For Step #3 you can use LVM Snapshots to make your copy of the data. It
would be a fairly quick operation and you won't have to wait for the
full data copy to happen, just for the snapshot to finish. Once the
snapshot is complete, you can do step 4 and 5 and then mount the
snapshot on a different mount point and copy it off to the remote host.
On 07/14/2014 12:41 PM, Clint Miller wrote:
We're running a large 0.90.10 cluster. Due to performance problems
we're seeing with has_parent queries (unrelated issue), upgrading to
1.x is not an option for us at the current time.
We're trying to figure out how to backup 0.90. The following link
gives some ideas for doing
backups: http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/setup-upgrade.html.
Essentially, the process is
1. Stop indexes from being flushed to disk.
2. Stop shard reallocation.
3. Copy the data.
4. Resume index flushing.
5. Resume shard reallocation.
The concern is step 3. We have a lot of data to backup. So, step 3
could take a longer amount of time than we'd want to keep indexes from
flushing to disk.
However, given that segments in the data directory are immutable, I'm
wondering if we could change step 3 to first create a parallel
directory structure off to the side somewhere and then to hard link
all the files in the data directory into the equivalent directories in
the parallel structure. Running through the files and directories and
creating hard links should be sub-second.
Then, we can resume index flushing and shard reallocation, backup the
parallel directory structure (waiting however long that takes), and
finally delete the parallel directory structure.
This approach is similar to how backups work in Solr.
Will that approach work, or are there any files in the data directory
that are mutable?
Thanks
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