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#SQL

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SQL is a standard language for storing, manipulating and retrieving data in relational databases.

Article
· Mar 18, 2024 10m read
Pandas for KPIs in InterSystems IRIS BI

Pandas is not just a popular software library. It is a cornerstone in the Python data analysis landscape. Renowned for its simplicity and power, it offers a variety of data structures and functions that are instrumental in transforming the complexity of data preparation and analysis into a more manageable form. It is particularly relevant in such specialized environments as ObjectScript for Key Performance Indicators (KPIs) and reporting, especially within the framework of the InterSystems IRIS platform, a leading data management and analysis solution.

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It is often necessary to sort the results of a query on a string field containing a combination of alphabetic and numeric characters. In cases like this the default string collation may not always return the data in the expected sequence.

An example of this may be where a select from Samples.Person should order the results by the street address, but firstly ordered by the street number part as numeric, and then by the street name.

The default query will return the results as follows:

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This is the second piece in our series on 2021.2 SQL enhancements delivering an adaptive, high-performance SQL experience. In this article, we'll zoom in on the innovations in gathering Table Statistics, which are of course the primary input for the Run Time Plan Choice capability we described in the previous article.

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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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Recently I wanted to get a list of all cached queries and their texts. Here's how to do that.

First create an SQL Procedure returning Cache Query text from a Cached Query routine name:

Class test.CQ
{

/// SELECT test.CQ_GetText()
ClassMethod GetText(routine As %String) As %String [ CodeMode = expression, SqlProc ]
{
##class(%SQLCatalog).GetCachedQueryInfo(routine)
}

}

And after that you can execute this query:

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Article
· Apr 19, 2023 2m read
Apache Superset now with IRIS

Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

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Article
· Apr 26, 2024 3m read
Geo Vector Search #2

Technical surprises using VECTORs
>>> UPDATED

Building my tech. example provided me with a bunch of findings htt I want to share.
The first vectors I touched appeared with text analysis and more than 200 dimensions.
I have to confess that I feel well with Einstein's 4 dimensional world.
7 to 15 dimensions populating the String Theory are somewhat across the border.
But 200 and more is definitely far beyond my mathematical horizon.

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Article
· Sep 11, 2024 9m read
Dates with InterSystems

Do not let the title of this article confuse you; we are not planning to take the InterSystems staff out to a fine Italian restaurant. Instead, this article will cover the principles of working with date and time data types in IRIS. When we use these data types, we should be aware of three different conversion issues:

  1. Converting between internal and ODBC formats.
  2. Converting between local time, UTC, and Posix time.
  3. Converting to and from various date display formats.

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Article
· Feb 22, 2024 4m read
IRIS 2024.1 Preview - New Feature

There is an interesting new feature in the recently announced 2024.1 preview, JSON_TABLE. JSON_TABLE is one of a family of functions introduced by the 2016 version of the SQL Standard (ISO Standard, published in early 2017). It allows for JSON values to be mapped to columns and queried using SQL. JSON_TABLE is valid in the FROM clause of some SQL statements.

The syntax of JSON_TABLE is quite large, allowing for exceptional conditions where the provided JSON values don't match expectations, nested structures and so on.

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Introduction

In some of the last few articles I've talked about types between IRIS and Python, and it is clear that it's not that easy to access objects from one side at another.

Fortunately, work has already been done to create SQLAlchemy-iris (follow the link to see it on Open Exchange), which makes everything much easier for Python to access IRIS' objects, and I'm going to show the starters for that.

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What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

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In the world of APIs, REST is very extended. But what happens when you need more flexibility in your data-fetching strategies? For instance letting the client to choose what fields is going to receive. Enter GraphQL, a query language for your APIs that provides a flexible alternative to REST.

In this post, we will:

  • Compare REST and GraphQL.
  • Dive into the basics of GraphQL: Queries, Mutations, and HTTP.
  • Build a simple GraphQL server implementation using Graphene, SQLAlchemy, and Flask over data in InterSystems IRIS.
  • Explore how to deploy your GraphQL server as a WSGI application in IRIS.
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Article
· May 13, 2016 1m read
mySQL data importer tool

Hi,

If you want to import data from a mySQL export file (exported with mysqldump), you will find here a little script that could help.

Only the INSERT commands in the sql file are executed into Caché. Indices are not computed for better performance.
%NOINDEX, %NOCHECK and %NOLOCK are generated on each INSERT line.

Currently, the file can not contain a "),(" pattern inside the values part of the INSERT command. If this is the case, the line is skipped. This feature may be implemented in the extractValuesList method.

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Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.

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When working with InterSystems IRIS, database developers and architects often face a critical decision: whether to use Dynamic SQL or Embedded SQL for querying and updating data. Both methods have their unique strengths and use cases, but understanding their performance implications is essential to making the right choice. Response time, a key metric in evaluating application performance, can vary significantly depending on the SQL approach used. Dynamic SQL offers flexibility, as queries can be constructed and executed at runtime, making it ideal for scenarios with unpredictable or highly variable query needs. Conversely, Embedded SQL emphasizes stability and efficiency by integrating SQL code directly into application logic, offering optimized response times for predefined query patterns.

In this article, I will explore the response times when using these two types of SQL and how they depend on different class structures and usage of parameters. So to do this, I'm going to use the following classes from the diagram:

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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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Article
· Mar 21, 2024 2m read
Left Side Functions in ObjectScript

In ObjectScript you have a wide collection of functions that return some value
typically:

set variable = $somefunction(param1,param2, ...)

There is nothing special about that.
But there is a set of functions that I classify as LEFT SIDED
The specialty of them is that you can use them also on the left of the equal operator
as a target in the SET command:

set $somefunction(param1,param2, ...) = value

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On the Latest GlobalSummit 2022, InterSystems Introduced Cloud SQL. So, you may have lightweight InterSystems IRIS with access to SQL only. Well, what if you would still need some Interoperability features in the cloud as well? There are various solutions on the market nowadays, which offer a bunch of integration adapters out of the box and can be extended with support from the community. Some time ago, I've implemented an adapter for the Node-RED project, which can be deployed manually everywhere you want. Now I would like to introduce a new integration with my recent discovery, n8n.io

Banner image

n8n.io is a workflow automation platform, that supports over 200 different integrations out of the box and from a community, and now including InterSystems IRIS.

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Article
· 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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Introduction

InterSystems IRIS allows you to build REST APIs using ObjectScript classes and the %CSP.REST framework. This enables the development of modern services to expose data for web apps, mobile apps, or system integrations.

In this article, you'll learn how to create a basic REST API in InterSystems IRIS, including:

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I implemented a Python Flask application for the 2024 Python Contest with a page that provides common form fields for an outgoing email such as the To and CC fields. And it lets you input a message as well as uploading text based attachments.

Then using LlamaIndex in Python, the app analyzes the content you put in and returns to you in a result box if there is anything that should stop you from sending that email.

Take a look at the Github repo here.

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So, you checked your server and saw that IRISTEMP is growing too much. There's no need to panic. Let’s investigate the issue before your storage runs out.

Step 1: Confirm the IRISTEMP Growth Issue

Before assuming IRISTEMP is the problem, let’s check its actual size.

Check the Free Space

Run the following command in the IRIS terminal:

%SYS>do ^%FREECNT

When prompted, enter:

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Case description

Let’s imagine that you are a Python developer or have a well-trained team specialized in Python, but the deadline you got to analyze some data in IRIS is tight. Of course, InterSystems offers many tools for all kinds of analyses and treatments. However, in the given scenario, it is better to get the job done using the good old Pandas and leave the IRIS for another time.

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We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.

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