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.
%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.
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.
A key part of Application Performance Management (APM) is recording the activity and performance of user activity. For many web applications the closest you can get to this is to record the CSP pages or CSP based services being dispatched.
You may have heard about our mg-dbx-napi interface for IRIS which provides insanely fast access from Node.js. If you've been following recent developments in the server-side JavaScript world, you'll be excited to know that mg-dbx-napi also works with Bun.js, the latter proving to be significantly faster than Node.js for many/most purposes.
Of course, if you're a Node.js user, you'll probably wonder how mg-dbx-napi compares with the Native API for Node.js that is included with IRIS.
Most transactional applications have a 70:30 RW profile. However, some special cases have extremely high write IO profiles.
I ran storage IO tests in the ap-southeast-2 (Sydney) AWS region to simulate IRIS database IO patterns and throughput similar to a very high write rate application.
The test aimed to determine whether the EC2 instance types and EBS volume types available in the AWS Australian regions will support the high IO rates and throughput required.
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).
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.
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.
In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph.
Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.
It has been noticed that some customers running JAVA programs (for example, FOP) on AIX would see the server eventually running low then out of memory. Customer would notice the system pages heavily and user experience becomes bad. And the server would crash when out of memory.
When the problem happens, we can see in ipcs a lot of shared memory segment marked for deletion (Capital D at the beginning of MODE section). This means they will not disappear until the last process attached to the segment detaches it.
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.
Continuing on with providing some examples of various storage technologies and their performance profiles, this time we looked at the growing trend of leveraging internal commodity-based server storage, specifically the new HPE Cloudline 3150 Gen10 AMD processor-based single socket servers with two 3.2TB Samsung PM1725a NVMe drives.
A practical guide to using the tools PERFMON and MONLBL.
Introduction
When investigating performance problems, I often use the utilities ^PERFMON and ^%SYS.MONLBL to identify exactly where in the application pieces of code are taking a long time to execute.
The Certification Team of InterSystems Learning Services is developing an InterSystems IRIS Developer Specialist certification exam, and we are reaching out to our community for feedback that will help us evaluate and establish the contents of this exam.
InterSystems IRIS offers various ways how to profile your code, in most cases it produces enough information to find the places where the most time is spent or where the most global sets. But sometimes it's difficult to understand the execution flow and how it ended at that point.
To solve this, I've decided to implement a way to build a report in a way, so, it's possible to dive by stack down
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.
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.
we're planning some work on our SQL Query Plan functionality for a future release of InterSystems IRIS and are interested to hear how you're using them today, or what'd keep you from using them. Rather than try and fit everything in a rigid survey, I thought a simple thread on our beloved DC might also reveal some use patterns that we support or could do a better job on.
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).
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:
By update
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