If you don't want to lose the data, which doesn't fit in RAM, on restarts
then go for Ignite persistence.
If it's fine to lose the data on restarts then the OS swapping is a good
option as well:
https://apacheignite.readme.io/docs/swap-space
--
Denis
On Thu, Aug 9, 2018 at 2:33 PM
We are trying to load and transform a large amount of data using the
IgniteDataStreamer using a custom StreamReceiver.We'd like this to run
a lot faster, and we cannot find anything that is close to saturated,
except the data-streamer threads, queues. This is 2.5, with Ignite
persistence,
Will Apache Ignite allow me to use more memory than is physically available
on my system?
If yes, Do I need to turn persistence on?
If I do not need to turn persistence on , how do I specify the location on
disk to which memory will be swapped?
--
Sent from:
attaching log of the tow nodes crashing everytime, I have 4 nodes but the
other two nodes ver rarely crashed. All nodes(VM) are 4CPU/16GB RAm/200GB
HDD(Shared Storage)
node 3:
[16:35:21,938][INFO][main][IgniteKernal]
>>>__
>>> / _/ ___/ |/ / _/_ __/ __/
Still looking for any suggestions on this. Anyone have any ideas for next
steps to troubleshoot?
--
Sent from: http://apache-ignite-users.70518.x6.nabble.com/
I've been struggling to find a configuration that works successfully for IGFS
with hadoop filesystem caching. Anytime I attempt to load more data than
what will fit into memory on my Ignite node, the ignite process crashes.
The behavior I am looking for is that old cache entries will be
Hello!
I have just created sample project with your code (by running two
standalone nodes with peerAssemblyLoadingMode="CurrentAppDomain" as well as
one client with project code) and it runs just fine. Both Hello World
closure and filter are executed just fine.
This is slightly puzzling to me,
Hello,
do i miss something or the message at
https://github.com/apache/ignite/blob/master/modules/core/src/main/java/org/apache/ignite/internal/IgniteKernal.java#L2461
is incorrect ?
I think it should refer to
DataStorageConfiguration.*systemRegionInitialSize*
and not to
Ah thank you very much! That indeed fixes the problem. All the examples I could
find had the full name specified there and since the classNames property were
also full names, I never thought of changing this to the simple name.
Apparently putting the full name there, triggers this strange
in first example i have not deployd HelloAction class manually but it works
correctly it means that class was successfully transfered and deployed. but
why this does not work in case of EmployeeEventFilter class? is it an error?
чт, 9 авг. 2018 г., 15:46 Ilya Kasnacheev :
> Hello!
>
> I'm not
Hello!
I am pretty confident that affinity key configuration is supported by C++.
There is one error in your configation file: you are using Simple Mapper,
but still specify package of class in question. This causes weird behavior
on Ignite side, but is trivial to fix:
After that,
I've been able to look at cache and thread pool statistics using JVisualVM
with Mbeans support. Has anyone found a way to get these statistics out
to a tool like NewRelic or DataDog?
Thanks,
Dave Harvey
Disclaimer
The information contained in this communication from the sender is
You're right! My fault!
On Thu, Aug 9, 2018 at 2:37 PM Ilya Kasnacheev
wrote:
> Hello!
>
> WriteInt8Array accepts pointer and len, so I don't see why you have to
> pass char by char.
>
> Regards,
>
> --
> Ilya Kasnacheev
>
> 2018-08-09 1:32 GMT+03:00 F.D. :
>
>> Ok, but I think it's the same
Just an update.. Affinity key is indeed *not* supported in C++ at the moment.
By digging into the C++ source I found the following..
core/src/impl/binary/binary_type_updater_impl.cpp
line 78:
rawWriter.WriteString(0); // Affinity key is not supported for now.
It just always passes in a null
Hello!
I'm not sure that C# has peer class loading. Are you sure that you have
this filter's code deployed on your server nodes?
Regards,
--
Ilya Kasnacheev
2018-08-09 15:23 GMT+03:00 Som Som <2av10...@gmail.com>:
> is there any information?
>
> -- Forwarded message -
> From:
Hello!
The expectation is that distributed grid will inherently work slower on
single node than dedicated DB.
However, distributed grid will also work on multiple node, when traditional
DB wouldn't.
So extracting every last bit of single-node performance takes a back seat
when also considering
Yes, I am running on a single node. Still ignite being in-memory, I expected
it to perform better than H2. Is there anything I can do to make it faster?
Like right now I have a single data region, does having multiple data
regions give me better performance? Something like that?
--
Sent from:
Hello!
WriteInt8Array accepts pointer and len, so I don't see why you have to pass
char by char.
Regards,
--
Ilya Kasnacheev
2018-08-09 1:32 GMT+03:00 F.D. :
> Ok, but I think it's the same like WriteArray.
>
> For the moment I solved in a different way,using a encode/decode functions.
>
>
is there any information?
-- Forwarded message -
From: Som Som <2av10...@gmail.com>
Date: ср, 8 авг. 2018 г., 16:46
Subject: continous query remote filter issue
To:
hello.
It looks like peerAssemblyLoadingMode flag doesn’t work correctly in case
of CacheEntryEventFilter:
As
Orel,
Could you share command that you used to deploy Web Console?
If in that command data folder was configured to some folder of you local
file system, it means that all data can be restored.
Otherwise, data was inside docker image and I'm afraid it was also deleted.
Also you may try to
Hi,
Are you sure that you access the same cache that is used for l2? Try to
invoke Ignite.cacheNames() - it will give you a list of caches in the
cluster, using that, you can check that use right cache.
Evgenii
--
Sent from: http://apache-ignite-users.70518.x6.nabble.com/
Hi,
I've built up a project on a web console deployed via docker and
accidentally deleted the image. This resulted in the whole project
disappearing server side (obviously). The problem is I can't upload the
project I have so that I can continue editing it on the Web Console. Is
this something
Hi!
H2 is optimized for what it does and it's data format, only the H2
parser is used in Ignite, if you run a single node you should expect it
to be a bit slower compared to H2, are you running a single node ?
It's only if you have multiple nodes you can expect it to be faster
(depending of
Hi,
When I tried running few db lookup scenarios in H2 and ignite, I got better
performance for H2. All were basic db operations like =, <=, >=.
These were the figures for 2million records(2 lookups for one record)
H2 : 3 minutes
Ignite (cache gets based on keys) : 3.4 minutes
Ignite
Hi,
Is there any mbean to get all deployed services in a grid (or in a node)?
Thanks,
Calvin
Calvin KL Wong
Sr. Lead Engineer, Execution Services
D +852 2600 7983 | M +852 9267 9471 | T +852 2600
5/F, One Island East, 18 Westlands Road, Island East, Hong Kong
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Thanks for your reply. It is indeed the case that both Java and C++
configuration files are the same, and the AffinityKeyMapped annotation is not
used in the Java declaration. Still, it is throwing an error.
I have attached here a minimal reproducing example.
My Java key class is the following:
Hello!
As far as my understanding goes, you have to supply cacheKeyConfiguration
in both Java and C++ configuration files, and remove @AffinityKeyMapped
from Java CustomKey class (or other ways of specifying it where applicable).
Regards,
--
Ilya Kasnacheev
2018-08-09 10:50 GMT+03:00 Floris
Hi,
I'm going to use Ignite instead of guava. During testing, I find that
CacheMode.LOCAL is often OOM. Please help me check what's wrong with it. Thank
you very much.
Ignite version : 2.6.0
jdk 1.8.0_151-b12
Test Code :
==
public class LocalCacheDemo {
Hi all,
I'm experiencing exactly the same issue as is described in a previous post on
this mailing list:
http://apache-ignite-users.70518.x6.nabble.com/Affinity-Key-field-is-not-identified-if-binary-configuration-is-used-on-cache-key-object-td15959.html
In short - defining an XML config with
Hi Akash,
1) Actually exchange is a short-time process when nodes remap partitions.
But Ignite uses late affinity assignment, that means affinity distribution
will be switched after rebalance completed. In other words after rebalance
it will atomically switch partition distribution.
But you don't
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