On 24/05/2023 10:22, Steven Blanchard wrote:
Hi Andy,
I tried it on a local disk and it had no impact on the average speed for
the Data stage.
SSD or rotating disk? (It shouldn't make an extreme difference or
xloader, because that's part of the point of the xloader.)
I checked with iostat, there was indeed an increase in the speed of
reading the input files. This step writes very little data so there was
no difference in the writing speed.
I also did a test with only 1 of the uniprot files (291 million tuples)
and the average speed was about 160,000 tuples/s. This value corresponds
to speeds obtained on other insertions.
On the exact same hardware?
Could this decrease of average speed be related to the amount of total
data?
Is it possible to run this Data step only file by file and all the other
steps with all files?
Not sure - there is a shared node table being built. The slowness is
presumably a consequence of the previous stages. The use of the some URI
needs to have the same internal NodeId everywhere - i.e. seeing all the
data.
I'm still not seeing why the data stage starts at a slow rate - I will
need to find time to explore the code.
(This is an argument for having NodeIds be hashes because that can be
computed without reference to the table unique ids and representation
storage. Downside - the NodeIds would be longer, 96 or 128 bits and
hashes have bad locality (i.e. none whatsoever)).
Andy
Thank you,
Steven
Le mar., mai 23 2023 at 11:30:36 +0100, Andy Seaborne <a...@apache.org>
a écrit :
On 22/05/2023 16:38, Steven Blanchard wrote:
Le lun., mai 22 2023 at 16:18:21 +0100, Andy Seaborne
<a...@apache.org <mailto:a...@apache.org>> a écrit :
Hello Andy,
Hi Steven,
How are you runnign xloader? Default settings?
Yes, we use your default settings.
The command line used is the following line :
tdb2.xloader --loc /nfs/uniprot_tmp/tdb2/UniProt_04_2022/ --tmpdir
/nfs/uniprot_tmp/ --threads 30
/nfs/uniprot_tmp/Download/2022_04/uniprotkb_*.rdf
Just looking at that, the use of NFS may be related.
NFS is shared, remote filing system so it has comparative high
overheads on every operation to give the semantics of sharing
(visibility on write).
Could you try using local disk to see if that makes a difference?
Andy
What's the storage being used?
We use a Block Storage from a cloud providers with ssd on a mouted
nsf volume.
On 22/05/2023 10:49, Steven Blanchard wrote:
Hello,
I am currently trying to load a very large dataset ( 54 billion
triples) with the tdb2.xloader command.
The first two steps (Nodes and Terms) are completed with an average
load speed of ~ 120,000.
The third stage (Data) has an average load speed of only 800.
is thet "Avg" is 800 from teh start of the phase or "the average
drops to 800" during the phase?
The Avg is 800 from the start of the phase and he stay at 800.
This average load speed is incompatible with the amount of data to
be loaded.
Looking at the status of the job, it is possible that there is an
excessive demand on memory which slows down the process extremely.
We saw with a top that java required many memories :
```
top
# PID USER PR NI VIRT RES SHR S %CPU
%MEM TIME+ COMMAND
# 867362 sblanch+ 20 0 289,0g 90,2g 88,4g S 3,3 72,1
1102:32 java
```
xloader does not have much requirement for java heap memory.
Ok, since our email we have try to increase the -xmx and we have not
an increase of the performance.
That space may be mapped files.
But with a free -g, we see that it actually uses very little memory.
```
free -g
# total used free shared buff/cache available
# Mem: 125 3 0 0 121 120
```
Are there any possibilities to speed up this step? (Give a -xms to
java?)
Can this significant drop in loading speed for this step be due to
memory usage? Do you know of any other limiting causes in this
loading stage?
For previous insertions on smaller datasets, this Data step was not
limiting and the average speed was even slightly higher than
the Nodes and Terms steps.
How small is "smaller"?
For example, we have upload UniRef RDF Database (Same providers like
UniProt) with 12 Milliards of triples with an average for Data task
of 230 000 tuples/s
That sounds like what I see when loading.
For information, the machine used has 32 CPUs and 128 Giga of Ram.
Thanks for your help,
Regards,
Steven
