Hi,

this is a public announcement for the first release of Intersystems Cache Object-Relational Mapper in Python 3. Project's main repository is located at Github (healiseu/IntersystemsCacheORM).

About the project

CacheORM module is an enhanced OOP porting of Intersystems Cache-Python binding. There are three classes implemented:

The intersys.pythonbind package is a Python C extension that provides Python application with transparent connectivity to the objects stored in the Caché database.

Last comment 23 July 2018
+ 2   1 2
366

views

+ 2

rating

Connected Data London Conference

TRIADB is an emerging unique and valuable technology in NoSQL database modelling and BI analytics. The following video is from a presentation and demonstration of TRIADB prototype implemented on top of Intersystems Cache database and driven with a CLI in Python (Jupyter-Pandas). In fact this is the second time in the past year that a prototype based on this technology is implemented and demonstrated. The first one was built on top of OrientDB multi-model database and driven by a Mathematica notebook.

+ 3   0 3
0

comments

401

views

+ 3

rating


Introduction

Have you noticed that what ever the model and data structure in databases we cannot escape from the fundamental principle of managing data allocation space with references, i.e. pointer based logic, memory addressing ? Isn’t this the fundamental mechanism of programming languages too ? The problem I see with all these modern NoSQL databases, especially graph databases is that they provide a higher level abstraction for the end developer but they hide and lock completely the access to the low level storage and retrieval mechanism including indexes. Even in key-value stores you cannot see or understand the sorting of indexes, you cannot easily reference data values in permanent storage locations

Last comment 12 September 2017
+ 7   0 5
268

views

+ 7

rating

Hi,

I am experimenting with Cache-Python binding.  In the following piece of Python code

import intersys.pythonbind3

conn = intersys.pythonbind3.connection( )
conn.connect_now('localhost[1972]:SAMPLES', '_SYSTEM', '123', None)
samplesDB = intersys.pythonbind3.database(conn)
p10 = samplesDB.openid("Sample.Person",'10',-1,-1)

p10.run_obj_method("PrintPerson",[])

I am opening the 10th record of Sample.Person class and then I am calling an object method (PrintPerson). 

Method PrintPerson()

  {

    Write !, "Name: ", ..Name

    Quit  

  }

How can I redirect the output of Write command so that Person's name is printed on Python interpreter

Last answer 29 August 2017 Last comment 10 September 2017
0   0 2
430

views

0

rating

This is a  rather personal view on the history before Caché.
It is in no sense meant to compete with the excellent books from Mike Kadow  discussed in an earlier article.
We have different history and so this is meant to create a different prospective of the past.

The whole story started in 1966 at MGH (Mass.General Hospital) on a PDP-7 Ser.#103
with 8K of memory (18-bit words) [ today = 18K byte ]  as a spare system

Last comment 12 September 2017
+ 16   0 11
878

views

+ 16

rating

In the previous parts (1, 2) we talked about globals as trees. In this article, we will look at them as sparse arrays.

A sparse array - is a type of array where most values assume an identical value.

In practice, you will often see sparse arrays so huge that there is no point in occupying memory with identical elements. Therefore, it makes sense to organize sparse arrays in such a way that memory is not wasted on storing duplicate values.

In some programming languages, sparse arrays are part of the language - for example, in J, MATLAB. In other languages, there are special libraries that let you use them. For C++, those would be Eigen and the like.

Globals are good candidates for implementing sparse arrays for the following reasons:

Last comment 1 days ago
+ 6   0 5
576

views

+ 6

rating