InterSystems IRIS

Hi all. Today we are going to install Jupyter Notebook and connect it to Apache Spark and InterSystems IRIS.

Note: I have done the following on Ubuntu 18.04,  Python 3.6.5.

Introduction

If you are looking for well-known, widely-spread and mainly popular among Python users notebook instead of Apache Zeppelin, you should choose Jupyter notebook. Jupyter notebook is a very powerful and great data science tool. it has a big community and a lot of additional software and integrations. Jupyter notebook allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. And most importantly, it is a big community that will help you solve the problems you face

(Originally posted by @Eduard Lebedyuk on Intersystems CODE, 6/26/14) This code snippet determines the difference between two ObjectScript lists. The class method "test" runs the code, and its parameters are detailed in the comments:

The following code allows a user to view the audit settings of their instance. Run the code by running the class method "test"

Often InterSystems technology architect team is asked about recommended storage arrays or storage technologies.  To provide this information to a wider audience as reference, a new series is started to provide some of the results we have encountered with various storage technologies.  As a general recommendation, all-flash storage is highly recommended with all InterSystems products to provide the lowest latency and predictable IOPS capabilities.

The first in the series was the most recently tested Netapp AFF A300 storage array.  This is middle-tier type storage array with several higher models above it.  This specific A300 model is capable of supporting a minimal configuration of only a few drives to hundreds of drives per HA pair, and also capable of being clustered with multiple controller pairs for tens of PB's of disk capacity and hundreds of thousands of IOPS or higher. 

Hi all. Yesterday I tried to connect Apache Spark, Apache Zeppelin, and InterSystems IRIS. During the process, I experienced troubles connecting it all together and I did not find a useful guide. So, I decided to write my own.

Introduction

What is Apache Spark and Apache Zeppelin and find out how it works together. Apache Spark is an open-source cluster-computing framework. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. So, it is very useful when you need to work with Big Data. And Apache Zeppelin is a notebook, that provides cool UI to work with analytics and machine learning. Together, it works like this: IRIS provides data, Spark reads provided data, and in a notebook we work with the data.

Note: I have done the following on Windows 10

++ Update: August 1, 2018

The use of the InterSystems Virtual IP (VIP) address built-in to Caché database mirroring has certain limitations. In particular, it can only be used when mirror members reside the same network subnet. When multiple data centers are used, network subnets are not often “stretched” beyond the physical data center due to added network complexity (more detailed discussion here). For similar reasons, Virtual IP is often not usable when the database is hosted in the cloud.

Network traffic management appliances such as load balancers (physical or virtual) can be used to achieve the same level of transparency, presenting a single address to the client applications or devices. The network traffic manager automatically redirects clients to the current mirror primary’s real IP address. The automation is intended to meet the needs of both HA failover and DR promotion following a disaster. 

This post provides useful links and an overview of best practice configuration for low latency storage IO by creating LVM Physical Extent (PE) stripes for database disks on InterSystems Data Platforms; InterSystems IRIS, Caché, and Ensemble.

Consistent low latency storage is key to getting the best database application performance. For applications running on Linux, Logical Volume Manager (LVM) is often used for database disks, for example because of the ability to grow volumes and filesystems or create snapshots for online backups. For database applications the parallelism of writes using LVM PE striped logical volumes can also help increase performance for large sequential reads and writes by improving the efficiency of the data I/O

With the release of InterSystems IRIS, we're also making available a nifty bit of software that allows you to get the best out of your InterSystems IRIS cluster when working with Apache Spark for data processing, machine learning and other data-heavy fun. Let's take a closer look at how we're making your life as a Data Scientist easier, as you're probably already facing tough big data challenges already, just from the influx of job offers in your inbox!

"Telegram" is a well-known instant messenger, which provides an API for creating bots. The features of this API allow you to create bots with a wide range of functionality including receiving payments. 
With the help of the telegram bot, I solved a simple task - sending Alerts from Ensemble to Telegram.

Advantages: Alerts come to the mobile phone, a notification appears, so there is no need to install any additional applications (in contrast to the solution https://community.intersystems.com/post/sending-alerts-mobile-phone-using-pushover- httpoutboundadapter).

The bot can be extended by adding new commands, for example, for managing the Production or for solving other tasks in Iris, Ensemble or Cache.

Container Images

In this second post on containers fundamentals, we take a look at what container images are.

What is a container image?

A container image is merely a binary representation of a container.

A running container or simply a container is the runtime state of the related container image.

Please see the first post that explains what a container is

Last week saw the launch of the InterSystems IRIS Data Platform in sunny California.

For the engaging eXPerience Labs (XP-Labs) training sessions, my first customer and favourite department (Learning Services), was working hard assisting and supporting us all behind the scene.

Before the event, Learning Services set up the most complicated part of public cloud :) "credentials-for-free" for a smooth and fast experience for all our customers at the summit. They did extensive testing before the event so that we could all spin up cloud infrastructures to test the various new features of the new InterSystems IRIS data platform without glitches

This post will show you an approach to size shared memory requirements for database applications running on InterSystems data platforms including global and routine buffers, gmheap, and locksize as well as some performance tips you should consider when configuring servers and when virtualizing Caché applications. As ever when I talk about Caché I mean all the data platform (Ensemble, HealthShare, iKnow and Caché).

A list of other posts in this series is here

Note (Sept 2018): There have been big changes since this post first appeared, I suggest using the Docker Container version, the project and details for running as a container are still in the same place  published on GitHub so you can download, run - and modify if you need to

In this post, I am going to detail how to set up a mirror using SSL, including generating the certificates and keys via the Public Key Infrastructure built in to Caché. The goal of this is to take you from new installations to a working mirror with SSL, including a primary, backup, and DR async member, along with a mirrored database. I will not go into security recommendations or restricting access to the files. This is meant to just simply get a mirror up and running. Example screenshots are taken on a 2016.1 version of Caché, so yours may look slightly different.

Step 1: Configure Certificate Authority (CA) Server