Add a link to the manual install procedure, use a
quote to make the manual install path more visible
and mention the --default-access-key option.
Closes: #1446
Rendered output:

Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1496
PostObject (presigned POST) returns `400 InvalidHeaderValue` when the upload involves a non-ASCII filename. The same key uploads fine vie PUT, as the issue #1489 noted.
The problem was that `handle_post_object` stores the form field values in an `http::HeaderMap`. values go in through `HeaderValue::from_str` but are read back with the strict `HeaderValue::to_str()` (ASCII-only) which fails on any UTF-8 value.
This is the same problem fixed in eab2b81b for `x-amz-meta-*` headers, and the fix is the same:
`std::str::from_utf8(value.as_bytes())` instead of `to_str()`. The `key` field in `post_object.rs` and the standard headers (`content-disposition` etc.) in `extract_metadata_headers`.
Added integration tests for PostObject (there were none): UTF-8 key, `${filename}` substitution, and UTF-8 `Content-Disposition` metadata. The substitution test passes even without the fix, proving that the filename path was never broken, only the form-params round-trip.
Co-authored-by: Mathew Storm <mathew@stormdevelopments.ca>
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1492
Reviewed-by: trinity-1686a <trinity-1686a@noreply.localhost>
Documenting the structure of a team can be helpful to integrate newcomers, avoid some conflicts and to accompany the evolution of that structure as a FOSS project grows.
This is an attempt to do that for garage, after discussions with multiple maintainers. The aim of this text is to document the status quo, offering a start for further incremental updates.
I propose to put it as `GOVERNANCE.md` file because this naming has emerged as a sort of standard in FOSS projects.
I would be happy to help facilitate such updates if there is interest (while being aware that project members have other things on their plates and that governance work can be taxing).
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1493
The rebalance op links to the multi-hdd page, but the multi-hdd page
does not refer to the rebalance op description and only states that an
operator can launch a repair procedure without stating which procedure.
Add a link to the rebalance repair procedure in the multi-hdd page to
make it easier to find what procedure to run for rebalance.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1491
save_peer_list() ran unconditionally every 60s from the discovery
loop, writing the same data to disk even when the peer set hadn't
changed. Compare the newly encoded peer list against the bytes
already on disk and skip the write when they match.
Fixes#1457
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1488
Reviewed-by: Alex <lx@deuxfleurs.fr>
This performs synchronous repair-on-read for K2V reads:
- uses the new get_*_monotonic operations in the table module
- implements repair-on-read for the K2V-specific poll operations
`parse_duration` is no longer maintained upstream; `fundu-system` seems like the best option to provide most of the functionality provided by parse_duration and minimize deps.
Fixes#1246
Dep diffstat: +3 -6
## Caveats
- I've done basically no testing of this PR beyond `cargo test`
- See my comment in #1246 for regression risk; this is a breaking change. Is there a document I should update to make note of this? Should I rebase this for `next-v3` instead?
- In theory CVE-2021-29932 is fixed by this PR as `fundu-systemd` doesn't support exponents, but I've done no verification that this is really the case beyond the info in #1246
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1468
Reviewed-by: Alex <lx@deuxfleurs.fr>
If the files configured in `api_token_file` or `rpc_secret_file` are not found, garage exits with no details (at least not enough) of what went wrong:
2026-06-14T23:40:34.517429Z INFO garage::server: Loading configuration from tmp/config1.toml...
Error: IO error: No such file or directory (os error 2)
This add the kind of file and the file path being read, to the returned error message:
2026-06-14T23:41:41.136213Z INFO garage::server: Loading configuration from tmp/config1.toml...
Error: Failed to read secret file /run/secrets/rpc_secret: No such file or directory (os error 2)
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1475
Reviewed-by: Alex <lx@deuxfleurs.fr>
The OPTIONS placeholder for buckets without a resolvable global alias returns` Access-Control-Allow-Origin: *` and `Access-Control-Allow-Methods: *` but omits `Access-Control-Allow-Headers`.
Bug verified against Garage v2.2.0 with a local-aliased bucket: OPTIONS placeholder doesn't have `Access-Control-Allow-Headers`, causes the browser to reject signed PUT preflights
The current placeholder fails open for unsigned simple requests but blocks every signed request, undermining the design intent flagged in the FIXME:
```rs
// We take the permissive approach of allowing everything,
// because we don't want to prevent web apps that use
// local bucket names from making API calls.
```
Adds `Access-Control-Allow-Headers: *` so the permissive default is actually permissive for the request shapes that exist in practice.
Refs #258. Does not address the broader FIXME (CORS rule resolution for local-aliased buckets); the placeholder approach is preserved.
All tests are fine locally:
```bash
▲ ~/opensource/garage cargo test -p garage_api_common cors::
running 5 tests
test cors::tests::preflight_with_single_allowed_origin_returns_request_origin ... ok
test cors::tests::preflight_with_multiple_allowed_origins_reflects_request_origin ... ok
test cors::tests::preflight_with_wildcard_allowed_origin_returns_wildcard ... ok
test xml::cors::tests::test_deserialize_norules ... ok
test xml::cors::tests::test_deserialize ... ok
test result: ok. 5 passed; 0 failed; 0 ignored; 0 measured; 16 filtered out; finished in 0.00s
```
Co-authored-by: smattymatty <smattymatt@gmail.com>
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1450
Reviewed-by: Alex <lx@deuxfleurs.fr>
## Title
fix(cors): return single matching origin instead of multiple values in `Access-Control-Allow-Origin`
## Summary
This PR fixes bucket CORS responses when a single CORS rule contains multiple `AllowedOrigins`.
Previously, Garage returned the configured origins as a comma-separated list in `Access-Control-Allow-Origin`, for example:
```http
Access-Control-Allow-Origin: https://app.example.test, https://admin.example.test
```
This is not the expected browser-facing behavior.
When a request origin matches a configured rule, the response should reflect **only the matching request origin**, unless the rule contains `*`.
## What changed
- `Access-Control-Allow-Origin` now behaves as follows:
- returns `*` when the matched rule contains a wildcard origin
- otherwise returns the request `Origin` as a **single value**
- added `Vary: Origin` when ACAO reflects the request origin
- added preflight-specific `Vary` handling in the preflight path for:
- `Origin`
- `Access-Control-Request-Method`
- `Access-Control-Request-Headers`
## Scope
This change applies to shared bucket CORS handling paths, including:
- S3 API responses
- K2V API responses
- S3 POST object responses
- web bucket responses
- preflight (`OPTIONS`) bucket CORS responses
This does **not** change admin API fixed CORS behavior.
## Reproduction
A direct repro script is included:
```bash
./script/test-cors-multi-origin.sh
```
It exercises two cases against a direct single-node Garage instance:
1. **single-origin control**
2. **multi-origin repro**
Before this fix, the multi-origin case returned a comma-separated ACAO value.
After this fix, both cases reflect only the request origin.
## Example behavior
### Before
```http
Access-Control-Allow-Origin: https://app.example.test, https://admin.example.test
```
### After
```http
Access-Control-Allow-Origin: https://app.example.test
```
## Tests
Added/updated tests in `src/api/common/cors.rs` for:
- single-origin control
- multiple allowed origins reflecting the request origin
- wildcard origin preserving `*`
- preserving existing `Vary` values while appending `Origin`
## Validation
Used for validation:
```bash
cargo test -p garage_api_common cors::tests -- --nocapture
cargo build -p garage --bin garage
./script/test-cors-multi-origin.sh
```
## Reproducibility
For reviewers who want to validate behavior by commit:
- Before fix: `aa368e4b`
- includes the direct repro script and the regression test setup
- multi-origin ACAO is reproduced as a comma-separated value
- After fix: `f630eb92`
- reflects only the matching request origin
- preserves wildcard behavior
- adds `Vary: Origin` and preflight-specific `Vary` handling
Branch:
- `fix/cors-multiple-allow-origin`
Base used during validation:
- `74ad3bf8` (`main-v2`)
ClosesDeuxfleurs/garage#1149
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1419
## Summary
Garage's SigV4 canonical-request builder trims leading/trailing whitespace from signed header values but does not collapse sequential internal whitespace, which the SigV4 spec requires:
> Convert sequential spaces to a single space.
— https://docs.aws.amazon.com/IAM/latest/UserGuide/create-signed-request.html
AWS SDKs apply this normalization before computing the signature, but transmit the raw value on the wire. The receiver must therefore apply the same normalization when reconstructing the canonical request, otherwise the recomputed hash differs and the request is rejected as `Invalid signature`.
Same class of canonicalization-drift bug as #1155 / !1382, but on the canonical-headers axis rather than the canonical-URI axis.
## Reproduction
Surfaces in practice with `gitlab-runner`'s S3 cache uploader. I was in the midst of migrating my runner cache from AWS S3 to garage, but I noticed some shared runner caches were no longer uploading.
I was using `sha256sum | sha256sum` to compute my cache keys, which leaves a trailing ` -` on the value. Once GitLab appends `-protected` for protected branches the resulting `x-amz-meta-cachekey` header value contains internal sequential whitespace and triggers the mismatch:
```
x-amz-meta-cachekey:php- --protected
^^
two spaces, preserved by Garage
```
Without the fix the included regression test (`test_presigned_put_with_user_metadata`) fails with HTTP 403; with the fix it returns 200.
`aws-cli` is unaffected because it signs `Content-Type` rather than user metadata, so the specific code path with whitespace-bearing signed header values isn't exercised.
## Fix
In `canonical_request` (`src/api/common/signature/payload.rs`), replace the `.trim()` call on the joined header value with the full SigV4 normalization — `split_whitespace().collect::<Vec<_>>().join(" ")` — which both trims edges and collapses internal runs.
## Tests
* New regression test `test_presigned_put_with_user_metadata` covering a presigned PUT whose `x-amz-meta-*` value contains internal sequential whitespace.
* Full integration suite passes: `40 passed; 0 failed; 2 ignored`.
* `garage_api_common` unit tests pass: `18 passed; 0 failed`.
## Notes
* Backwards-compatible: any signature that validated before still validates, because clients are spec-required to collapse on their side; Garage was only rejecting requests where the client had collapsed correctly but Garage hadn't.
* No config or migration changes.
* Fix applies to both presigned-URL and Authorization-header code paths since they share the canonical-request builder.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1424
Reviewed-by: Alex <lx@deuxfleurs.fr>
Merging this first version as a baseline. For future work: write a security.md document to explain how to report security vulnerabilities, and split off the release process in a separate releasing.md document
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1406
Otherwise the rustls dependency might be built with both aws-lc and ring backends,
leading to the following error in the k2v_client tests when
consul-discovery feature is enabled (including the reqwest dependency):
```
Could not automatically determine the process-level CryptoProvider from Rustls crate features.
Call CryptoProvider::install_default() before this point to select a provider manually, or make sure exactly one of the 'aws-lc-rs' and 'ring' features is enabled.
See the documentation of the CryptoProvider type for more information.
```
Co-authored-by: Yureka <yuka@yuka.dev>
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1412
Reviewed-by: Alex <lx@deuxfleurs.fr>
known_addrs in PeerInfoInternal is append-only — addresses accumulate
via add_addr() and PeerList gossip but are never removed. In dynamic
environments (k8s pod restarts, DHCP, NAT traversal), this list grows
unboundedly with stale addresses.
Combined with sequential iteration in try_connect() and no TCP connect
timeout in netapp.rs, each unreachable address blocks reconnection for
the kernel's TCP SYN timeout (75-130s on Linux). With 10+ stale
addresses, worst-case reconnection exceeds 750s — a full outage for
replication_factor=3 clusters.
This commit contains the two following changes:
1. Address failure tracking and pruning (peering.rs): Track consecutive
connection failures per address in PeerInfoInternal. After 3 failures,
prune from known_addrs. Reset count when address is re-advertised via
gossip or incoming connection. Prevents unbounded list growth.
2. Shuffle before connecting (peering.rs): Randomize address order in
try_connect() so the valid address (often appended last) gets a fair
chance instead of always trying stale addresses first.
known_addrs in PeerInfoInternal is append-only — addresses accumulate
via add_addr() and PeerList gossip but are never removed. In dynamic
environments (k8s pod restarts, DHCP, NAT traversal), this list grows
unboundedly with stale addresses.
Combined with sequential iteration in try_connect() and no TCP connect
timeout in netapp.rs, each unreachable address blocks reconnection for
the kernel's TCP SYN timeout (75-130s on Linux). With 10+ stale
addresses, worst-case reconnection exceeds 750s — a full outage for
replication_factor=3 clusters.
This patches includes a first change to fix this issue:
1. TCP connect timeout (netapp.rs): Wrap TcpStream::connect() in
tokio::time::timeout(10s). Caps per-address attempt from 75-130s
to 10s, reducing worst-case 10-addr reconnection from ~750s to ~100s.
## Summary
This PR ensures that the `LifecycleWorker` yields at least once to the Tokio scheduler in between each batch of 100 objects.
## Problem being solved
I'm administrating a Garage cluster which has been experiencing timeouts on all endpoints while the lifecycle worker is running at midnight UTC : `Ping timeout` error messages and even requests eventually failing due to `Could not reach quorum ...`.
I have found that this happens while the lifecycle worker is working on a big bucket (containing millions of objects) with a lifecycle rule that applies to very few objects.
The `process_object()` function does not hit any `await`:
- `last_bucket` is always the same, so the `bucket_table` is not read asynchronously
- no transaction is made on the `object_table` because my lifecycle rule (almost) never applies to any object
The first commit in this PR adds an executable which reproduces the problem that I've been experiencing in a self-contained way : the lifecycle worker starves the Tokio scheduler so much that no other task is able to run (or very rarely).
To run it : `cargo run -p garage_model --bin lifecycle-starvation-test`.
This commit can be dropped post-review, as it's only useful to demonstrate the starvation.
The error messages completely stopped after adding the extra yield to the nodes of my cluster.
The duration of the lifecycle worker task does not appear to have changed at all from what I can see (looking at the timestamps produced either by the self-contained binary or by each of my nodes with the `Lifecycle worker finished` message).
## Note
An other potential fix would have been to force the `WorkerProcessor` to yield before re-enqueuing a busy task, but this would have affected all Garage workers even though it's only the `LifecycleWorker` being uncooperative.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1396
Reviewed-by: Alex <lx@deuxfleurs.fr>
Co-authored-by: Gauthier Zirnhelt <gauthier.zirnhelt@insimo.fr>
Co-committed-by: Gauthier Zirnhelt <gauthier.zirnhelt@insimo.fr>
This fixes a regression wrt garage-v1, likely caused by the version upgrade of quick_xml.
Currently, garage-v2 will emit empty ErrorDocument/IndexDocument/RedirectAllRequestsTo attributes in the response of GetBucketWebsite if there are no corresponding values.
This is somewhat wrong; at least, the S3 documentation for RedirectAllRequestsTo (https://docs.aws.amazon.com/AmazonS3/latest/API/API_RedirectAllRequestsTo.html) writes that it has a required HostName field. So emitting an empty RedirectAllRequestsTo is invalid.
This PR skips emitting XML attributes for these parameters if they contain no value.
Co-authored-by: Armaël Guéneau <armael.gueneau@ens-lyon.org>
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1391
Co-authored-by: Armael <armael@noreply.localhost>
Co-committed-by: Armael <armael@noreply.localhost>
## Summary
This PR fixes S3 `DeleteObjects` XML parsing when the request body is pretty-printed (contains indentation/newlines as whitespace text nodes).
Although PR #1324 already tried to address this, parsing could still fail with:
`InvalidRequest: Bad request: Invalid delete XML query`
because non-element nodes were validated but not actually skipped in the parsing loop.
## What changed
- In `src/api/s3/delete.rs`:
- Properly skip non-element whitespace text nodes while iterating over `<Delete>` children.
- Keep rejecting non-whitespace stray text content.
- Parse the root `<Delete>` element more robustly by selecting the first element child.
## Tests added
New unit tests in `src/api/s3/delete.rs`:
- `parse_delete_objects_xml_with_formatting`
- pretty-printed valid XML is accepted.
- `parse_delete_objects_xml_accepts_compact_valid_xml`
- compact valid XML is accepted.
- `parse_delete_objects_xml_rejects_non_whitespace_text_node`
- compact XML with stray text is rejected.
- `parse_delete_objects_xml_rejects_pretty_print_with_stray_text`
- pretty-printed XML with stray text is rejected.
## Validation
Executed:
```bash
cargo test -p garage_api_s3 parse_delete_objects_xml -- --nocapture
```
Result: all parser tests pass.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1374
Co-authored-by: milouz1985 <francois.hoyez@gmail.com>
Co-committed-by: milouz1985 <francois.hoyez@gmail.com>
## Problem
`hugo deploy` is broken with Garage on recent hugo versions when using gzip matchers
## Why?
We don't support multi-value headers correctly, in this case this specific headers combination:
```
Content-Encoding: gzip
Content-Encoding: aws-chunked
```
is interpreted as:
```
Content-Encoding: gzip
```
instead of:
```
Content-Encoding: gzip,aws-chunked
```
It fails both 1. the signature check and 2. the streaming check.
## Proposed fix
- Taking into account multi-value headers when building Canonical Request (validated with hugo deploy + AWS SDK v2)
- Taking into account multi-value headers (both comma separated and HeaderEntry separated) when removing `aws-chunked` (validated with hugo deploy + AWS SDK v2)
## Full explanation
Currently, `hugo deploy` on version `hugo v0.152.2` or more recent uses AWS SDK v2 only and supports for sending gzipped content.
That's configured with a matcher like that:
```yaml
deployment:
matchers:
- pattern: "^.+\\.(woff2|woff|svg|ttf|otf|eot|js|css)$"
cacheControl: "max-age=31536000, no-transform, public"
gzip: true # <-------- here
```
Also, with SDK v2, hugo is streaming all of its files.
Thus, it sends that kind of requests:
```python
Request {
method: PUT,
uri: /sebou/pagefind/pagefind.js?x-id=PutObject,
version: HTTP/1.1,
headers: {
"host": "localhost",
"user-agent": "aws-sdk-go-v2/1.39.2 ua/2.1 os/linux lang/go#1.25.6 md/GOOS#linux md/GOARCH#amd64 api/s3#1.84.0 ft/s3-transfer m/E,G,Z,g",
"content-length": "10026",
"accept-encoding": "identity",
"amz-sdk-invocation-id": "aed6df34-a67c-4bab-b63b-2b3777b751a0",
"amz-sdk-request": "attempt=1; max=3",
"authorization": "AWS4-HMAC-SHA256 Credential=GKxxxxx/20260227/garage/s3/aws4_request, SignedHeaders=accept-encoding;amz-sdk-invocation-id;amz-sdk-request;cache-control;content-encoding;content-length;content-type;host;x-amz-content-sha256;x-amz-date;x-amz-decoded-content-length;x-amz-meta-md5chksum;x-amz-trailer, Signature=76cd9b77f693ca89c2e6dd2a4dc55f83d4a82eca0f563d9d095ff96076f7b057",
"cache-control": "max-age=31536000, no-transform, public",
"content-encoding": "gzip", # <---- see here 1st instance of Content-Encoding
"content-encoding": "aws-chunked", # <---- 2nd instance of Content-Encoding
"content-type": "text/javascript",
"via": "2.0 Caddy",
"x-amz-content-sha256": "STREAMING-UNSIGNED-PAYLOAD-TRAILER",
"x-amz-date": "20260227T132212Z",
"x-amz-decoded-content-length": "9982",
"x-amz-meta-md5chksum": "aad88ac0bf704e91584b8d9ad9796670",
"x-amz-trailer": "x-amz-checksum-crc32",
"x-forwarded-for": "::1",
"x-forwarded-host": "localhost",
"x-forwarded-proto": "https"
},
body: Body(Streaming)
}
```
But our canonical request function only calls `HeaderMap.get()` that returns only the 1st value and not `HeaderMap.get_all()` that returns all the values for a header.
Leading to the following invalid `CanonicalRequest` value:
```python
PUT
/sebou/pagefind/pagefind.js
x-id=PutObject
accept-encoding:identity
amz-sdk-invocation-id:aed6df34-a67c-4bab-b63b-2b3777b751a0
amz-sdk-request:attempt=1; max=3
cache-control:max-age=31536000, no-transform, public
content-encoding:gzip # <----- see here, we kept only gzip and dropped aws-chunked
content-length:10026
content-type:text/javascript
host:localhost
x-amz-content-sha256:STREAMING-UNSIGNED-PAYLOAD-TRAILER
x-amz-date:20260227T132212Z
x-amz-decoded-content-length:9982
x-amz-meta-md5chksum:aad88ac0bf704e91584b8d9ad9796670
x-amz-trailer:x-amz-checksum-crc32
accept-encoding;amz-sdk-invocation-id;amz-sdk-request;cache-control;content-encoding;content-length;content-type;host;x-amz-content-sha256;x-amz-date;x-amz-decoded-content-length;x-amz-meta-md5chksum;x-amz-trailer
```
Amazon is crystal clear that, instead of dropping the other values, we should concatenate them with a comma:

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_sigv-create-signed-request.html#create-canonical-request
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1369
Reviewed-by: Alex <lx@deuxfleurs.fr>
Co-authored-by: Quentin Dufour <quentin@deuxfleurs.fr>
Co-committed-by: Quentin Dufour <quentin@deuxfleurs.fr>
- use `trim` method of `str` instead of manual implementation with `trim_matches(char::is_whitespace)`
- use result of `trim` for xml parsing instead of use the `str` before trim.
Garage RPC connections have no TCP keepalive enabled. When a connection dies silently (proxy pod restart, NAT timeout, network partition), it's only detected by application-level pings after ~60s (4 failed pings x 15s interval). During this window, the node appears connected but all RPC calls to it fail.
Enable TCP keepalive on both outgoing and incoming RPC connections via socket2:
- Idle time before first probe: 30s (TCP_KEEPALIVE_TIME)
- Probe interval after first: 10s (TCP_KEEPALIVE_INTERVAL)
A helper set_keepalive() function avoids duplicating the socket2 setup. Incoming connection keepalive failures are logged as warnings but don't reject the connection.
Companion to #1345 (stale address pruning + connect timeout). Together they address both halves of the reconnection problem: faster detection (this PR) and faster recovery.
Co-authored-by: Raj Singh <raj@tailscale.com>
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1348
Reviewed-by: maximilien <git@mricher.fr>
Co-authored-by: rajsinghtech <rajsinghtech@noreply.localhost>
Co-committed-by: rajsinghtech <rajsinghtech@noreply.localhost>
Even when using the catalog an dedicated token for authentication
might be needed.
**Approach**: Support the token header even with client certs was the simplist approach and somebody might need/want to use it.
**Background**: I want to run garage via Nomad but within containers (with host volumes). Nomad generates consul tokens (but at least not at the moment client certs). I need to use the catalog as with the services API garage tries to use the host/node IPs (instead of the actual service IPs).
**Tests**: I deployed this version and it works well.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1353
Reviewed-by: Alex <lx@deuxfleurs.fr>
Co-authored-by: Malte Swart <mswart@devtation.de>
Co-committed-by: Malte Swart <mswart@devtation.de>
add add some related tests.
catched from clippy lint `format_collect`
message: use of `format!` to build up a string from an iterator
--> src/api/common/encoding.rs:12:17
|
12 | let value = format!("{}", c)
| _____________________________^
13 | | .bytes()
14 | | .map(|b| format!("%{:02X}", b))
15 | | .collect::<String>();
| |________________________________________^
|
help: call `fold` instead
--> src/api/common/encoding.rs:14:7
|
14 | .map(|b| format!("%{:02X}", b))
| ^^^
help: ... and use the `write!` macro here
--> src/api/common/encoding.rs:14:15
|
14 | .map(|b| format!("%{:02X}", b))
| ^^^^^^^^^^^^^^^^^^^^^
= note: this can be written more efficiently by appending to a `String` directly
= help: for further information visit https://rust-lang.github.io/rust-clippy/rust-1.93.0/index.html#format_collect
and use workspace configuration in each package.
This allow to customize clippy and rust lint configuration for project.
No particular configuration in this commit.
- thin LTO is much much faster to compile, this will help when making
release builds
- strip="debuginfo" allows to still have symbols when unwinding stack
traces, which are usefull to have in user's bug reports. Binary size
is increased from 23M to 27M, so a reasonable increase
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1342
Co-authored-by: Alex Auvolat <lx@deuxfleurs.fr>
Co-committed-by: Alex Auvolat <lx@deuxfleurs.fr>
this avoid rust-analyzer indicate invalid field error on `Redirect` for
`replace_prefix` and `replace_full` because of a conflict between struct :
`api::s3::website::Redirect` and `model::bucket_table::Redirect`
- migration from `ByteSlice` to `Bytes` heed type.
- not sure about the impact of only one `read_txn` for the entire function `list_trees`, whereas before `open_database` call uses their own access controller.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1318
Co-authored-by: Gwen Lg <me@gwenlg.fr>
Co-committed-by: Gwen Lg <me@gwenlg.fr>
- clippy::nonminimal_bool disabled for check_size_filter function
clippy message: this boolean expression can be simplified
help: for further information visit https://rust-lang.github.io/rust-clippy/rust-1.92.0/index.html#nonminimal_bool
- clippy::large_enum_variant for `DecryptStreamState` and `State`
- clippy::too_many_arguments for `put_block_and_meta` and
`test_read_encrypted`
- clippy::deref_addrof for specific unsafe code
- clippy::doc_overindented_list_items and clippy::doc_lazy_continuation
- include the lifetime instead of hide it
lint message: hiding a lifetime that's elided elsewhere is confusing
help: the same lifetime is referred to in inconsistent ways, making the signature confusing
- remove useless lifetime
lint message: the following explicit lifetimes could be elided: 'a
help: for further information visit https://rust-lang.github.io/rust-clippy/rust-1.92.0/index.html#needless_lifetimes
I have successfully setup my Pleroma instance with GarageHQ and would share this in the documentation.
The part with the loop is totally optionnal, but, as I said, in case of old link in Pleroma database I prefer run it.
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1294
Co-authored-by: Dryusdan <contact@dryusdan.fr>
Co-committed-by: Dryusdan <contact@dryusdan.fr>
Put the features flags into alphabetical order in both the cookbook and
the version information, so that it is easier to document additional
feature flags.
The version control site was migrated from Gitea to Forgejo some time
ago, and Forgejo has declared a hard fork from Gitea[1]. Update the
documentation and links to refer to the site as a Forgejo instance
instead of a Gitea instance.
[1] https://forgejo.org/2024-02-forking-forward/
Buypass Go SSL has stopped providing SSL/TLS certificates, including
from their ACME API endpoints, as of October 2025, and the service is to
be completely phased out by April 2026:
https://community.buypass.com/t/y4y130p
Remove them from the docs as an ACME-capable provider.
When not used. Especially with a lot of small files on the PVC's Garage uses/is configured with,
Pod upstart time can get quite extreme. For more context see a very good description
of the issues and the technical intricacies behind it here: https://github.com/longhorn/longhorn/issues/2131#issuecomment-778897129
I think it's sane to have fsGroupChangePolicy at "OnRootMismatch" as Garage is an S3 system and thereby
is likely to process a lot of files. And somewhat likely that many of these will be small files as well.
---
Using `fsGroupChangePolicy: "OnRootMismatch"` in my env. changed the Garage Pod upstart. E.g. in an
upgrade case from 5-7minutes ( around 50GB of data ). Many small files though. To 1-15sec.
Thank you.
Signed-off-by: larssb <larssb@noreply.localhost>
The systemd-journald is used in most major Linux distros that use systemd.
This enables logging using the systemd-journald native protocol, instead
of just writing to stderr.
i went with a `500`/`InternalError`/`Please try again.` because that is something i've seen AWS S3 report while developing other software, and i'm not convinced all clients would understand a 409 conflict properly (GET don't usually conflict)
Reviewed-on: https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/1053
Co-authored-by: trinity-1686a <trinity@deuxfleurs.fr>
Co-committed-by: trinity-1686a <trinity@deuxfleurs.fr>
- Remove the replication_factor.replication_factor() in favor of
usize::from(replication_factor) to make the conversion more explicit.
- Implement Display on ReplicationFactor so that it can be formatted
without converting to usize
- Use ReplicationFactor in the constructor of LayoutVersion and add a
method to get a ReplicationFactor from a LayoutVersion, despite
LayoutVersion still storing it as usize internally.
See #1006
LMDB files never shrink, so we can end up with a large database that
contains a smaller amount of actual data.
Compacting the snapshots is an easy win: it will write faster to disk,
take less space, and if needed you can reimport an already-compacted
snapshot as the main database.
In particular, it means that "garage meta snapshot --all" will get an exit
code of 1 if any node fails to snapshot.
This makes sure that any external tool trying to snapshot nodes (e.g. from
cron) will be aware of the failure.
Fix#920
Snapshot errors on remote nodes were not reported at all.
We now get proper error output such as:
0fa0f35be69528ab error: Internal error: DB error: LMDB: No space left on device (os error 28)
88d92e2971d14bae ok
Fix#920
admin api: rename EndpointHandler into RequestHandler to avoid confusion with RPC
wip: infrastructure for local api calls
admin api: fix things
admin api: first local endpoint to work with new scheme
admin api: implement SetWorkerVariable
This removes our dependency on cargo2nix, which was causing us some
issues. Whereas cargo2nix creates one Nix derivation per crate, Crane
uses only two derivations:
1. Build dependencies only
2. Build the final binary
This means that during the second step, no caching can be done. For
instance, if we do a change in garage_model, we need to recompile all of
the Garage crates including those that do not depend on garage_model.
On the upside, this allows all of the Garage crates to be built at once
using cargo build logic, which is optimized for high parallelism and
better pipelining between all of the steps of the build. All in all,
this makes most builds faster than cargo2nix.
A few other changes have been made to the build scripts and CI:
- Unit tests are now run within a Nix derivation. In fact, we have
different derivations to run the tests using LMDB and Sqlite as
metadata db engines.
- For debug builds, most CI steps now run in parallel (with the notable
exception of the smoke test that runs after the build, which is
inevitable).
- We no longer pass the GIT_VERSION argument when building debug builds
and running the tests. This means that dev binaries and test
binaries don't know the exact version of Garage they are from. That
shouldn't be an issue in most cases.
- The not-dynamic.sh scripts has been fixed to fail if the file does not
exist.
This change is probably not a proper fix, somebody with more expertise on
this code should look at it.
Here is how to reproduce the crash:
- start with a layout with two zones
- move all nodes of a zone to gateway mode: `garage layout assign fea54bcc081f318 -g`
- `garage layout show` will panic with a backtrace
Fortunately, the crash is only on the RPC client side, not on the Garage
server itself, and `garage layout revert` still works to go back to the
previous state.
As far as I can tell, this bug is present since Garage 0.9.0 which
includes the new layout assignation algorithm:
https://git.deuxfleurs.fr/Deuxfleurs/garage/pulls/296
Currently, taking a snapshot of the metadata database with sqlite creates
a sqlite file without extension with the following format:
snapshots/2025-01-26T15:29:17Z
This makes it hard to understand what kind of data this is, and is not
consistent with LMDB:
snapshots/2025-01-26T15:29:17Z/data.mdb
With this change, we now get a directory with a single db.sqlite file:
snapshots/2025-01-26T15:29:17Z/db.sqlite
I followed the documentation and got an error if the layout's version was not specified
```
garage layout apply
Error: Internal error:
Please pass the new layout version number to ensure that you are writing the correct version of the cluster layout.
To know the correct value of the new layout version, invoke `garage layout show` and review the proposed changes.
```
This fixes that
In case `rpc_public_addr` is not set, but autodiscovery is used, this
allows filtering the list of automatically discovered IPs to a specific
subnet.
For example, if nodes should pick *their* IP inside a specific subnet,
but you don't want to explicitly write the IP down (as it's dynamic, or
you want to share configs across nodes), you can use this option.
- We always recalculate the reference count of a block before deleting
it locally, to make sure that it is indeed zero.
- If we had to fetch a remote block but we were not able to get it,
check that refcount is indeed > 0.
- Repair procedure that checks everything
This page of the AWS docs indicate that Content-Type should be part of
the CanonicalHeaders (and therefore SignedHeaders) strings in signature
calculation:
https://docs.aws.amazon.com/AmazonS3/latest/API/sig-v4-header-based-auth.html
However, testing with Minio Client revealed that it did not sign the
Content-Type header, and therefore we broke CI by expecting it to be
signed. With this commit, we don't mandate Content-Type to be signed
anymore, for better compatibility with the ecosystem. Testing against
the official behavior of S3 on AWS has not been done.
For some users, this might be their first time being interacting with
the `env_logger` crate.
As such, they might not be aware that less verbose log levels exist.
Some might not want to log every incoming request, for example.
This commit also adds syntax hints to the code-fence for bash for better
syntax highlighting of that section, and repeats itself multiple times,
that `info` is, in fact, the default.
No changes to the recommendation of log levels were made.
Having all Engine enum variants conditional causes compilation errors
when *none* of the DB engine features is enabled. This is not an issue
for full garage build, but affects crates that use garage_db as
dependency.
Change all variants to be present at all times. It solves compilation
errors and also allows us to better differentiate between invalid DB
engine name and engine with support not compiled in current binary.
Use optional DB open overrides for both input and output database.
Duplicating the same override flag for input/output would result in too
many, too long flags. It would be too costly for very rare edge-case
where converting between same DB engine, just with different flags.
Because overrides flags for different engines are disjoint and we are
preventing conversion between same input/ouput DB engine, we can have
only one set.
The override flag will be passed either to input or output, based on
engine type it belongs to. It will never be passed to both of them and
cause unwelcome surprise to user.
Garage v0.8.5
This minor release includes the following improvements and fixes:
New features:
- Configuration: make LMDB's `map_size` configurable and make `block_size` and `sled_cache_capacity` expressable as strings (such as `10M`) (#628, #630)
- Add support for binding to Unix sockets for the S3, K2V, Admin and Web API servers (#640)
- Move the `convert_db` command into the main Garage binary (#645)
- Add support for specifying RPC secret and admin tokens as environment variables (#643)
- Add `allow_world_readable_secrets` option to config file (#663, #685)
Bug fixes:
- Use `statvfs` instead of mount list to determine free space in metadata/data directories (#611, #631)
- Add missing casts to fix 32-bit build (#632)
- Fix error when none of the HTTP servers (S3/K2V/Admin/Web) is started and fix shutdown hang (#613, #633)
- Add missing CORS headers to PostObject response (#609, #656)
- Monitoring: finer histogram boundaries in Prometheus exported metrics (#531, #686)
Other:
- Documentation improvements (#641)
- self.node_id_vec was not properly updated when the previous ring was empty
- ClusterLayout::merge was not considering changes in the layout parameters
- have consistent error return types
- store the zone redundancy in a Lww
- print the error and message in the CLI (TODO: for the server Api, should msg be returned in the body response?)
It takes as paramters the replication factor and the zone redundancy, computes the
largest partition size reachable with these constraints, and among the possible
assignation with this partition size, it computes the one that moves the least number
of partitions compared to the previous assignation.
This computation uses graph algorithms defined in graph_algo.rs
The function now computes an optimal assignation (with respect to partition size) that minimizes the distance to the former assignation, using flow algorithms.
This commit was written by Mendes Oulamara <mendes.oulamara@pm.me>
2022-05-01 09:54:19 +02:00
512 changed files with 87916 additions and 23712 deletions
To ensure the quality of the codebase and documentation, the use of AI,
including LLMs and coding agents, is strictly restricted in the following way:
- AI **must not** be used to write documentation
- **Do not** use AI to write bug reports, commit descriptions and pull request
messages
- **Do not** use AI agents to make contributions to Garage, all contributions
must be led by a human that know what they are doing at all times
- AI **may** be used for some tedious code generation tasks, limited to very
mechanical translations from API docs or boilerplate writing. The code
generated must be so simple as to make it clear that it cannot be covered by
copyright.
You are free to make use of AI privately to explore the codebase and solve
conceptual problems, but please restrain from copying the output from an LLM
anywhere in your code or on the issue tracker, or from letting an agent edit
the codebase directly.
## Asking questions
Read the documentation before asking questions.
Do not use the issue tracker to ask questions about Garage.
Questions asked on the issue tracker will be closed.
Ask questions on the Matrix channel `#garage:deuxfleurs.fr` so that any
community member can see your question and help you out.
If you need in-depth support from the Garage developers specifically, write to
`garagehq@deuxfleurs.fr`. Even if you do so, we do not commit to giving you an
answer.
## Reporting bugs
When writing a bug report, use this checklist:
- For bugs that can be reproduced:
- confirm that you are using the latest version of Garage and that the bug still exists in this version
- set the log level to debug using the `RUST_LOG=garage=debug` environment variable and reproduce the bug to get more verbose logs
- Check whether there is already an open issue in the bug tracker. If so, your bug report is still valuable but please add it as a comment to the existing issue instead of opening a new one.
- Collect as much information as possible:
- logs of the Garage daemon at the time the issue happened, including logs that show what was happening before the issue occurred
- the output of `garage status`
- the output of `garage stats -a`
- the output of `garage layout history`
- Write a detailed bug report, including:
- a description of your cluster (number of nodes, hardware, operating system, networking, etc)
- a detailed description of what you did that led to the issue, including any code or command line that invoked a Garage API
- what you were expecting
- what actually happened, and how that's different from what you expected
- the information collected previously
- if possible, simple steps to help the developers reproduce the issue locally
Bug reports that are imprecise or otherwise unactionable will be closed.
## Suggesting new features
Garage can be improved in many ways, but just suggesting a new feature does not mean we will implement it.
Feature requests that may lead to an actual implementation are feature requests that:
- are precise and actionable, i.e. include a precise description of the expected behavior and any necessary architectural details required for the implementation
- are motivated by actual need from a variety of users
Moreover, a certain number of features are defined as out-of-scope for Garage, including but not limited to:
- extensions to the S3 API that are not present on AWS
- features that require the implementation of a consensus algorithm
- more generally, features that are incompatible with the architecture of Garage and its goal of staying simple
Only feature requests in one of the following category may stay open in the issue tracker:
- features that the Garage team wants to work on
- features that are being actively worked on by an external contributor which is clearly identified
- features that are easy to implement and could be an easy task for a new contributor that wants to get to know the codebase
All other feature requests will be closed after a few months of inactivity, so as to keep the number of open issues to a manageable level.
Feature requests that are clearly out of scope will be closed directly.
## Improving the documentation
An easy way to contribute to Garage which also adds a lot of value is to
improve the documentation. Make sure to write in clear technical English, and
write unambiguously. Documentation contributions are very appreciated if they
are well-written.
## For developers
We welcome code contributions to Garage that adhere to our standards for quality:
- Changes should be reviewed from a functional perspective to ensure that they work well with the existing codebase and do not introduce bugs or subtle issues.
- You must have tested your contribution to make sure that it does what it says. The amount of testing required is proportional to the complexity of the change introduced.
- Any new feature must be properly documented following existing practices (see below).
- Unit tests should be included when relevant.
- Contributions should pass basic lints for syntactic quality (`cargo fmt`, `cargo clippy`, `typos`).
- Contributions should pass our CI test suite.
- No user-facing breaking changes may be introduced between major releases.
- No internal data model change may be introduced between major releases, to
ensure that Garage daemons with different minor/patch versions numbers can
work together in a cluster. For major releases, a proper migration path
should be implemented and tested thoroughly.
Please follow up on your work when changes are requested, to avoid stale PRs.
Do not take it personally if a Garage developer pushes directly to your branch
to modify your contribution, as this might be necessary to get it merged
faster.
### Properly documenting your contribution
#### Configuration options
New configuration options should be documented in
`doc/book/reference-manual/configuration.md`. The documentation for a
configuration option should be exhaustive. For instance, for choice options all
choices should be listed explicitly with a precise description of their
meaning.
In terms of syntax, all configuration options should appear in three places:
- in the example at the top, with an example value
- in the index of all configuration options which is sorted by alphabetical order
- in its dedicated subsection with full reference text
#### CLI commands and command flags
CLI commands are self-documented using the doc commends in the codebase.
Make sure to write clear and precise comments for all options you are adding.
#### S3 features
If you implement new S3 features, make sure to update the compatibility matrix in `doc/book/reference-manual/s3-compatibility.md`.
#### Admin API
The admin API has an OpenAPI specification that is automatically generated
using Utoipa, from a description of each endpoint that is given in
`src/api/admin/openapi.rs` and a description of data structure schemas in
`src/api/admin/api.rs`. The code in `openapi.rs` is only used to generate the
OpenAPI specification document and not for the actual implementation in Garage,
whereas structures defined in `api.rs` are also used for the implementation of
API calls. Make sure to write good doc comments for all of these items so that
the OpenAPI specification will be precise and accurate.
An up-to-date version of the OpenAPI specification document should be kept in
the repository in `doc/api/garage-admin-v2.json`. When you are making changes
to the admin API, update this document with the following command:
```
cargo run -- admin-api-schema > doc/api/garage-admin-v2.json
```
## Garage team organization
Alex (handle `lx`) is the lead developer and is responsible of ensuring the
correctness of Garage and stability between version upgrades.
The other maintainers are Trinity (handle `trinity-1686a`), Quentin (handle `quentin`) and Maximilien (handle `halfa`).
Maximilien is responsible for coordinating effort on the Kubernetes integration / Helm chart.
## Pull request merging criteria
The following PRs should only be merged after review and approval from Alex:
- PRs that introduce architectural changes, such as changes in the data model
or change in the coordination protocols between nodes
- PRs that introduce changes on the format of data structures used for
persistent disk storage and internal cluster communication (RPC)
- PRs that are suspected of introducing some kind of breakage or unexpected
behavior due to their complexity
PRs that introduce breaking change for users but don't fall in one of the
previous category should be discussed between maintainers to evaluate the
impact on users when upgrading. Alex's approval is not required to merge them
as long as they are clearly identified as breaking in the PR title, and are
properly merged in the branch for the next major version and not in the current
main branch.
All other PRs can be merged by any maintainer on their own, once they are
confident that the quality standards defined in this document are respected
before merging.
## Merging strategy
When merging PRs, maintainers should ensure that a Git commit is created by
Forgejo that records the PR number, its title and its text in the commit
message. If a PR is fixing an issue, make sure that the issue number is
included in the PR title as well. This is to ensure that when releasing a new
version of Garage, the changelog in the release notes can be properly
constructed by reading the Git log since the last release.
We also want to keep the history "almost linear" to facilitate the use of `git
bisect` if it ever were necessary. This leaves the following two merging
strategies:
- For PRs that consist of many commits that should stay independent, the
"rebase and create merge commit" strategy should be used. The merge commit is
created automatically by Forgejo and saves the PR's number, title and text in
the commit message.
- For PRs that consist of only one commit, or a few number of commits that can
be merged, the "create squash commit" strategy should be used. This way a
single commit will be created by Forgejo which also saves the PR's number,
title and text in the commit message.
When cherry-picking commits from one branch to the other, a simple fast-forward
merging strategy can be used if the commit message already references a PR
This documents how the Garage project operates. It reflects the state of the project as of July 2026 and is not optimal. The team is interested to improve it in the future.
## Team organization
* **Contributors**: anyone can contribute by proposing changes in issues and pull requests.
* **Maintainers**: they are responsible for reviewing, merging pull requests, publishing releases and triaging issues.
The current maintainers are:
* Alex (handle `lx`)
* Trinity (handle `trinity-1686a`)
* Quentin (handle `quentin`)
* Maximilien (handle `halfa`), who is in particular responsible for coordinating effort on the Kubernetes integration / Helm chart.
They are added to a white-list of the branch protection rule of the repository to enable them to merge pull requests.
To become a maintainer, you need to be a long-term contributor and earn the personal trust of Alex.
There is no set process for leaving the maintainer role.
* **Lead developer**: Alex (handle `lx`) is the lead developer and is responsible of ensuring the
correctness of Garage and stability between version upgrades. He may transfer this role to someone else as he sees fit.
## Communication channels
The team coordinates in the following channels:
* The issue tracker and pull requests of the official repository.
* The `#garage:deuxfleurs.fr` matrix channel (in English), open to anyone.
On this channel, users may ask for support and discussions about development also happen.
* The `#garage-dev:deuxfleurs.fr` matrix channel (in French), not advertised to contributors but de facto accessible to anyone.
Discussions about development and project coordination happen there.
The moderators for those discussion channels are the Garage maintainers.
## Decision procedures
Decisions are taken by lazy consensus, with the lead developer settling discussions when a consensus cannot be reached.
## Governance changes
There is no set process for changing the governance of garage.
If you wish to report responsibly a security vulnerability about Garage, we ask that you follow the following process.
Please report each security vulnerabilities by filling out the following template:
- PROJECT: A URL to the code repository containing the vulnerable version - be reminded that the source of truth is at https://git.deuxfleurs.fr/deuxfleurs/garage
- PUBLIC: Please let us know if this vulnerability has been made or discussed publicly already, and if so, please let us know where.
- DESCRIPTION: Please provide precise description of the security vulnerability you have found with as much information as you are able and willing to provide.
Please send the above info, along with any other information you feel is pertinent by emailing the core team at: garagehq@deuxfleurs.fr
The Garage Core Team will let you know within a few weeks whether or not your report has been accepted or rejected.
We ask that you please keep the report confidential until we have either responded or made a public announcement.
Administrate your Garage cluster programatically, including status, layout, keys, buckets, and maintainance tasks.
*Disclaimer: The API is not stable yet, hence its v0 tag. The API can change at any time, and changes can include breaking backward compatibility. Read the changelog and upgrade your scripts before upgrading. Additionnaly, this specification is very early stage and can contain bugs, especially on error return codes/types that are not tested yet. Do not expect a well finished and polished product!*
paths:
Administrate your Garage cluster programmatically, including status, layout, keys, buckets, and maintenance tasks.
*Disclaimer: The API is not stable yet, hence its v0 tag. The API can change at any time, and changes can include breaking backward compatibility. Read the changelog and upgrade your scripts before upgrading. Additionally, this specification is very early stage and can contain bugs, especially on error return codes/types that are not tested yet. Do not expect a well finished and polished product!*
If you are developping a new application, you may want to use Garage to store your user's media.
If you are developing a new application, you may want to use Garage to store your user's media.
The S3 API that Garage uses is a standard REST API, so as long as you can make HTTP requests,
you can query it. You can check the [S3 REST API Reference](https://docs.aws.amazon.com/AmazonS3/latest/API/API_Operations_Amazon_Simple_Storage_Service.html) from Amazon to learn more.
Developping your own wrapper around the REST API is time consuming and complicated.
Instead, there are some libraries already avalaible.
Developing your own wrapper around the REST API is time consuming and complicated.
Instead, there are some libraries already available.
Some of them are maintained by Amazon, some by Minio, others by the community.
Nextcloud will now make Garage encrypt files at rest in the storage bucket.
These files will not be readable by an S3 client that has credentials to the
bucket but doesn't also know the secret encryption key.
### External Storage
**From the GUI.** Activate the "External storage support" app from the "Applications" page (click on your account icon on the top right corner of your screen to display the menu). Go to your parameters page (also located below your account icon). Click on external storage (or the corresponding translation in your language).
@@ -88,7 +136,7 @@ To test your new configuration, just reload your Nextcloud webpage and start sen
*Click on the picture to zoom*
Add a new external storage. Put what you want in "folder name" (eg. "shared"). Select "Amazon S3". Keep "Access Key" for the Authentication field.
In Configuration, put your bucket name (eg. nextcloud), the host (eg. 127.0.0.1), the port (eg. 3900 or 443), the region (garage). Tick the SSL box if you have put an HTTPS proxy in front of garage. You must tick the "Path access" box and you must leave the "Legacy authentication (v2)" box empty. Put your Key ID (eg. GK...) and your Secret Key in the last two input boxes. Finally click on the tick symbol on the right of your screen.
In Configuration, put your bucket name (eg. nextcloud), the host (eg. 127.0.0.1), the port (eg. 3900 or 443), the region ("garage" if you use the default, or the one your configured in your `garage.toml`). Tick the SSL box if you have put an HTTPS proxy in front of garage. You must tick the "Path access" box and you must leave the "Legacy authentication (v2)" box empty. Put your Key ID (eg. GK...) and your Secret Key in the last two input boxes. Finally click on the tick symbol on the right of your screen.
Now go to your "Files" app and a new "linked folder" has appeared with the name you chose earlier (eg. "shared").
@@ -139,15 +187,15 @@ a reasonable trade-off for some instances.
Create a key for Peertube:
```bash
garage key new --name peertube-key
garage key create peertube-key
```
Keep the Key ID and the Secret key in a pad, they will be needed later.
We need two buckets, one for normal videos (named peertube-video) and one for webtorrent videos (named peertube-playlist).
We need two buckets, one for normal videos (named peertube-videos) and one for webtorrent videos (named peertube-playlists).
```bash
garage bucket create peertube-video
garage bucket create peertube-playlist
garage bucket create peertube-videos
garage bucket create peertube-playlists
```
Now we allow our key to read and write on these buckets:
@@ -191,7 +239,7 @@ object_storage:
# Put localhost only if you have a garage instance running on that node
endpoint: 'http://localhost:3900' # or "garage.example.com" if you have TLS on port 443
# Garage supports only one region for now, named garage
# Garage default region is named "garage", edit according to your config
region: 'garage'
credentials:
@@ -206,7 +254,7 @@ object_storage:
proxify_private_files: false
streaming_playlists:
bucket_name:'peertube-playlist'
bucket_name: 'peertube-playlists'
# Keep it empty for our example
prefix: ''
@@ -216,7 +264,7 @@ object_storage:
# Same settings but for webtorrent videos
videos:
bucket_name:'peertube-video'
bucket_name: 'peertube-videos'
prefix: ''
# You must fill this field to make Peertube use our reverse proxy/website logic
Unfortunately, [old avatars and headers cannot currently be cleaned up](https://github.com/mastodon/mastodon/issues/9567).
### Migrating your data
Data migration should be done with an efficient S3 client.
@@ -366,7 +413,7 @@ mc mirror --newer-than "3h" ./public/system/ garage/mastodon-data
## Matrix
Matrix is a chat communication protocol. Its main stable server implementation, [Synapse](https://matrix-org.github.io/synapse/latest/), provides a module to store media on a S3 backend. Additionally, a server independent media store supporting S3 has been developped by the community, it has been made possible thanks to how the matrix API has been designed and will work with implementations like Conduit, Dendrite, etc.
Matrix is a chat communication protocol. Its main stable server implementation, [Synapse](https://matrix-org.github.io/synapse/latest/), provides a module to store media on a S3 backend. Additionally, a server independent media store supporting S3 has been developed by the community, it has been made possible thanks to how the matrix API has been designed and will work with implementations like Conduit, Dendrite, etc.
### synapse-s3-storage-provider (synapse only)
@@ -379,7 +426,7 @@ Supposing you have a working synapse installation, you can add the module with p
Now create a bucket and a key for your matrix instance (note your Key ID and Secret Key somewhere, they will be needed later):
Ente is an alternative for Google Photos and Apple Photos. It [can be selfhosted](https://help.ente.io/self-hosting/) and is working fine with Garage as of May 2024.
As a first step we need to create a bucket and a key for Ente:
```bash
garage bucket create ente
garage key create ente-key
# For the CORS setup to work, the key needs to be --owner as well, at least temporarily.
garage bucket allow ente --read --write --owner --key ente-key
```
We also need to setup some CORS rules to allow the Ente frontend to access the bucket:
aws s3api put-bucket-cors --bucket ente --cors-configuration $CORS
```
Now we need to configure ente-server to use our bucket. This is explained [in the Ente S3 documentation](https://help.ente.io/self-hosting/guides/external-s3).
Prepare a configuration file for ente's backend as `museum.yaml`:
```yaml
credentials-file: /credentials.yaml
apps:
public-albums: https://albums.example.tld # If you want to use the share album feature
internal:
hardcoded-ott:
local-domain-suffix: "@example.com" # Your domain
local-domain-value: 123456 # Custom One-Time Password since we are not sending mail by default
key:
# WARNING -- You MUST CHANGE the values below
# Someone has made an image that can do it for you : https://github.com/EdyTheCow/ente-selfhost/blob/main/images/ente-server-tools/Dockerfile
# Simply build it yourself or run docker run --rm ghcr.io/edythecow/ente-server-tools go run tools/gen-random-keys/main.go
encryption: yvmG/RnzKrbCb9L3mgsmoxXr9H7i2Z4qlbT0mL3ln4w= # CHANGE THIS VALUE
hash: KXYiG07wC7GIgvCSdg+WmyWdXDAn6XKYJtp/wkEU7x573+byBRAYtpTP0wwvi8i/4l37uicX1dVTUzwH3sLZyw== # CHANGE THIS VALUE
jwt:
secret: i2DecQmfGreG6q1vBj5tCokhlN41gcfS2cjOs9Po-u8= # CHANGE THIS VALUE
```
The full configuration file can be found [here](https://github.com/ente-io/ente/blob/main/server/configurations/local.yaml)
Then prepare a credentials file as `credentials.yaml`
```yaml
db:
host: postgres
port: 5432
name: <ente_db_name>
user: <pguser>
password: <pgpass>
s3:
# Override the primary and secondary hot storage. The commented out values
# are the defaults.
#
hot_storage:
primary: b2-eu-cen
# secondary: wasabi-eu-central-2-v3
# If true, enable some workarounds to allow us to use a local minio instance
# for object storage.
#
# 1. Disable SSL.
# 2. Use "path" style S3 URLs (see `use_path_style_urls` below).
# 3. Directly download the file during replication instead of going via the
# Cloudflare worker.
# 4. Do not specify storage classes when uploading objects (since minio does
# not support them, specifically it doesn't support GLACIER).
are_local_buckets: true
# To use "path" style S3 URLs instead of DNS-based bucket access
# default to true if you set "are_local_buckets: true"
# use_path_style_urls: true
b2-eu-cen: # Don't change this key, it is hardcoded
key: <keyID>
secret: <keySecret>
endpoint: garage:3900 # publicly accessible endpoint of your garage instance
region: garage
bucket: <yourbucketName>
use_path_style: true
# you can specify secondary locations, names are hardcoded as well
# wasabi-eu-central-2-v3:
# scw-eu-fr-v3:
# and you can also specify a bucket to be used for embeddings, preview etc..
We also need to expose these buckets publicly to serve their content to users:
```bash
garage bucket website --allow pleroma
```
Note the Key ID and Secret Key.
### Configure Pleroma
Update your Pleroma configuration like that in `/etc/pleroma/config.exs`.
```
config :pleroma, Pleroma.Upload,
uploader: Pleroma.Uploaders.S3,
base_url: "https://pleroma.garage.example.tld"
config :ex_aws, :s3,
access_key_id: "GW...",
secret_access_key: "XXX",
region: "garage",
host: "api.garage.example.tld"
```
And restart Pleroma.
You can found more information in [Pleroma Documentation > Pleroma.Uploaders.S3](https://docs-develop.pleroma.social/backend/configuration/cheatsheet/#pleromauploaderss3)
### Migrating your data
Pleroma have an internal migration tool that can encounter some fatal error
```
** (EXIT from #PID<0.98.0>) an exception was raised:
** (File.Error) could not stream "/var/lib/pleroma/uploads/09/f8": illegal operation on a directory
(elixir 1.17.3) lib/file/stream.ex:100: anonymous fn/3 in Enumerable.File.Stream.reduce/3
(elixir 1.17.3) lib/stream.ex:1675: anonymous fn/5 in Stream.resource/3
On Plakar server, add your Garage as a storage location:
```bash
plakar store add garageS3 s3://my-garage.tld/plakar-backups \
region=garage # Or as you've specified in garage.toml \
access_key=<Key ID from "garage key info my-plakar-key"> \
secret_access_key=<Secret key from "garage key info my-plakar-key">
```
Then create the repository.
```bash
plakar at @garageS3 create -plaintext # Unencrypted
# or
plakar at @garageS3 create #encrypted
```
If you encrypt your backups (Plakar default), you will need to define a strong passphrase. Do not forget to save your password safely. It will be needed to decrypt your backups.
After the repository has been created, check that everything works as expected (that might give an empty result as no file has been added yet, but no error message):
```bash
plakar at @garageS3 check
```
Now that everything is configure, you can use Garage as your backups storage. For instance sync it with a local backup storage:
```bash
$ plakar at ~/backups sync to @garageS3
```
Or list the S3 storage content:
```bash
$ plakar at @garageS3 ls
```
More information in Plakar documentation: https://www.plakar.io/docs/main/quickstart/
## Synology HyperBackup
HyperBackup can be configured to upload backups to garage using a custom S3 destination. However, the HyperBackup client hardcodes the `us-east-1` region that is a critical input to the v4 signature process. If garage is not set to `us-east-1`, HyperBackup will recognize available buckets, but fail during the final setup stage.
This will tremendously accelerate operations such as `rclone sync` or `rclone ncdu` by reducing the number
of ListObjects calls that are made.
**Garage behind Cloudflare proxy:** when running Garage behind Cloudflare proxy, you might see `Response: error 403 Forbidden, Forbidden: Invalid signature` error in your garage logs or `AccessDenied: Forbidden: Invalid signature` error in rclone logs. Try adding `--s3-sign-accept-encoding=false` flag to your rclone command and see if the issue is resolved.
@@ -17,13 +17,13 @@ Garage can also help you serve this content.
## Gitea
You can use Garage with Gitea to store your [git LFS](https://git-lfs.github.com/) data, your users' avatar, and their attachements.
You can use Garage with Gitea to store your [git LFS](https://git-lfs.github.com/) data, your users' avatar, and their attachments.
You can configure a different target for each data type (check `[lfs]` and `[attachment]` sections of the Gitea documentation) and you can provide a default one through the `[storage]` section.
Let's start by creating a key and a bucket (your key id and secret will be needed later, keep them somewhere):
which contains all of the released versions as well as the code
for the developpement of the next version.
for the development of the next version.
Clone the repository and enter it as follows:
@@ -41,7 +41,7 @@ git tag # List available tags
git checkout v0.8.0 # Change v0.8.0 with the version you wish to build
```
Otherwise you will be building a developpement build from the `main` branch
Otherwise you will be building a development build from the `main` branch
that includes all of the changes to be released in the next version.
Be careful that such a build might be unstable or contain bugs,
and could be incompatible with nodes that run stable versions of Garage.
@@ -85,11 +85,14 @@ The following feature flags are available in v0.8.0:
| Feature flag | Enabled | Description |
| ------------ | ------- | ----------- |
| `bundled-libs` | *by default* | Use bundled version of sqlite3, zstd, lmdb and libsodium |
| `system-libs` | optional | Use system version of sqlite3, zstd, lmdb and libsodium<br>if available (exclusive with `bundled-libs`, build using<br>`cargo build --no-default-features --features system-libs`) |
| `consul-discovery` | optional | Enable automatic registration and discovery<br>of cluster nodes through the Consul API |
| `fjall` | experimental | Enable using Fjall to store Garage's metadata |
| `journald` | optional | Enable logging to systemd-journald with<br>`GARAGE_LOG_TO_JOURNALD=true` environment variable set |
| `k2v` | optional | Enable the experimental K2V API (if used, all nodes on your<br>Garage cluster must have it enabled as well) |
| `kubernetes-discovery` | optional | Enable automatic registration and discovery<br>of cluster nodes through the Kubernetes API |
| `lmdb` | *by default* | Enable using LMDB to store Garage's metadata |
| `metrics` | *by default* | Enable collection of metrics in Prometheus format on the admin API |
| `sqlite` | *by default* | Enable using Sqlite3 to store Garage's metadata |
| `syslog` | optional | Enable logging to Syslog with<br>`GARAGE_LOG_TO_SYSLOG=true` environment variable set |
| `system-libs` | optional | Use system version of sqlite3, zstd, lmdb and libsodium<br>if available (exclusive with `bundled-libs`, build using<br>`cargo build --no-default-features --features system-libs`) |
| `telemetry-otlp` | optional | Enable collection of execution traces using OpenTelemetry |
| `sled` | *by default* | Enable using Sled to store Garage's metadata |
| `lmdb` | optional | Enable using LMDB to store Garage's metadata |
| `sqlite` | optional | Enable using Sqlite3 to store Garage's metadata |
If you want to manage the CustomResourceDefinition used by garage for its `kubernetes_discovery` outside of the helm chart, add `garage.kubernetesSkipCrd: true` to your custom values and use the kustomization before deploying the helm chart:
After deploying, cluster layout must be configured manually as described in [Creating a cluster layout](@/documentation/quick-start/_index.md#creating-a-cluster-layout). Use the following command to access garage CLI:
```bash
@@ -40,12 +47,12 @@ All possible configuration values can be found with:
helm show values ./garage
```
This is an example `values.overrride.yaml` for deploying in a microk8s cluster with a https s3 api ingress route:
This is an example `values.override.yaml` for deploying in a microk8s cluster with a https s3 api ingress route:
If your `StorageClass` does not support volume expansion, double check if you can enable it. Otherwise, your only real option is to spin up a new Garage cluster with increased size and migrate all data over.
If your `StorageClass` supports expansion, you are free to continue.
### Increase the size of the PVCs
Increase the size of all PVCs to your desired size.
```bash
kubectl -n garage edit pvc data-garage-0
kubectl -n garage edit pvc data-garage-1
kubectl -n garage edit pvc data-garage-2
kubectl -n garage edit pvc meta-garage-0
kubectl -n garage edit pvc meta-garage-1
kubectl -n garage edit pvc meta-garage-2
```
### Increase the size of the `StatefulSet` PVC template
This is an optional step, but if not done, future instances of Garage will be created with the original size from the template.
This will remove the Garage `StatefulSet` but leave the pods running. It may seem destructive but needs to be done this way since edits to the size of PVC templates are prohibited.
### Redeploy the `StatefulSet`
Now the size of future PVCs can be increased, and the Garage Helm chart can be upgraded. The new `StatefulSet` should take ownership of the orphaned pods again.
@@ -19,14 +19,15 @@ To run a real-world deployment, make sure the following conditions are met:
- You have at least three machines with sufficient storage space available.
- Each machine has a public IP address which is reachable by other machines. It
is highly recommended that you use IPv6 for this end-to-end connectivity. If
IPv6 is not available, then using a mesh VPN such as
- Each machine has an IP address which makes it directly reachable by all other machines.
In many cases, nodes will be behind a NAT and will not each have a public
IPv4 addresses. In this case, is recommended that you use IPv6 for this
end-to-end connectivity if it is available. Otherwise, using a mesh VPN such as
[Nebula](https://github.com/slackhq/nebula) or
[Yggdrasil](https://yggdrasil-network.github.io/) are approaches to consider
in addition to building out your own VPN tunneling.
- This guide will assume you are using Docker containers to deploy Garage on each node.
- This guide will assume you are using Docker containers to deploy Garage on each node.
Garage can also be run independently, for instance as a [Systemd service](@/documentation/cookbook/systemd.md).
You can also use an orchestrator such as Nomad or Kubernetes to automatically manage
Docker containers on a fleet of nodes.
@@ -42,7 +43,7 @@ For our example, we will suppose the following infrastructure with IPv6 connecti
| Brussels | Mars | fc00:F::1 | 1.5 TB |
Note that Garage will **always** store the three copies of your data on nodes at different
locations. This means that in the case of this small example, the available capacity
locations. This means that in the case of this small example, the usable capacity
of the cluster is in fact only 1.5 TB, because nodes in Brussels can't store more than that.
This also means that nodes in Paris and London will be under-utilized.
To make better use of the available hardware, you should ensure that the capacity
@@ -52,9 +53,9 @@ to store 2 TB of data in total.
### Best practices
- If you have fast dedicated networking between all your nodes, and are planing to store
very large files, bump the `block_size` configuration parameter to 10 MB
(`block_size = 10485760`).
- If you have reasonably fast networking between all your nodes, and are planing to store
mostly large files, bump the `block_size` configuration parameter to 10 MB
(`block_size = "10M"`).
- Garage stores its files in two locations: it uses a metadata directory to store frequently-accessed
small metadata items, and a data directory to store data blocks of uploaded objects.
@@ -67,36 +68,42 @@ to store 2 TB of data in total.
EXT4 is not recommended as it has more strict limitations on the number of inodes,
which might cause issues with Garage when large numbers of objects are stored.
-If you only have an HDD and no SSD, it's fine to put your metadata alongside the data
on the same drive. Having lots of RAM for your kernel to cache the metadata will
help a lot with performance. Make sure to use the LMDB database engine,
instead of Sled, which suffers from quite bad performance degradation on HDDs.
Sled is still the default for legacy reasons, but is not recommended anymore.
-Servers with multiple HDDs are supported natively by Garage without resorting
to RAID, see [our dedicated documentation page](@/documentation/operations/multi-hdd.md).
- For the metadata storage, Garage does not do checksumming and integrity
verification on its own. If you are afraid of bitrot/data corruption,
put your metadata directory on a BTRFS partition. Otherwise, just use regular
EXT4 or XFS.
verification on its own, so it is better to use a robust filesystem such as
BTRFS or ZFS. Users have reported that when using the LMDB database engine
(the default), database files have a tendency of becoming corrupted after an
unclean shutdown (e.g. a power outage), so you should take regular snapshots
to be able to recover from such a situation. This can be done using Garage's
built-in automatic snapshotting (since v0.9.4), or by using filesystem level
snapshots. If you cannot do so, you might want to switch to Sqlite which is
more robust.
-Having a single server with several storage drives is currently not very well
supported in Garage ([#218](https://git.deuxfleurs.fr/Deuxfleurs/garage/issues/218)).
For an easy setup, just put all your drives in a RAID0 or a ZFS RAIDZ array.
If you're adventurous, you can try to format each of your disk as
a separate XFS partition, and then run one `garage` daemon per disk drive,
or use something like [`mergerfs`](https://github.com/trapexit/mergerfs) to merge
all your disks in a single union filesystem that spreads load over them.
-LMDB is the fastest and most tested database engine, but it has the following
weaknesses: 1/ data files are not architecture-independent, you cannot simply
move a Garage metadata directory between nodes running different architectures,
and 2/ LMDB is not suited for 32-bit platforms. Sqlite is a viable alternative
if any of these are of concern.
- If you only have an HDD and no SSD, it's fine to put your metadata alongside
the data on the same drive, but then consider your filesystem choice wisely
(see above). Having lots of RAM for your kernel to cache the metadata will
help a lot with performance. The default LMDB database engine is the most
tested and has good performance.
## Get a Docker image
Our docker image is currently named `dxflrs/garage` and is stored on the [Docker Hub](https://hub.docker.com/r/dxflrs/garage/tags?page=1&ordering=last_updated).
We encourage you to use a fixed tag (eg. `v0.8.0`) and not the `latest` tag.
For this example, we will use the latest published version at the time of the writing which is `v0.8.0` but it's up to you
We encourage you to use a fixed tag (eg. `v2.3.0`) and not the `latest` tag.
For this example, we will use the latest published version at the time of the writing which is `v2.3.0` but it's up to you
to check [the most recent versions on the Docker Hub](https://hub.docker.com/r/dxflrs/garage/tags?page=1&ordering=last_updated).
For example:
```
sudo docker pull dxflrs/garage:v0.8.0
docker pull dxflrs/garage:v2.3.0
```
## Deploying and configuring Garage
@@ -119,8 +126,9 @@ A valid `/etc/garage.toml` for our cluster would look as follows:
metadata_dir="/var/lib/garage/meta"
data_dir="/var/lib/garage/data"
db_engine="lmdb"
metadata_auto_snapshot_interval="6h"
replication_mode="3"
replication_factor=3
compression_level=2
@@ -144,6 +152,8 @@ Check the following for your configuration files:
- Make sure `rpc_public_addr` contains the public IP address of the node you are configuring.
This parameter is optional but recommended: if your nodes have trouble communicating with
one another, consider adding it.
Alternatively, you can also set `rpc_public_addr_subnet`, which can filter
the addresses announced to other peers to a specific subnet.
- Make sure `rpc_secret` is the same value on all nodes. It should be a 32-bytes hex-encoded secret key.
You can generate such a key with `openssl rand -hex 32`.
@@ -161,12 +171,13 @@ docker run \
-v /etc/garage.toml:/etc/garage.toml \
-v /var/lib/garage/meta:/var/lib/garage/meta \
-v /var/lib/garage/data:/var/lib/garage/data \
dxflrs/garage:v0.8.0
dxflrs/garage:v2.3.0
```
It should be restarted automatically at each reboot.
Please note that we use host networking as otherwise Docker containers
can not communicate with IPv6.
With this command line, Garage should be started automatically at each boot.
Please note that we use host networking as otherwise the network indirection
added by Docker would prevent Garage nodes from communicating with one another
(especially if using IPv6).
If you want to use `docker-compose`, you may use the following `docker-compose.yml` file as a reference:
@@ -174,7 +185,7 @@ If you want to use `docker-compose`, you may use the following `docker-compose.y
version:"3"
services:
garage:
image:dxflrs/garage:v0.8.0
image:dxflrs/garage:v2.3.0
network_mode:"host"
restart:unless-stopped
volumes:
@@ -183,12 +194,14 @@ services:
- /var/lib/garage/data:/var/lib/garage/data
```
Upgrading between Garage versions should be supported transparently,
but please check the relase notes before doing so!
To upgrade, simply stop and remove this container and
start again the command with a new version of Garage.
If you wish to upgrade your cluster, make sure to read the corresponding
[documentation page](@/documentation/operations/upgrading.md) first, as well as
the documentation relevant to your version of Garage in the case of major
upgrades. With the containerized setup proposed here, the upgrade process
will require stopping and removing the existing container, and re-creating it
with the upgraded version.
## Controling the daemon
## Controlling the daemon
The `garage` binary has two purposes:
- it acts as a daemon when launched with `garage server`
@@ -246,7 +259,7 @@ You can then instruct nodes to connect to one another as follows:
@@ -272,7 +339,7 @@ Add the following configuration section [to compress response](https://doc.traef
### Add caching response
Traefik's caching middleware is only available on [entreprise version](https://doc.traefik.io/traefik-enterprise/middlewares/http-cache/), however the freely-available [Souin plugin](https://github.com/darkweak/souin#tr%C3%A6fik-container) can also do the job. (section to be completed)
Traefik's caching middleware is only available on [enterprise version](https://doc.traefik.io/traefik-enterprise/middlewares/http-cache/), however the freely-available [Souin plugin](https://github.com/darkweak/souin#tr%C3%A6fik-container) can also do the job. (section to be completed)
### Complete example
@@ -472,3 +539,32 @@ https:// {
More information on how this endpoint is implemented in Garage is available
in the [Admin API Reference](@/documentation/reference-manual/admin-api.md) page.
@@ -10,7 +10,7 @@ perspective. It will allow you to understand if Garage is a good fit for
you, how to better use it, how to contribute to it, what can Garage could
and could not do, etc.
- **[Goals and use cases](@/documentation/design/goals.md):** This page explains why Garage was concieved and what practical use cases it targets.
- **[Goals and use cases](@/documentation/design/goals.md):** This page explains why Garage was conceived and what practical use cases it targets.
- **[Related work](@/documentation/design/related-work.md):** This pages presents the theoretical background on which Garage is built, and describes other software storage solutions and why they didn't work for us.
@@ -31,5 +31,3 @@ We love to talk and hear about Garage, that's why we keep a log here:
- [(en, 2021-04-28) Distributed object storage is centralised](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/b1f60579a13d3c5eba7f74b1775c84639ea9b51a/doc/talks/2021-04-28_spirals-team/talk.pdf)
- [(fr, 2020-12-02) Garage : jouer dans la cour des grands quand on est un hébergeur associatif](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/b1f60579a13d3c5eba7f74b1775c84639ea9b51a/doc/talks/2020-12-02_wide-team/talk.pdf)
@@ -15,14 +15,14 @@ The more a user request will require intra-cluster requests to complete, the mor
This is especially true for sequential requests: requests that must wait the result of another request to be sent.
We designed Garage without consensus algorithms (eg. Paxos or Raft) to minimize the number of sequential and parallel requests.
This serie of benchmarks quantifies the impact of this design choice.
This series of benchmarks quantifies the impact of this design choice.
### On a simple simulated network
We start with a controlled environment, all the instances are running on the same (powerful enough) machine.
To control the network latency, we simulate the network with [mknet](https://git.deuxfleurs.fr/trinity-1686a/mknet) (a tool we developped, based on `tc` and the linux network stack).
To mesure S3 endpoints latency, we use our own tool [s3lat](https://git.deuxfleurs.fr/quentin/s3lat/) to observe only the intra-cluster latency and not some contention on the nodes (CPU, RAM, disk I/O, network bandwidth, etc.).
To control the network latency, we simulate the network with [mknet](https://git.deuxfleurs.fr/trinity-1686a/mknet) (a tool we developed, based on `tc` and the linux network stack).
To measure S3 endpoints latency, we use our own tool [s3lat](https://git.deuxfleurs.fr/quentin/s3lat/) to observe only the intra-cluster latency and not some contention on the nodes (CPU, RAM, disk I/O, network bandwidth, etc.).
Compared to other benchmark tools, S3Lat sends only one (small) request at the same time and measures its latency.
We selected 5 standard endpoints that are often in the critical path: ListBuckets, ListObjects, GetObject, PutObject and RemoveObject.
@@ -32,7 +32,7 @@ In this first benchmark, we consider 5 instances that are located in a different
Compared to garage, minio latency drastically increases on 3 endpoints: GetObject, PutObject, RemoveObject.
We suppose that these requests on minio make transactions over Raft, involving 4 sequential requests: 1) sending the message to the leader, 2) having the leader dispatch it to the other nodes, 3) waiting for the confirmation of followers and finally 4) commiting it. With our current configuration, one Raft transaction will take around 400 ms. GetObject seems to correlate to 1 transaction while PutObject and RemoveObject seems to correlate to 2 or 3. Reviewing minio code would be required to confirm this hypothesis.
We suppose that these requests on minio make transactions over Raft, involving 4 sequential requests: 1) sending the message to the leader, 2) having the leader dispatch it to the other nodes, 3) waiting for the confirmation of followers and finally 4) committing it. With our current configuration, one Raft transaction will take around 400 ms. GetObject seems to correlate to 1 transaction while PutObject and RemoveObject seems to correlate to 2 or 3. Reviewing minio code would be required to confirm this hypothesis.
Conversely, garage uses an architecture similar to DynamoDB and never require global cluster coordination to answer a request.
Instead, garage can always contact the right node in charge of the requested data, and can answer in as low as one request in the case of GetObject and PutObject. We also observed that Garage latency, while often lower to minio, is more dispersed: garage is still in beta and has not received any performance optimization yet.
@@ -50,7 +50,7 @@ We plot a similar graph as before:
This new graph is very similar to the one before, neither minio or garage seems to benefit from this new topology, but they also do not suffer from it.
Considering garage, this is expected: nodes in the same DC are put in the same zone, and then data are spread on different zones for data resiliency and availaibility.
Considering garage, this is expected: nodes in the same DC are put in the same zone, and then data are spread on different zones for data resiliency and availability.
Then, in the default mode, requesting data requires to query at least 2 zones to be sure that we have the most up to date information.
These requests will involve at least one inter-DC communication.
In other words, we prioritize data availability and synchronization over raw performances.
@@ -48,7 +48,24 @@ locations. They use Garage themselves for the following tasks:
- As a backup target using `rclone` and `restic`
- In the Drone continuous integration platform to store task logs
The Deuxfleurs Garage cluster is a multi-site cluster currently composed of
9 nodes in 3 physical locations.
### Triplebit
[Triplebit](https://www.triplebit.org) is a non-profit hosting provider and
ISP focused on improving access to privacy-related services. They use
Garage themselves for the following tasks:
- Hosting of their homepage, [privacyguides.org](https://www.privacyguides.org/), and various other static sites
- As a PowerDNS authoritative zone backend through [Lightning Stream](https://doc.powerdns.com/lightningstream/latest/index.html) and [LMDB](https://doc.powerdns.com/authoritative/backends/lmdb.html)
- As a Mastodon media storage backend for [mstdn.party](https://mstdn.party/) and [mstdn.plus](https://mstdn.plus/)
- As a PeerTube storage backend for [neat.tube](https://neat.tube/)
- As a [Matrix media backend](https://github.com/matrix-org/synapse-s3-storage-provider)
Triplebit's Garage cluster is a multi-site cluster currently composed of
@@ -37,7 +37,7 @@ However, Amazon S3 source code is not open but alternatives were proposed.
We identified Minio, Pithos, Swift and Ceph.
Minio/Ceph enforces a total order, so properties similar to a (relaxed) filesystem.
Swift and Pithos are probably the most similar to AWS S3 with their consistent hashing ring.
However Pithos is not maintained anymore. More precisely the company that published Pithos version 1 has developped a second version 2 but has not open sourced it.
However Pithos is not maintained anymore. More precisely the company that published Pithos version 1 has developed a second version 2 but has not open sourced it.
Some tests conducted by the [ACIDES project](https://acides.org/) have shown that Openstack Swift consumes way more resources (CPU+RAM) that we can afford. Furthermore, people developing Swift have not designed their software for geo-distribution.
There were many attempts in research too. I am only thinking to [LBFS](https://pdos.csail.mit.edu/papers/lbfs:sosp01/lbfs.pdf) that was used as a basis for Seafile. But none of them have been effectively implemented yet.
@@ -63,11 +63,11 @@ Due to its industry oriented design, Ceph is also far from being *Simple* to ope
In a certain way, Ceph and MinIO are closer together than they are from Garage or OpenStack Swift.
**[Pithos](https://github.com/exoscale/pithos):**
Pithos has been abandonned and should probably not used yet, in the following we explain why we did not pick their design.
Pithos has been abandoned and should probably not used yet, in the following we explain why we did not pick their design.
Pithos was relying as a S3 proxy in front of Cassandra (and was working with Scylla DB too).
From its designers' mouth, storing data in Cassandra has shown its limitations justifying the project abandonment.
They built a closed-source version 2 that does not store blobs in the database (only metadata) but did not communicate further on it.
We considered there v2's design but concluded that it does not fit both our *Self-contained & lightweight* and *Simple* properties. It makes the development, the deployment and the operations more complicated while reducing the flexibility.
We considered their v2's design but concluded that it does not fit both our *Self-contained & lightweight* and *Simple* properties. It makes the development, the deployment and the operations more complicated while reducing the flexibility.
If you use submodules in your crate (like `crdt` and `replication` in `garage_table`), you must list them in `default.nix`
The Windows target does not work. it might be solvable through [overrides](https://github.com/cargo2nix/cargo2nix/blob/master/overlay/overrides.nix). Indeed, we pass `x86_64-pc-windows-gnu` but mingw need `x86_64-w64-mingw32`
We have a simple [PR on cargo2nix](https://github.com/cargo2nix/cargo2nix/pull/201) that fixes critical bugs but the project does not seem very active currently. We must use [my patched version of cargo2nix](https://github.com/superboum/cargo2nix) to enable i686 and armv6l compilation. We might need to contribute to cargo2nix in the future.
## Nix
Nix has no armv7 + musl toolchains but armv7l is backward compatible with armv6l.
Garage automatically resyncs all entries stored in the metadata tables every hour,
@@ -113,5 +141,7 @@ blocks may still be held by Garage. If you suspect that such corruption has occu
in your cluster, you can run one of the following repair procedures:
- `garage repair versions`: checks that all versions belong to a non-deleted object, and purges any orphan version
- `garage repair block_refs`: checks that all block references belong to a non-deleted object version, and purges any orphan block reference (this will then allow the blocks to be garbage-collected)
- `garage repair block-refs`: checks that all block references belong to a non-deleted object version, and purges any orphan block reference (this will then allow the blocks to be garbage-collected)
- `garage repair block-rc`: checks that the reference counters for blocks are in sync with the actual number of non-deleted entries in the block reference table
@@ -9,18 +9,30 @@ a certain capacity, or a gateway node that does not store data and is only
used as an API entry point for faster cluster access.
An introduction to building cluster layouts can be found in the [production deployment](@/documentation/cookbook/real-world.md) page.
In Garage, all of the data that can be stored in a given cluster is divided
into slices which we call *partitions*. Each partition is stored by
one or several nodes in the cluster
(see [`replication_factor`](@/documentation/reference-manual/configuration.md#replication_factor)).
The layout determines the correspondence between these partitions,
which exist on a logical level, and actual storage nodes.
## How cluster layouts work in Garage
In Garage, a cluster layout is composed of the following components:
A cluster layout is composed of the following components:
- a table of roles assigned to nodes
- a table of roles assigned to nodes, defined by the user
- an optimal assignation of partitions to nodes, computed by an algorithm that is ran once when calling `garage layout apply` or the ApplyClusterLayout API endpoint
- a version number
Garage nodes will always use the cluster layout with the highest version number.
Garage nodes also maintain and synchronize between them a set of proposed role
changes that haven't yet been applied. These changes will be applied (or
canceled) in the next version of the layout
canceled) in the next version of the layout.
All operations on the layout can be realized using the `garage` CLI or using the
[administration API endpoint](@/documentation/reference-manual/admin-api.md).
We give here a description of CLI commands, the admin API semantics are very similar.
The following commands insert modifications to the set of proposed role changes
for the next layout version (but they do not create the new layout immediately):
@@ -51,7 +63,7 @@ commands will fail otherwise.
## Warnings about Garage cluster layout management
**Warning: never make several calls to `garage layout apply` or `garage layout
**⚠️ Never make several calls to `garage layout apply` or `garage layout
revert` with the same value of the `--version` flag. Doing so can lead to the
creation of several different layouts with the same version number, in which
case your Garage cluster will become inconsistent until fixed.** If a call to
@@ -65,13 +77,198 @@ shell, you shouldn't have much issues as long as you run commands one after
the other and take care of checking the output of `garage layout show`
before applying any changes.
If you are using the `garage` CLI to script layout changes, follow the following recommendations:
If you are using the `garage` CLI or the admin API to script layout changes,
follow the following recommendations:
- Make all of your `garage` CLI calls to the same RPC host. Do not use the
`garage` CLI to connect to individual nodes to send them each a piece of the
layout changes you are making, as the changes propagate asynchronously
between nodes and might not all be taken into account at the time when the
new layout is applied.
- If using the CLI, make all of your `garage` CLI calls to the same RPC host.
If using the admin API, make all of your API calls to the same Garage node. Do
not connect to individual nodes to send them each a piece of the layout changes
you are making, as the changes propagate asynchronously between nodes and might
not all be taken into account at the time when the new layout is applied.
- **Only call `garage layout apply` once**, and call it**strictly after** all
of the `layout assign` and `layout remove` commands have returned.
- **Only call `garage layout apply`/ApplyClusterLayout once**, and call it
**strictly after** all of the `layout assign` and `layout remove`
commands/UpdateClusterLayout API calls have returned.
## Understanding unexpected layout calculations
When adding, removing or modifying nodes in a cluster layout, sometimes
unexpected assignations of partitions to node can occur. These assignations
are in fact normal and logical, given the objectives of the algorithm. Indeed,
**the layout algorithm prioritizes moving less data between nodes over
achieving equal distribution of load. It also tries to use all links between
pairs of nodes in equal proportions when moving data.** This section presents
two examples and illustrates how one can control Garage's behavior to obtain
the desired results.
### Example 1
In this example, a cluster is originally composed of 3 nodes in 3 different
zones (data centers). The three nodes are of equal capacity, therefore they
are all fully exploited and all store a copy of all of the data in the cluster.
Then, a fourth node of the same size is added in the datacenter `dc1`.
As illustrated by the following, **Garage will by default not store any data on the new node**:
For admin API calls that take both query parameters and a JSON body, combine them in the following fashion:
```
$ garage json-api UpdateAdminToken '{"id":"b0e6e0ace2c0b2aca4cdb2de", "body":{"name":"not a test"}}'
```
## Special administration API endpoints
### Metrics `GET /metrics`
@@ -80,13 +209,13 @@ content-length: 102
date: Tue, 08 Aug 2023 07:22:38 GMT
Garage is fully operational
Consult the full health check API endpoint at /v0/health for more details
Consult the full health check API endpoint at /v2/GetClusterHealth for more details
```
### On-demand TLS `GET /check`
To prevent abuses for on-demand TLS, Caddy developpers have specified an endpoint that can be queried by the reverse proxy
to know if a given domain is allowed to get a certificate. Garage implements this endpoints to tell if a given domain is handled by Garage or is garbage.
To prevent abuse for on-demand TLS, Caddy developers have specified an endpoint that can be queried by the reverse proxy
to know if a given domain is allowed to get a certificate. Garage implements these endpoints to tell if a given domain is handled by Garage or is garbage.
Garage responds with the following logic:
- If the domain matches the pattern `<bucket-name>.<s3_api.root_domain>`, returns 200 OK
@@ -99,7 +228,7 @@ You must manually declare the domain in your reverse-proxy. Idem for K2V.*
*Note 2: buckets in a user's namespace are not supported yet by this endpoint. This is a limitation of this endpoint currently.*
**Example:** Suppose a Garage instance configured with `s3_api.root_domain = .s3.garage.localhost` and `s3_web.root_domain = .web.garage.localhost`.
**Example:** Suppose a Garage instance is configured with `s3_api.root_domain = .s3.garage.localhost` and `s3_web.root_domain = .web.garage.localhost`.
With a private `media` bucket (name in the global namespace, website is disabled), the endpoint will feature the following behavior:
- [Add option for a backend check to approve use of on-demand TLS](https://github.com/caddyserver/caddy/pull/1939)
- [Serving tens of thousands of domains over HTTPS with Caddy](https://caddy.community/t/serving-tens-of-thousands-of-domains-over-https-with-caddy/11179)
### Cluster operations
These endpoints are defined on a dedicated [Redocly page](https://garagehq.deuxfleurs.fr/api/garage-admin-v0.html). You can also download its [OpenAPI specification](https://garagehq.deuxfleurs.fr/api/garage-admin-v0.yml).
Requesting the API from the command line can be as simple as running:
and the hashes of individual blocks are used to dispatch them to storage nodes
and to deduplicate them.
### No RAFT slowing you down
It might seem strange to tout the absence of something as a desirable feature,
@@ -46,14 +61,7 @@ directed to a Garage cluster can be handled independently of one another instead
of going through a central bottleneck (the leader node).
As a consequence, requests can be handled much faster, even in cases where latency
between cluster nodes is important (see our [benchmarks](@/documentation/design/benchmarks/index.md) for data on this).
This is particularly usefull when nodes are far from one another and talk to one other through standard Internet connections.
### Several replication modes
Garage supports a variety of replication modes, with 1 copy, 2 copies or 3 copies of your data,
and with various levels of consistency, in order to adapt to a variety of usage scenarios.
Read our reference page on [supported replication modes](@/documentation/reference-manual/configuration.md#replication-mode)
to select the replication mode best suited to your use case (hint: in most cases, `replication_mode = "3"` is what you want).
This is particularly useful when nodes are far from one another and talk to one other through standard Internet connections.
### Web server for static websites
@@ -76,13 +84,13 @@ exposing the same content under different domain names.
Garage also supports bucket aliases which are local to a single user:
this allows different users to have different buckets with the same name, thus avoiding naming collisions.
This can be helpfull for instance if you want to write an application that creates per-user buckets with always the same name.
This can be helpful for instance if you want to write an application that creates per-user buckets with always the same name.
This feature is totally invisible to S3 clients and does not break compatibility with AWS.
### Cluster administration API
Garage provides a fully-fledged REST API to administer your cluster programatically.
Garage provides a fully-fledged REST API to administer your cluster programmatically.
Functionality included in the admin API include: setting up and monitoring
cluster nodes, managing access credentials, and managing storage buckets and bucket aliases.
A full reference of the administration API is available [here](@/documentation/reference-manual/admin-api.md).
@@ -92,7 +100,7 @@ A full reference of the administration API is available [here](@/documentation/r
Garage makes some internal metrics available in the Prometheus data format,
which allows you to build interactive dashboards to visualize the load and internal state of your storage cluster.
For developpers and performance-savvy administrators,
For developers and performance-savvy administrators,
Garage also supports exporting traces of what it does internally in OpenTelemetry format.
This allows to monitor the time spent at various steps of the processing of requests,
in order to detect potential performance bottlenecks.
@@ -121,5 +129,5 @@ related to objects stored in an S3 bucket.
In the context of our research project, [Aérogramme](https://aerogramme.deuxfleurs.fr),
K2V is used to provide metadata and log storage for operations on encrypted e-mail storage.
Learn more on the specification of K2V [here](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/branch/k2v/doc/drafts/k2v-spec.md)
Learn more on the specification of K2V [here](https://git.deuxfleurs.fr/Deuxfleurs/garage/src/commit/f8be15c37db857e177d543de7be863692628d567/doc/drafts/k2v-spec.md)
and on how to enable it in Garage [here](@/documentation/reference-manual/k2v.md).
In case of disconnected nodes, when changing the cluster layout to remove these
nodes and add other nodes instead, Garage might not be able to properly evict
the old nodes from the system. This is a built-in security measure to avoid any
inconsistent cluster states.
This manifests by several cluster layout versions staying active even after a
full resync. You can diagnose this situation with `garage layout history`,
which will give you instructions to fix it.
### Tag assignment
In the `garage layout assign` command, the `-t` argument has to be repeated
multiple times to set multiple tags on a node. Writing multiple tags separated
by commas will result in a single string.
## General footguns
Choices made by the developers that users must be aware of if they don't want
to run into potential issues.
### Resync tranquility is conservative by default
By default, the worker parameters `resync-tranquility` and `resync-worker-count` are set to very conservative values, to avoid overloading nodes with I/O when data needs to be resynchronized between nodes.
This can cause issues where the resync queue grows faster than it can be cleared, which in turn causes performance issues in the rest of Garage.
This situation is indicated by a big resync queue with few resync errors (the queue is not caused by a disconnected/malfunctionning node).
To fix it, increase the number of resync workers and reduce the resync tranquility. For instance, if you want to resync as fast as possible:
@@ -42,7 +42,7 @@ The general principle are similar, but details have not been updated.**
A version is defined by the existence of at least one entry in the blocks table for a certain version UUID.
We must keep the following invariant: if a version exists in the blocks table, it has to be referenced in the objects table.
We explicitly manage concurrent versions of an object: the version timestamp and version UUID columns are index columns, thus we may have several concurrent versions of an object.
Important: before deleting an older version from the objects table, we must make sure that we did a successfull delete of the blocks of that version from the blocks table.
Important: before deleting an older version from the objects table, we must make sure that we did a successful delete of the blocks of that version from the blocks table.
Thus, the workflow for reading an object is as follows:
@@ -68,7 +68,7 @@ Workflow for DELETE:
1. Check write permission (LDAP)
2. Get current version (or versions) in object table
3. Do the deletion of those versions NOT IN A BACKGROUND JOB THIS TIME
4. Return succes to the user if we were able to delete blocks from the blocks table and entries from the object table
4. Return success to the user if we were able to delete blocks from the blocks table and entries from the object table
To delete a version:
@@ -92,10 +92,10 @@ Known issue: if someone is reading from a version that we want to delete and the
- file path = /meta/(first 3 hex digits of hash)/(rest of hash)
- map block hash -> set of version UUIDs where it is referenced
Usefull metadata:
Useful metadata:
- list of versions that reference this block in the Casandra table, so that we can do GC by checking in Cassandra that the lines still exist
- list of other nodes that we know have acknowledged a write of this block, usefull in the rebalancing algorithm
- list of other nodes that we know have acknowledged a write of this block, useful in the rebalancing algorithm
Write strategy: have a single thread that does all write IO so that it is serialized (or have several threads that manage independent parts of the hash space). When writing a blob, write it to a temporary file, close, then rename so that a concurrent read gets a consistent result (either not found or found with whole content).
**This guide explains how to migrate to 0.9 if you have an existing 0.8 cluster.
We don't recommend trying to migrate to 0.9 directly from 0.7 or older.**
This migration procedure has been tested on several clusters without issues.
However, it is still a *critical procedure* that might cause issues.
**Make sure to back up all your data before attempting it!**
You might also want to read our [general documentation on upgrading Garage](@/documentation/operations/upgrading.md).
The following are **breaking changes** in Garage v0.9 that require your attention when migrating:
- LMDB is now the default metadata db engine and Sled is deprecated. If you were using Sled, make sure to specify `db_engine = "sled"` in your configuration file, or take the time to [convert your database](https://garagehq.deuxfleurs.fr/documentation/reference-manual/configuration/#db-engine-since-v0-8-0).
- Capacity values are now in actual byte units. The translation from the old layout will assign 1 capacity = 1Gb by default, which might be wrong for your cluster. This does not cause any data to be moved around, but you might want to re-assign correct capacity values post-migration.
- Multipart uploads that were started in Garage v0.8 will not be visible in Garage v0.9 and will have to be restarted from scratch.
- Changes to the admin API: some `v0/` endpoints have been replaced by `v1/` counterparts with updated/uniformized syntax. All other endpoints have also moved to `v1/` by default, without syntax changes, but are still available under `v0/` for compatibility.
## Simple migration procedure (takes cluster offline for a while)
The migration steps are as follows:
1. Disable API and web access. You may do this by stopping your reverse proxy or by commenting out
the `api_bind_addr` values in your `config.toml` file and restarting Garage.
2. Do `garage repair --all-nodes --yes tables` and `garage repair --all-nodes --yes blocks`,
check the logs and check that all data seems to be synced correctly between
nodes. If you have time, do additional checks (`versions`, `block_refs`, etc.)
3. Check that the block resync queue and Merkle queue are empty:
run `garage stats -a` to query them or inspect metrics in the Grafana dashboard.
4. Turn off Garage v0.8
5. **Backup the metadata folder of all your nodes!** For instance, use the following command
if your metadata directory is `/var/lib/garage/meta`: `cd /var/lib/garage ; tar -acf meta-v0.8.tar.zst meta/`
6. Install Garage v0.9
7. Update your configuration file if necessary.
8. Turn on Garage v0.9
9. Do `garage repair --all-nodes --yes tables` and `garage repair --all-nodes --yes blocks`.
Wait for a full table sync to run.
10. Your upgraded cluster should be in a working state. Re-enable API and Web
access and check that everything went well.
11. Monitor your cluster in the next hours to see if it works well under your production load, report any issue.
12. You might want to assign correct capacity values to all your nodes. Doing so might cause data to be moved
in your cluster, which should also be monitored carefully.
## Minimal downtime migration procedure
The migration to Garage v0.9 can be done with almost no downtime,
by restarting all nodes at once in the new version.
The migration steps are as follows:
1. Do `garage repair --all-nodes --yes tables` and `garage repair --all-nodes --yes blocks`,
check the logs and check that all data seems to be synced correctly between
nodes. If you have time, do additional checks (`versions`, `block_refs`, etc.)
2. Turn off each node individually; back up its metadata folder (see above); turn it back on again.
This will allow you to take a backup of all nodes without impacting global cluster availability.
You can do all nodes of a single zone at once as this does not impact the availability of Garage.
3. Prepare your binaries and configuration files for Garage v0.9
4. Shut down all v0.8 nodes simultaneously, and restart them all simultaneously in v0.9.
Use your favorite deployment tool (Ansible, Kubernetes, Nomad) to achieve this as fast as possible.
Garage v0.9 should be in a working state as soon as it starts.
5. Proceed with repair and monitoring as described in steps 9-12 above.
@@ -28,16 +28,16 @@ We should try to test in least invasive ways, i.e. minimize the impact of the te
- Not making `garage` a shared library (launch using `execve`, it's perfectly fine)
Instead, we should focus on building a clean outer interface for the `garage` binary,
for example loading configuration using environnement variables instead of the configuration file if that's helpfull for writing the tests.
for example loading configuration using environment variables instead of the configuration file if that's helpful for writing the tests.
There are two reasons for this:
- Keep the soure code clean and focused
- Keep the source code clean and focused
- Test something that is as close as possible as the true garage that will actually be running
Reminder: rules of simplicity, concerning changes to Garage's source code.
Always question what we are doing.
Never do anything just because it looks nice or because we "think" it might be usefull at some later point but without knowing precisely why/when.
Never do anything just because it looks nice or because we "think" it might be useful at some later point but without knowing precisely why/when.
Only do things that make perfect sense in the context of what we currently know.
## References
@@ -71,5 +71,3 @@ Interesting blog posts on the blog of the Sled database:
Misc:
- [mutagen](https://github.com/llogiq/mutagen) - mutation testing is a way to assert our test quality by mutating the code and see if the mutation makes the tests fail
- [fuzzing](https://rust-fuzz.github.io/book/) - cargo supports fuzzing, it could be a way to test our software reliability in presence of garbage data.
Garage\footnote{\url{https://garagehq.deuxfleurs.fr/}} is an open-source distributed object storage service tailored for self-hosting. It was designed by the Deuxfleurs association\footnote{\url{https://deuxfleurs.fr/}} to enable small structures (associations, collectives, small companies) to share storage resources to reliably self-host their data, possibly with old and non-reliable machines.
To achieve these reliability and availability goals, the data is broken into \emph{partitions} and every partition is replicated over 3 different machines (that we call \emph{nodes}). When the data is queried, a consensus algorithm allows to fetch it from one of the nodes. A \emph{replication factor} of 3 ensures the best guarantees in the consensus algorithm \cite{ADD RREF}, but this parameter can be different.
Moreover, if the nodes are spread over different \emph{zones} (different houses, offices, cities\dots), we can ask the data to be replicated over nodes belonging to different zones, to improve the storage robustness against zone failure (such as power outage). To do so, we set a \emph{redundancy parameter}, that is no more than the replication factor, and we ask that any partition is replicated over this number of zones at least.
In this work, we propose a repartition algorithm that, given the nodes specifications and the replication and redundancy parameters, computes an optimal assignation of partitions to nodes. We say that the assignation is optimal in the sense that it maximizes the size of the partitions, and hence the effective storage capacity of the system.
Moreover, when a former assignation exists, which is not optimal anymore due to nodes or zones updates, our algorithm computes a new optimal assignation that minimizes the amount of data to be transferred during the assignation update (the \emph{transfer load}).
We call the set of nodes cooperating to store the data a \emph{cluster}, and a description of the nodes, zones and the assignation of partitions to nodes a \emph{cluster layout}
\subsection{Notations}
Let $k$ be some fixed parameter value, typically 8, that we call the ``partition bits''.
Every object to be stored in the system is split into data blocks of fixed size. We compute a hash $h(\mathbf{b})$ of every such block $\mathbf{b}$, and we define the $k$ last bits of this hash to be the partition number $p(\mathbf{b})$ of the block. This label can take $P=2^k$ different values, and hence there are $P$ different partitions. We denote $\mathbf{P}$ the set of partition labels (i.e. $\mathbf{P}=\llbracket1,P\rrbracket$).
We are given a set $\mathbf{N}$ of $N$ nodes and a set $\mathbf{Z}$ of $Z$ zones. Every node $n$ has a non-negative storage capacity $c_n\ge0$ and belongs to a zone $z_n\in\mathbf{Z}$. We are also given a replication parameter $\rho_\mathbf{N}$ and a redundancy parameter $\rho_\mathbf{Z}$ such that $1\le\rho_\mathbf{Z}\le\rho_\mathbf{N}$ (typical values would be $\rho_N=3$ and $\rho_Z=2$).
Our goal is to compute an assignment $\alpha=(\alpha_p^1, \ldots, \alpha_p^{\rho_\mathbf{N}})_{p\in\mathbf{P}}$ such that every partition $p$ is associated to $\rho_\mathbf{N}$ distinct nodes $\alpha_p^1, \ldots, \alpha_p^{\rho_\mathbf{N}}\in\mathbf{N}$ and these nodes belong to at least $\rho_\mathbf{Z}$ distinct zones. Among the possible assignations, we choose one that \emph{maximizes} the effective storage capacity of the cluster. If the layout contained a previous assignment $\alpha'$, we \emph{minimize} the amount of data to transfer during the layout update by making $\alpha$ as close as possible to $\alpha'$. These maximization and minimization are described more formally in the following section.
\subsection{Optimization parameters}
To link the effective storage capacity of the cluster to partition assignment, we make the following assumption:
\begin{equation}
\tag{H1}
\text{\emph{All partitions have the same size $s$.}}
\end{equation}
This assumption is justified by the dispersion of the hashing function, when the number of partitions is small relative to the number of stored blocks.
Every node $n$ will store some number $p_n$ of partitions (it is the number of partitions $p$ such that $n$ appears in the $\alpha_p$). Hence the partitions stored by $n$ (and hence all partitions by our assumption) have there size bounded by $c_n/p_n$. This remark leads us to define the optimal size that we will want to maximize:
\begin{equation}
\label{eq:optimal}
\tag{OPT}
s^* = \min_{n \in N}\frac{c_n}{p_n}.
\end{equation}
When the capacities of the nodes are updated (this includes adding or removing a node), we want to update the assignment as well. However, transferring the data between nodes has a cost and we would like to limit the number of changes in the assignment. We make the following assumption:
\begin{equation}
\tag{H2}
\text{\emph{Nodes updates happen rarely relatively to block operations.}}
\end{equation}
This assumption justifies that when we compute the new assignment $\alpha$, it is worth to optimize the partition size \eqref{eq:optimal} first, and then, among the possible optimal solution, to try to minimize the number of partition transfers. More formally, we minimize the distance between two assignments defined by
where the symmetric difference $\alpha_p \triangle\alpha'_p$ denotes the nodes appearing in one of the assignations but not in both.
\section{Computation of an optimal assignment}
The algorithm that we propose takes as inputs the cluster layout parameters $\mathbf{N}$, $\mathbf{Z}$, $\mathbf{P}$, $(c_n)_{n\in\mathbf{N}}$, $\rho_\mathbf{N}$, $\rho_\mathbf{Z}$, that we defined in the introduction, together with the former assignation $\alpha'$ (if any). The computation of the new optimal assignation $\alpha^*$ is done in three successive steps that will be detailed in the following sections. The first step computes the largest partition size $s^*$ that an assignation can achieve. The second step computes an optimal candidate assignment $\alpha$ that achieves $s^*$ and a heuristic is used in the computation to make it hopefully close to $\alpha'$. The third steps modifies $\alpha$ iteratively to reduces $d(\alpha, \alpha')$ and yields an assignation $\alpha^*$ achieving $s^*$, and minimizing $d(\cdot, \alpha')$ among such assignations.
We will explain in the next section how to represent an assignment $\alpha$ by a flow $f$ on a weighted graph $G$ to enable the use of flow and graph algorithms. The main function of the algorithm can be written as follows.
\State$f^*\leftarrow$\Call{Minimize transfer load}{$G$, $f$, $\alpha'$}
\State Build $\alpha^*$ from $f^*$
\State\Return$\alpha^*$
\EndFunction
\end{algorithmic}
\subsubsection*{Complexity}
As we will see in the next sections, the worst case complexity of this algorithm is $O(P^2 N^2)$. The minimization of transfer load is the most expensive step, and it can run with a timeout since it is only an optimization step. Without this step (or with a smart timeout), the worst cas complexity can be $O((PN)^{3/2}\log C)$ where $C$ is the total storage capacity of the cluster.
\subsection{Determination of the partition size $s^*$}
We will represent an assignment $\alpha$ as a flow in a specific graph $G$. We will not compute the optimal partition size $s^*$ a priori, but we will determine it by dichotomy, as the largest size $s$ such that the maximal flow achievable on $G=G(s)$ has value $\rho_\mathbf{N}P$. We will assume that the capacities are given in a small enough unit (say, Megabytes), and we will determine $s^*$ at the precision of the given unit.
Given some candidate size value $s$, we describe the oriented weighted graph $G=(V,E)$ with vertex set $V$ arc set $E$ (see Figure \ref{fig:flowgraph}).
The set of vertices $V$ contains the source $\mathbf{s}$, the sink $\mathbf{t}$, vertices
$\mathbf{p^+, p^-}$ for every partition $p$, vertices $\mathbf{x}_{p,z}$ for every partition $p$ and zone $z$, and vertices $\mathbf{n}$ for every node $n$.
The set of arcs $E$ contains:
\begin{itemize}
\item ($\mathbf{s}$,$\mathbf{p}^+$, $\rho_\mathbf{Z}$) for every partition $p$;
\item ($\mathbf{s}$,$\mathbf{p}^-$, $\rho_\mathbf{N}-\rho_\mathbf{Z}$) for every partition $p$;
\item ($\mathbf{p}^+$,$\mathbf{x}_{p,z}$, 1) for every partition $p$ and zone $z$;
\item ($\mathbf{p}^-$,$\mathbf{x}_{p,z}$, $\rho_\mathbf{N}-\rho_\mathbf{Z}$) for every partition $p$ and zone $z$;
\item ($\mathbf{x}_{p,z}$,$\mathbf{n}$, 1) for every partition $p$, zone $z$ and node $n\in z$;
\item ($\mathbf{n}$, $\mathbf{t}$, $\lfloor c_n/s \rfloor$) for every node $n$.
\caption{An example of graph $G(s)$. Arcs are oriented from left to right, and unlabeled arcs have capacity 1. In this example, nodes $n_1,n_2,n_3$ belong to zone $z_1$, and nodes $n_4,n_5$ belong to zone $z_2$.}
\label{fig:flowgraph}
\end{figure}
In the following complexity calculations, we will use the number of vertices and edges of $G$. Remark from now that $\# V = O(PZ)$ and $\# E = O(PN)$.
\begin{proposition}
An assignment $\alpha$ is realizable with partition size $s$ and the redundancy constraints $(\rho_\mathbf{N},\rho_\mathbf{Z})$ if and only if there exists a maximal flow function $f$ in $G$ with total flow $\rho_\mathbf{N}P$, such that the arcs ($\mathbf{x}_{p,z}$,$\mathbf{n}$, 1) used are exactly those for which $p$ is associated to $n$ in $\alpha$.
\end{proposition}
\begin{proof}
Given such flow $f$, we can reconstruct a candidate $\alpha$. In $f$, the flow passing through $\mathbf{p^+}$ and $\mathbf{p^-}$ is $\rho_\mathbf{N}$, and since the outgoing capacity of every $\mathbf{x}_{p,z}$ is 1, every partition is associated to $\rho_\mathbf{N}$ distinct nodes. The fraction $\rho_\mathbf{Z}$ of the flow passing through every $\mathbf{p^+}$ must be spread over as many distinct zones as every arc outgoing from $\mathbf{p^+}$ has capacity 1. So the reconstructed $\alpha$ verifies the redundancy constraints. For every node $n$, the flow between $\mathbf{n}$ and $\mathbf{t}$ corresponds to the number of partitions associated to $n$. By construction of $f$, this does not exceed $\lfloor c_n/s \rfloor$. We assumed that the partition size is $s$, hence this association does not exceed the storage capacity of the nodes.
In the other direction, given an assignment $\alpha$, one can similarly check that the facts that $\alpha$ respects the redundancy constraints, and the storage capacities of the nodes, are necessary condition to construct a maximal flow function $f$.
\end{proof}
\textbf{Implementation remark:} In the flow algorithm, while exploring the graph, we explore the neighbours of every vertex in a random order to heuristically spread the associations between nodes and partitions.
\subsubsection*{Algorithm}
With this result mind, we can describe the first step of our algorithm. All divisions are supposed to be integer divisions.
To compute the maximal flow, we use Dinic's algorithm. Its complexity on general graphs is $O(\#V^2\#E)$, but on graphs with edge capacity bounded by a constant, it turns out to be $O(\#E^{3/2})$. The graph $G$ does not fall in this case since the capacities of the arcs incoming to $\mathbf{t}$ are far from bounded. However, the proof of this complexity function works readily for graphs where we only ask the edges \emph{not} incoming to the sink $\mathbf{t}$ to have their capacities bounded by a constant. One can find the proof of this claim in \cite[Section 2]{even1975network}.
The dichotomy adds a logarithmic factor $\log(C)$ where $C=\sum_{n \in\mathbf{N}} c_n$ is the total capacity of the cluster. The total complexity of this first function is hence
$O(\#E^{3/2}\log C )= O\big((PN)^{3/2}\log C\big)$.
\subsubsection*{Metrics}
We can display the discrepancy between the computed $s^*$ and the best size we could have hoped for the given total capacity, that is $C/\rho_\mathbf{N}$.
\subsection{Computation of a candidate assignment}
Now that we have the optimal partition size $s^*$, to compute a candidate assignment it would be enough to compute a maximal flow function $f$ on $G(s^*)$. This is what we do if there is no former assignation $\alpha'$.
If there is some $\alpha'$, we add a step that will heuristically help to obtain a candidate $\alpha$ closer to $\alpha'$. We fist compute a flow function $\tilde{f}$ that uses only the partition-to-node associations appearing in $\alpha'$. Most likely, $\tilde{f}$ will not be a maximal flow of $G(s^*)$. In Dinic's algorithm, we can start from a non maximal flow function and then discover improving paths. This is what we do by starting from $\tilde{f}$. The hope\footnote{This is only a hope, because one can find examples where the construction of $f$ from $\tilde{f}$ produces an assignment $\alpha$ that is not as close as possible to $\alpha'$.} is that the final flow function $f$ will tend to keep the associations appearing in $\tilde{f}$.
More formally, we construct the graph $G_{|\alpha'}$ from $G$ by removing all the arcs $(\mathbf{x}_{p,z},\mathbf{n}, 1)$ where $p$ is not associated to $n$ in $\alpha'$. We compute a maximal flow function $\tilde{f}$ in $G_{|\alpha'}$. The flow $\tilde{f}$ is also a valid (most likely non maximal) flow function on $G$. We compute a maximal flow function $f$ on $G$ by starting Dinic's algorithm on $\tilde{f}$.
\State$ f \leftarrow$\Call{Maximal flow from flow}{$G$, $\tilde{f}$}
\State\Return$f$
\EndFunction
\end{algorithmic}
~
\textbf{Remark:} The function ``Maximal flow'' can be just seen as the function ``Maximal flow from flow'' called with the zero flow function as starting flow.
\subsubsection*{Complexity}
With the considerations of the last section, we have the complexity of the Dinic's algorithm $O(\#E^{3/2})= O((PN)^{3/2})$.
\subsubsection*{Metrics}
We can display the flow value of $\tilde{f}$, which is an upper bound of the distance between $\alpha$ and $\alpha'$. It might be more a Debug level display than Info.
\subsection{Minimization of the transfer load}
Now that we have a candidate flow function $f$, we want to modify it to make its corresponding assignation $\alpha$ as close as possible to $\alpha'$. Denote by $f'$ the maximal flow corresponding to $\alpha'$, and let $d(f, \alpha')=d(f, f'):=d(\alpha,\alpha')$\footnote{It is the number of arcs of type $(\mathbf{x}_{p,z},\mathbf{n})$ saturated in one flow and not in the other.}.
We want to build a sequence $f=f_0, f_1, f_2\dots$ of maximal flows such that $d(f_i, \alpha')$ decreases as $i$ increases. The distance being a non-negative integer, this sequence of flow functions must be finite. We now explain how to find some improving $f_{i+1}$ from $f_i$.
For any maximal flow $f$ in $G$, we define the oriented weighted graph $G_f=(V, E_f)$ as follows. The vertices of $G_f$ are the same as the vertices of $G$. $E_f$ contains the arc $(v_1,v_2, w)$ between vertices $v_1,v_2\in V$ with weight $w$ if and only if the arc $(v_1,v_2)$ is not saturated in $f$ (i.e. $c(v_1,v_2)-f(v_1,v_2)\ge1$, we also consider reversed arcs). The weight $w$ is:
\begin{itemize}
\item$-1$ if $(v_1,v_2)$ is of type $(\mathbf{x}_{p,z},\mathbf{n})$ or $(\mathbf{x}_{p,z},\mathbf{n})$ and is saturated in only one of the two flows $f,f'$;
\item$+1$ if $(v_1,v_2)$ is of type $(\mathbf{x}_{p,z},\mathbf{n})$ or $(\mathbf{x}_{p,z},\mathbf{n})$ and is saturated in either both or none of the two flows $f,f'$;
\item$0$ otherwise.
\end{itemize}
If $\gamma$ is a simple cycle of arcs in $G_f$, we define its weight $w(\gamma)$ as the sum of the weights of its arcs. We can add $+1$ to the value of $f$ on the arcs of $\gamma$, and by construction of $G_f$ and the fact that $\gamma$ is a cycle, the function that we get is still a valid flow function on $G$, it is maximal as it has the same flow value as $f$. We denote this new function $f+\gamma$.
\begin{proposition}
Given a maximal flow $f$ and a simple cycle $\gamma$ in $G_f$, we have $d(f+\gamma, f')- d(f,f')= w(\gamma)$.
\end{proposition}
\begin{proof}
Let $X$ be the set of arcs of type $(\mathbf{x}_{p,z},\mathbf{n})$. Then we can express $d(f,f')$ as
Remark that since we passed on unit of flow in $\gamma$ to construct $f+\gamma$, we have for any $e\in X$, $f(e)=f'(e)$ if and only if $(f+\gamma)(e)\neq f'(e)$.
Plugging this in the previous equation, we find that
$$d(f,f')+w(\gamma)= d(f+\gamma, f').$$
\end{proof}
This result suggests that given some flow $f_i$, we just need to find a negative cycle $\gamma$ in $G_{f_i}$ to construct $f_{i+1}$ as $f_i+\gamma$. The following proposition ensures that this greedy strategy reaches an optimal flow.
\begin{proposition}
For any maximal flow $f$, $G_f$ contains a negative cycle if and only if there exists a maximal flow $f^*$ in $G$ such that $d(f^*, f') < d(f, f')$.
\end{proposition}
\begin{proof}
Suppose that there is such flow $f^*$. Define the oriented multigraph $M_{f,f^*}=(V,E_M)$ with the same vertex set $V$ as in $G$, and for every $v_1,v_2\in V$, $E_M$ contains $(f^*(v_1,v_2)- f(v_1,v_2))_+$ copies of the arc $(v_1,v_2)$. For every vertex $v$, its total degree (meaning its outer degree minus its inner degree) is equal to
The last two sums are zero for any inner vertex since $f,f^*$ are flows, and they are equal on the source and sink since the two flows are both maximal and have hence the same value. Thus, $\deg v =0$ for every vertex $v$.
This implies that the multigraph $M_{f,f^*}$ is the union of disjoint simple cycles. $f$ can be transformed into $f^*$ by pushing a mass 1 along all these cycles in any order. Since $d(f^*, f')<d(f,f')$, there must exists one of these simple cycles $\gamma$ with $d(f+\gamma, f') < d(f, f')$. Finally, since we can push a mass in $f$ along $\gamma$, it must appear in $G_f$. Hence $\gamma$ is a cycle of $G_f$ with negative weight.
\end{proof}
In the next section we describe the corresponding algorithm. Instead of discovering only one cycle, we are allowed to discover a set $\Gamma$ of disjoint negative cycles.
\subsubsection*{Algorithm}
\begin{algorithmic}[1]
\Function{Minimize transfer load}{$G$, $f$, $\alpha'$}
The distance $d(f,f')$ is bounded by the maximal number of differences in the associated assignment. If these assignment are totally disjoint, this distance is $2\rho_N P$. At every iteration of the While loop, the distance decreases, so there is at most $O(\rho_N P)= O(P)$ iterations.
The detection of negative cycle is done with the Bellman-Ford algorithm, whose complexity should normally be $O(\#E\#V)$. In our case, it amounts to $O(P^2ZN)$. Multiplied by the complexity of the outer loop, it amounts to $O(P^3ZN)$ which is a lot when the number of partitions and nodes starts to be large. To avoid that, we adapt the Bellman-Ford algorithm.
The Bellman-Ford algorithm runs $\#V$ iterations of an outer loop, and an inner loop over $E$. The idea is to compute the shortest paths from a source vertex $v$ to all other vertices. After $k$ iterations of the outer loop, the algorithm has computed all shortest path of length at most $k$. All simple paths have length at most $\#V-1$, so if there is an update in the last iteration of the loop, it means that there is a negative cycle in the graph. The observation that will enable us to improve the complexity is the following:
\begin{proposition}
In the graph $G_f$ (and $G$), all simple paths have a length at most $4N$.
\end{proposition}
\begin{proof}
Since $f$ is a maximal flow, there is no outgoing edge from $\mathbf{s}$ in $G_f$. One can thus check than any simple path of length 4 must contain at least two node of type $\mathbf{n}$. Hence on a path, at most 4 arcs separate two successive nodes of type $\mathbf{n}$.
\end{proof}
Thus, in the absence of negative cycles, shortest paths in $G_f$ have length at most $4N$. So we can do only $4N+1$ iterations of the outer loop in the Bellman-Ford algorithm. This makes the complexity of the detection of one set of cycle to be $O(N\#E)= O(N^2 P)$.
With this improvement, the complexity of the whole algorithm is, in the worst case, $O(N^2P^2)$. However, since we detect several cycles at once and we start with a flow that might be close to the previous one, the number of iterations of the outer loop might be smaller in practice.
\subsubsection*{Metrics}
We can display the node and zone utilization ratio, by dividing the flow passing through them divided by their outgoing capacity. In particular, we can pinpoint saturated nodes and zones (i.e. used at their full potential).
We can display the distance to the previous assignment, and the number of partition transfers.
Garage is an open-source distributed storage service blablabla$\dots$
Every object to be stored in the system falls in a partition given by the last $k$ bits of its hash. There are $P=2^k$ partitions. Every partition will be stored on distinct nodes of the system. The goal of the assignment of partitions to nodes is to ensure (nodes and zone) redundancy and to be as efficient as possible.
\subsection{Formal description of the problem}
We are given a set of nodes $\mathbf{N}$ and a set of zones $\mathbf{Z}$. Every node $n$ has a non-negative storage capacity $c_n\ge0$ and belongs to a zone $z\in\mathbf{Z}$. We are also given a number of partition $P>0$ (typically $P=256$).
We would like to compute an assignment of nodes to partitions. We will impose some redundancy constraints to this assignment, and under these constraints, we want our system to have the largest storage capacity possible. To link storage capacity to partition assignment, we make the following assumption:
\begin{equation}
\tag{H1}
\text{\emph{All partitions have the same size $s$.}}
\end{equation}
This assumption is justified by the dispersion of the hashing function, when the number of partitions is small relative to the number of stored large objects.
Every node $n$ will store some number $k_n$ of partitions. Hence the partitions stored by $n$ (and hence all partitions by our assumption) have there size bounded by $c_n/k_n$. This remark leads us to define the optimal size that we will want to maximize:
\begin{equation}
\label{eq:optimal}
\tag{OPT}
s^* = \min_{n \in N}\frac{c_n}{k_n}.
\end{equation}
When the capacities of the nodes are updated (this includes adding or removing a node), we want to update the assignment as well. However, transferring the data between nodes has a cost and we would like to limit the number of changes in the assignment. We make the following assumption:
\begin{equation}
\tag{H2}
\text{\emph{Updates of capacity happens rarely relatively to object storing.}}
\end{equation}
This assumption justifies that when we compute the new assignment, it is worth to optimize the partition size \eqref{eq:optimal} first, and then, among the possible optimal solution, to try to minimize the number of partition transfers.
For now, in the following, we ask the following redundancy constraint:
\textbf{Parametric node and zone redundancy:} Given two integer parameters $1\le\rho_\mathbf{Z}\le\rho_\mathbf{N}$, we ask every partition to be stored on $\rho_\mathbf{N}$ distinct nodes, and these nodes must belong to at least $\rho_\mathbf{Z}$ distinct zones.
\textbf{Mode 3-strict:} every partition needs to be assignated to three nodes belonging to three different zones.
\textbf{Mode 3:} every partition needs to be assignated to three nodes. We try to spread the three nodes over different zones as much as possible.
\textbf{Warning:} This is a working document written incrementally. The last version of the algorithm is the \textbf{parametric assignment} described in the next section.
\section{Computation of a parametric assignment}
\textbf{Attention : }We change notations in this section.
Notations : let $P$ be the number of partitions, $N$ the number of nodes, $Z$ the number of zones. Let $\mathbf{P,N,Z}$ be the label sets of, respectively, partitions, nodes and zones.
Let $s^*$ be the largest partition size achievable with the redundancy constraints. Let $(c_n)_{n\in\mathbf{N}}$ be the storage capacity of every node.
In this section, we propose a third specification of the problem. The user inputs two redundancy parameters $1\le\rho_\mathbf{Z}\le\rho_\mathbf{N}$. We compute an assignment $\alpha=(\alpha_p^1, \ldots, \alpha_p^{\rho_\mathbf{N}})_{p\in\mathbf{P}}$ such that every partition $p$ is associated to $\rho_\mathbf{N}$ distinct nodes $\alpha_p^1, \ldots, \alpha_p^{\rho_\mathbf{N}}$ and these nodes belong to at least $\rho_\mathbf{Z}$ distinct zones.
If the layout contained a previous assignment $\alpha'$, we try to minimize the amount of data to transfer during the layout update by making $\alpha$ as close as possible to $\alpha'$.
In the following subsections, we describe the successive steps of the algorithm we propose to compute $\alpha$.
\State$f^*\leftarrow$\Call{Minimize transfer load}{$G$, $f$, $\alpha'$}
\State Build $\alpha^*$ from $f^*$
\State\Return$\alpha^*$
\EndFunction
\end{algorithmic}
\subsubsection*{Complexity}
As we will see in the next sections, the worst case complexity of this algorithm is $O(P^2 N^2)$. The minimization of transfer load is the most expensive step, and it can run with a timeout since it is only an optimization step. Without this step (or with a smart timeout), the worst cas complexity can be $O((PN)^{3/2}\log C)$ where $C$ is the total storage capacity of the cluster.
\subsection{Determination of the partition size $s^*$}
Again, we will represent an assignment $\alpha$ as a flow in a specific graph $G$. We will not compute the optimal partition size $s^*$ a priori, but we will determine it by dichotomy, as the largest size $s$ such that the maximal flow achievable on $G=G(s)$ has value $\rho_\mathbf{N}P$. We will assume that the capacities are given in a small enough unit (say, Megabytes), and we will determine $s^*$ at the precision of the given unit.
Given some candidate size value $s$, we describe the oriented weighted graph $G=(V,E)$ with vertex set $V$ arc set $E$.
The set of vertices $V$ contains the source $\mathbf{s}$, the sink $\mathbf{t}$, vertices
$\mathbf{p^+, p^-}$ for every partition $p$, vertices $\mathbf{x}_{p,z}$ for every partition $p$ and zone $z$, and vertices $\mathbf{n}$ for every node $n$.
The set of arcs $E$ contains:
\begin{itemize}
\item ($\mathbf{s}$,$\mathbf{p}^+$, $\rho_\mathbf{Z}$) for every partition $p$;
\item ($\mathbf{s}$,$\mathbf{p}^-$, $\rho_\mathbf{N}-\rho_\mathbf{Z}$) for every partition $p$;
\item ($\mathbf{p}^+$,$\mathbf{x}_{p,z}$, 1) for every partition $p$ and zone $z$;
\item ($\mathbf{p}^-$,$\mathbf{x}_{p,z}$, $\rho_\mathbf{N}-\rho_\mathbf{Z}$) for every partition $p$ and zone $z$;
\item ($\mathbf{x}_{p,z}$,$\mathbf{n}$, 1) for every partition $p$, zone $z$ and node $n\in z$;
\item ($\mathbf{n}$, $\mathbf{t}$, $\lfloor c_n/s \rfloor$) for every node $n$.
\end{itemize}
In the following complexity calculations, we will use the number of vertices and edges of $G$. Remark from now that $\# V = O(PZ)$ and $\# E = O(PN)$.
\begin{proposition}
An assignment $\alpha$ is realizable with partition size $s$ and the redundancy constraints $(\rho_\mathbf{N},\rho_\mathbf{Z})$ if and only if there exists a maximal flow function $f$ in $G$ with total flow $\rho_\mathbf{N}P$, such that the arcs ($\mathbf{x}_{p,z}$,$\mathbf{n}$, 1) used are exactly those for which $p$ is associated to $n$ in $\alpha$.
\end{proposition}
\begin{proof}
Given such flow $f$, we can reconstruct a candidate $\alpha$. In $f$, the flow passing through $\mathbf{p^+}$ and $\mathbf{p^-}$ is $\rho_\mathbf{N}$, and since the outgoing capacity of every $\mathbf{x}_{p,z}$ is 1, every partition is associated to $\rho_\mathbf{N}$ distinct nodes. The fraction $\rho_\mathbf{Z}$ of the flow passing through every $\mathbf{p^+}$ must be spread over as many distinct zones as every arc outgoing from $\mathbf{p^+}$ has capacity 1. So the reconstructed $\alpha$ verifies the redundancy constraints. For every node $n$, the flow between $\mathbf{n}$ and $\mathbf{t}$ corresponds to the number of partitions associated to $n$. By construction of $f$, this does not exceed $\lfloor c_n/s \rfloor$. We assumed that the partition size is $s$, hence this association does not exceed the storage capacity of the nodes.
In the other direction, given an assignment $\alpha$, one can similarly check that the facts that $\alpha$ respects the redundancy constraints, and the storage capacities of the nodes, are necessary condition to construct a maximal flow function $f$.
\end{proof}
\textbf{Implementation remark:} In the flow algorithm, while exploring the graph, we explore the neighbours of every vertex in a random order to heuristically spread the association between nodes and partitions.
\subsubsection*{Algorithm}
With this result mind, we can describe the first step of our algorithm. All divisions are supposed to be integer division.
To compute the maximal flow, we use Dinic's algorithm. Its complexity on general graphs is $O(\#V^2\#E)$, but on graphs with edge capacity bounded by a constant, it turns out to be $O(\#E^{3/2})$. The graph $G$ does not fall in this case since the capacities of the arcs incoming to $\mathbf{t}$ are far from bounded. However, the proof of this complexity works readily for graph where we only ask the edges \emph{not} incoming to the sink $\mathbf{t}$ to have their capacities bounded by a constant. One can find the proof of this claim in \cite[Section 2]{even1975network}.
The dichotomy adds a logarithmic factor $\log(C)$ where $C=\sum_{n \in\mathbf{N}} c_n$ is the total capacity of the cluster. The total complexity of this first function is hence
$O(\#E^{3/2}\log C )= O\big((PN)^{3/2}\log C\big)$.
\subsubsection*{Metrics}
We can display the discrepancy between the computed $s^*$ and the best size we could hope for a given total capacity, that is $C/\rho_\mathbf{N}$.
\subsection{Computation of a candidate assignment}
Now that we have the optimal partition size $s^*$, to compute a candidate assignment, it would be enough to compute a maximal flow function $f$ on $G(s^*)$. This is what we do if there was no previous assignment $\alpha'$.
If there was some $\alpha'$, we add a step that will heuristically help to obtain a candidate $\alpha$ closer to $\alpha'$. to do so, we fist compute a flow function $\tilde{f}$ that uses only the partition-to-node association appearing in $\alpha'$. Most likely, $\tilde{f}$ will not be a maximal flow of $G(s^*)$. In Dinic's algorithm, we can start from a non maximal flow function and then discover improving paths. This is what we do in starting from $\tilde{f}$. The hope\footnote{This is only a hope, because one can find examples where the construction of $f$ from $\tilde{f}$ produces an assignment $\alpha$ that is not as close as possible to $\alpha'$.} is that the final flow function $f$ will tend to keep the associations appearing in $\tilde{f}$.
More formally, we construct the graph $G_{|\alpha'}$ from $G$ by removing all the arcs $(\mathbf{x}_{p,z},\mathbf{n}, 1)$ where $p$ is not associated to $n$ in $\alpha'$. We compute a maximal flow function $\tilde{f}$ in $G_{|\alpha'}$. $\tilde{f}$ is also a valid (most likely non maximal) flow function in $G$. We compute a maximal flow function $f$ on $G$ by starting Dinic's algorithm on $\tilde{f}$.
\State$ f \leftarrow$\Call{Maximal flow from flow}{$G$, $\tilde{f}$}
\State\Return$f$
\EndFunction
\end{algorithmic}
\textbf{Remark:} The function ``Maximal flow'' can be just seen as the function ``Maximal flow from flow'' called with the zero flow function as starting flow.
\subsubsection*{Complexity}
From the consideration of the last section, we have the complexity of the Dinic's algorithm $O(\#E^{3/2})= O((PN)^{3/2})$.
\subsubsection*{Metrics}
We can display the flow value of $\tilde{f}$, which is an upper bound of the distance between $\alpha$ and $\alpha'$. It might be more a Debug level display than Info.
\subsection{Minimization of the transfer load}
Now that we have a candidate flow function $f$, we want to modify it to make its associated assignment as close as possible to $\alpha'$. Denote by $f'$ the maximal flow associated to $\alpha'$, and let $d(f, f')$ be distance between the associated assignments\footnote{It is the number of arcs of type $(\mathbf{x}_{p,z},\mathbf{n})$ saturated in one flow and not in the other.}.
We want to build a sequence $f=f_0, f_1, f_2\dots$ of maximal flows such that $d(f_i, \alpha')$ decreases as $i$ increases. The distance being a non-negative integer, this sequence of flow functions must be finite. We now explain how to find some improving $f_{i+1}$ from $f_i$.
For any maximal flow $f$ in $G$, we define the oriented weighted graph $G_f=(V, E_f)$ as follows. The vertices of $G_f$ are the same as the vertices of $G$. $E_f$ contains the arc $(v_1,v_2, w)$ between vertices $v_1,v_2\in V$ with weight $w$ if and only if the arc $(v_1,v_2)$ is not saturated in $f$ (i.e. $c(v_1,v_2)-f(v_1,v_2)\ge1$, we also consider reversed arcs). The weight $w$ is:
\begin{itemize}
\item$-1$ if $(v_1,v_2)$ is of type $(\mathbf{x}_{p,z},\mathbf{n})$ or $(\mathbf{x}_{p,z},\mathbf{n})$ and is saturated in only one of the two flows $f,f'$;
\item$+1$ if $(v_1,v_2)$ is of type $(\mathbf{x}_{p,z},\mathbf{n})$ or $(\mathbf{x}_{p,z},\mathbf{n})$ and is saturated in either both or none of the two flows $f,f'$;
\item$0$ otherwise.
\end{itemize}
If $\gamma$ is a simple cycle of arcs in $G_f$, we define its weight $w(\gamma)$ as the sum of the weights of its arcs. We can add $+1$ to the value of $f$ on the arcs of $\gamma$, and by construction of $G_f$ and the fact that $\gamma$ is a cycle, the function that we get is still a valid flow function on $G$, it is maximal as it has the same flow value as $f$. We denote this new function $f+\gamma$.
\begin{proposition}
Given a maximal flow $f$ and a simple cycle $\gamma$ in $G_f$, we have $d(f+\gamma, f')- d(f,f')= w(\gamma)$.
\end{proposition}
\begin{proof}
Let $X$ be the set of arcs of type $(\mathbf{x}_{p,z},\mathbf{n})$. Then we can express $d(f,f')$ as
Remark that since we passed on unit of flow in $\gamma$ to construct $f+\gamma$, we have for any $e\in X$, $f(e)=f'(e)$ if and only if $(f+\gamma)(e)\neq f'(e)$.
Plugging this in the previous equation, we find that
$$d(f,f')+w(\gamma)= d(f+\gamma, f').$$
\end{proof}
This result suggests that given some flow $f_i$, we just need to find a negative cycle $\gamma$ in $G_{f_i}$ to construct $f_{i+1}$ as $f_i+\gamma$. The following proposition ensures that this greedy strategy reaches an optimal flow.
\begin{proposition}
For any maximal flow $f$, $G_f$ contains a negative cycle if and only if there exists a maximal flow $f^*$ in $G$ such that $d(f^*, f') < d(f, f')$.
\end{proposition}
\begin{proof}
Suppose that there is such flow $f^*$. Define the oriented multigraph $M_{f,f^*}=(V,E_M)$ with the same vertex set $V$ as in $G$, and for every $v_1,v_2\in V$, $E_M$ contains $(f^*(v_1,v_2)- f(v_1,v_2))_+$ copies of the arc $(v_1,v_2)$. For every vertex $v$, its total degree (meaning its outer degree minus its inner degree) is equal to
The last two sums are zero for any inner vertex since $f,f^*$ are flows, and they are equal on the source and sink since the two flows are both maximal and have hence the same value. Thus, $\deg v =0$ for every vertex $v$.
This implies that the multigraph $M_{f,f^*}$ is the union of disjoint simple cycles. $f$ can be transformed into $f^*$ by pushing a mass 1 along all these cycles in any order. Since $d(f^*, f')<d(f,f')$, there must exists one of these simple cycles $\gamma$ with $d(f+\gamma, f') < d(f, f')$. Finally, since we can push a mass in $f$ along $\gamma$, it must appear in $G_f$. Hence $\gamma$ is a cycle of $G_f$ with negative weight.
\end{proof}
In the next section we describe the corresponding algorithm. Instead of discovering only one cycle, we are allowed to discover a set $\Gamma$ of disjoint negative cycles.
\subsubsection*{Algorithm}
\begin{algorithmic}[1]
\Function{Minimize transfer load}{$G$, $f$, $\alpha'$}
The distance $d(f,f')$ is bounded by the maximal number of differences in the associated assignment. If these assignment are totally disjoint, this distance is $2\rho_N P$. At every iteration of the While loop, the distance decreases, so there is at most $O(\rho_N P)= O(P)$ iterations.
The detection of negative cycle is done with the Bellman-Ford algorithm, whose complexity should normally be $O(\#E\#V)$. In our case, it amounts to $O(P^2ZN)$. Multiplied by the complexity of the outer loop, it amounts to $O(P^3ZN)$ which is a lot when the number of partitions and nodes starts to be large. To avoid that, we adapt the Bellman-Ford algorithm.
The Bellman-Ford algorithm runs $\#V$ iterations of an outer loop, and an inner loop over $E$. The idea is to compute the shortest paths from a source vertex $v$ to all other vertices. After $k$ iterations of the outer loop, the algorithm has computed all shortest path of length at most $k$. All simple paths have length at most $\#V-1$, so if there is an update in the last iteration of the loop, it means that there is a negative cycle in the graph. The observation that will enable us to improve the complexity is the following:
\begin{proposition}
In the graph $G_f$ (and $G$), all simple paths have a length at most $4N$.
\end{proposition}
\begin{proof}
Since $f$ is a maximal flow, there is no outgoing edge from $\mathbf{s}$ in $G_f$. One can thus check than any simple path of length 4 must contain at least two node of type $\mathbf{n}$. Hence on a path, at most 4 arcs separate two successive nodes of type $\mathbf{n}$.
\end{proof}
Thus, in the absence of negative cycles, shortest paths in $G_f$ have length at most $4N$. So we can do only $4N+1$ iterations of the outer loop in Bellman-Ford algorithm. This makes the complexity of the detection of one set of cycle to be $O(N\#E)= O(N^2 P)$.
With this improvement, the complexity of the whole algorithm is, in the worst case, $O(N^2P^2)$. However, since we detect several cycles at once and we start with a flow that might be close to the previous one, the number of iterations of the outer loop might be smaller in practice.
\subsubsection*{Metrics}
We can display the node and zone utilization ratio, by dividing the flow passing through them divided by their outgoing capacity. In particular, we can pinpoint saturated nodes and zones (i.e. used at their full potential).
We can display the distance to the previous assignment, and the number of partition transfers.
\section{Properties of an optimal 3-strict assignment}
\subsection{Optimal assignment}
\label{sec:opt_assign}
For every zone $z\in Z$, define the zone capacity $c_z =\sum_{v, z_v=z} c_v$ and define $C =\sum_v c_v =\sum_z c_z$.
One can check that the best we could be doing to maximize $s^*$ would be to use the nodes proportionally to their capacity. This would yield $s^*=C/(3N)$. This is not possible because of (i) redundancy constraints and (ii) integer rounding but it gives and upper bound.
\subsubsection*{Optimal utilization}
We call an \emph{utilization} a collection of non-negative integers $(n_v)_{v\in V}$ such that $\sum_v n_v =3N$ and for every zone $z$, $\sum_{v\in z} n_v \le N$. We call such utilization \emph{optimal} if it maximizes $s^*$.
We start by computing a node sub-utilization $(\hat{n}_v)_{v\in V}$ such that for every zone $z$, $\sum_{v\in z}\hat{n}_v \le N$ and we show that there is an optimal utilization respecting the constraints and such that $\hat{n}_v \le n_v$ for every node.
Assume that there is a zone $z_0$ such that $c_{z_0}/C \ge1/3$. Then for any $v\in z_0$, we define
which is the universal upper bound on $s^*$. Hence any optimal utilization $(n_v)$ can be modified to another optimal utilization such that $n_v\ge\hat{n}_v$
Because $z_0$ cannot store more than $N$ partition occurrences, in any assignment, at least $2N$ partitions must be assignated to the zones $Z\setminus\{z_0\}$. Let $C_0= C-c_{z_0}$. Suppose that there exists a zone $z_1\neq z_0$ such that $c_{z_1}/C_0\ge1/2$. Then, with the same argument as for $z_0$, we can define
Now we can assign the remaining partitions. Let $(\hat{N}, \hat{C})$ to be
\begin{itemize}
\item$(3N,C)$ if we did not find any $z_0$;
\item$(2N,C-c_{z_0})$ if there was a $z_0$ but no $z_1$;
\item$(N,C-c_{z_0}-c_{z_1})$ if there was a $z_0$ and a $z_1$.
\end{itemize}
Then at least $\hat{N}$ partitions must be spread among the remaining zones. Hence $s^*$ is upper bounded by $\hat{C}/\hat{N}$ and without loss of generality, we can define, for every node that is not in $z_0$ nor $z_1$,
We constructed a sub-utilization $\hat{n}_v$. Now notice that $3N-\sum_v \hat{n}_v \le\# V$ where $\# V$ denotes the number of nodes. We can iteratively pick a node $v^*$ such that
\begin{itemize}
\item$\sum_{v\in z_{v^*}}\hat{n}_v < N$ where $z_{v^*}$ is the zone of $v^*$;
\item$v^*$ maximizes the quantity $c_v/(\hat{n}_v+1)$ among the vertices satisfying the first condition (i.e. not in a saturated zone).
\end{itemize}
We iterate these instructions until $\sum_v \hat{n}_v=3N$, and at this stage we define $(n_v)=(\hat{n}_v)$. It is easy to prove by induction that at every step, there is an optimal utilization that is pointwise larger than $\hat{n}_v$, and in particular, that $(n_v)$ is optimal.
\subsubsection*{Existence of an optimal assignment}
As for now, the \emph{optimal utilization} that we obtained is just a vector of numbers and it is not clear that it can be realized as the utilization of some concrete assignment. Here is a way to get a concrete assignment.
Define $3N$ tokens $t_1,\ldots, t_{3N}\in V$ as follows:
\begin{itemize}
\item Enumerate the zones $z$ of $Z$ in any order;
\item enumerate the nodes $v$ of $z$ in any order;
\item repeat $n_v$ times the token $v$.
\end{itemize}
Then for $1\le i \le N$, define the triplet $T_i$ to be
$(t_i, t_{i+N}, t_{i+2N})$. Since the same nodes of a zone appear contiguously, the three nodes of a triplet must belong to three distinct zones.
However simple, this solution to go from an utilization to an assignment has the drawback of not spreading the triplets: a node will tend to be associated to the same two other nodes for many partitions. Hence, during data transfer, it will tend to use only two link, instead of spreading the bandwidth use over many other links to other nodes. To achieve this goal, we will reframe the search of an assignment as a flow problem. and in the flow algorithm, we will introduce randomness in the order of exploration. This will be sufficient to obtain a good dispersion of the triplets.
\caption{On the left, the creation of a concrete assignment with the naive approach of repeating tokens. On the right, the zones containing the nodes.}
\end{figure}
\subsubsection*{Assignment as a maximum flow problem}
We describe the flow problem via its graph $(X,E)$ where $X$ is a set of vertices, and $E$ are directed weighted edges between the vertices. For every zone $z$, define $n_z=\sum_{v\in z} n_v$.
The set of vertices $X$ contains the source $\mathbf{s}$ and the sink $\mathbf{t}$; a vertex $\mathbf{x}_z$ for every zone $z\in Z$, and a vertex $\mathbf{y}_i$ for every partition index $1\le i\le N$.
The set of edges $E$ contains
\begin{itemize}
\item the edge $(\mathbf{s}, \mathbf{x}_z, n_z)$ for every zone $z\in Z$;
\item the edge $(\mathbf{x}_z, \mathbf{y}_i, 1)$ for every zone $z\in Z$ and partition $1\le i\le N$;
\item the edge $(\mathbf{y}_i, \mathbf{t}, 3)$ for every partition $1\le i\le N$.
\caption{Flow problem to compute and optimal assignment.}
\end{figure}
We first show the equivalence between this problem and and the construction of an assignment. Given some optimal assignment $(n_v)$, define the flow $f:E\to\mathbb{N}$ that saturates every edge from $\mathbf{s}$ or to $\mathbf{t}$, takes value $1$ on the edge between $\mathbf{x}_z$ and $\mathbf{y}_i$ if partition $i$ is stored in some node of the zone $z$, and $0$ otherwise. One can easily check that $f$ thus defined is indeed a flow and is maximum.
Reciprocally, by the existence of maximum flows constructed from optimal assignments, any maximum flow must saturate the edges linked to the source or the sink. It can only take value 0 or 1 on the other edge, and every partition vertex is associated to exactly three distinct zone vertices. Every zone is associated to exactly $n_z$ partitions.
A maximum flow can be constructed using, for instance, Dinic's algorithm. This algorithm works by discovering augmenting path to iteratively increase the flow. During the exploration of the graph to find augmenting path, we can shuffle the order of enumeration of the neighbours to spread the associations between zones and partitions.
Once we have such association, we can randomly distribute the $n_z$ edges picked for every zone $z$ to its nodes $v\in z$ such that every such $v$ gets $n_z$ edges. This defines an optimal assignment of partitions to nodes.
\subsection{Minimal transfer}
Assume that there was a previous assignment $(T'_i)_{1\le i\le N}$ corresponding to utilizations $(n'_v)_{v\in V}$. We would like the new computed assignment $(T_i)_{1\le i\le N}$ from some $(n_v)_{v\in V}$ to minimize the number of partitions that need to be transferred. We can imagine two different objectives corresponding to different hypotheses:
\begin{equation}
\tag{H3A}
\label{hyp:A}
\text{\emph{Transfers between different zones cost much more than inside a zone.}}
\end{equation}
\begin{equation}
\tag{H3B}
\label{hyp:B}
\text{\emph{Changing zone is not the largest cost when transferring a partition.}}
\end{equation}
In case $A$, our goal will be to minimize the number of changes of zone in the assignment of partitions to zone. More formally, we will maximize the quantity
$$
Q_Z :=
\sum_{1\le i\le N}
\#\{z\in Z ~|~ z\cap T_i \neq\emptyset, z\cap T'_i \neq\emptyset\}
.$$
In case $B$, our goal will be to minimize the number of changes of nodes in the assignment of partitions to nodes. We will maximize the quantity
$$
Q_V :=
\sum_{1\le i\le N}\#(T_i \cap T'_i).
$$
It is tempting to hope that there is a way to maximize both quantity, that having the least discrepancy in terms of nodes will lead to the least discrepancy in terms of zones. But this is actually wrong! We propose the following counter-example to convince the reader:
We consider eight nodes $a, a', b, c, d, d', e, e'$ belonging to five different zones $\{a,a'\}, \{b\}, \{c\}, \{d,d'\}, \{e, e'\}$. We take three partitions ($N=3$), that are originally assigned with some utilization $(n'_v)_{v\in V}$ as follows:
$$
T'_1=(a,b,c) \qquad
T'_2=(a',b,d) \qquad
T'_3=(b,c,e).
$$
This assignment, with updated utilizations $(n_v)_{v\in V}$ minimizes the number of zone changes:
$$
T_1=(d,b,c) \qquad
T_2=(a,b,d) \qquad
T_3=(b,c,e').
$$
This one, with the same utilization, minimizes the number of node changes:
$$
T_1=(a,b,c) \qquad
T_2=(e',b,d) \qquad
T_3=(b,c,d').
$$
One can check that in this case, it is impossible to minimize both the number of zone and node changes.
Because of the redundancy constraint, we cannot use a greedy algorithm to just replace nodes in the triplets to try to get the new utilization rate: this could lead to blocking situation where there is still a hole to fill in a triplet but no available node satisfies the zone separation constraint. To circumvent this issue, we propose an algorithm based on finding cycles in a graph encoding of the assignment. As in section \ref{sec:opt_assign}, we can explore the neighbours in a random order in the graph algorithms, to spread the triplets distribution.
\subsubsection{Minimizing the zone discrepancy}
First, notice that, given an assignment of partitions to \emph{zones}, it is easy to deduce an assignment to \emph{nodes} that minimizes the number of transfers for this zone assignment: For every zone $z$ and every node $v\in z$, pick in any way a set $P_v$ of partitions that where assigned to $v$ in $T'$, to $z_v$ in $T$, with the cardinality of $P_v$ smaller than $n_v$. Once all these sets are chosen, complement the assignment to reach the right utilization for every node. If $\#P_v > n_v$, it means that all the partitions that could stay in $v$ (i.e. that were already in $v$ and are still assigned to its zone) do stay in $v$. If $\#P_v = n_v$, then $n_v$ partitions stay in $v$, which is the number of partitions that need to be in $v$ in the end. In both cases, we could not hope for better given the partition to zone assignment.
Our goal now is to find a assignment of partitions to zones that minimizes the number of zone transfers. To do so we are going to represent an assignment as a graph.
Let $G_T=(X,E_T)$ be the directed weighted graph with vertices $(\mathbf{x}_i)_{1\le i\le N}$ and $(\mathbf{y}_z)_{z\in Z}$. For any $1\le i\le N$ and $z\in Z$, $E_T$ contains the arc:
\begin{itemize}
\item$(\mathbf{x}_i, \mathbf{y}_z, +1)$, if $z$ appears in $T_i'$ and $T_i$;
\item$(\mathbf{x}_i, \mathbf{y}_z, -1)$, if $z$ appears in $T_i$ but not in $T'_i$;
\item$(\mathbf{y}_z, \mathbf{x}_i, -1)$, if $z$ appears in $T'_i$ but not in $T_i$;
\item$(\mathbf{y}_z, \mathbf{x}_i, +1)$, if $z$ does not appear in $T'_i$ nor in $T_i$.
\end{itemize}
In other words, the orientation of the arc encodes whether partition $i$ is stored in zone $z$ in the assignment $T$ and the weight $\pm1$ encodes whether this corresponds to what happens in the assignment $T'$.
\caption{On the left: the graph $G_T$ encoding an assignment to minimize the zone discrepancy. On the right: the graph $G_T$ encoding an assignment to minimize the node discrepancy.}
\end{figure}
Notice that at every partition, there are three outgoing arcs, and at every zone, there are $n_z$ incoming arcs. Moreover, if $w(e)$ is the weight of an arc $e$, define the weight of $G_T$ by
&=\#Z \times N - 4 \sum_{1\le i\le N} 3- \#\{z\in Z ~|~ z\cap T_i \neq\emptyset, z\cap T'_i \neq\emptyset\}\\
&= (\#Z-12)N + 4 Q_Z.
\end{align*}
Hence maximizing $Q_Z$ is equivalent to maximizing $w(G_T)$.
Assume that their exist some assignment $T^*$ with the same utilization $(n_v)_{v\in V}$. Define $G_{T^*}$ similarly and consider the set $E_\mathrm{Diff}= E_T \setminus E_{T^*}$ of arcs that appear only in $G_T$. Since all vertices have the same number of incoming arcs in $G_T$ and $G_{T^*}$, the vertices of the graph $(X, E_\mathrm{Diff})$ must all have the same number number of incoming and outgoing arrows. So $E_\mathrm{Diff}$ can be expressed as a union of disjoint cycles. Moreover, the edges of $E_\mathrm{Diff}$ must appear in $E_{T^*}$ with reversed orientation and opposite weight. Hence, we have
Hence, if $T$ is not optimal, there exists some $T^*$ with $w(G_T) < w(G_{T^*})$, and by the considerations above, there must exist a cycle in $E_\mathrm{Diff}$, and hence in $G_T$, with negative weight. If we reverse the edges and weights along this cycle, we obtain some graph. Since we did not change the incoming degree of any vertex, this is the graph encoding of some valid assignment $T^+$ such that $w(G_{T^+}) > w(G_T)$. We can iterate this operation until there is no other assignment $T^*$ with larger weight, that is until we obtain an optimal assignment.
\subsubsection{Minimizing the node discrepancy}
We will follow an approach similar to the one where we minimize the zone discrepancy. Here we will directly obtain a node assignment from a graph encoding.
Let $G_T=(X,E_T)$ be the directed weighted graph with vertices $(\mathbf{x}_i)_{1\le i\le N}$, $(\mathbf{y}_{z,i})_{z\in Z, 1\le i\le N}$ and $(\mathbf{u}_v)_{v\in V}$. For any $1\le i\le N$ and $z\in Z$, $E_T$ contains the arc:
\begin{itemize}
\item$(\mathbf{x}_i, \mathbf{y}_{z,i}, 0)$, if $z$ appears in $T_i$;
\item$(\mathbf{y}_{z,i}, \mathbf{x}_i, 0)$, if $z$ does not appear in $T_i$.
\end{itemize}
For any $1\le i\le N$ and $v\in V$, $E_T$ contains the arc:
\begin{itemize}
\item$(\mathbf{y}_{z_v,i}, \mathbf{u}_v, +1)$, if $v$ appears in $T_i'$ and $T_i$;
\item$(\mathbf{y}_{z_v,i}, \mathbf{u}_v, -1)$, if $v$ appears in $T_i$ but not in $T'_i$;
\item$(\mathbf{u}_v, \mathbf{y}_{z_v,i}, -1)$, if $v$ appears in $T'_i$ but not in $T_i$;
\item$(\mathbf{u}_v, \mathbf{y}_{z_v,i}, +1)$, if $v$ does not appear in $T'_i$ nor in $T_i$.
\end{itemize}
Every vertex $\mathbb{x}_i$ has outgoing degree 3, every vertex $\mathbf{y}_{z,v}$ has outgoing degree 1, and every vertex $\mathbf{u}_v$ has incoming degree $n_v$.
Remark that any graph respecting these degree constraints is the encoding of a valid assignment with utilizations $(n_v)_{v\in V}$, in particular no partition is stored in two nodes of the same zone.
Exactly like in the previous section, the existence of an assignment with larger weight implies the existence of a negatively weighted cycle in $G_T$. Reversing this cycle gives us the encoding of a valid assignment with a larger weight. Iterating this operation yields an optimal assignment.
\subsubsection{Linear combination of both criteria}
In the graph $G_T$ defined in the previous section, instead of having weights $0$ and $\pm1$, we could be having weights $\pm\alpha$ between $\mathbf{x}$ and $\mathbf{y}$ vertices, and weights $\pm\beta$ between $\mathbf{y}$ and $\mathbf{u}$ vertices, for some $\alpha,\beta>0$ (we have positive weight if the assignment corresponds to $T'$ and negative otherwise). Then
\begin{align*}
w(G_T) &= \sum_{e\in E_T} w(e) =
\alpha\big( (\#Z-12)N + 4 Q_Z\big) +
\beta\big( (\#V-12)N + 4 Q_V\big) \\
&= \mathrm{const}+ 4(\alpha Q_Z + \beta Q_V).
\end{align*}
So maximizing the weight of such graph encoding would be equivalent to maximizing a linear combination of $Q_Z$ and $Q_V$.
\subsection{Algorithm}
We give a high level description of the algorithm to compute an optimal 3-strict assignment. The operations appearing at lines 1,2,4 are respectively described by Algorithms \ref{alg:util},\ref{alg:opt} and \ref{alg:mini}.
\State$T \leftarrow$\Call{Minimization of transfers}{$(T_i)_{1\le i\le N}$, $(T'_i)_{1\le i\le N}$}
\EndIf
\State\Return$T$.
\EndFunction
\end{algorithmic}
\end{algorithm}
We give some considerations of worst case complexity for these algorithms. In the following, we assume $N>\#V>\#Z$. The complexity of Algorithm \ref{alg:total} is $O(N^3\# Z)$ if we assume \eqref{hyp:A} and $O(N^3\#Z \#V)$ if we assume \eqref{hyp:B}.
Algorithm \ref{alg:util} can be implemented with complexity $O(\#V^2)$. The complexity of the function call at line \ref{lin:subutil} is $O(\#V)$. The difference between the sum of the subutilizations and $3N$ is at most the sum of the rounding errors when computing the $\hat{n}_v$. Hence it is bounded by $\#V$ and the loop at line \ref{lin:loopsub} is iterated at most $\#V$ times. Finding the minimizing $v$ at line \ref{lin:findmin} takes $O(\#V)$ operations (naively, we could also use a heap).
Algorithm \ref{alg:opt} can be implemented with complexity $O(N^3\times\#Z)$. The flow graph has $O(N+\#Z)$ vertices and $O(N\times\#Z)$ edges. Dinic's algorithm has complexity $O(\#\mathrm{Vertices}^2\#\mathrm{Edges})$ hence in our case it is $O(N^3\times\#Z)$.
Algorithm \ref{alg:mini} can be implemented with complexity $O(N^3\# Z)$ under \eqref{hyp:A} and $O(N^3\#Z \#V)$ under \eqref{hyp:B}.
The graph $G_T$ has $O(N)$ vertices and $O(N\times\#Z)$ edges under assumption \eqref{hyp:A} and respectively $O(N\times\#Z)$ vertices and $O(N\times\#V)$ edges under assumption \eqref{hyp:B}. The loop at line \ref{lin:repeat} is iterated at most $N$ times since the distance between $T$ and $T'$ decreases at every iteration. Bellman-Ford algorithm has complexity $O(\#\mathrm{Vertices}\#\mathrm{Edges})$, which in our case amounts to $O(N^2\# Z)$ under \eqref{hyp:A} and $O(N^2\#Z \#V)$ under \eqref{hyp:B}.
\State Compute the maximal flow $f$ using Dinic's algorithm with randomized neighbours enumeration
\State Construct the assignment $(T_i)_{1\le i\le N}$ from $f$
\State\Return$(T_i)_{1\le i\le N}$
\EndFunction
\end{algorithmic}
\end{algorithm}
\begin{algorithm}
\caption{Minimization of the number of transfers}
\label{alg:mini}
\begin{algorithmic}[1]
\Function{Minimization of transfers}{$(T_i)_{1\le i\le N}$, $(T'_i)_{1\le i\le N}$}
\State Construct the graph encoding $G_T$
\Repeat\label{lin:repeat}
\State Find a negative cycle $\gamma$ using Bellman-Ford algorithm on $G_T$
\State Reverse the orientations and weights of edges in $\gamma$
\Until{no negative cycle is found}
\State Update $(T_i)_{1\le i\le N}$ from $G_T$
\State\Return$(T_i)_{1\le i\le N}$
\EndFunction
\end{algorithmic}
\end{algorithm}
\newpage
\section{Computation of a 3-non-strict assignment}
\subsection{Choices of optimality}
In this mode, we primarily want to store every partition on three nodes, and only secondarily try to spread the nodes among different zone. So we make the choice of not taking the zone repartition in the criterion of optimality.
We try to maximize $s^*$ defined in \eqref{eq:optimal}. So we can compute the optimal utilizations $(n_v)_{v\in V}$ with the only constraint that $n_v \le N$ for every node $v$. As in the previous section, we start with a sub-utilization proportional to $c_v$ (and capped at $N$), and we iteratively increase the $\hat{n}_v$ that is less than $N$ and maximizes the quantity $c_v/(\hat{n}_v+1)$, until the total sum is $3N$.
\subsection{Computation of a candidate assignment}
To compute a candidate assignment (that does not optimize zone spreading nor distance to a previous assignment yet), we can use the following flow problem.
Define the oriented weighted graph $(X,E)$. The set of vertices $X$ contains the source $\mathbf{s}$, the sink $\mathbf{t}$, vertices
$\mathbf{x}_p, \mathbf{u}^+_p, \mathbf{u}^-_p$ for every partition $p$, vertices $\mathbf{y}_{p,z}$ for every partition $p$ and zone $z$, and vertices $\mathbf{z}_v$ for every node $v$.
The set of edges is composed of the following arcs:
\begin{itemize}
\item ($\mathbf{s}$,$\mathbf{x}_p$, 3) for every partition $p$;
\item ($\mathbf{x}_p$,$\mathbf{u}^+_p$, 3) for every partition $p$;
\item ($\mathbf{x}_p$,$\mathbf{u}^-_p$, 2) for every partition $p$;
\item ($\mathbf{u}^+_p$,$\mathbf{y}_{p,z}$, 1) for every partition $p$ and zone $z$;
\item ($\mathbf{u}^-_p$,$\mathbf{y}_{p,z}$, 2) for every partition $p$ and zone $z$;
\item ($\mathbf{y}_{p,z}$,$\mathbf{z}_v$, 1) for every partition $p$, zone $z$ and node $v\in z$;
\item ($\mathbf{z}_v$, $\mathbf{t}$, $n_v$) for every node $v$;
\end{itemize}
One can check that any maximal flow in this graph corresponds to an assignment of partitions to nodes. In such a flow, all the arcs from $\mathbf{s}$ and to $\mathbf{t}$ are saturated. The arc from $\mathbf{y}_{p,z}$ to $\mathbf{z}_v$ is saturated if and only if $p$ is associated to~$v$.
Finally the flow from $\mathbf{x}_p$ to $\mathbf{y}_{p,z}$ can go either through $\mathbf{u}^+_p$ or $\mathbf{u}^-_p$.
\subsection{Maximal spread and minimal transfers}
Notice that if the arc $\mathbf{u}_p^+\mathbf{y}_{p,z}$ is not saturated but there is some flow in $\mathbf{u}_p^-\mathbf{y}_{p,z}$, then it is possible to transfer a unit of flow from the path $\mathbf{x}_p\mathbf{u}_p^-\mathbf{y}_{p,z}$ to the path $\mathbf{x}_p\mathbf{u}_p^+\mathbf{y}_{p,z}$. So we can always find an equivalent maximal flow $f^*$ that uses the path through $\mathbf{u}_p^-$ only if the path through $\mathbf{u}_p^+$ is saturated.
We will use this fact to consider the amount of flow going through the vertices $\mathbf{u}^+$ as a measure of how well the partitions are spread over nodes belonging to different zones. If the partition $p$ is associated to 3 different zones, then a flow of 3 will cross $\mathbf{u}_p^+$ in $f^*$ (i.e. a flow of 0 will cross $\mathbf{u}_p^+$). If $p$ is associated to two zones, a flow of $2$ will cross $\mathbf{u}_p^+$. If $p$ is associated to a single zone, a flow of $1$ will cross $\mathbf{u}_p^+$.
Let $N_1, N_2, N_3$ be the number of partitions associated to respectively 1,2 and 3 distinct zones. We will optimize a linear combination of these variables using the discovery of positively weighted circuits in a graph.
At the same step, we will also optimize the distance to a previous assignment $T'$. Let $\alpha> \beta> \gamma\ge0$ be three parameters.
Given the flow $f$, let $G_f=(X',E_f)$ be the multi-graph where $X' = X\setminus\{\mathbf{s},\mathbf{t}\}$. The set $E_f$ is composed of the arcs:
\begin{itemize}
\item As many arcs from $(\mathbf{x}_p, \mathbf{u}^+_p,\alpha), (\mathbf{x}_p, \mathbf{u}^+_p,\beta), (\mathbf{x}_p, \mathbf{u}^+_p,\gamma)$ (selected in this order) as there is flow crossing $\mathbf{u}^+_p$ in $f$;
\item As many arcs from $(\mathbf{u}^+_p, \mathbf{x}_p,-\gamma), (\mathbf{u}^+_p, \mathbf{x}_p,-\beta), (\mathbf{u}^+_p, \mathbf{x}_p,-\alpha)$ (selected in this order) as there is flow crossing $\mathbf{u}^-_p$ in $f$;
\item As many copies of $(\mathbf{x}_p, \mathbf{u}^-_p,0)$ as there is flow through $\mathbf{u}^-_p$;
\item As many copies of $(\mathbf{u}^-_p,\mathbf{x}_p,0)$ so that the number of arcs between these two vertices is 2;
\item$(\mathbf{u}^+_p,\mathbf{y}_{p,z}, 0)$ if the flow between these vertices is 1, and the opposite arc otherwise;
\item as many copies of $(\mathbf{u}^-_p,\mathbf{y}_{p,z}, 0)$ as the flow between these vertices, and as many copies of the opposite arc as 2~$-$~the flow;
\item$(\mathbf{y}_{p,z},\mathbf{z}_v, \pm1)$ if it is saturated in $f$, with $+1$ if $v\in T'_p$ and $-1$ otherwise;
\item$(\mathbf{z}_v,\mathbf{y}_{p,z}, \pm1)$ if it is not saturated in $f$, with $+1$ if $v\notin T'_p$ and $-1$ otherwise.
\end{itemize}
To summarize, arcs are oriented left to right if they correspond to a presence of flow in $f$, and right to left if they correspond to an absence of flow. They are positively weighted if we want them to stay at their current state, and negatively if we want them to switch. Let us compute the weight of such graph.
As for the mode 3-strict, one can check that the difference of two such graphs corresponding to the same $(n_v)$ is always eulerian. Hence we can navigate in this class with the same greedy algorithm that discovers positive cycles and flips them.
The function that we optimize is
$$
2Q_V + \beta N_2 + (\beta+\gamma) N_3.
$$
The choice of parameters $\beta$ and $\gamma$ should be lead by the following question: For $\beta$, where to put the tradeoff between zone dispersion and distance to the previous configuration? For $\gamma$, do we prefer to have more partitions spread between 2 zones, or have less between at least 2 zones but more between 3 zones.
The quantity $Q_V$ varies between $0$ and $3N$, it should be of order $N$. The quantity $N_2+N_3$ should also be of order $N$ (it is exactly $N$ in the strict mode). So the two terms of the function are comparable.
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