Docker File System Performance Code And Expect

One very mature option is file synchronisation based upon rsync.

They fixed many issues, but the bitter truth is they missed something important.

Benchmarks We can spin up a container and write to a mounted volume by executing the following commands: Start a container Mount the current directory Write random data to a file in this directory docker run --rm -it -v (PWD):pwd -w pwd alpine time dd ifdevzero ofspeedtest bs1024 count100000 Lets compare the results of Windows, Cent OS and Mac OS: Windows 10 1000000 records in.

When you develop a big dockerized application then you are in a bad spot.

Docker File System Performance Code And Expect

Usually you would work on your source code and expect no slowdowns for building.

There is a lot of hate so better listen to the members instead of reading all the frustrations.

Additionally, osxfs integrates a mapping between OS Xs FSEvents API and Linuxs inotify API which is implemented inside of the file system itself complicating matters further (cache behavior in particular).

At the highest level, there are two dimensions to file system performance: throughput (readwrite IO) and latency (roundtrip time).

In a traditional file system on a modern SSD, applications can generally expect throughput of a few GBs.

With large sequential IO operations, osxfs can achieve throughput of around 250 MBs which, while not native speed, will not be the bottleneck for most applications which perform acceptably on HDDs.

Latency is the time it takes for a file system system call to complete.

For instance, the time between a thread issuing write in a container and resuming with the number of bytes written.

With a classical block-based file system, this latency is typically under 10s (microseconds).

With osxfs, latency is presently around 200s for most operations or 20x slower.

For workloads which demand many sequential roundtrips, this results in significant observable slow down.

To reduce the latency, we need to shorten the data path from a Linux system call to OS X and back again.

This requires tuning each component in the data path in turn -- some of which require significant engineering effort.

Even if we achieve a huge latency reduction of 100sroundtrip, we will still only see a doubling of performance.

This is typical of performance engineering, which requires significant effort to analyze slowdowns and develop optimized components.

Some of them use nfs, Docker in Docker, Unison 2 way sync or rsync.

I tried some solutions but non of them worked for my docker container that contains a big Java monolith.

Either they install extra tools like vagrant to reduce the pain.

Vagrant uses nfs but this is still slow compared to native write and read performance.

Docker For Mac File System Performance