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.

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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.

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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",[])

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This is a translation of the following article. Thanks [@Evgeny Shvarov] for the help in translation.

This post is also available on Habrahabrru.

The post was inspired by this Habrahabr article: Interval-associative arrayru→en.

Since the original implementation relies on Python slices, the Caché public may find the following article useful: Everything you wanted to know about slicesru→en.

Note: Please note that the exact functional equivalent of Python slices has never been implemented in Caché, since this functionality has never been required.

And, of course, some theory: Interval treeru→en.

All right, let’s cut to the chase and take a look at some examples.

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It is possible to update Cache object property from Python using the following Python code, with import of intersys.pythonbind3:

my_object.set("my_property",["A","B","C"])

However, I am unable to save 2D %List with 2D Python array like the following:

my_object.set("my_property",[["A","B","C"],["1","2","3"]])

I am not sure whether this is Python-Cache bind flaw or design issue. Is there any alternative/ workaround to do the same for above?

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I have done Python - Cache binding setup following the guide from http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=.... I have also run test.py from sample3 folder and it able to run and complete successfully.

However, when I try to run the same test.py code via $zf, it gives error with exit code 1.

I've tried running help("intersys.pythonbind3") via $zf and also running from Cache terminal as follows:

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In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph.
Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.

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