By ILikeBeer
Hi, I’m using a postgres database deployed as a docker container on a $15/mo Droplet (with swap). I’ve replicated the container for an A vs. B ‘Volume vs local filing system’ speed test.
Where the database files are : [A] on local machine file system/disk <---- takes 10s [B] on a 10GB Volume <— takes 34s
A query on a 7 million row table (see below) takes 10 secs on [A] and 34 secs on [B]
SELECT * FROM ( SELECT val1,val2, cdatetime, a, b, c, d FROM events_table WHERE cdatetime >= ‘2022-01-16T12:26’ AND cdatetime < ‘2022-01-17T15:26’ ORDER BY cdatetime DESC ) as sub_sigevent WHERE sub_sigevent.d = 59 AND sub_sigevent.a = 6 AND sub_sigevent.b = 3 AND sub_sigevent.c = 7
As Volumes are networked memory in effect, I assume this is caused by the slow connection to the Volume compared to local filing system. So it would appear there’s no way round this, other than to try a time series specific data base such as ClickHouse (OSS version of InfluxDB) that scales better.
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Hi there,
I was just about to share the link to the same discussion. Indeed as mentioned in that discussion, you will always see a bit worse performance on a Volume compared to local storage, as Volumes are network-based storage which adds some overhead.
You could try doing some tweaking in order to optimize the performance of the Postgres instance itself using the following script:
https://github.com/jfcoz/postgresqltuner
Also, a good related read would be this tutorial here on how to benchmark DigitalOcean volumes:
https://www.digitalocean.com/community/tutorials/how-to-benchmark-digitalocean-volumes
Best, Bobby
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