I did find another thread that isn't being cleaned up correctly.
I'll put up a fix soon.
Thanks,
Emilio
On 08/30/2017 12:09 PM, Emilio
Lahr-Vivaz wrote:
Hi Rémi,
Is it possible that you were still using the data store (or
associated feature stores/writers) after calling dispose()? I've
noticed that if you call dispose() on a datastore, then write
features, it may open a connection in the metadata class that is
never closed. This may be why your initial thread dump showed so
many metadata connections. Other than that, it appears things are
being cleaned up correctly, but I will keep looking into it.
Thanks,
Emilio
On 08/30/2017 11:34 AM, GRISOT, REMI
wrote:
I use geomesa 1.3.1 but I tested
other versions with the same problem : 1.3.0 and 1.3.3.
Thank you for the link, I will follow this.
Rémi
Ah, good to hear. I've created an issue here to track
the .dispose() issue:
https://geomesa.atlassian.net/browse/GEOMESA-2003
Which version of geomesa are you using?
Thanks,
Emilio
On 08/30/2017 11:18 AM,
GRISOT, REMI wrote:
Hi Emilio,
Using a single data store instance shared between all
the threads, solved the problem: the number of threads
remains stable during the ingest.
Thank you very much!
Rémi
Hi Rémi,
I gave you code a quick look and I don't see
any obvious resource leaks. Generally if you
use getFeatureStore.addFeatures(), you don't
need to clean up any feature writers. If you
use getFeatureWriter*, then you need to close
it yourself. Calling .dispose on the datastore
should clean up the metadata connections, but
won't close any feature writers.
You might try keeping a single data store
instance around for the entire ingest -
generally a data store object is fairly
expensive to create, so only one is created
and shared across threads. Possibly the
datastore.dispose() method is not cleaning
things up appropriately, but that may not be
apparent unless many datastore instances are
created (as you're doing). I will check into
that.
Thanks,
Emilio
On 08/30/2017 03:58
AM, GRISOT, REMI wrote:
Hi Emilio,
Thank you for your quick answer. I tested an
ingest based on the Quickstart example, and
the problem definitely comes from my
implementation : in the test, the number of
threads stayed stable.
For my insertions, I do not use a distributed
setup: I have only one machine, with a lot of
RAM and CPUs. In a later time, I will probably
split it into VMs, but for now, I would like
to make it work on a single node.
The ingest program works as follow:
- Datas are collected, and when a limit is
reached, a thread is created (in a
ThreadPoolExecutor). - That thread create an
instance of DataStore, send datas and then
destroy the instance of Datastore (which I
thought should have closed feature writers).
It is really nice to you to propose check the
code for resource cleanup! The code is not
public (I write it in the field of my
internship), but I changed some variables
name, and it should be OK. I attached an
archive containing the relevant files of the
project.
I will have a look to the documentation links
and try to imrove my conf with this.
Thank you,
Rémi Grisot
Hi Rémi,
What is your ingest setup like? If you're
seeing thread exhaustion, my first guess
would be that you are not cleaning up
resources correctly (e.g. closing feature
writers). Are you distributing your
ingest? Usually for data sets that large
you would distribute your ingestion across
a cluster using spark or map/reduce, so
I'm not sure we've tested billions of
points from a single JVM.
The metadata batch writer is configured to
use 2 threads, so there may be others
generated by Accumulo but I wouldn't
expect it to swamp the JVM by itself. It
will also only write mutations when you
call 'flush' or 'close' on a feature
writer, so minimizing those calls would
reduce the writes.
There are other batch writers associated
with each feature writer you create; those
are more likely to be causing issues than
the metadata writer as there are more of
them, with more threads and greater
throughput.
You can control the underlying batch
writer configuration via system properties
as described here [1], although the
metadata writer overrides max memory and
max threads [2].
[1]
http://www.geomesa.org/documentation/user/accumulo/configuratiissues.on.html#batch-writer-properties,
[2]
https://github.com/locationtech/geomesa/blob/master/geomesa-accumulo/geomesa-accumulo-datastore/src/main/scala/org/locationtech/geomesa/accumulo/data/AccumuloBackedMetadata.scala#L30
If you code is public, I'd be glad to give
it a quick check for resource cleanup.
Thanks,
Emilio
On 08/29/2017
09:55 AM, GRISOT, REMI wrote:
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