I saw someone recently refer to ECP as magic. It certainly seems so, and there is a lot of very clever engineering to make it work. But the following sequence of diagrams is a simple view of how data is retrieved and used across a distributed architecture.

For more more on ECP including capacity planning follow this link: Data Platforms and Performance - Part 7 ECP for performance, scalability and availability

Created by Daniel Kutac, Sales Engineer, InterSystems

Warning: if you get confused by URLs used: the original series used screens from machine called dk-gs2016. The new screenshots are taken from a different machine. You can safely treat url WIN-U9J96QBJSAG as if it was dk-gs2016.

Part 2. Authorization server, OpenID Connect server

Question:

My journal files are growing rapidly - what could be the cause? How can I minimize the phenomena?

1.About this article

Just like Caché pattern matching, Regular Expressions can be used in Caché to identify patterns in text data – only with a much higher expressive power. This article provides a brief introduction into Regular Expressions and what you can do with it in Caché. The information provided herein is based on various sources, most notably the book “Mastering Regular Expressions” by Jeffrey Friedl and of course the Caché online documentation. The article is not intended to discuss all the possibilities and details of regular expressions. Please refer to the information sources listed in chapter 5 if you would like to learn more. If you prefer to read off-line you can also download the PDF version of this article.

For each defined property, query or an index, several corresponding methods would be automatically generated on a class compilation. These methods can be very useful. In this article, I would describe some of them.

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.

Hi!

I believe the simplest is (to work with csv delimited by ";"):

set file = ##class(%File).%New( "data.csv" )
    set sc = file.Open( "R" ) 
    if $$$ISERR(sc) quit    ; or do smth

    while 'file.AtEnd {
        set str=file.ReadLine() 
        for i=1:1:$length( str, ";" ) {
            set id=$piece( str, ";" ,i ) 
            write !, id  // or do smth
        }
    }
    do file.Close()

Possible options:

different variants of error handling with sc code.

Embrace while loop into try/catch block.

And what's yours?

Hi!

Want to share with you code snippet of try catch block I usually use in methods which should return %Status.

{ 
 try {
  	$$$TOE(sc,StatusMethod())
 }
 catch e {
 	set sc=e.AsStatus()
 	do e.Log()
 }

Quit sc 
}

Here $$$TOE is a short form of $$$TROWONERROR macro.

Inside macro StatusMethod is any method you call which will return %Status value. This value will be placed into sc variable.

Presenter: Dan Kutac
Task: Use a common login identity and a central mechanism of authentication across environments from multiple entities
Approach: Provide examples and code samples of an application environment using OpenID Connect and OAuth 2.0

Description: In this session we will demonstrate an application environment using OpenID Connect and OAuth 2.0. Hear how this is done and what options you have; and yes, you get to keep the code.

Problem: How to use a a common login identity (e.g. Facebook credentials) and a central mechanism of authorization cross environments from multiple entities.

Solution: Create awareness and interest in using OAuth 2.0

Content related to this session, including slides, video and additional learning content can be found here.

Previously I showed you how to run pButtons to start collecting performance metrics that we are looking at in this series of posts.

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:

Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?