[转帖]Measuring Docker IO overhead

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**Content Summary** This blog post discusses the Docker container overhead in different data volume patterns, including: * Using data volume containers * Using external data volumes * Using data volume containers The post concludes by stating that there is no Docker IO overhead for any of the patterns tested. **Key Points** * Docker's philosophy is to provide ephemeral containers, but it does not work well for data. * Using data volume containers avoids the network overhead associated with external data volumes. * There is no Docker IO overhead for any of the data volume patterns tested. **Conclusion** The post confirms that there is no Docker IO overhead for any of the data volume patterns tested. This is an important finding, as it suggests that Docker can be used to efficiently store and access data in a multi-host network environment.

正文

https://www.percona.com/blog/2016/02/11/measuring-docker-io-overhead/

 

Docker IO overheadThis will be another post on using Percona Server via a Docker image. I want to follow up on my previous post regarding CPU/Network overhead in Docker “Measuring Percona Server Docker CPU/network overhead” by measuring  if there is any docker IO overhead on operations.

After running several tests, it appears (spoiler alert) that there is no Docker IO overhead. I still think it is useful to understand the different ways Docker can be used with data volumes, however. Docker’s philosophy is to provide ephemeral containers, but ephemeral does not work well for data – we do not want our data to disappear.

So, the first pattern is to create data inside a docker container. This is the default mode:

(I am using --net=host to avoid network overhead; check the previous post for more information.)

The second pattern is to use an external data volume, there we need to substitute the data volume with -v /data/flash/d1/:/var/lib/mysql. The full command is:

Finally, there is third pattern: using data volume containers. For this example, I created a dummy container:

After stopping the ps13-data-volume container, we can start a real one using the data volume from ps13-data-volume  as:

I compared all these modes with Percona Server running on a bare metal box, and direct mounted in sysbench, for both read-intensive and write-intensive IO workloads. For the reference, sysbench command is:

I’m not going to show the final numbers or charts, as the results are identical for all docker modes and for the bare metal case. So I can confidently say there is NO IO overhead for any docker data volume pattern described above.

As next experiment, I want to measure the Docker container overhead in a multi-host network environment.

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