For those of you who might be new to IRIS, and even those who have used Cache or IRIS for some time but want to explore beyond its usually-assumed boundaries and practices, you might want to dive into this detailed exploration of the database engine that is at its heart, and discover just what you can really do with it, going way beyond what InterSystems have done with it for you.
I'd like to share with you some storage features that also exist in Caché but are almost unknown and mostly not used. They are of course available in IRIS and gain importance with large and distributed storage architectures.
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:
Standard error logs in IRIS / Caché / Ensemble are written global ^ERRORS. As this piece dates back some decades back to previous millennium its structure is far from the typical SQL storage structures.
To anticipate the obvious reply that InterSystems IRIS supports WebSockets natively as a client: This is correct and here is my aged related article with the link to the example in OEX
Since I started to use internet (late 90's), I always had a CMS (content management system) present to make easier post
any information in a blog, social media or even an enterprise page. And later years putting all my code into github I
used to document it on a markdown file. Observing how easy could be persisting data into Intersystems IRIS with the
Native API I decided to make this application and force myself to forget a little of SQL and stay open to key-value database
model.
QEWD is assumed by most people to only integrate with IRIS (or Cache) via a connection through IRIS's high-performance C interface. This requires QEWD (and its Node.js environment) to be installed and configured on the same machine as IRIS.
I'm frequently asked if QEWD can run on a separate server (or servers), and access IRIS (or Cache) over a network connection. The answer is yes it can, but the information on how to set it up in this way has been admittedly a bit tricky to discover.
This is a coding example working on IRIS 2020.1 and on Caché 2018.1.3
It will not be kept in sync with new versions
It is also NOT serviced by InterSystems Support !
Every now and then you may encounter a situation where for various reasons ODBC is the only option to access a remote system. Which is sufficient as long as you need to examine or change tables. But you can't directly execute some commands or change some Global.
Special thanks @Anna Golitsyna for inspiring me to publish this.
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