Date range queries going too slow for you? SQL Performance got you down? I have one weird trick that might just help you out! (SQL Developers hate this!)*
If you have a class that records timestamps when the data is added, then that data will be in sequence with your IDKEY values - that is, TimeStamp1 < TimeStamp2 if and only if ID1 < ID2 for all IDs and TimeStamp values in table - then you can use this knowledge to increase performance for queries against TimeStamp ranges. Consider the following table:
A good writer is supposed to draw you in with the title and bury the answer somewhere in the article. I suppose this makes me a bad writer – don’t think less of me, my whole feeling of self-worth comes from the opinions of internet strangers!
In this article I will explain how to display management portal dashboard by using Python Flask web and Boostrap framework with the help of embedded python
With IRIS 2021.1, we released a significant revision our SQL utilities API at %SYSTEM.SQL. Yes, that's a while ago now, but last week a customer asked a few questions about this and then @Tom Woodfin applied gentle mental pressure ;-) to make me describe the rationale of these changes in a little more detail on the Developer Community. So here we go!
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.
I just wrote up a quick sample to help a colleague load data into IRIS from R using RJDBC, and figured it's worth sharing here for future reference.
Ultimately it was pretty simple, aside from IRIS not liking "." in column names; the workaround is to just rename the columns. Someone better at R than me could probably provide some generic approach.
As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.
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
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.
In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!
An interesting pattern around unique indices came up recently (in internal discussion re: isc.rest) and I'd like to highlight it for the community.
As a motivating use case: suppose you have a class representing a tree, where each node also has a name, and we want nodes to be unique by name and parent node. We want each root node to have a unique name too. A natural implementation would be:
In the good old days (tm) determining the size of the data, streams, and indices for a class/table was easy - you just ran %GSIZE and check D, S, and I globals respectively.
However, nowadays sharding, optimized global names, and indices in separate globals produce %GSIZE output looking like this:
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.
Sometimes we need to import data into InterSystems IRIS from CSV. It can be done e.g. via csvgen tool that generates a class and imports all the data into it.
But what if you already have your own class and want to import data from CSV into your existing table?
There are numerous ways to do that but you can use csvgen (or csvgen-ui) again! I prepared and and example and happy to share. Here we go!
Finding errors in your code or examining unexpected behavior is the main purpose of Debugging I will try to refresh the traditional tools away from the helpers you have in Studio, VScode, Serenji, .... to the basics which have been there before your preferred EDI used it in the background.
We recently encountered an Embedded SQL issue while upgrading to IRIS 2021.1, and thought the issue and workaround might be interesting to share.
Key takeaway: Host variables in an ORDER BY clause of an embedded SQL query that is inside of a method don't work as expected. IRIS versions starting with 2020.1 are affected. As a workaround, add the host variable to the Method's PublicList list and "new" them so the embedded query has access to them.
Application to import 12 Datasets along with 43 tables dynamically by using LOAD DATA command which loads data from a source into an IRIS SQL table. List of Datasets
One of my colleagues had developed an interface in Health Connect (HealthShare 2019.1) to add large amounts of data to an external SQL Server database. The data comes from many text files with delimited rows and data for one table per file. There is a business process to read a file line by line and send an Insert Request to an operation.
The 2021.2 release of the InterSystems IRIS Data Platform includes many exciting new features for fast, flexible and secure development of your mission-critical applications. Embedded Python definitely takes the limelight (and for good reason!), but in SQL we've also made a massive step forward towards a more adaptive engine that gathers detailed statistical information about your table data and exploits it to deliver the best query plans. In this brief series of articles, we'll take a closer at three elements that are new in 2021.2 and work together towards this goal, starting with Run Time Plan Choice.
It's hard to figure out the right order to talk about these (you can't imagine how often I've reshuffled them in writing this article!) because they fit together in such a nice way. As such, feel free to go on a limb and read these in random order .
This is the third article in our short series around innovations in IRIS SQL that deliver a more adaptive, high-performance experience for analysts and applications querying relational data on IRIS. It may be the last article in this series for 2021.2, but we have several more enhancements lined up in this area. In this article, we'll dig a little deeper into additional table statistics we're starting to gather in this release: Histograms
One of our apps uses a class query to support a ZEN Report and works just fine in that report, producing the expected results every time. We’ve since migrated to InterSystems Reports and noticed that, for a report using the same class query, 100s of extra rows with the same column values appear at its bottom.
Benjamin De Boe wrote this great article about Universal Cached Queries, but what the heck is a Universal Cached Query (UCQ) and why should I care about it if I am writing good old embedded SQL? In Caché and Ensemble, Cached Queries would be generated to resolve xDBC and Dynamic SQL. Now i
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