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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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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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

https://www.youtube.com/embed/3KClL5zT6MY
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Running Yape in a container.

2. Yape Container SQLite iostat InterSystems

https://www.youtube.com/embed/cuMLSO9NQCM
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Extracting and plotting pButtons data including timeframes and iostat.

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Hyper-Converged Infrastructure (HCI) solutions have been gaining traction for the last few years with the number of deployments now increasing rapidly. IT decision makers are considering HCI when scoping new deployments or hardware refreshes especially for applications already virtualised on VMware. Reasons for choosing HCI include; dealing with a single vendor, validated interoperability between all hardware and software components, high performance especially IO, simple scalability by addition of hosts, simplified deployment and simplified management.

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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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Article
· 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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I am often asked to review customers' IRIS application performance data to understand if system resources are under or over-provisioned.

This recent example is interesting because it involves an application that has done a "lift and shift" migration of a large IRIS database application to the Cloud. AWS, in this case.

A key takeaway is that once you move to the Cloud, resources can be right-sized over time as needed. You do not have to buy and provision on-premises infrastructure for many years in the future that you expect to grow into.

Continuous monitoring is required. Your application transaction rate will change as your business changes, the application use or the application itself changes. This will change the system resource requirements. Planners should also consider seasonal peaks in activity. Of course, an advantage of the Cloud is resources can be scaled up or down as needed.

For more background information, there are several in-depth posts on AWS and IRIS in the community. A search for "AWS reference" is an excellent place to start. I have also added some helpful links at the end of this post.

AWS services are like Lego blocks, different sizes and shapes can be combined. I have ignored networking, security, and standing up a VPC for this post. I have focused on two of the Lego block components;
- Compute requirements.
- Storage requirements.

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Article
· Jul 7, 2017 19m read
Indexing of non-atomic attributes

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 <...>".

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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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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

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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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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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.

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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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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.

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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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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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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.

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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.

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Article
· 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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