Alteryx Server – AMP is slowing down server?

[grossal]

Recently a couple of customers turned on AMP on Server and migrated the majority of the workflows to use AMP. While the promise is very simple „AMP provides significant performance and efficiency improvements over the original Engine“ (link) – the results look very different in reality. We have noticed a significant (actually dramatic) decrease in performance.

(screenshot from the SERVER documentation)

I was able to also verify this when testing it on my private server and the reason is very simple: While AMP will increase the performance of a single workflow significantly, it doesn’t do this very efficiently. And this is a false claim by Alteryx stated in the documentation.

I have a 10-core server – therefore I was able to run 5 workflows in parallel on a server with the old E1 engine.

I used a sample workflow that roughly took 10 minutes with E1. Using the E1 setting, it would take 10 minutes to execute this workflow five times.

Switching the workflow to AMP resulted in a great performance increase and took down the time of execution to only four minutes – but it was the only thing running. When running it five times… it takes 20 minutes instead of 10 and therefore decreases the performance by 50%.

The difference became even bigger as soon as we were dependent on internet speeds and interface response times. In many workflows, a lot of time is taken by the Read/Write of files / APIs / DB. For example, let’s assume we have a workflow that simply grabs data from an API and saves it to a database.

The API doesn’t run faster at all when querying it with AMP – the same as the DB when writing it. In these extreme scenarios, this leads to a performance decrease of 80%. This might be an extreme example (that we actually have on many customers), but the premise is the same: As long as read/write are the major time eaters in our workflows, AMP is not a good idea to be used – especially when put on the server.

My friends (who pointed me to this in the beginning) asked me to open this up here as a discussion-starter about AMP and core efficiency as it seems, that Alteryx claims about "efficiency" aren't correctly stated in the documentation or "efficiency" doesn't mean "CPU core-efficiency" and "significant performance improvements" does only mean "single workflow performance improvements" and not "overall performance improvements".

In one of the cases, this became a huge problem. The customer was running his Alteryx server at 90% load factor over the whole week span. Now ... they can't run all the workflows anymore and had to go back to the pre-AMP migration version of it in order to run their business successfully. 100s of hours were wasted optimizing and changing the workflow to be "AMPed" properly and it didn't result in the desired improvements.

I'd hope that we can get some clarification on what Alteryx means by "efficiency" and "performance improvements" as it seems, that this isn't universal and customers are misled by these promises in the server "AMP Engine Best Practices" guide.

Best

Alex

[TonyAdam]

Great discussion! Are there any updates from Alteryx on this topic?

We are currently facing similar issues. Our IT simply activated AMP without making changes to hardware or configuration, which actually crashed our gallery... I am now trying to find the best / most efficient configuration for our Server + 10 Capacity licenses on workflows that are mostly limited by data transfer speed and not calculations.

[harsh_alang]

Hello All,

Wanted to provide an update.

We at Alteryx are working towards providing a better 'best practice and recommendation' documentation for our users that will allow them to set up environment that will best cater to their needs. 

While a more detailed recommendation is in works, we have also recently updated our best practice document as well.

AMP Engine Best Practices | Alteryx Help

[deeptimathew]

thank you, this response helps me reset my system settings after the upgrade. How about the Memory Limit and Default processing threads as the minimum Memory Limit from 2022.1 seems to be 8192 MB with 6 Default processing threads. I had it set to 2047 MB Memory Limit and 3 Default processing threads before the upgrade. Can I switch back to this same setting after the upgrade?

Thank You in advance, 

Deepti Mathew

[Watermark]

Thx  Alex

[nickdreach]

@harsh_alang do you have any update? 

NB the link you posted doesn't appear to be valid
https://help.alteryx.com/20223/server/amp-engine-best-practices

It just results in 'No results found', for me at least.

[nickdreach]

Posting a link that works for me:
https://help.alteryx.com/20221/en/server/best-practices/amp-engine-best-practices.html##

[TonyaS]

I suggest using this link, it has more recent updates: https://help.alteryx.com/20231/en/server/best-practices/amp-engine-best-practices.html## 

[nickdreach]

Thanks for sharing that link @TonyaS .  AMP is new to us, so the more information we have the better.  (We are currently on 2022.1 - so I wasn't thinking about the wider community when I posted the 20221 link 😀)

[nickdreach]

Ok, I've read the link and have the simplest of workflows which is giving us trouble.  The workflow reads in a single worksheet from a google sheet typical of our organisation.  It contains 76 worksheets, each of which are relatively small in content- for example the worksheet we read has 104 rows and 37 columns of data.  The deprecated google sheet input tool will read this worksheet and write it out via the Output Data tool to a local Excel spreadsheet in 2 seconds or less.  No complicated workflow - just google sheet input tool to Output Data tool.

The latest version of the Google Drive tool, which requires AMP to be turned on, runs in an average of 20s.  Checking 'Engine compatibility mode' makes no difference.
Neither workflow make any impact on the memory being used, the non-AMP workflow spikes CPU once to 10%, the AMP workflow spikes twice to 15% drops back down and spikes twice more.
So, we've gained 18s - bit of a pain, will anyone notice?  The real problem is we actually have 36 of these worksheets to read and will gain another 12 at the end of the year.  In the real workflow they are read one by one, via a macro.  So today I'm looking at an increase in execution time - all things being equal of 18s per worksheet multiplied by 36 worksheets - call it 11 minutes.  And by the end of the year that'll increase to 14 and a half minutes.  The resultant spreadsheet is 100Kb in size.  This is a very hard sell to management who don't understand why replacing a deprecated tool makes things run slower.

I did a further test with a copy of the google sheet that only contained the single worksheet I am reading.  In that case, the Google Drive tool-based workflow ran in 13 or 14s, the Google Sheet tool-based workflow in 1s.  As already mentioned, neither workflow has any impact on memory - but I upped my memory anyway such that the Memory Limit was 16384 Mb (was 8192) - this made no difference.

All help or suggestions greatfully received!