Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace
Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.
Happy new year! I’m Raj Singh, InterSystems’ product manager for Developer Experience and I’d love your feedback on how you use IDEs today and your thoughts for the future.
Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.
After >40 years writing in-countable lines of code in M*/COS/ISOS (and a bunch of archaic languages) I decided for myself to set a strong signal for the future. We have Embedded Python available (still pre-release)! I just felt it as a sacrilege to ignore this excellent NEW opportunity and stay with the old sermon that I had used for decades.
In the first part, I've shown how to start a new project on Django, as well as define new models and add already existing models.
This time, I'll introduce an admin panel, available out of the box and how it can be useful.
Important note: do not expect that if you try to repeat actions from this post it will work for you, it does not. During the article, I had to do some fixes in the django-iris project, and even in DB-API driver made by InterSystems to fix some issues there as well, and I think this driver is still in development, and we will get more stable driver in future. Let's decide that this article only explains how it could be if we would have all done.
This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.
This is the first article of a series diving into visualization tools and analysis of time series data. Obviously we are most interested in looking at performance related data we can gather from the Caché family of products. However, as we'll see down the road, we are absolutely not limited to that. For now we are exploring python and the libraries/tools available within that ecosystem.
Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.
To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.
This is a demo to make use of a simple WebSocket Client with Embedded Python in IRIS.
To continue my series of WebSocket Client I have added an example written in Python.
The most impressive experience was how easy the writing and testing of the client was
which happened total offline from IRIS.
Embedding, running and feeding the client with data from IRIS was also incredibly simple.
Following this GitHub we will see how the FIX protocol can be implemented easily using IRIS and Python.
If you don't have much time focus on the Send a Quote before the Order part near the end, as it will, in a matter of minute, tell you how to send a Quote Request followed by an Order Request and show you the result from the server, and that in no more than five clicks.
This was my first attempt to use Embedded Python in IRIS The Python code is adapted from solutions for Advent of Code 2020 contest. Test data are all input to my personal challenge.
Continuing the description of work on the use of the openhl python module in productive mode.
Since the version of iris with Embedded Python, does not yet have a final release, it is already necessary to use it in production now. We decided to back up the service for exporting requests to a xlsx file on a separate server, and save the query result in a global in a separate database.
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 just created a new Global Master Topic, "IRIS Cheatsheets". IRIS has introduced a lot of new functionality, especially in scripting languages, FHIR R4 support, enhanced Interoperability Tools, and IRIS Analytics. Having spent 35 years working on Windows-based PC's and Laptops, I have surprisingly little knowledge of Linux, Docker and Git. Furthermore, I have written almost every application and Interface in ObjectScript with splatterings of SQL, .Net, and Java Gateways and the most basic knowledge of WinSCP, Putty, SSH. All that changed when I received my first Raspberry Pi.
import grongier.pex
import iris
import MyResponse
class MyBusinessOperation(grongier.pex.BusinessOperation):
def OnInit(self):
print("[Python] ...MyBusinessOperation:OnInit() is called")
self.LOGINFO("Operation OnInit")
return
def OnTeardown(self):
print("[Python] ...MyBusinessOperation:OnTeardown() is called")
return
def OnMessage(self, messageInput):
if hasattr(messageInput,"_IsA"):
if messageInput._IsA("Ens.StringRequest"):
self.LOGINFO(f"[Python] ...This iris class is a Ens.StringRequest with this message {messageInput.StringValue}")
self.LOGINFO("Operation OnMessage")
response = MyResponse.MyResponse("...MyBusinessOperation:OnMessage() echos")
return response
Welcome to day 3 of Virtual Summit 2021! We have incredible content available to you this year and I'd just like to bring your attention to the 4(!) sessions that we have on Embedded Python.
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