This is the story of cold blocks and mismatched instances and how they will cause you pain and cost you money until you understand why.
Most of the clients that we support run on the Amazon cloud using either RDS or running MySQL on plain EC2 instances using (Provisioned IOPS) PIOPS EBS for data storage.
As expected the common architecture is running a master with one or more slaves handling the read traffic.
A common problem is that after the slaves are provisioned (normally created from an EBS snapshot) they lag badly due to slow IO performance.
Unfortunately what tends to be lost in the “speed of provisioning new resources” fetish is some limitations in terms of data persistence layer (EBS).
If you are using EBS and you have created the EBS volume from snapshot or created a new volume you have to pre-warm the EBS volume otherwise you will suffer a bad (I mean seriously bad) first usage penalty. Bad? I am talking up to 50% performance drop. So that expensive PIOPS EBS volume you created is going to perform like rubbish every time it reads/writes a cold block.
The other thing which also tends to happen is mixing up the wrong instance (network performance) with the PIOPS EBS. This the classic networked storage, the network is the bottleneck. If your instance type has limited network performance, having a higher PIOPS than the network can handle means you are wasting money (on PIOPS) you can’t use. A bit like in the old days (of dedicated servers and SAN storage) where the SAN could deliver 200-300Mbytes per sec, but the 1 Gigabit network could only do 40-50Mbytes per sec.
Here is the real downside, using the cloud you can provision new resources to handle peak load (in the case more MySQL slaves to handle read load) as fast as you can click, or faster using API calls, or even automagically, if you have some algo forecast the need for additional resources. But… the EBS is all cold blocks, so these new instances will be up and available in minutes but the IO performance will be poor until you either pre-warm or the slave gets around to writing/reading all blocks.
So the common solution is to pre-warm the blocks using dd to read the EBS device (and warm the block) to /dev/null
sudo dd if=/dev/xvdf of=/dev/null bs=1M
Consider how long this will take for any reasonable sized DB (200GBytes) using an instance with 1 Gigabit network.
200Gigabytes read at 50Mbytes/sec = 200,000 Mbytes/50 = 4000 secs = 3600 (1hr) + 400 (6 mins 40 secs) =~ more than 1 hr.
So you or your algo provisioned a new EC2 instance for the database in minutes but either your IO will be rubbish for an extended period, or you wait more than 1 hr per 200GB to have the EBS pre-warmed.
What are the solutions?
- Forecast further in advance depending on the size of your db (or any other persistent storage layer eg NoSQL etc)
- Use ephemeral storage and manage the increased risk of data loss in the event of instance termination.
- Break your DB or your application into smaller pieces aka micro services.
- Pay more $ and have your databases stay around longer so waiting for a instance to be ready in the beginning is not a problem.
As you can expect, most businesses are happy with option 4. Pay more, leave instances around like they were dedicated servers (base load). Amazon is happy too.
Option 3 whilst requiring some thought (argh) and additional complexity is where the real speed of provisioning, dare I say it, agile nature of the cloud will bear the most fruit.
Hm, how about option 5 to have a pool of pre-warmed EBS volumes ready in each AZ? As a new instance comes up, attach the volume, ‘mkfs’ to wipe the volume and then deploy the current version of the DB.
Having EBS sitting around is a lot cheaper than more EC2 running 24/7 (also, no time or IO penalty waiting for the ‘dd’ each time)
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