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.

I’ve a server that is running Windows Server 2003 R2 Enterprise Edition SP2 x86.

I just noted that you cannot allocate shared memory beyond 1.6GB.

Is this a known problem between Cache and this OS architecture, and has anyone configured it beyond this?

Since Caché 2017 the SQL engine has included new set of statistics. These record the number of times a query is executed and the time it takes to run.

In the previous parts (1, 2) we talked about globals as trees. In this article, we will look at them as sparse arrays.

A sparse array - is a type of array where most values assume an identical value.

In practice, you will often see sparse arrays so huge that there is no point in occupying memory with identical elements. Therefore, it makes sense to organize sparse arrays in such a way that memory is not wasted on storing duplicate values.

In some programming languages, sparse arrays are part of the language - for example, in J, MATLAB. In other languages, there are special libraries that let you use them. For C++, those would be Eigen and the like.

Globals are good candidates for implementing sparse arrays for the following reasons:

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 :)

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 -

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.

Quotes (1NF/2NF/3NF)^ru:

Every row-and-column intersection contains exactly one value from the applicable domain (and nothing else).
The same value can be atomic or non-atomic depending on the purpose of this value. For example, “4286” can be

• atomic, if its denotes “a credit card’s PIN code” (if it’s broken down or reshuffled, it is of no use any longer)
• non-atomic, if it’s just a “sequence of numbers” (the value still makes sense if broken down into several parts or reshuffled)

This article explores the standard methods of increasing the performance of SQL queries involving the following types of fields: string, date, simple list (in the $LB format), "list of <...>" and "array of <...>".

Hi Guys,

Can you please advise on the below queries.

Query 1:

Example 1:

 S a="345",b="arun",c="kumar",d="hi",e="yello",f="orange"

Example 2:

S a="345"

S b="arun"

S c="kumar"

S d="hi"

S e="yello"

S f="orange"

Can you please advise me, which one is performance wise is better.

Query 2:

Example 1:

S:a=1 R="Arun"

Example 2:

I a=2 R="Arun"

Please advise me, which one is giving better performance in this.

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.

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

Cache version: Cache for Windows (x86-64) 2017.2.1 (Build 801_3U)

Good Afternoon,

I have a co-worker who is trying to run the below query via ODBC. The issue is that the query appears to be running extremely slow (nearly 2 hours).

Currently, we are running 2014.1 on two different servers (OpenVMS, RHEL). The plan is to transition from OpenVMS to RHEL, but our Write Daemon is in a Troubled state on both servers.

On the OpenVMS server, we have a WIJ file that's 26G and can grow to 40G (size of database cache). Since it hasn't grown to 40G, we don't believe the size of the WIJ file to be the issue.

What else should we be looking at regarding the performance of the Write Daemon?

AWS has officially released their second-generation Arm-based Graviton2 processors and associated Amazon EC2 M6g instance type, which boasts up to 40% better price performance over current generation Intel Xeon based M5 instances.

A few months ago, InterSystems participated in the M6g preview program, and we ran a few benchmarks with InterSystems IRIS that showed compelling results. This led us to support ARM64 architectures for the first time.

Now you can try InterSystems IRIS and InterSystems IRIS for Health on Graviton2-based Amazon EC2 M6g instances for yourselves through the AWS Marketplace!