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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