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Author Topic: Ways to speed up the JRiver MC server?  (Read 1661 times)

luvmich

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Ways to speed up the JRiver MC server?
« on: April 29, 2020, 05:52:51 pm »

My setup is as follows.  I have a dedicated W10 pro running only JRiver MC server V26 (Music only).  It is a HP Prodesk 405 with a Ryzen Pro 2400GE.  256GB NVME boot/app.  There are 165k mp3 are on a W10 pro application/file server.  All gigabit connections.

The issue I'm having is slow library updates.   When I add mp3's to the file server they take a long time to show on MC server/clients.  The analyze audio has taken hours to performed.  (At times I thought it skipped the analyze audio and I manually do it to speed it up) The mp3's on the files server are on a dedicated WD 3TB Red and the server has 3 gigabit Intel nics LAG'ed together.

My questions:
Would putting the mp3 on a SSD in the MC server PC speed things up? Drastically?   
Does using network files really slow down MC server?
Is this updated speed normal when having this many files?

MP3 collection:
all mp3 have waveform
99% have embedded cover art
90% have embedded lyrics

MC server:
=== Running Benchmarks (please do not interrupt) ===

Running 'Math' benchmark...
    Single-threaded integer math... 1.879 seconds
    Single-threaded floating point math... 2.015 seconds
    Multi-threaded integer math... 1.133 seconds
    Multi-threaded mixed math... 0.663 seconds
Score: 3339

Running 'Image' benchmark...
    Image creation / destruction... 1.057 seconds
    Flood filling... 0.375 seconds
    Direct copying... 1.016 seconds
    Small renders... 0.959 seconds
    Bilinear rendering... 0.625 seconds
    Bicubic rendering... 0.474 seconds
Score: 4882

Running 'Database' benchmark...
    Create database... 0.114 seconds
    Populate database... 1.171 seconds
    Save database... 0.298 seconds
    Reload database... 0.066 seconds
    Search database... 1.427 seconds
    Sort database... 0.844 seconds
    Group database... 0.713 seconds
Score: 4642

JRMark (version 26.0.69 64 bit): 4288
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luvmich

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Re: Ways to speed up the JRiver MC server?
« Reply #1 on: May 04, 2020, 09:00:37 pm »

Fine... I will bench it myself :P

Simple test is to see how fast thumbnail cache can be done on 165k files
Local SSD 39min :)
NAS WD3tb Red 2h11min @gigabit speed :'(

The cpu was at 45%ish when building thumbnail cache off the SSD. (I could hear the cpu fan spinning up a bunch)
The cpu was at 12% when building off the NAS WD3tb drive.  It was only at 2.6Mhz of the 3.2Mhz. (Fan was silent)
This was all a no brainer I guess.

As far as the speed when adding new music, they are about the same.  The album is added rather quickly to the MC server. (SSD or NAS)  Press F5 and wait less then a minute and the music will appear on the server.  The SSD is quicker like 30s compared to 60s. (not a deal breaker for a NAS)  Anyway I'm guessing the audio is only analyzed only so often when set to auto.  The waveform eventually shows up, unless I force it manually. (Less then a hour on NAS or SSD).  As far as the speed of it showing up on the client, I find it is easier to just restart the client and the new albums will show up. (SSD and NAS are the same speed)

So in conclusion, an SSD is faster at thumbnail creation. duh!  Once it is created then there is no benefit to putting the actual mp3s on a SSD.   I'm sure a SSD in a NAS would be much faster then a mechanical HD in a NAS.  The point is a SSD is not going to speed anything up except the initial library scan.  Unless I connect 10000s clients an SSD is not needed.  The MC server Library is on a NVMe, that is all the speed it needs.  My client computer is so fast anyway I can't tell any difference between a NAS and SSD.  Once the library is loaded, then only thing that speeds up the server is CPU and NIC speed. 

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ths61

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Re: Ways to speed up the JRiver MC server?
« Reply #2 on: August 01, 2020, 01:04:29 pm »

Running multiple SSDs always helps, providing your MoBo has an interface sufficient to handle the SSDs speeds and protocols. 

Put your OS on one SSD and your data on other SSD(s), preferably on separate buses to minimize read/write bottlenecks.

Distribute the work load across buses, cores and threads.
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