Letsgetdugg

CouchDB was made for next generation filesystems such as ZFS and BTRFS. First off, unlike PostgreSQL or MySQL, CouchDB can be snapshot while in production without any flushing or locking trickery since it uses an append only B-Tree storage approach. That alone makes it a compelling database choice on ZFS/BTRFS.

Second, CouchDB works hand-in-hand with ZFS’s block level compression. ZFS can compress blocks of data as they are being written out to the disk. However, it only does it for new blocks and not retroactively. Now, the awesome part, CouchDB on compaction writes out a brand new database file which can utilize the new gzip compression settings on ZFS. This means you can try out different gzip compression settings just by compacting your CouchDB.

Some tips on running CouchDB on ZFS:

1. Use automated snapshots to prevent $admin error, it is painless with ZFS and CouchDB loves being snapshot

You can give my little ruby script a try for daily snapshots; I use it both on Mac OSX and Solaris for automated ZFS snapshot goodness.

2. Try out various gzip compression schemes on your CouchDB workload, re-compact the database to use the new gzip compression settings. I personally use the gzip-4 compression for our workload which strikes the perfect balance between space and cpu utilization.

3. Set the ZFS dataset to 4k block record size and turn off atime. Yes the B-Tree append only approach is elastic on writes but you can have near perfect tiny writes with a small 4k block record size.

I used to backup our database using the following statement;

Once our dataset grew into the gigabytes, it took a very long time to do database dumps. Today, I stumbled upon yet another awesome blog post done by Ted Dzibua mentioning two useful parallel compression utilities. So why not try parallel compression with PostgreSQL dumps?

pbzip2 – Parallel BZIP2: Parallel implementation of BZIP2. BZIP2 is well known for being balls slow, so speed it up using multiple CPUs.

pigz – Parallel GZIP: Parallel implementation of GZIP written by Mark Adler.

Time to try this out with our PostgreSQL dump, here are the result times.

• This was done on a quad core xeon 2.66ghz machine.

The original database was 1.6gigs. The compressed files came out to….

And just to make this post complete, to pipe the SQL dump back into PostgreSQL