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.

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

But what represents your master map and row level? is that are these 14 entries all corresponding to one and the same row, presumably with some sort of IDKEY consisting of "AC" and "MD"? We cannot say much meaningful without understanding that broader picture.

This is indeed expected behaviour. It's not the SELECT * itself, but the fact that this query is not applying any filtering or doing any other calculations that are worth parallelizing. So the query is asking to return all rows as-is, passing them back through a single connection/process. Therefore the optimizer argues there's no benefit in parallelizing that work, as the work of collating the per-process results back into a single resultset is pure overhead.

The actual formula being applied is a little more subtle (a WHERE clause that is expected to only filter out a small fraction of the rows would not be enough to parallelize either) and as has been suggested you still need to hit the AutoParallel threshold for the process mgmt code not to outweigh the benefits (e.g. if there's only a few hundred rows).

Thanks,
benjamin

I have only this morning updated my settings about notifications, which had a few things switched off that I didn't know existed as separate options. I'm wondering if some recent enhancement to the DC refining control on what to subscribe to was a little conservative in starting with notifications disabled.

I believe I should be all set now, as I got a notification for your post right away now.

Hi @Andreas Schneider ,

I have just downloaded the released 2024.2 container as well as the latest 2024.3, set up foreign server connections from the former to the latter, and all THROUGH SERVER commands work as expected:

[SQL]USER>>through server myhost select $zv
2.      through server myhost select $zv

| Expression_1 |
| -- |
| IRIS for UNIX (Ubuntu Server LTS for x86-64 Containers) 2024.3 (Build 206U) Fri Oct 11 2024 16:59:56 EDT |

1 Rows(s) Affected
statement prepare time(s)/globals/cmds/disk: 0.0485s/6,382/43,267/0ms
          execute time(s)/globals/cmds/disk: 0.0028s/40/576/0ms
                                query class: %SQL.PassthroughResult.Dynamic
---------------------------------------------------------------------------
[SQL]USER>>through server myself select $zv
3.      through server myself select $zv

| Expression_1 |
| -- |
| IRIS for UNIX (Ubuntu Server LTS for x86-64 Containers) 2024.2 (Build 247U) Tue Jul 16 2024 09:29:04 EDT |

1 Rows(s) Affected
statement prepare time(s)/globals/cmds/disk: 0.1686s/6,388/43,347/0ms
          execute time(s)/globals/cmds/disk: 0.0031s/40/576/0ms
                                query class: %SQL.PassthroughResult.Dynamic
---------------------------------------------------------------------------
[SQL]USER>>select $zv
4.      select $zv

| Expression_1 |
| -- |
| IRIS for UNIX (Ubuntu Server LTS for x86-64 Containers) 2024.2 (Build 247U) Tue Jul 16 2024 09:29:04 EDT |

1 Rows(s) Affected
statement prepare time(s)/globals/cmds/disk: 0.0852s/35,278/160,395/0ms
          execute time(s)/globals/cmds/disk: 0.0001s/0/411/0ms
                                query class: %sqlcq.USER.cls24
---------------------------------------------------------------------------
[SQL]USER>>

I'm afraid I have no other suggestion than to re-check whether your 2024.2 instance and the JDBC driver it uses are in sync and there are no stale classpath entries in your SQL Gateway connection or SQL Data Lens config.

FWIW, Foreign Tables use new xDBC Gateway infrastructure that only relies on the SQL Gateway connection definition for getting the server URL and credentials, so the "test" button in the SQL Gateway definition screen is not necessarily representative for xDBC Gateway connectivity. This is a temporary situation until the IRIS Wallet (secure credential score) is ready for the new Gateway (and Foreign Tables) to adopt, but we've never run into any differences in behaviour like the one you are facing.

To validate whether the xDBC Gateway can connect and possibly glean more detail from the error message, please try this, replacing the host/port/credentials as appropriate for your setup:

w ##class(%XDBC.Gateway.Connection).%GetConnection({ "host": "host.docker.internal", "port": 51774, "databasename": "USER", "datasource": "com.intersystems.jdbc.IRISDataSource", "user":"_SYSTEM", "password":"SYS","type":"jdbc" },.sc)
d $system.OBJ.DisplayError(sc)

Please note that documentation link is for InterSystems Caché. In IRIS, we've gotten more strict around the deprecation of those older classes and that section of the documentation has been removed, and the associated classes are marked as deprecated and/or internal to further encourage you to take advantage of %SQL.Statement

another question to ask yourself is whether you really want that CSV file data to be in IRIS tables. Maybe if it's rarely accessed projecting it as a foreign table is more cost effective?