#Performance

Having been inspired with Shared code execution speed question/discussion, I dare to ask another one which is annoying me and my colleagues for several weeks.

We have a routine called Lib that comprises 200 $$-functions of 1500 code lines total. It was noticed that after calling _any_ function of another rather big routine (1900 functions, 32000 lines) the next call of $$someFunction^Lib(x) is getting 10-20% slower than previous call of the same function. This effect doesn't depend on:

Hi,

If I test the Native api for Node.js from the documentation, I noticed (if I'm correct) all methods and calls are synchronous. By default due to the nature of Node.js, there is only one thread of execution and normally all JavaScript methods and all calls should be asynchronous and use either a callback function (the "old way") or promises or the async/await contruct to return their result, e.g.:

Hi Guys,

Can you please advise on the below queries.

Query 1:

Example 1:

 S a="345",b="arun",c="kumar",d="hi",e="yello",f="orange"

Example 2:

S a="345"

S b="arun"

S c="kumar"

S d="hi"

S e="yello"

S f="orange"

Can you please advise me, which one is performance wise is better.

Query 2:

Example 1:

S:a=1 R="Arun"

Example 2:

I a=2 R="Arun"

Please advise me, which one is giving better performance in this.

I have a database that is 2TB in size (from a LIVE system).

I would like to defragment it but I have some questions. I took a look at official documentation but it does not help.

Hello,

I want to process more requests per second in Ensemble 2015 (soap service). My problem is in a business process that makes a great transformation. I thought that I can put its group size to 4 (the current value is 1), or put 4 business processes and apply, for example, the round-robin algorithm.
Which alternative is better?

Hello Community,

I recently encountered a issue with Caché and I can't figure out where the problem is coming from.

I noticed that the license limit (200) was reached whenever I was opening my Studio (so it seems). When this occurs, I restart Caché (with the Cube in the Taskbar), and the number of license used is back to 1%, but grows back after. The time taken before the number of license grows back again looks pretty random.

Here is a couple of screenshots :

Can someone direct me to where in the documentation we can find how consumption may be calculated for global storage?

EDIT: After receiving some responses, it seems I was unclear in my initial inquiry. I am looking to determine our rate of consumption of storage; however, I am having some difficulty in doing that.

Suppose we need to store millions of values temporarily, that means, we don't care about them if we lose them but our application use them to get realtime information. Should I use Cachetemp or whatever other DB without journaling enabled? If answer is Cachetemp, shouldn't be a problem if we decide to scale using App Server + ECP? I'm not sure what would happen with the app logic in such architecture as I guess I couldn't map and share cachetemp...

Any idea/suggestion?

Is there anyway to improve the performance of the display of DeepSee dashboards?

It takes minutes to do the cell calculations of computing the axis members.

Any direction or help would be appreciated.

Thanks,
LG

I work in a small development company that uses Caché as a database. In some support cases I have doubts about whether the client's infrastructure environment is not affecting Caché's response time. Reading a bit about comparing installations in different environments, both in production as testing and homologation environments , I understood that the TPC-E is a benchmarking method accepted in the market.

Running cache 5.0.21 64 bit on Windows server 2016 in virtual environment. Trying to understand why every single process disk read speed (simple sql data walks) caps around ~20MB/s, however 2 paralell such tasks on different data areas can reach 19MB/s each, four - 17MB/s each, that is 70MB/s total, etc. Also simple copy file to nul on that system reach ~400MB/s.

What can keep single query on idle system from reaching for example 200MB/s? Virtualization? Windows? Cache? Processors are below 1-3%

I use the following code to start a start a job :

Class MyClass Extends (...)
{
    ClassMethod Foo()
    {
       job $CLASSMETHOD("MyClass","MyMethod") //take forever depending hardware
    }

    ClassMethod MyMethod()
    {
       //do database related stuff
    }
}

On local environment, calling Foo() is instantaneous (a few ms). On production/test servers (which have much better hardware than local) calling this function is slow and take between 200 ms to 800 ms. Obviously starting a new job with "job" command take lot of time on those environments.

Cache version: Cache for Windows (x86-64) 2017.2.1 (Build 801_3U)

Good Afternoon,

I have a co-worker who is trying to run the below query via ODBC. The issue is that the query appears to be running extremely slow (nearly 2 hours).

Hi all.

A long time ago I enabled Activity Monitoring to be able to save myself headaches in the future when looking at the performance of various message routes through our productions. It's served it's purpose of answering questions on how many messages we process a week etc but I had not had the chance to really dig down into the stats for specific message types or destinations to pin point issues.