#HealthShare

I already talked about GraphQL and the ways of using it in this article. Now I am going to tell you about the tasks I was facing and the results that I managed to achieve in the process of implementing GraphQL for InterSystems platforms.

What this article is about

• Generation of an AST for a GraphQL request and its validation
• Generation of documentation
• Generation of a response in the JSON format

Fun or No Fun - how serious is it?

Large language models are stirring up some phenomena in recent months. So inevitably I was playing ChatGPT too over last weekend, to probe whether it would be a complimentary to some BERT based "traditional" AI chatbots I was knocking up, or rather would it simply sweep them away.

Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

After upgrading an Information Exchange/HealthShare instance that has a registry namespace, you may notice that the Caché audit log gets filled with frequent login failures by the HS_Services user (check this by going to Management Portal > System Administration > Security > Auditing > View Audit Database). Clicking the “details” shows event data like:

Error message: Access Denied
CSP Application: /csp/healthshare/hsregistry
Authentication: Delegated

Purpose

This set of tools (RanRead, RanWrite, and the combined RanIO) is used to generate random read and write events within a database (or pair of databases) to test the IO speed of IRIS running on a specified hardware setup. While Read operations can be measured in the usual Input/Output operations per second (IOPS) since they're direct disk reads, write events are sent to the database and thus their physical writes are managed by IRIS's write daemon.

Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests, ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis. The suggested method is by running the System Performance tool that comes bundled within IRIS. Please note that this is an update to a previous release, which can be found here.

If you work with Portal on multiple instances of Caché, Ensemble or HealthShare you might find it useful to set the System Mode of the various instances, so as to give yourself a visual reminder of the role of the one you're currently working on.

For instance:

Getting around to wrapping up a short tour in Professional Playtime, I wanted to share my experience at the HIMSS FHIR Connectathon I participated in out in Indy in April.

For practical reasons, it may be desirable that after a Linux server restart, the IRIS instance is automatically started.

Below you will find the steps to follow to automate the startup of IRIS during a reboot of the Linux server, via systemd :

1. Create an iris.service file in /etc/systemd/system/iris.service containing the following information

Hi Community,

This is my first post in the developer community - would appreciate any feedback!

For testing or demo purposes, you may want to send emails from your Interoperability Production. In this post then I will walk you through connecting an InterSystems IRIS Production to Gmail so you can use it to send emails alerts.

Hi, if you develop production with HealthShare, you may have noticed there is a nice tracing feature available - HS.Util.Trace.Operations. This feature allows visually tracing debug information, in structured way. It complements good old logging macros like $$$LOGINFO, $$$TRACE and alike.

I use this feature a lot. However, it has limited functionality, it works nice with classes that extend XML Adapter, streams or data types, but it doesn't work well with arbitrary objects that do no extend from XML Adapter.

InterSystems FAQ rubric

To create a user-defined error you need to prepare the XML that describes the error code and corresponding message that you want to use as a user-defined error.

Please set the error code as a negative integer.

In the previous article we have reviewed how to install our EMPI in standalone, so we are ready to start the basic configuration of our EMPI.

First of all we have to do an initial basic configuration, we can access to the configuration from the Configuration menu of our Registry.

Selecting that option will allow to us to edit the basic configuration table of the Registry:

Deploying InterSystems HealthShare code, supporting lookups and artifacts like ssl certs, keys etc is relatively straight forward using Gitlab Runners. Not only does this approach enable managing the code base and deploying with git type workflows, but it also lends to a speedy recovery and repeatable environments for some implementations.

In the world of Big Data, selecting the right file format is crucial for efficient data storage, processing, and analysis. With the massive amount of data generated every day, choosing the appropriate format can greatly impact the speed, cost, and accuracy of data processing tasks. There are several file formats available, each with its own set of advantages and disadvantages, making the decision of which one to use complex. Some of the popular Big Data file formats include CSV, JSON, Avro, ORC, and Parquet.