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#Monitoring

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Monitoring is a process of controlling and management of performance and availability of software applications.

Article
· Mar 3, 2024 5m read
How to send messages to Microsoft Teams

Hi community,

The aim of this article is to explain how to create messaging between IRIS and Microsoft Teams.

In my company, we wanted to monitor error messages, and we used the Ens.Alert class to redirect those error messages through a Business Operation that sent an email.
The problem was that we sent those error messages to a support account where there were many emails. We wanted something specific for a specific team.

So we investigated how to make these messages reach the development team directly and they could have, in real time, a notification of an error in our production.
In our company we use Microsoft Teams as a corporate tool, so we asked ourselves: How could we make these messages reach the IRIS development team?

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Monitoring your IRIS deployment is crucial. With the deprecation of System Alert and Monitoring (SAM), a modern, scalable solution is necessary for real-time insights, early issue detection, and operational efficiency. This guide covers setting up Prometheus and Grafana in Kubernetes to monitor InterSystems IRIS effectively.

This guide assumes you already have an IRIS cluster deployed using the InterSystems Kubernetes Operator (IKO), which simplifies deployment, integration and mangement.

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The following steps show you how to display a sample list of metrics available from the /api/monitor service.

In the last post, I gave an overview of the service that exposes IRIS metrics in Prometheus format. The post shows how to set up and run IRIS preview release 2019.4 in a container and then list the metrics.

This post assumes you have Docker installed. If not, go and do that now for your platform :)

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In this post I would like to talk about the syslog table. I will cover what it is, how you look at it, what the entries really are, and why it may be important to you. The syslog table can contain important diagnostic information. If your system is having any problems, it is important to understand how to look at this table and what information is contained there.

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Article
· Aug 16, 2023 11m read
Http request response time monitoring

Hi developers!

Today I would like to address a subject that has given me a hard time. I am sure this must have been the case for quite a number of you already (so-called “the bottleneck”). Since this is a broad topic, this article will only focus on identifying incoming HTTP requests that could be causing slowness issues. I will also provide you with a small tool I have developed to help identify them.

Our software is becoming more and more complex, processing a large number of requests from different sources, be it front-end or third-party back-end applications. To ensure optimal performance, it is essential to have a logging system capable of taking a few key measurements, such as the response time, the number of global references and the number of lines of code executed for each HTTP response. As part of my work, I get involved in the development of EMR software as well as incident analysis. Since user load comes mostly from HTTP requests (REST API or CSP application), the need to have this type of measurement when generalized slowness issues occur has become obvious.

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Article
· Feb 13, 2017 14m read
Creating custom SNMP OIDs

This post is dedicated to the task of monitoring a Caché instance using SNMP. Some users of Caché are probably doing it already in some way or another. Monitoring via SNMP has been supported by the standard Caché package for a long time now, but not all the necessary parameters are available “out of the box”. For example, it would be nice to monitor the number of CSP sessions, get detailed information about the use of the license, particular KPI’s of the system being used and such. After reading this article, you will know how to add your parameters to Caché monitoring using SNMP.

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Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

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If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).

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Article
· Aug 26, 2016 8m read
Enterprise Monitor and HealthShare

Enterprise Monitor is a component of Ensemble and can help organizations monitor multiple productions running on different namespaces within the same instance or namespaces running on multiple instances.

Documentation can be found at:

http://docs.intersystems.com/ens20161/csp/docbook/DocBook.UI.Page.cls?KEY=EMONITOR_all#EMONITOR_enterprise

In Ensemble 2016.1 there were changes made to make this utility work with HealthShare environments.

This article will:

  • Show how to set up Enterprise Monitor for HealthShare sites
  • Show some features of Enterprise Monitor
  • Show some features of Enterprise Message Viewer

For this article, I used the following version of HealthShare:

Cache for Windows (x86-64) 2016.1 (Build 656U) Fri Mar 11 2016 17:42:42 EST [HealthShare Modules:Core:14.02.2415 + Linkage Engine:14.02.2415 + Patient Index:14.02.2415 + Clinical Viewer:14.02.2415 + Active Analytics:14.02.2415]

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Article
· Dec 22, 2016 2m read
The Polymetric Dashboard

> Customizable System Monitoring. ## Introduction The Polymetric Dashboard is a stand-alone module that provides enhanced monitoring tools for a Caché environment. Equipped with over one hundred sensors that monitor key system metrics, a robust REST API, and a modular AngularJS user interface, the Polymetric Dashboard is fully functional out of the box. However, the Polymetric Dashboard is designed to be customizable; any system metric can be monitored by creating a new sensor, and the visualization of collected data can be tailored to specific requirements and purposes.

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Article
· Feb 25, 2019 4m read
Using Grafana directly from IRIS

There have been some very helpful articles in the community that show how to use Grafana with IRIS (or Cache/Ensemble) by using an intermediate database.

But I wanted to get at IRIS structures directly. In particular, i wanted to access the Cache History monitor data that is accessible by SQL as described here

https://community.intersystems.com/post/apm-using-cach%C3%A9-history-mon...

and didn't want anything between me and the data.

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From time to time, we get the previous question in support, something or someone is using more licenses than expected, and we need to find what.

We have two scenarios. The first scenario is when we realize that the licenses are exhausted when the application does not work or when we try to connect through the terminal and get the "lovely"

<LICENSE LIMIT EXCEEDED> message:

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Hi Community,

This article assumes that you are familiar with Zabbix and SNMP monitoring, if not, there are some very interesting posts on the Community, especially this one (https://community.intersystems.com/post/creating-custom-snmp-oids) which contains a lot of information on how to configure and request an SNMP Cache server.

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Preview releases are now available for the first version (v1.0) of InterSystems System Alerting and Monitoring (InterSystems SAM for short).

InterSystems SAM v1.0 provides a modern monitoring solution for InterSystems IRIS-based products. It allows high-level views of clusters and single-node drilled down metrics-visualization together with alerts notifications. This first version provides visualization for more than one hundred InterSystems IRIS kernel metrics, and users can extend the default-supplied Grafana template to their liking.

V1.0 is meant to be a simple and intuitive baseline. Please help us make it great by trying it and sending us feedback!

SAM can display information from InterSystems-based instance starting with version 2019.4

SAM is only available in container format. You will need the SAM Manager container plus a small set of additional open-source components (Prometheus and Grafana) that are added automatically by the composition file.

SAM components and the SAM Manager Community Edition are available from

If you are traveling or prefer a voice-based Q&A description on what SAM is, here is a podcast we have prepared for you:

https://5e18edf067eb59-03854285.castos.com/player/198587
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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Article
· Jul 12, 2019 2m read
Basic Database Metrics example

This is a self contained class that can be run from the Intersystems Task Scheduler which records peak usage details for databases and licenses built up throughout the day and retaining 30 days history.

To schedule the task to run every hour:

d ##class(Metrics.Task).Schedule()

You can also specify your own start time, stop time, and run interval:

d ##class(Metrics.Task).Schedule(startTime, stopTime, intervalMins)

Metrics are stored in ^Metrics in the namespace that the class resides in/is run from.

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APM normally focuses on the activity of the application but gathering information about system usage gives you important background information that helps understand and manage the performance of your application so I am including the IRIS History Monitor in this series.

In this article I will briefly describe how you start the IRIS or Caché History Monitor to build a record of the system level activity to go with the application activity and performance information you gather. I will also give examples of SQL to access the information.

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Introduction

Database performance has become a critical success factor in a modern application environment. Therefore identifying and optimizing the most resource-intensive SQL queries is essential for guaranteeing a smooth user experience and maintaining application stability.

This article will explore a quick approach to analyzing SQL query execution statistics on an InterSystems IRIS instance to identify areas for optimization within a macro-application.

Rather than focusing on real-time monitoring, we will set up a system that collects and analyzes statistics pre-calculated by IRIS once an hour. This approach, while not enabling instantaneous monitoring, offers an excellent compromise between the wealth of data available and the simplicity of implementation.

We will use Grafana for data visualization and analysis, InfluxDB for time series storage, and Telegraf for metrics collection. These tools, recognized for their power and flexibility, will allow us to obtain a clear and exploitable view.

More specifically, we will detail the configuration of Telegraf to retrieve statistics. We will also set up the integration with InfluxDB for data storage and analysis, and create customized dashboards in Grafana. This will help us quickly identify queries requiring special attention.

To facilitate the orchestration and deployment of these various components, we will employ Docker.

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