It is part of my job description to say you shouldn't touch internal globals ;-)

see you in Orlando!

FWIW, I would not recommend users touch this global. It is internal and InterSystems may (and does) change how it's used without notice.

Love the article! Very well-phrased considerations on the use of AI, almost all of which I share. Especially in the context of #1, we should not forget that the second L in LLM is for Language, and not for Fact or Solution (otherwise it would be a really bad acronym!). Therefore, if we're not qualified to spot what hallucinations crept into the response, its nicely-phrased language will probably make sure we never will.

PS: so glad you passed that Stats test and joined InterSystems :-) 

Hi, welcome to the club!

At https://learning.intersystems.com/, we have a fair number of videos and exercises that help you get started with IntegratedML. This learning path maps a broad range of resources for you, and this shorter one should offer a decent intro course as well.

I believe what you're looking at is the new, more fine-grained set of %Native_* resources you need to use native functions. Look for DP-423341 in the upgrade guide. It seems we failed to describe this requirement in the  Native API documentation (or at least I didn't find it where I expected it), so we'll get that addressed.

I'd also recommend defaulting to the new, dynamic upgrade guide that makes it easier to filter on particular types of issues. This is now replacing the old, static pages that were more reliant on / vulnerable to manual curation. In fact you'll no longer find those static pages from the menu in the 2025.1 doc.

Thanks for the excellent writeup @Ron Sweeney , and your neatly themed series is bound to inspire many to dive into the classic literature and/or tourism pond!!🤩

No, I would leave out the semicolon at the end of that query. It's typically used as a statement separator, but not really part of query syntax itself. IRIS (as of 2023.2) will tolerate it at the end of a statement, but it doesn't seem that Spark really does anything with it as it wraps what you sent to dbtable with further queries, causing the error you saw.

You may also want to apply 

  .option(“pushDownLimit”, false)

You can install any Python distribution you like (such as Anaconda), but the deep integration with IRIS is only available from 2022.1 onwards, so it'd be running "external" to IRIS and have to talk to IRIS through a client such as the Native API or DB-API.

Unfortunately, no. 2017.2 is indeed rather vintage.

Hi, while others have already addressed your specific XML projection question, I'm wondering why you aren't considering to use IRIS' built-in support for Bulk FHIR exports: https://docs.intersystems.com/irisforhealthlatest/csp/docbook/DocBook.UI...

See also my response to Vitaliy's post -- we intend to refresh the driver distribution page on GitHub to reflect the new distribution channel shortly.

Hi David,

AutoParallel is based on a comprehensive formula weighing the cost (setup cost for the coordination work, writing and then recombining per-process results) against the benefits (independent work that can run in parallel).

For queries not doing any aggregation, iow when the result rows correspond directly to rows in the table being queried (and especially if there are no JOINs), having to write and then read the per-process results, even when done in parallel, does not add value if there's no significant compute cost associated with for example validating a filter predicate. 

For the SELECT COUNT(*), the optimizer is satisfying that from the index (you don't seem to have a bitmap extent index, but that index on age is still plenty small), which is very cheap to read so 1M rows still don't weigh up against the setup costs. 

Anyway, AutoParallel works very well for most of our customers. It's based on a cost formula that incorporates a few constants representing the cost of IO and computations that generalize across infrastructure, so for some environments there may be cases where it over-estimates or under-estimates the exact cost, leading to edge cases where the other option might have yielded a slightly faster query, but generally the formula holds well and every now and then we review whether the constants need to be tuned (wrt newer standards for hardware).

As for the particular example with 0.06 vs 0.24s, I think there may be something different at play there. The (presumed!) non-parallel case does 600k grefs whereas the parallel one only needs 318. Even if your result should only have 300 rows, I would expect it to need at least twice as many grefs (index lookup + master map), so I'd recommend giving that another try after ensuring table stats are up to date and comparing the query plans (for the %PARALLEL, %NOPARALLEL, and query without either hint). A possible explanation might be that your query tool (the SMP?) at the UI level was only retrieving the first 100 rows for the first query, and all of them for the second test.

Then you'd need to define those four subscript levels as part of your IDKEY and have your data map look like this:

<Data name="ModifyDDDD">
<Delimiter>":"</Delimiter>
<Piece>1</Piece>
</Data>

Please see the examples attached to the series of articles I referenced earlier