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?

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In this article, I would like to talk about the spec-first approach to REST API development.

While traditional code-first REST API development goes like this:

  • Writing code
  • REST-enabling it
  • Documenting it (as a REST API)

Spec-first follows the same steps but reverse. We start with a spec, also doubling as documentation, generate a boilerplate REST app from that and finally write some business logic.

This is advantageous because:

  • You always have relevant and useful documentation for external or frontend developers who want to use your REST API
  • Specification created in OAS (Swagger) can be imported into a variety of tools allowing editing, client generation, API Management, Unit Testing and automation or simplification of many other tasks
  • Improved API architecture. In code-first approach, API is developed method by method so a developer can easily lose track of the overall API architecture, however with the spec-first developer is forced to interact with an API from the position if API consumer which usually helps with designing cleaner API architecture
  • Faster development - as all boilerplate code is automatically generated you won't have to write it, all that's left is developing business logic.
  • Faster feedback loops - consumers can get a view of the API immediately and they can easier offer suggestions simply by modifying the spec

Let's develop our API in a spec-first approach!

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This formation, accessible on my GitHub, will cover, in half a hour, how to read and write in csv and txt files, insert and get inside the IRIS database and a distant database using Postgres or how to use a FLASK API, all of that using the Interoperability framework using ONLY Python following the PEP8 convention.

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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 -

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Article
Robert Cemper · Jan 2, 2022 3m read
DB Migration using SQLgateway

Thanks to @Yuri Marx we have seen a very nice example for DB migration from Postgres to IRIS.
My personal problem is the use of DBeaver as a migration tool.
Especially as one of the strengths of IRIS ( and also Caché) before is the availability of the
SQLgateways that allow access to any external Db as long as for them an access usinig
JDBC or ODBC is available. So I extended the package to demonstrate this.

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Article
Timothy Leavitt · Mar 17, 2021 3m read
Making the most of $Query

I ran into an interesting ObjectScript use case today with a general solution that I wanted to share.

Use case:

I have a JSON array (specifically, in my case, an array of issues from Jira) that I want to aggregate over a few fields - say, category, priority, and issue type. I then want to flatten the aggregates into a simple list with the total for each of the groups. Of course, for the aggregation, it makes sense to use a local array in the form:

agg(category, priority, type) = total

Such that for each record in the input array I can just:

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There's an easy new way to add certificate authority (CA) certificates to your SSL/TLS configurations on InterSystems IRIS 2019.1 (and 2018.1.2) on Windows and Mac. You can ask IRIS to use the operating system's certificate store by entering:

%OSCertificateStore

in the field for "File containing Trusted Certificate Authority X.509 certificate(s)". Here's an image of how to do this in the portal:

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Article
Dmitry Maslennikov · Dec 3, 2021 1m read
VSCode-ObjectScript on GitHub

Not so while ago GitHub introduced, ability to very quickly run VSCode in the browser for any repository hosted there. Press the . key on any repository or pull request, or swap .com with .dev in the URL, to go directly to a VS Code environment in your browser.

github dev

This VSCode is a light version of the Desktop version but works entirely in Browser. And due to this, it has a limitation for extensions which was allowed to work this way. And let me introduce the new version 1.2.1 of VSCode-ObjectScript extension which now supports running in Browser mode.

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Article
Evgeny Shvarov · Jun 13, 2016 1m read
Debug: using locks for breakpoints

Hi, Community!

Want to share with you one debugging approach from the Russian forum.

Suppose I want to debug the application and I want it to stop the execution on a particular line.

I add in code this line:

l +d,-d

When I want to start debugging in this line I block d in terminal

USER> l +d

And execute the app.

The app stops on this line and lets me connect to it with Studio debugger.

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InterSystems IRIS Business Intelligence allows you to keep your cubes up to date in multiple ways. This article will cover building vs synchronizing. There are also ways to manually keep cubes up to date, but these are very special cases and almost always cubes are kept current by building or synchronizing.

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In this post I show strategies for backing up Caché using External Backup with examples of integrating with snapshot based solutions. The majority of solutions I see today are deployed on Linux on VMware so a lot of the post shows how solutions integrate VMware snapshot technology as examples.

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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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Article
Peter Steiwer · Nov 26, 2019 3m read
Designing valid hierarchies in DeepSee

When designing a hierarchy in DeepSee, a child member must have only one parent member. In the case where a child corresponds to two parents, the results can become unreliable. In the case where two similar members exist, their keys must be changed so that they are unique. We will take a look at two examples to see when this happens and how to prevent it.

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In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

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Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

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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

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In this article, we will run an InterSystems IRIS cluster using docker and Merge CPF files - a new feature allowing you to configure servers with ease.

On UNIX® and Linux, you can modify the default iris.cpf using a declarative CPF merge file. A merge file is a partial CPF that sets the desired values for any number of parameters upon instance startup. The CPF merge operation works only once for each instance.

Our cluster architecture is very simple, it would consist of one Node1 (master node) and two Data Nodes (check all available roles). Unfortunately, docker-compose cannot deploy to several servers (although it can deploy to remote hosts), so this is useful for local development of sharding-aware data models, tests, and such. For a productive InterSystems IRIS Cluster deployment, you should use either ICM or IKO.

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Over the past year or so, my team (Application Services at InterSystems - tasked with building and maintaining many of our internal applications, and providing tools and best practices for other departmental applications) has embarked on a journey toward building Angular/REST-based user interfaces to existing applications originally built using CSP and/or Zen. This has presented an interesting challenge that may be familiar to many of you - building out new REST APIs to existing data models and business logic.

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This post provides useful links and an overview of best practice configuration for low latency storage IO by creating LVM Physical Extent (PE) stripes for database disks on InterSystems Data Platforms; InterSystems IRIS, Caché, and Ensemble.

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I wanted to write it as a comment to article of @Evgeny Shvarov . But it happens to be so long, so, decided to post it separately.

Image result for docker clean all images

I would like to add a bit of clarification about how docker uses disk space and how to clean it. I use macOS, so, everything below, is mostly for macOS, but docker commands suit any platform.

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