Mirroring 101

Caché mirroring is a reliable, inexpensive, and easy to implement high availability and disaster recovery solution for Caché and Ensemble-based applications. Mirroring provides automatic failover under a broad range of planned and unplanned outage scenarios, with application recovery time typically limited to seconds. Logical data replication eliminates storage as a single point of failure and a source of data corruption. Upgrades can be executed with little or no downtime.

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Usualy, if you want to deploy a solution, you need to add the items, configure your lookup tables and default configuration manually.
It's okay if you have all the permissions and privileges to perform these actions. If you want to deploy to a client's production server, and you don't have the permissions, you need to indicate in a document ALL the steps that the deployment manager has to perform.

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This article will describe and include an example of how to embed an external PDF file into an HL7 segment, specifically ADT_A01:2.3.1 OBX(). This can be useful when attempting to insert pictures or other external data into an HL7 message. In this example, the name of the PDF file to be embedded is provided in the incoming HL7 message in OBX(1):ObservationValue field.

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Each business component in Ensemble picks up its allocated jobs from a pool, and the Pool Size setting for each component determines how many jobs each component can work on each time. In this article we will be specifically discussing different Pool Size settings for Business Processes.

Pool Size = 1

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Hi Community,

In this article I will demonstrate the functionality of my app iris-energy-isodata .
Application is accessing energy data (production, demand and supply) from the major Independent System Operators (ISOs) in the United States to ensure sustainable consumption and production patterns (SDG's 12)

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Article
· Sep 23, 2016 6m read
Creating a RESTful Service using Ensemble

This is a detailed guide to develop RESTful services using InterSystems Ensemble. The goal of this guide is to make you understanding the basic concept and building blocks of a RESTful service. The service is going to provide a very basic functionality (a “Hello world!”).

You will learn how to create required components as Ensemble classes, configure the run-time as an Ensemble Production and create a service configuration as a web application.

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Healthcare interoperability is instrumental in improving patient care, decreasing healthcare provider costs, and providing a more accurate picture to providers. However, with so many different systems, data is formatted in many different ways. There are many standards that have been created to try to solve this problem, including HL7v2, HL7v3, and CDA but each one has its drawbacks.

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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.

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While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".

After this session I thought it would be a nice exercise to try and provide some easier human interface for this.

The first step in this was to wrap a class-based API to the existing pButtons routine.

I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.

The next step was to add on top of this API, a REST API class.

With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.

For example -

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Requirement: Transform source XML message to target JSON.

step1: First create json equivalent xml from any online tool.

step2: use add in studio utility create persistent classes from json equivalent xml and compile it , now target persistent classes are ready

step3: Do the above step for source xml or xsd and generate persistent classes for source and compile it

step4: complete the DTL by selecting root node from source, target persistent classes and complete the mappings and compile it

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DTL Transformations and GetValueAt/SetValueAt calls on HL7 messages will truncate any fields longer than 32K. To avoid this, the methods GetFieldStreamRaw and StoreFieldStreamRaw must be used when dealing with fields that might be larger than 32K. OBX:5 is a frequent example. These methods have some subtleties and must be used carefully.

This can't be done by simply dragging from left to right in a DTL. It must be done with a code action. Also, the StoreFieldStreamRaw call must be the last edit made to the segment because the segment becomes immutable after that.

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Created by Daniel Kutac, Sales Engineer, InterSystems

Warning: if you get confused by URLs used: the original series used screens from machine called dk-gs2016. The new screenshots are taken from a different machine. You can safely treat url WIN-U9J96QBJSAG as if it was dk-gs2016.

Part 2. Authorization server, OpenID Connect server

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Article
· Apr 9, 2019 3m read
IRIS/Ensemble as an ETL

IRIS and Ensemble are designed to act as an ESB/EAI. This mean they are build to process lots of small messages.

But some times, in real life we have to use them as ETL. The down side is not that they can't do so, but it can take a long time to process millions of row at once.

To improve performance, I have created a new SQLOutboundAdaptor who only works with JDBC.

BatchSqlOutboundAdapter

Extend EnsLib.SQL.OutboundAdapter to add batch batch and fetch support on JDBC connection.

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