YASPE is the successor to YAPE (Yet Another pButtons Extractor). YASPE has been written from the ground up with many internal changes to allow easier maintenance and enhancements.

YASPE functions:

  • Parse and chart InterSystems Caché pButtons and InterSystems IRIS SystemPerformance files for quick performance analysis of Operating System and IRIS metrics.
  • Allow a deeper dive by creating ad-hoc charts and by creating charts combining the Operating System and IRIS metrics with the "Pretty Performance" option.
  • The "System Overview" option saves you from searching your SystemPerformance files for system details or common configuration options.

YASPE is written in Python and is available on GitHub as source code or for Docker containers at:


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One of my colleagues had developed an interface in Health Connect (HealthShare 2019.1) to add large amounts of data to an external SQL Server database. The data comes from many text files with delimited rows and data for one table per file. There is a business process to read a file line by line and send an Insert Request to an operation.

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Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

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While the integrity of Caché and InterSystems IRIS databases is completely protected from the consequences of system failure, physical storage devices do fail in ways that corrupt the data they store. For that reason, many sites choose to run regular database integrity checks, particularly in coordination with backups to validate that a given backup could be relied upon in a disaster.

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Dynamic PoolSize (DPS) Experiment

Purpose:

Enhance Ensemble or IRIS production so it can dynamically allocate pool size for adapter-based components based on their utilization.

Sometimes, an unexpected traffic volume occurs, and default pool size allocated to production components may become a bottleneck. To avoid such situations, I created a demonstrator project some 2 years ago to see, whether it would be possible and feasible to modify production, so it allowed for dynamically modifying its components per their load.

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A More Industrial-Looking Global Storage Scheme

In the first article in this series, we looked at the entity–attribute–value (EAV) model in relational databases, and took a look at the pros and cons of storing those entities, attributes and values in tables. We learned that, despite the benefits of this approach in terms of flexibility, there are some real disadvantages, in particular a basic mismatch between the logical structure of the data and its physical storage, which causes various difficulties.

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Introduction

In the first article in this series, we’ll take a look at the entity–attribute–value (EAV) model in relational databases to see how it’s used and what it’s good for. Then we'll compare the EAV model concepts to globals.

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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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Introduction

InterSystems has recently completed a performance and scalability benchmark of IRIS for Health 2020.1, focusing on HL7 version 2 interoperability. This article describes the observed throughput for various workloads, and also provides general configuration and sizing guidelines for systems where IRIS for Health is used as an interoperability engine for HL7v2 messaging.

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InterSystems and Intel recently conducted a series of benchmarks combining InterSystems IRIS with 2nd Generation Intel® Xeon® Scalable Processors, also known as “Cascade Lake”, and Intel® Optane™ DC Persistent Memory (DCPMM). The goals of these benchmarks are to demonstrate the performance and scalability capabilities of InterSystems IRIS with Intel’s latest server technologies in various workload settings and server configurations. Along with various benchmark results, three different use-cases of Intel DCPMM with InterSystems IRIS are provided in this report.

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What is %SQLRESTRICT

%SQLRESTRICT is a special %FILTER clause for use in MDX queries in InterSystems IRIS Business Intelligence. Since this function begins with %, it means this is a special MDX extension created by InterSystems. It allows users to insert an SQL statement that will be used to restrict the returned records in the MDX Result Set. This SQL statement must return a set of Source Record IDs to limit the results by. Please see the documentation for more information.

Why is this useful?

This is useful because there are often times users want to restrict the results in their MDX Result Set based on information that is not in their cubes. It may be the case that this information may not make sense to be in the cube. Other times this can be useful when there is a large set of values you want to restrict. As mentioned before, this is not a standard MDX function, it was created by InterSystems to handle cases were queries were not performing well or cases that were not easily solved by existing functions.

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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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A few years ago, I was teaching the basics of our %UnitTest framework during Caché Foundations class (now called Developing Using InterSystems Objects and SQL). A student asked if it was possible to collect performance statistics while running unit tests. A few weeks later, I added some additional code to the %UnitTest examples to answer this question. I’m finally sharing it on the Community.

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Our team is reworking an application to use REST services that use the same database as our current ZEN application. One of the new REST endpoints uses a query that ran very slowly when first implemented. After some analysis, we found that an index on one of the fields in the table greatly improved performance (a query that took 35 seconds was now taking a fraction of a second).

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If a picture is worth a thousand words, what's a video worth? Certainly more than typing a post.

Please check out my "Coding talks" on InterSystems Developers YouTube:

1. Analysing InterSystems IRIS System Performance with Yape. Part 1: Installing Yape

Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

Extracting and plotting pButtons data including timeframes and iostat.

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Article
Guillaume Rongier · 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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In my earlier article on Adopting Bitmaps I described the technique already.
Now you can find a code example also on Open Exchange.

This is a coding example working on Caché 2018.1.3 and IRIS 2020.2 
It will not be kept in sync with new versions 
It is also NOT serviced by InterSystems Support !

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There are often questions surrounding the ideal Apache HTTPD Web Server configuration for HealthShare. The contents of this article will outline the initial recommended web server configuration for any HealthShare product.

As a starting point, Apache HTTPD version 2.4.x (64-bit) is recommended. Earlier versions such as 2.2.x are available, however version 2.2 is not recommended for performance and scalability of HealthShare.

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Article
Ben Spead · Jan 11, 2019 4m read
SQL Performance Resources

There are three things most important to any SQL performance conversation: Indices, TuneTable, and Show Plan. The attached PDFs includes historical presentations on these topics that cover the basics of these 3 things in one place. Our documentation provides more detail on these and other SQL Performance topics in the links below. The eLearning options reinforces several of these topics. In addition, there are several Developer Community articles which touch on SQL performance, and those relevant links are also listed.

There is a fair amount of repetition in the information listed below. The most important aspects of SQL performance to consider are:

  1. The types of indices available
  2. Using one index type over another
  3. The information TuneTable gathers for a table and what it means to the Optimizer
  4. How to read a Show Plan to better understand if a query is good or bad
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Article
Sergei Sarkisian · Oct 1, 2018 4m read
Profiling code using Caché Monitor

Not everyone knows that InterSystems Caché has a built-in tool for code profiling called Caché Monitor.

Its main purpose (obviously) is the collection of statistics for programs running in Caché. It can provide statistics by program, as well as detailed Line-by-Line statistics for each program.

Using Caché Monitor

Let’s take a look at a potential use case for Caché Monitor and its key features. So, in order to start the profiler, you need to go to the terminal and switch to the namespace that you want to monitor, then launch the %SYS.MONLBL system routine:

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