#SQL

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.

Earlier this year I announced availability of a VS Code extension for coding in ObjectScript, Embedded Python or SQL using the notebook paradigm popularized by Jupyter. Today I published a maintenance release to correct a "getting started" problem.

Here's a video of the installation steps from the extension's README:

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:

IRIS External Table is an InterSystems Community Open Source Project, that allows you to use files, stored in the local file system and cloud object storage such as AWS S3 as SQL Tables.

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).

Suppose you have an application that allows users to write posts and comment on them. (Wait... that sounds familiar...)

For a given user, you want to be able to list all of the published posts with which that user has interacted - that is, either authored or commented on. How do you make this as fast as possible?

Here's what our %Persistent class definitions might look like as a starting point (storage definitions are important, but omitted for brevity):

You need to install the application first. If not installed, please refer to the previous article

Application demonstration

After successfully running the iris image vector search application, some data needs to be stored to support image retrieval as it is not initialized in the library.

While working on getting JSON support for some Python libraries, I discovered some capabilities IRIS provided.

• JSON_OBJECT - A conversion function that returns data as a JSON object.
• JSON_ARRAY - A conversion function that returns data as a JSON array.
• IS JSON - Determines if a data value is in JSON format.
• JSON_TABLE function returns a table that can be used in a SQL query by mapping JSON.
• JSONPath support - is a query language for querying values in JSON.

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.

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.

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.

Hello,

We are sometimes asked for examples of how to do 3rd party SQL database selects/updates/inserts/stored procedures from an Ensemble SQL Business Operation.

In the attached file there are 4 sample Ensemble Operations that demonstrate four different types of transactions with an external SQL DB:

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