Recovering Riak data if it can no longer load in memory

Matthew Von-Maszewski matthewv at
Tue Jul 12 12:26:14 EDT 2016

It would be helpful if you described the physical characteristics of the servers:  memory size, logical cpu count, etc.

Google created leveldb to be highly reliable in the face of crashes.  If it is not restarting, that suggests to me that you have a low memory condition that is not able to load leveldb's MANIFEST file.  That is easily fixed by moving the dataset to a machine with larger memory.

There is also a special flag to reduce Riak's leveldb memory foot print during development work.  The setting reduces the leveldb performance, but lets you run with less memory.

In riak.conf, set:

leveldb.limited_developer_mem = true


> On Jul 12, 2016, at 11:56 AM, Vikram Lalit <vikramlalit at> wrote:
> Hi - I've been testing a Riak cluster (of 3 nodes) with an ejabberd messaging cluster in front of it that writes data to the Riak nodes. Whilst load testing the platform (by creating 0.5 million ejabberd users via Tsung), I found that the Riak nodes suddenly crashed. My question is how do we recover from such a situation if it were to occur in production?
> To provide further context / details, the leveldb log files storing the data suddenly became too huge, thus making the AWS Riak instances not able to load them in memory anymore. So we get a core dump if 'riak start' is fired on those instances. I had an n_val = 2, and all 3 nodes went down almost simultaneously, so in such a scenario, we cannot even rely on a 2nd copy of the data. One way to of course prevent it in the first place would be to use auto-scaling, but I'm wondering is there a ex post facto / post the event recovery that can be performed in such a scenario? Is it possible to simply copy the leveldb data to a larger memory instance, or to curtail the data further to allow loading in the same instance?
> Appreciate if you can provide inputs - a tad concerned as to how we could recover from such a situation if it were to happen in production (apart from leveraging auto-scaling as a preventive measure).
> Thanks!
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