How we improved database performance 1000% with just one command

In my current contract we have a nice database server with 4 datacentre quality SSD’s arranged in raid5, a battery-backed (Dell PERC / megaraid) controller and very poor disk performance. As I was doing a dump of the database (with parallel process, obviously), iostat was showing 100% disk utilization at 200-300 iops and 40-60mb/sec. Really not what it should be. After poking the raid card a lot with the MegaCli tool, I discovered that by changing the Linux IO scheduler to noop or deadline we suddenly got great performance – 50% disk utilization at 1300 iops and 200mb/sec read. So, an issue with the CFQ scheduler then as everywhere seems to suggest? In fact not – it turns out the root of the problem is that because of the raid controller the kernel did not detect that the underlying disks were SSD rather than HDD. Just by tweaking this setting CFQ provides the same performance as noop or deadline schedulers (in fact better for our postgres workload). So, here’s the command we used to get a 1000% improvement in database performance:

Getting value/count grouping in a single row with postgres

In one of my contracts we’re doing some very complex data storage and querying using jsonb in postgres. One common display in the system we’re replacing is basically something like:

Which is horrible, inefficient and doesn’t let you know if anything occurred outside the range. As we’re already doing a lot with JSON, I was wondering if rather than creating new columns, we could return these counts in a JSON format. It turns out that with postgres this is really easy – here’s how:

First, we want to create a function that given the existing aggregated value and a new key to inclement, returns a jsonb object

Then, you define the aggregate:

If you noticed the STRICT in the function (which means that NULL’s are not allowed), the aggregate we created notices that and will never pass NULL values in to the function meaning we don’t need to special-case them.

If you then run the query first casting the floating-point value into an integer and then turning that into a text datatype for the json key:

You get a JSON result back with the counts:

Obviously your first port of call when faced with something like this is ‘can I group by the value’, but in this case we want the row to be based on something different so we needed the sub-aggregation of the values.

Happy days