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Dmitry Maslennikov · Apr 24, 2023

There is no way, to catch the possible issues for the previous version of IRIS.

The best case scenario is if you automate the build process, for instance with Docker, and test a compile stage on different versions of IRIS. But the ability to successfully compile may not prove that it will work, it would be better to have some unit tests, which will check it.

One more thing, may help, to check it, using the such tool as ObjectScript Quality, can help with checking System's API version. Where you can set the oldest supported version of IRIS, and during the scan, it can check if Methods are available in that particular version.

Dmitry Maslennikov · Apr 3, 2023

Implemented it with SQL Procedure

CREATE OR REPLACE PROCEDURE %ZDJANGO.CLONE_DATABASE(sourceNS %String, targetNS %String)
LANGUAGE OBJECTSCRIPT
{
	new $namespace
	set $namespace = "%SYS"
	
	$$$ThrowOnError(##class(Config.Namespaces).Get(sourceNS, .sourceNSparams))
	$$$ThrowOnError(##class(Config.Namespaces).Get(targetNS, .targetNSparams))
	
	for kind="Globals", "Routines" {
		$$$ThrowOnError(##class(Config.Databases).Get(sourceNSparams(kind), .sourceDBparams))
		$$$ThrowOnError(##class(Config.Databases).Get(targetNSparams(kind), .targetDBparams))
		
		set from = sourceDBparams("Directory")
		set to = targetDBparams("Directory")
		
		quit:$Data(done(to))
		set done(to) = "" 
		
		$$$ThrowOnError(##class(SYS.Database).Copy(from, to, , , 4))
	}
}

DANGER: Do not use it, made specifically for my case, it may overwrite database

Dmitry Maslennikov · Apr 3, 2023

Example of using SQLAlchemy+Pandas, works with this Cloud SQL as well

from sqlalchemy import create_engine
import pandas as pd

server = '<your-server-hostname>'
port = 1972
namespace = 'USER'
username = 'SQLAdmin'
password = '<YOUR_PASSWORD>'

url = f"iris://{username}:{password}@{server}:{port}/{namespace}"
print(url)
engine = create_engine(url)

df = pd.DataFrame({
    'int': [1, 2, 3, 4, 5],
    'float': [1.1, 2.2, 3.3, 4.4, 5.5],
    'string': ['a', 'b', 'c', 'd', 'e'],
    'datetime': pd.date_range('20130101', periods=5),
    'bool': [True, False, True, False, True]
})

# create a table in IRIS
df.to_sql('iris_table', engine, if_exists='replace', schema='sqlalchemy')

# read the table back from IRIS 
df2 =  pd.read_sql_table('iris_table', engine, schema='sqlalchemy')
# print the dataframe
print(df2)