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Dmitry Maslennikov · Apr 16

Did you try using more appropriate package %Stream for it?

In your case, you would need to use %Stream.FileCharacter

 Set stream=##class(%Stream.FileCharacter).%New()
 $$$ThrowOnError(stream.LinkToFile("c:\export.csv"))
 set rs = ##class(%SQL.Statement).%ExecDirect(, "SELECT a, b FROM table")
 if rs.%SQLCODE'=0 {
     throw ##class(%Exception.SQL).CreateFromSQLCODE(rs.%SQLCODE, rs.%Message)
 }
 while rs.%Next() {
    set line = $listbuild(rs.a, rs.b)
    do f.WriteLine($listtostring(line, ","))
 }
 $$$ThrowOnError(stream.%Save())

Additionally, nowadays using embedded classes does not give much advantage, if it's not for one row result, only makes the code a bit harder to read

Dmitry Maslennikov · Mar 25

Implemented for Interoperability: it can check status, including items in it, restart, update, recover, and check for queues and errors.
There is also SQL Query executon

https://www.youtube.com/embed/EVzZnkjIvoM
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Dmitry Maslennikov · Mar 16

While we moved our application to AWS, and we have some data, which we need to keep for a while. With this feature, we can move old data to a cheaper storage.

I believe the ability to move to a cheaper storage is mostly the case. Another option is that some table is too big, and someone would like to split it to be stored in multiple different databases, together with the indexes.

Dmitry Maslennikov · Mar 13

I don't see any difference

Time taken: 23.218561416957527 seconds to insert 100000 at  4306.899045302852  records per second.

Time taken: 23.179011167027056 seconds to insert 100000 at  4314.247889152987  records per second.

from sqlalchemy import create_engine
import numpy as np
import time
import iris

hostname = "localhost"
port = 1972
namespace = "USER"
username = "_SYSTEM"
password = "SYS"

# Create the SQLAlchemy engine
DATABASE_URL = (
    f"iris+intersystems://{username}:{password}@{hostname}:{port}/{namespace}"
)
engine = create_engine(DATABASE_URL, echo=True)
args = {
    "hostname": hostname,
    "port": port,
    "namespace": namespace,
    "username": username,
    "password": password,
}
connection = iris.connect(**args)
# connection = engine.raw_connection()

# Generate data for each row (50 fields)
columns_count = 50
drop_table_sql = f"DROP TABLE IF EXISTS test_table"
columns = [f"field_{i + 1}" for i in range(columns_count)]
create_table_sql = f"CREATE TABLE test_table ({', '.join([f'{col} DOUBLE' for col in columns])})"

num_records = 100000
# Define SQL insert statement
sql_insert = f"INSERT INTO SQLUser . test_table ({', '.join(columns)}) VALUES ({', '.join(['?'] * columns_count)})"
record_values = []
# Execute SQL insert
try:
    start_time = time.perf_counter()  # Capture start time
    batch = 0
    cursor = connection.cursor()
    cursor.execute(drop_table_sql)
    cursor.execute(create_table_sql)
    connection.commit()
    for _ in range(num_records):
        record_values = [np.random.rand() for _ in range(columns_count)]
        cursor.execute(sql_insert, record_values)
        batch = batch + 1
        if batch >= 10000:
            connection.commit()
            print("Batch inserted successfully!")
            batch = 0
    connection.commit()
    end_time = time.perf_counter()  # Capture end time
    elapsed_time = end_time - start_time
    print(
        f"Time taken: {elapsed_time} seconds to insert {num_records} at ",
        num_records / elapsed_time,
        " records per second.",
    )
except Exception as e:
    print("Error inserting record:", e)
finally:
    cursor.close()
    connection.close()
    engine.dispose()
Dmitry Maslennikov · Mar 4

I think you just confusing $listbuild with list in BPL context. In BPL when you define it as List Collection, it will use class %Collection.ListOfDT, or in case of array it will be %Collection.ArrayOfDT

That means, that you should use 

 if 'context.Facilities.Find(##class(Ens.Rule.FunctionSet).SubString(context.EpicDepartmentID,1,4))
 {
   do context.Facilities.Insert(##class(Ens.Rule.FunctionSet).SubString(context.EpicDepartmentID,1,4))
 }
Dmitry Maslennikov · Mar 4

The support for NodeJS in IRIS is quite primitive and limited to only native functions, globals, methods, no SQL

check in IRIS folder, what do you have installed with IRIS for nodejs

I don't have windows, only docker, and in my case

/usr/irissys/bin/iris1200.node
/usr/irissys/bin/iris800.node
/usr/irissys/bin/iris1600.node
/usr/irissys/bin/iris1400.node
/usr/irissys/bin/iris1000.node
/usr/irissys/dev/nodejs/intersystems-iris-native/bin/lnxubuntuarm64/irisnative.node

If you want to use IRIS SQL from nodejs, you can try my package, which you can install with npm

npm install intersystems-iris
const { IRIS } = require("intersystems-iris");

async function main() {
    const db = new IRIS('localhost', 1972, 'USER', '_SYSTEM', 'SYS')
    console.log('connected')
    let res = await db.sql("select 1 one, 2 two")
    console.log(res.rows);
    await db.close()
}

main()

It's quite simple at the moment, only supports SQL with no parameters, but should work I believe