In current setup there are two Mongo Docker containers, running on hosts A and B, with Mongo version of 3.4 and running in a replica set. I would like to upgrade them to 3.6 and increase a member so the containers would run on hosts A, B and C. Containers have 8GB memory limit and no swap allocated (currently), and are administrated in Rancher. So my plan was to boot up the three new containers, initialize a replica set for those, take a dump from the 3.4 container, and restore it the the new replica set master.
Taking the dump went fine, and its size was about 16GB. When I tried to restore it to the new 3.6 master, restoring starts fine, but after it has restored roughly 5GB of the data, mongo process seems to be killed by OS/Rancher, and while the container itself doesn't restart, MongoDB process just crashes and reloads itself back up again. If I run mongorestore to the same database again, it says unique key error for all the already inserted entries and then continue where it left off, only to do the same again after 5GB or so. So it seems that mongorestore loads all the entries it restores to memory.
So I've got to get some solution to this, and:
Increasing the swap size as the other answer pointed out worked out for me. Also, The --numParallelCollections
option controls the number of collections mongodump
/mongorestore
should dump/restore in parallel. The default is 4 which may consume a lot of memory.
Since it sounds like you're not running out of disk space due to mongorestore continuing where it left off successfully, focusing on memory issues is the correct response. You're definitely running out of memory during the mongorestore process.
I would highly recommend going with the swap space, as this is the simplest, most reliable, least hacky, and arguably the most officially supported way to handle this problem.
Alternatively, if you're for some reason completely opposed to using swap space, you could temporarily use a node with a larger amount of memory, perform the mongorestore on this node, allow it to replicate, then take the node down and replace it with a node that has fewer resources allocated to it. This option should work, but could become quite difficult with larger data sets and is pretty overkill for something like this.
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