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Copy on write
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# Copy-on-write filesystems
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FIXME
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Container engines rely on copy-on-write to be able
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to start containers quickly, regardless of their size.
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We will explain how that works, and review some of
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the copy-on-write storage systems available on Linux.
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---
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## What is copy-on-write?
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- Copy-on-write is a mechanism allowing to share data.
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- The data appears to be a copy, but is only
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a link (or reference) to the original data.
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- The actual copy happens only when someone
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tries to change the shared data.
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- Whoever changes the shared data ends up
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using their own copy instead of the shared data.
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---
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## A few metaphors
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--
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- First metaphor:
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<br/>white board and tracing paper
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--
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- Second metaphor:
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<br/>magic books with shadowy pages
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--
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- Third metaphor:
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<br/>just-in-time house building
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---
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## Copy-on-write is *everywhere*
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- Process creation with `fork()`.
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- Consistent disk snapshots.
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- Efficient VM provisioning.
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- And, of course, containers.
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---
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## Copy-on-write and containers
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Copy-on-write is essential to give us "convenient" containers.
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- Creating a new container (from an existing image) is "free".
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(Otherwise, we would have to copy the image first.)
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- Customizing a container (by tweaking a few files) is cheap.
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(Adding a 1 KB configuration file to a 1 GB container takes 1 KB, not 1 GB.)
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- We can take snapshots, i.e. have "checkpoints" or "save points"
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when building images.
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---
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## AUFS overview
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- The original (legacy) copy-on-write filesystem used by first versions of Docker.
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- Combine multiple *branches* in a specific order.
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- Each branch is just a normal directory.
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- You generally have:
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- at least one read-only branch (at the bottom),
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- exactly one read-write branch (at the top).
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(But other fun combinations are possible too!)
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---
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## AUFS operations: opening a file
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- With `O_RDONLY` - read-only access:
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- look it up in each branch, starting from the top
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- open the first one we find
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- With `O_WRONLY` or `O_RDWR` - write access:
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- if the file exists on the top branch: open it
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- if the file exists on another branch: "copy up"
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<br/>
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(i.e. copy the file to the top branch and open the copy)
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- if the file doesn't exist on any branch: create it on the top branch
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That "copy-up" operation can take a while if the file is big!
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---
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## AUFS operations: deleting a file
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- A *whiteout* file is created.
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- This is similar to the concept of "tombstones" used in some data systems.
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```
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# docker run ubuntu rm /etc/shadow
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# ls -la /var/lib/docker/aufs/diff/$(docker ps --no-trunc -lq)/etc
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total 8
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drwxr-xr-x 2 root root 4096 Jan 27 15:36 .
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drwxr-xr-x 5 root root 4096 Jan 27 15:36 ..
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-r--r--r-- 2 root root 0 Jan 27 15:36 .wh.shadow
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```
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---
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## AUFS performance
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- AUFS `mount()` is fast, so creation of containers is quick.
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- Read/write access has native speeds.
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- But initial `open()` is expensive in two scenarios:
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- when writing big files (log files, databases ...),
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- when searching many directories (PATH, classpath, etc.) over many layers.
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- Protip: when we built dotCloud, we ended up putting
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all important data on *volumes*.
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- When starting the same container multiple times:
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- the data is loaded only once from disk, and cached only once in memory;
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- but `dentries` will be duplicated.
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---
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## Device Mapper
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Device Mapper is a rich subsystem with many features.
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It can be used for: RAID, encrypted devices, snapshots, and more.
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In the context of containers (and Docker in particular), "Device Mapper"
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means:
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"the Device Mapper system + its *thin provisioning target*"
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If you see the abbreviation "thinp" it stands for "thin provisioning".
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---
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## Device Mapper principles
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- Copy-on-write happens on the *block* level
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(instead of the *file* level).
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- Each container and each image get their own block device.
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- At any given time, it is possible to take a snapshot:
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- of an existing container (to create a frozen image),
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- of an existing image (to create a container from it).
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- If a block has never been written to:
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- it's assumed to be all zeros,
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- it's not allocated on disk.
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(That last property is the reason for the name "thin" provisioning.)
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---
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## Device Mapper operational details
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- Two storage areas are needed:
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one for *data*, another for *metadata*.
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- "data" is also called the "pool"; it's just a big pool of blocks.
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(Docker uses the smallest possible block size, 64 KB.)
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- "metadata" contains the mappings between virtual offsets (in the
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snapshots) and physical offsets (in the pool).
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- Each time a new block (or a copy-on-write block) is written,
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a block is allocated from the pool.
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- When there are no more blocks in the pool, attempts to write
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will stall until the pool is increased (or the write operation
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aborted).
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- In other words: when running out of space, containers are
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frozen, but operations will resume as soon as space is available.
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---
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## Device Mapper performance
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- By default, Docker puts data and metadata on a loop device
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backed by a sparse file.
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- This is great from a usability point of view,
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since zero configuration is needed.
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- But it is terrible from a performance point of view:
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- each time a container writes to a new block,
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- a block has to be allocated from the pool,
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- and when it's written to,
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- a block has to be allocated from the sparse file,
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- and sparse file performance isn't great anyway.
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- If you use Device Mapper, make sure to put data (and metadata)
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on devices!
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---
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## BTRFS principles
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- BTRFS is a filesystem (like EXT4, XFS, NTFS...) with built-in snapshots.
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- The "copy-on-write" happens at the filesystem level.
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- BTRFS integrates the snapshot and block pool management features
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at the filesystem level.
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(Instead of the block level for Device Mapper.)
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- In practice, we create a "subvolume" and
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later take a "snapshot" of that subvolume.
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Imagine: `mkdir` with Super Powers and `cp -a` with Super Powers.
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- These operations can be executed with the `btrfs` CLI tool.
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---
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## BTRFS in practice with Docker
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- Docker can use BTRFS and its snapshotting features to store container images.
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- The only requirement is that `/var/lib/docker` is on a BTRFS filesystem.
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(Or, the directory specified with the `--data-root` flag when starting the engine.)
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---
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class: extra-details
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## BTRFS quirks
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- BTRFS works by dividing its storage in *chunks*.
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- A chunk can contain data or metadata.
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- You can run out of chunks (and get `No space left on device`)
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even though `df` shows space available.
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(Because chunks are only partially allocated.)
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- Quick fix:
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```
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# btrfs filesys balance start -dusage=1 /var/lib/docker
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```
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---
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## Overlay2
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- Overlay2 is very similar to AUFS.
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- However, it has been merged in "upstream" kernel.
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- It is therefore available on all modern kernels.
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(AUFS was available on Debian and Ubuntu, but required custom kernels on other distros.)
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- It is simpler than AUFS (it can only have two branches, called "layers").
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- The container engine abstracts this detail, so this is not a concern.
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- Overlay2 storage drivers generally use hard links between layers.
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- This improves `stat()` and `open()` performance, at the expense of inode usage.
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---
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## ZFS
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- ZFS is similar to BTRFS (at least from a container user's perspective).
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- Pros:
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- high performance
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- high reliability (with e.g. data checksums)
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- optional data compression and deduplication
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- Cons:
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- high memory usage
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- not in upstream kernel
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- It is available as a kernel module or through FUSE.
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---
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## Which one is the best?
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- According to Michael Crosby (core Docker maintainer), overlay2!
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- Overlay2 is available on all modern systems.
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- Its memory usage is better than Device Mapper, BTRFS, or ZFS.
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- The remarks about *write performance* shouldn't bother you:
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<br/>
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data should always be stored in volumes anyway!
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