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#Performance

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Performance tag groups posts regarding software performance issues and the best practices on solving and monitoring performance issues.

Myself and the other Technology Architects often have to explain to customers and vendors Caché IO requirements and the way that Caché applications will use storage systems. The following tables are useful when explaining typical Caché IO profile and requirements for a transactional database application with customers and vendors. The original tables were created by Mark Bolinsky.

In future posts I will be discussing more about storage IO so am also posting these tables now as a reference for those articles.

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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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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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Article
· May 25, 2016 5m read
Random Read IO Storage Performance Tool

New Tool Available

Please see PerfTools IO Test Suite for a later version of the Random Read IO tool.

Purpose

This tool is used to generate random read Input/Output (IO) from within the database. The goal of this tool is to drive as many jobs as possible to achieve target IOPS and ensure acceptable disk response times are sustained. Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis.

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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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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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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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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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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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This week I am going to look at CPU, one of the primary hardware food groups :) A customer asked me to advise on the following scenario; Their production servers are approaching end of life and its time for a hardware refresh. They are also thinking of consolidating servers by virtualising and want to right-size capacity either bare-metal or virtualized. Today we will look at CPU, in later posts I will explain the approach for right-sizing other key food groups - memory and IO.

So the questions are:

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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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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:

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Article
· 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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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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Often InterSystems technology architect team is asked about recommended storage arrays or storage technologies. To provide this information to a wider audience as reference, a new series is started to provide some of the results we have encountered with various storage technologies. As a general recommendation, all-flash storage is highly recommended with all InterSystems products to provide the lowest latency and predictable IOPS capabilities.

The first in the series was the most recently tested Netapp AFF A300 storage array. This is middle-tier type storage array with several higher models above it. This specific A300 model is capable of supporting a minimal configuration of only a few drives to hundreds of drives per HA pair, and also capable of being clustered with multiple controller pairs for tens of PB's of disk capacity and hundreds of thousands of IOPS or higher.

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In the last post we scheduled 24-hour collections of performance metrics using pButtons. In this post we are going to be looking at a few of the key metrics that are being collected and how they relate to the underlying system hardware. We will also start to explore the relationship between Caché (or any of the InterSystems Data Platforms) metrics and system metrics. And how you can use these metrics to understand the daily beat rate of your systems and diagnose performance problems.

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Here is a snippet that I learned yesterday

You can define an index on a collection property but when I tried to use it, I failed. I was using

     Select ….. where …. :xx %INLIST collproperty

But this will not use an index, but the equivalent syntax

     SELECT .. WHERE ... FOR SOME %ELEMENT(collproperty) (%VALUE=:xx)

will use the index

Check out

http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=...

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