Date range queries going too slow for you? SQL Performance got you down? I have one weird trick that might just help you out! (SQL Developers hate this!)*

If you have a class that records timestamps when the data is added, then that data will be in sequence with your IDKEY values - that is, TimeStamp₁ < TimeStamp₂ if and only if ID₁ < ID₂ for all IDs and TimeStamp values in table - then you can use this knowledge to increase performance for queries against TimeStamp ranges. Consider the following table:

A good writer is supposed to draw you in with the title and bury the answer somewhere in the article. I suppose this makes me a bad writer – don’t think less of me, my whole feeling of self-worth comes from the opinions of internet strangers!

This article is translation of one from Habrahabr InterSystems blog (Russian).
The original post can be found here: https://habrahabr.ru/company/intersystems/blog/251611/

Introduction

InterSystems IRIS 2020.1 includes PEX (Production EXtension Framework) to facilitate the development of IRIS Interoperability productions with components written in Java or .NET.

Thanks to PEX, an integration developer with knowledge of Java or .NET can benefit from the power, scalability, and robustness of the InterSystems IRIS Interoperability framework and be productive in no time.

In this article, we will discuss about Orphaned Messages.

** Revised Feb-12, 2018

While this article is about InterSystems IRIS, it also applies to Caché, Ensemble, and HealthShare distributions.

Introduction

Memory is managed in pages. The default page size is 4KB on Linux systems. Red Hat Enterprise Linux 6, SUSE Linux Enterprise Server 11, and Oracle Linux 6 introduced a method to provide an increased page size in 2MB or 1GB sizes depending on system configuration know as HugePages.

At first HugePages required to be assigned at boot time, and if not managed or calculated appropriately could result in wasted resources. As a result various Linux distributions introduced Transparent HugePages with the 2.6.38 kernel as enabled by default. This was meant as a means to automate creating, managing, and using HugePages. Prior kernel versions may have this feature as well however may not be marked as [always] and potentially set to [madvise].

Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages. However in current Linux releases THP can only map individual process heap and stack space.

The topic of for/while loop performance in Caché ObjectScript came up in discussion recently, and I'd like to share some thoughts/best practices with the rest of the community. While this is a basic topic in itself, it's easy to overlook the performance implications of otherwise-reasonable approaches.

Overview

We started to use Azure Service Bus (ASB) as an enterprise messaging solution 3 years ago. It is being used to publish and consume data between many applications in the organization. Since the data flow is complex, and one application’s data is usually needed in multi applications the “publisher” ---> ”multiple subscribers” model was a great fit. The ASB usage in the organization is dozens of millions of messages per day, while IRIS platform is having around 2-3 million messages/day.

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 InterSystems IRIS Data Platform. I did a similar post in the past for Caché, so feel free to check that out here if you are not running InterSystems IRIS. Much like the original, 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 2018.1.1 version of IRIS, so yours may look slightly different.

We are ridiculously good at mastering data. The data is clean, multi-sourced, related and we only publish it with resulting levels of decay that guarantee the data is current. We chose the HL7 Reference Information Model (RIM) to land the data, and enable exchange of the data through Fast Healthcare Interoperability Resources (FHIR®).

The InterSystems IRIS is a great data platform and it is met the current features required by the market. In this article, you see the top 10:

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.

Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?

Background

InterSystems IRIS 2019 is going to introduce new and exciting features. One of the areas with new interesting must-to-know things is the API Management.