Thread: fast-archiver tool, useful for pgsql DB backups
Hi pgsql-general,
Has anyone else ever noticed how slow it can be to rsync or tar a pgdata directory with hundreds of thousands or millions of files? I thought this could be done faster with a bit of concurrency, so I wrote a little tool called fast-archiver to do so. My employer (Replicon) has allowed me to release this tool under an open source license, so I wanted to share it with everyone.
fast-archiver is written in Go, and makes uses of Go's awesome concurrency capabilities to read and write files in parallel. When you've got lots of small files, this makes a big throughput improvement.
For a 90GB PostgreSQL database with over 2,000,000 data files, fast-archiver can create an archive in 27 minutes, as compared to tar in 1hr 23 min.
Piped over an ssh connection, fast-archiver can transfer and write the same dataset on a gigabit network in 1hr 20min, as compared to rsync taking 3hrs for the same transfer.
fast-archiver is available at GitHub: https://github.com/replicon/fast-archiver
I hope this is useful to others. :-)
Mathieu
$ time fast-archiver -c -o /dev/null /db/data
skipping symbolic link /db/data/pg_xlog
1008.92user 663.00system 27:38.27elapsed 100%CPU (0avgtext+0avgdata 24352maxresident)k
0inputs+0outputs (0major+1732minor)pagefaults 0swaps
$ time tar -cf - /db/data | cat > /dev/null
tar: Removing leading `/' from member names
tar: /db/data/base/16408/12445.2: file changed as we read it
tar: /db/data/base/16408/12464: file changed as we read it
32.68user 375.19system 1:23:23elapsed 8%CPU (0avgtext+0avgdata 81744maxresident)k
0inputs+0outputs (0major+5163minor)pagefaults 0swaps
On Fri, 2012-08-24 at 15:48 -0600, Mathieu Fenniak wrote: > Hi pgsql-general, > > Has anyone else ever noticed how slow it can be to rsync or tar a pgdata > directory with hundreds of thousands or millions of files? Yes: http://petereisentraut.blogspot.com/2012/05/base-backup-compression-options.html My analysis showed that the archiving was CPU-bound on the compression task. It might different when you are dealing with a lot of small files as opposed to a few big files. So parallelizing the archiving itself could still be useful.