pyfstree

fstree - filesystem tree sharing and sync

Fstree a tool to share a filesystem tree over the network and keep it in sync.

It is similar to rsync, but it is designed to be used in a client-server model, where trees are pushed and pulled between the server and the clients. Merkle trees are used to efficiently determine what parts of the tree need to be downloaded or uploaded.

Files and directories can be ignored using a .fstreeignore file in the root of the tree. It uses the same syntax as .gitignore files.

A simple gRPC protocol is used to communicate between the client and the server. A reference server implementation is available as the robrt/jolt-cache Docker image on Docker Hub.

HTTP remotes are also supported for basic upload and download of objects. This allows integration with existing HTTP file servers or CDNs. Note that HTTP remotes are significantly less efficient than the gRPC server, as they do not support querying for the presence of multiple objects and trees at once.

Usage

To start the server, run:

docker run -v /path/to/data:/data -p 9090:9090 robrt/jolt-cache -i

To push a tree to the server, run:

fstree write-tree-push --remote jolt://localhost:9090 /path/to/data

The steps above can be split into two separate commands:

fstree write-tree /path/to/data
fstree push --remote jolt://localhost:9090 <digest>

To push a tree to a standard HTTP server, run:

fstree write-tree-push --remote http://localhost:8080 /path/to/data'

To pull a tree from the server, run:

fstree pull-checkout --remote jolt://localhost:9090 <digest> /path/to/data

The steps above can be split into two separate commands:

fstree pull --remote jolt://localhost:9090 <digest>
fstree checkout <digest> /path/to/data

To pull a tree from a standard HTTP server, run:

fstree pull-checkout --remote http://localhost:8080 <digest> /path/to/data

To list the contents of a tree, run:

fstree ls-tree <digest>

Configuration

Configuration can be passed as command line arguments or environment variables.

The following environment variables are supported:

FSTREE_CACHE: The directory where the local object cache is stored. Defaults to ~/.cache/fstree on Linux and macOS and ~/AppData/Local/fstree on Windows.
FSTREE_IGNORE: The relative path to the ignore file. Defaults to .fstreeignore in the root of the tree.
FSTREE_REMOTE: The remote address of the server to connect to. Defaults to jolt://localhost:9090.
FSTREE_THREADS: The number of threads to use for parallel operations. Defaults to the number of CPU cores.

The following command line arguments are supported:

--cache: See FSTREE_CACHE.
--ignore: See FSTREE_IGNORE.
--remote: See FSTREE_REMOTE.
--threads: See FSTREE_THREADS.

Building

The project can be built using CMake, but developers typically use Jolt as a higher level orchestrator. To build the project using Jolt, run:

pip install jolt
jolt build fstree

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

pyfstree-26.9.192-cp314-cp314-win_amd64.whl (6.5 MB view details)

Uploaded Sep 7, 2026 CPython 3.14Windows x86-64

pyfstree-26.9.192-cp314-cp314-musllinux_1_2_x86_64.whl (12.9 MB view details)

Uploaded Sep 7, 2026 CPython 3.14musllinux: musl 1.2+ x86-64

pyfstree-26.9.192-cp314-cp314-manylinux_2_28_x86_64.whl (11.7 MB view details)

Uploaded Sep 7, 2026 CPython 3.14manylinux: glibc 2.28+ x86-64

pyfstree-26.9.192-cp314-cp314-macosx_11_0_arm64.whl (8.4 MB view details)

Uploaded Sep 7, 2026 CPython 3.14macOS 11.0+ ARM64

pyfstree-26.9.192-cp313-cp313-win_amd64.whl (6.3 MB view details)

Uploaded Sep 7, 2026 CPython 3.13Windows x86-64

pyfstree-26.9.192-cp313-cp313-musllinux_1_2_x86_64.whl (12.8 MB view details)

Uploaded Sep 7, 2026 CPython 3.13musllinux: musl 1.2+ x86-64

pyfstree-26.9.192-cp313-cp313-manylinux_2_28_x86_64.whl (11.7 MB view details)

Uploaded Sep 7, 2026 CPython 3.13manylinux: glibc 2.28+ x86-64

pyfstree-26.9.192-cp313-cp313-macosx_11_0_arm64.whl (8.4 MB view details)

Uploaded Sep 7, 2026 CPython 3.13macOS 11.0+ ARM64

pyfstree-26.9.192-cp312-cp312-win_amd64.whl (6.3 MB view details)

Uploaded Sep 7, 2026 CPython 3.12Windows x86-64

pyfstree-26.9.192-cp312-cp312-musllinux_1_2_x86_64.whl (12.8 MB view details)

Uploaded Sep 7, 2026 CPython 3.12musllinux: musl 1.2+ x86-64

pyfstree-26.9.192-cp312-cp312-manylinux_2_28_x86_64.whl (11.7 MB view details)

Uploaded Sep 7, 2026 CPython 3.12manylinux: glibc 2.28+ x86-64

pyfstree-26.9.192-cp312-cp312-macosx_11_0_arm64.whl (8.4 MB view details)

Uploaded Sep 7, 2026 CPython 3.12macOS 11.0+ ARM64

pyfstree-26.9.192-cp311-cp311-win_amd64.whl (6.3 MB view details)

Uploaded Sep 7, 2026 CPython 3.11Windows x86-64

pyfstree-26.9.192-cp311-cp311-musllinux_1_2_x86_64.whl (12.8 MB view details)

Uploaded Sep 7, 2026 CPython 3.11musllinux: musl 1.2+ x86-64

pyfstree-26.9.192-cp311-cp311-manylinux_2_28_x86_64.whl (11.7 MB view details)

Uploaded Sep 7, 2026 CPython 3.11manylinux: glibc 2.28+ x86-64

pyfstree-26.9.192-cp311-cp311-macosx_11_0_arm64.whl (8.4 MB view details)

Uploaded Sep 7, 2026 CPython 3.11macOS 11.0+ ARM64

pyfstree-26.9.192-cp310-cp310-win_amd64.whl (6.3 MB view details)

Uploaded Sep 7, 2026 CPython 3.10Windows x86-64

pyfstree-26.9.192-cp310-cp310-musllinux_1_2_x86_64.whl (12.8 MB view details)

Uploaded Sep 7, 2026 CPython 3.10musllinux: musl 1.2+ x86-64

pyfstree-26.9.192-cp310-cp310-manylinux_2_28_x86_64.whl (11.7 MB view details)

Uploaded Sep 7, 2026 CPython 3.10manylinux: glibc 2.28+ x86-64

pyfstree-26.9.192-cp310-cp310-macosx_11_0_arm64.whl (8.4 MB view details)

Uploaded Sep 7, 2026 CPython 3.10macOS 11.0+ ARM64

File details

Details for the file pyfstree-26.9.192-cp314-cp314-win_amd64.whl.

File metadata

Download URL: pyfstree-26.9.192-cp314-cp314-win_amd64.whl
Upload date: Sep 7, 2026
Size: 6.5 MB
Tags: CPython 3.14, Windows x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/7.0.0 CPython/3.12.14

File hashes

Hashes for pyfstree-26.9.192-cp314-cp314-win_amd64.whl
Algorithm	Hash digest
SHA256	`b4bc1db42ffe042f5e4ba3a86e8d3c4d6ad29120c7bdad6051af8d13f9d4c49e`
MD5	`252339bf470dee1cd7026d19636deb87`
BLAKE2b-256	`69120fb2808facc667fbe93a910f961aa69d60c93aa77c40a946a2df5f54e108`

Algorithm	Hash digest
SHA256	`981522c0f6a246f7ec192e352127a3f9dce77af4d839a3ad23fd71692b8dd007`
MD5	`386ae7303381c58c6214e02ee7a417ba`
BLAKE2b-256	`ff075b54deed0e8c52ab0f71d47a7016beb7430e32136fba379898c6fa3ee36c`

Algorithm	Hash digest
SHA256	`4e43c73442a1c330ecc84f7ef1d6e52d5c3ddbee01223fa460160d816a228a33`
MD5	`c7ac2d748d98f87c417e931e70ec4a34`
BLAKE2b-256	`fd24e7f69dc7955698b663e53b9283aefd10d372e646cb3f274f3d6d98ee25da`

Algorithm	Hash digest
SHA256	`af2a590f8295981bb993dd454561cbfc01199e64bb689e86c426c0162c0e2989`
MD5	`1a472e20699007f7d321d1de3d69c5d6`
BLAKE2b-256	`01ddffae1b467dc3256915d15a037d30f05b92ba1cff2c43b08bcd0639a139fd`

Algorithm	Hash digest
SHA256	`18c35f2d96589fe3ffaa3af234b5ff19d894dbaa148df549d9164b6b73df58d5`
MD5	`f00e29360d87bfe72887306f1d8f43ae`
BLAKE2b-256	`7ea56128dc612c36b5079dc488f4adf04a8bcc938dafc0d4cac7d665b464e365`

Algorithm	Hash digest
SHA256	`64ff26c60e091bf54096a3397b2a017671d8cad1b58afdb91bee59ac6b91f75c`
MD5	`b0612babe8b19ec2124237cd4ce80215`
BLAKE2b-256	`233b1fe46ea6c25a99a22ce0b8a470b39110147b67e9f34e2b8c5367e6e8dd18`

Algorithm	Hash digest
SHA256	`4a4506952108c30eb3a7db092e53d52aab59260e5652452b2f8f3067bc68cab1`
MD5	`945ed4521305a26f8cd7a1e21a86c762`
BLAKE2b-256	`79905b1b76eddb6e9d41381a314caa04f11d7bd12448f96e3e5e60249d6486c1`

Algorithm	Hash digest
SHA256	`f0c8a2298a7b32a8078415d0af48229251e1d277bcf3586d06b6463098172048`
MD5	`fc592f0b603526c5dce4454684f56779`
BLAKE2b-256	`d332c9cb1878625bc6e50a4982e7e20d3026e3c4a9e1379a9796c1a95664d38e`

Algorithm	Hash digest
SHA256	`c1fe12e86f11a838ffdaf42298a5f11a9a2f7108fa011a484d3320a9d13cf564`
MD5	`f162d3734cf54c98b8933dcc0413f377`
BLAKE2b-256	`6f393cf83c5f800daf2f8a7d511317381039e31a2444272eff3bab7958380870`

Algorithm	Hash digest
SHA256	`ac0954fa55819f8a317357fe0208ad03cf4c802cd1dab71bacceda437004dd71`
MD5	`8f28a9f2e4ecf431d95ac95d8e528b35`
BLAKE2b-256	`09b4a578d6a5ddf03ceef9e0043f01263cc1f28943922d0bc25c997ade453b43`

Algorithm	Hash digest
SHA256	`fb67a1e7bac2672f5f61e41ea06821474e05eceb73bb182ded9f7de04055f94a`
MD5	`262ecb4aeec2c3480e520f102ba2cc33`
BLAKE2b-256	`432aca79239d8241ec723d232191b016067ea43876c0acb33abe5fce17ada3d1`

Algorithm	Hash digest
SHA256	`af1c0499aa55c7b7eb05ba7c287f21a93c5cac56b5e09bcd416ee04c695b4ac2`
MD5	`4a237477af2408b934885c96d4b238ac`
BLAKE2b-256	`71c1488a5faf2832f195962537ed9a03f855908fa451f979f804e163de5bcb87`

Algorithm	Hash digest
SHA256	`fcaa5fca891d123f7d9ab652300100ec86e839844614c7bd6fe8296cfcf2f67c`
MD5	`ecc06441c6de704eb42737fc3f2ef9bc`
BLAKE2b-256	`6af04f40f02ab50eee99a5be20fd4c6b16717b4bf0abafeb3bcb99ef6461f965`

Algorithm	Hash digest
SHA256	`50167db2f35d4561125061709be7b276cb5e1788f6c2f3400fa56b8f5341e32c`
MD5	`ef3d04ccdc68604bdbbcac6450638c8f`
BLAKE2b-256	`0b9c125d21d5ea7437c202cae2d2100bb1e17da9073d08e1ae20045305f0b862`

Algorithm	Hash digest
SHA256	`027a7a8b9cf20ee62c4384f226f7eec71a1e6e32813ad891992565fb1181f207`
MD5	`46ac7cd79c4bf838c6d8f06230bcc187`
BLAKE2b-256	`167dc2b629fbfffd8a1babdca0ced4bb44a62ec8b255c9c5a5bc4b670043fa53`

Algorithm	Hash digest
SHA256	`bc515af8b146f041e4447dcd521e74beef0140903330deb1f5c3b6619eb450b0`
MD5	`722c995fd72496d11cf366ed1726dfe3`
BLAKE2b-256	`68e0e0974bf450c65f4602d6337c9a333e3a9f73acc9cefa58d1ce457b33a26e`

Algorithm	Hash digest
SHA256	`71f7548a85285a19715eb5830f53c52a8ca76041b2cbf073d0c97866337704c3`
MD5	`30854ec05e5e861e487e457bb66ea017`
BLAKE2b-256	`2fa3b5b185f59c0233d9a517ad201ce6f84c4f768c2c82937b8b57d6006c0674`

Algorithm	Hash digest
SHA256	`add6d96d289af9f29a6965b378f0afd1192ba67da25d801c4646eb5dea4f8e72`
MD5	`3de42b990d5cf0300a7607e4eb8abff9`
BLAKE2b-256	`567ae1aa9f41a7e6d4e4d578e8a22836a530dd1b8bea32802e0c34160d3955e8`

Algorithm	Hash digest
SHA256	`9ecf54ee157b6f44452c34d8e8b5a4d214b737df39b2e8b2a9622ce8d6ccb73b`
MD5	`62ca3aab00f1c84ce2027aaba9fba12b`
BLAKE2b-256	`7813ba60447f471bd03984d5edffd55cc4a7f599a70663896f3e26d1d76e8674`

Algorithm	Hash digest
SHA256	`77318d69e971a1bfa589c0aabf424405061d4118d7e40fdaedafb600b4915fac`
MD5	`672a9d53cdb891b1d7aa8c121f0c3ff3`
BLAKE2b-256	`6a7ee0f10db99d44327bb2bbb2aa268d5fca3ad32cd3b2da2b603abd02b0bec5`

pyfstree 26.9.192

fstree - filesystem tree sharing and sync

Usage

Configuration

Building

Project links

Key dates

1 maintainer

Credits

License

Requires

Tags

Classifiers

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Release history Release notifications | RSS feed