Chris LuandGitHub
7063b3e14c
* s3 lifecycle: bound the daily-replay subscription at the pass boundary A pass opens one meta-log subscription and 16 shard drains, then waits on all of them. Nothing told the subscription where the pass ends, so the only exit was the fan-out spotting an event past runNow — i.e. some unrelated write landing under /buckets after the pass started. On a cluster that goes quiet the reader parks in Recv, every shard drain starves on an empty channel, and Run never returns. The job sits at stage "starting" with the executor slot held and no log line, so expiry stops cluster-wide until someone restarts the worker. The pass covers (globalStartTsNs, runNow], so say that: UntilNs on the subscribe request makes the filer end the stream once it has shipped that range. The reader then closes the event channel on the way out, which is what unblocks the fan-out and the drains when the stream finishes on its own rather than by cancellation. Same fix retires the other silent hang: a reader that failed early (subscribe error, stream error) also left every drain waiting forever. * s3 lifecycle: keep a halted shard from starving the shared fan-out A drain that halts mid-stream (BLOCKED / RETRY_LATER / an RPC error on dispatch) returns while the fan-out is still routing that shard's events. After 256 of them the per-shard buffer is full and the fan-out blocks on the send, so no other shard sees another event. Run's WaitGroup never drains, and the teardown that would cancel the reader sits behind that wait — the pass wedges exactly like an idle subscription did, with one S3 hiccup as the trigger. Keep discarding the channel after runShard returns. The events are past this shard's saved cursor and get re-scanned next pass anyway. * s3 lifecycle: assert the starved shard actually made progress The fan-out test only checked that Run returned, which a version that quietly dropped the second shard's events would also satisfy. Assert the dispatch landed and the cursor moved. recordingClient gains a per-object outcome map: the two shards dispatch from separate goroutines, so pinning BLOCKED by call index was a race waiting to pick the wrong shard. * s3 lifecycle: fail the pass when the shared subscription dies Closing the event channel on reader exit is what unblocks the shard drains, but it also means a subscribe that never opened, or a stream that broke mid-pass, now ends every drain cleanly. Run logged that at V(2) and returned the shard result — so a filer failure produced a green lifecycle job that had processed nothing. Surface it as the pass error. Cursors still hold what was processed and tomorrow resumes there; what changes is that the job stops claiming success. Cancellation has to stay a non-error — the shell driver's -runtime cap is a truncated pass, not a failed one — and a canceled gRPC stream arrives as a status code, not a wrapped context.Canceled, so isCanceled checks both forms the way the rest of the tree does. * s3 lifecycle: decide reader cancellation by intent, not status code A stream we cancel and a stream the filer cancels both arrive as codes.Canceled, so classifying the reader's exit by its error let a truncated pass report success whenever the failure happened to carry a cancellation status. Intent is knowable exactly, so read that instead: the pass stops on purpose only when the caller's context ended (the shell driver's -runtime cap) or the fan-out hit the pass boundary itself. Everything else is a broken subscription and fails the pass. TestRun_ServerSideCancelFailsThePass and TestRun_CappedPassIsNotAFailure are the same codes.Canceled from the reader with opposite verdicts — the pair only passes because the decision no longer looks at the error. * s3 lifecycle: time out a subscription that stops delivering UntilNs ends a healthy stream and gRPC keepalive catches a dead connection, but neither reaches a filer that keeps answering pings while its handler has stopped producing. The pass would wait on that forever, since s3_lifecycle is the one job type with no execution timeout. Bound the wait for each response at 20 minutes, and opt into the filer's idle heartbeats so a caught-up stream proves liveness instead of looking stalled. The default sits above the filer's 15-minute metadata-gap recovery budget, so a subscriber legitimately parked on a gap is never mistaken for a stalled one. Recv is only interruptible by killing the RPC, so it moves to its own goroutine behind a per-response deadline. The timer covers only the wait on the filer — dispatch to Events happens outside it, so a slow consumer can't trip the watchdog. Approach and the 20-minute figure are from #10577 by way of comparing the two fixes; the wiring differs because the reader here ends the pass by closing its event channel rather than cancelling the fan-out. * s3 lifecycle: trim the comments added by this branch Keep the non-obvious why, drop the prose restating what the code says. * s3 lifecycle: snapshot reader intent where the reader stops Sampling ctx.Err() during teardown reads it after the drains and cursor saves have run. A reader that failed while the deadline was still live, on a pass whose teardown then outlives that deadline, was classified as an intentional stop and reported success. Sampling earlier in Run is not the fix either: before the shard wait, a legitimately capped pass has not reached its deadline yet and would be misclassified the other way. Intent belongs where the reader actually stops, so the reader goroutine records it next to the error it returns. Reported by greptile on #10578. * s3 lifecycle: cover the worker-dispatched pass with nothing due The e2e suite drives the shell command in 14 of 15 files; the one test on the real admin->worker path backdates an object, so its own delete pushes a meta-log event past the pass boundary and ends the pass. The branch where a pass has nothing to dispatch was never exercised through the worker. Cover it, asserting the pass returns on its own: no admin cancellation, and the executor slot free for the next one. This is not a regression test for the wedge. A pass used to end when any write landed past its boundary, and on a shared test cluster something usually does — the whole suite passes on the unfixed build, verified. The deterministic guards stay the dailyrun unit tests; this one would catch a pass that hangs unconditionally.
SeaweedFS
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- Wiki Documentation
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- SeaweedFS Introduction Slides 2025.5
- SeaweedFS Introduction Slides 2021.5
- SeaweedFS Introduction Slides 2019.3
Table of Contents
- Quick Start
- Introduction
- Features
- Example: Using Seaweed Object Store
- Architecture
- Compared to Other File Systems
- Dev Plan
- Installation Guide
- Disk Related Topics
- Benchmark
- Enterprise
- License
Quick Start
Quick Start with weed mini
Download the latest binary from https://github.com/seaweedfs/seaweedfs/releases and unzip the single weed (or weed.exe) file, or run go install github.com/seaweedfs/seaweedfs/weed@latest. Then start a ready-to-use S3 object store with credentials and a pre-created bucket in one command:
AWS_ACCESS_KEY_ID=admin \
AWS_SECRET_ACCESS_KEY=secret \
S3_BUCKET=my-bucket \
./weed mini -dir=/data
That's it — the S3 endpoint is at http://localhost:8333, my-bucket already exists, and admin/secret are valid credentials. S3_BUCKET accepts a comma-separated list (e.g. raw,processed); use S3_TABLE_BUCKET for S3 Tables (Iceberg) buckets. Drop any of the env vars to skip that piece (no AWS keys → S3 runs in unauthenticated "Allow All" mode for development).
The same command starts everything else too:
- S3 Endpoint: http://localhost:8333
- Master UI: http://localhost:9333
- Volume Server: http://localhost:9340
- Filer UI: http://localhost:8888
- WebDAV: http://localhost:7333
- Admin UI: http://localhost:23646
macOS: if the binary is quarantined, run
xattr -d com.apple.quarantine ./weedfirst.
Perfect for development, testing, learning SeaweedFS, and single-node deployments. To scale out, add more volume servers by running weed volume -dir="/some/data/dir2" -master="<master_host>:9333" -port=8081 locally, on another machine, or on thousands of machines.
Quick Start for S3 API on Docker
docker run -p 8333:8333 \
-e AWS_ACCESS_KEY_ID=admin \
-e AWS_SECRET_ACCESS_KEY=secret \
-e S3_BUCKET=my-bucket \
chrislusf/seaweedfs
Same behavior as the weed mini command above — the S3 endpoint is at http://localhost:8333 with my-bucket pre-created. Drop the env vars to run anonymously for development.
Introduction
SeaweedFS is a simple and highly scalable distributed file system. There are two objectives:
- to store billions of files!
- to serve the files fast!
SeaweedFS started as a blob store to handle small files efficiently. Instead of managing all file metadata in a central master, the central master only manages volumes on volume servers, and these volume servers manage files and their metadata. This relieves concurrency pressure from the central master and spreads file metadata into volume servers, allowing faster file access (O(1), usually just one disk read operation).
There is only 40 bytes of disk storage overhead for each file's metadata. It is so simple with O(1) disk reads that you are welcome to challenge the performance with your actual use cases.
SeaweedFS started by implementing Facebook's Haystack design paper. Also, SeaweedFS implements erasure coding with ideas from f4: Facebook’s Warm BLOB Storage System, and has a lot of similarities with Facebook’s Tectonic Filesystem and Google's Colossus File System
On top of the blob store, optional Filer can support directories and POSIX attributes. Filer is a separate linearly-scalable stateless server with customizable metadata stores, e.g., MySql, Postgres, Redis, Cassandra, HBase, Mongodb, Elastic Search, LevelDB, RocksDB, Sqlite, MemSql, TiDB, Etcd, CockroachDB, YDB, etc.
SeaweedFS can transparently integrate with the cloud. With hot data on local cluster, and warm data on the cloud with O(1) access time, SeaweedFS can achieve both fast local access time and elastic cloud storage capacity. What's more, the cloud storage access API cost is minimized. Faster and cheaper than direct cloud storage!
SeaweedFS also ships a built-in Iceberg REST Catalog, turning the same cluster into a self-contained lakehouse. Spark, Trino, Dremio, DuckDB, and RisingWave can query Iceberg tables directly — no Hive Metastore, Glue, or external catalog service required. Storage and table metadata live in one system, simplifying on-prem and small-team analytics stacks.
Features
Additional Blob Store Features
- Support different replication levels, with rack and data center aware.
- Automatic master servers failover - no single point of failure (SPOF).
- Automatic compression depending on file MIME type.
- Automatic compaction to reclaim disk space after deletion or update.
- Automatic entry TTL expiration.
- Flexible Capacity Expansion: Any server with some disk space can add to the total storage space.
- Adding/Removing servers does not cause any data re-balancing unless triggered by admin commands.
- Optional picture resizing.
- Support ETag, Accept-Range, Last-Modified, etc.
- Support in-memory/leveldb/readonly mode tuning for memory/performance balance.
- Support rebalancing the writable and readonly volumes.
- Customizable Multiple Storage Tiers: Customizable storage disk types to balance performance and cost.
- Transparent cloud integration: unlimited capacity via tiered cloud storage for warm data.
- Erasure Coding for warm storage Rack-Aware 10.4 erasure coding reduces storage cost and increases availability. Enterprise version can customize EC ratio.
Filer Features
- Filer server provides "normal" directories and files via HTTP.
- File TTL automatically expires file metadata and actual file data.
- Mount filer reads and writes files directly as a local directory via FUSE.
- Filer Store Replication enables HA for filer meta data stores.
- Active-Active Replication enables asynchronous one-way or two-way cross cluster continuous replication.
- Amazon S3 compatible API accesses files with S3 tooling.
- Hadoop Compatible File System accesses files from Hadoop/Spark/Flink/etc or even runs HBase.
- Async Replication To Cloud has extremely fast local access and backups to Amazon S3, Google Cloud Storage, Azure, BackBlaze.
- WebDAV accesses as a mapped drive on Mac and Windows, or from mobile devices.
- AES256-GCM Encrypted Storage safely stores the encrypted data.
- Super Large Files stores large or super large files in tens of TB.
- Cloud Drive mounts cloud storage to local cluster, cached for fast read and write with asynchronous write back.
- Gateway to Remote Object Store mirrors bucket operations to remote object storage, in addition to Cloud Drive
Data Lakehouse Features
- S3 Table Buckets expose a dedicated namespace for Iceberg tables with strict layout validation.
- Built-in Iceberg REST Catalog runs alongside the S3 endpoint — no external metastore needed.
- Native integrations with Apache Spark, Trino, Dremio, DuckDB, and RisingWave.
- Automated table maintenance: compaction, snapshot expiration, orphan removal, manifest rewriting.
- Granular IAM at the bucket, namespace, and table level via standard S3 bucket policies.
Kubernetes
- Kubernetes CSI Driver A Container Storage Interface (CSI) Driver.
- SeaweedFS Operator
Example: Using Seaweed Blob Store
By default, the master node runs on port 9333, and the volume nodes run on port 8080. Let's start one master node, and two volume nodes on port 8080 and 8081. Ideally, they should be started from different machines. We'll use localhost as an example.
SeaweedFS uses HTTP REST operations to read, write, and delete. The responses are in JSON or JSONP format.
Start Master Server
> ./weed master
Start Volume Servers
> weed volume -dir="/tmp/data1" -max=5 -master="localhost:9333" -port=8080 &
> weed volume -dir="/tmp/data2" -max=10 -master="localhost:9333" -port=8081 &
Write A Blob
A blob, also referred as a needle, a chunk, or mistakenly as a file, is just a byte array. It can have attributes, such as name, mime type, create or update time, etc. But basically it is just a byte array of a relatively small size, such as 2 MB ~ 64 MB. The size is not fixed.
To upload a blob: first, send a HTTP POST, PUT, or GET request to /dir/assign to get an fid and a volume server URL:
> curl http://localhost:9333/dir/assign
{"count":1,"fid":"3,01637037d6","url":"127.0.0.1:8080","publicUrl":"localhost:8080"}
Second, to store the blob content, send a HTTP multi-part POST request to url + '/' + fid from the response:
> curl -F file=@/home/chris/myphoto.jpg http://127.0.0.1:8080/3,01637037d6
{"name":"myphoto.jpg","size":43234,"eTag":"1cc0118e"}
To update, send another POST request with updated blob content.
For deletion, send an HTTP DELETE request to the same url + '/' + fid URL:
> curl -X DELETE http://127.0.0.1:8080/3,01637037d6
Save Blob Id
Now, you can save the fid, 3,01637037d6 in this case, to a database field.
The number 3 at the start represents a volume id. After the comma, it's one file key, 01, and a file cookie, 637037d6.
The volume id is an unsigned 32-bit integer. The file key is an unsigned 64-bit integer. The file cookie is an unsigned 32-bit integer, used to prevent URL guessing.
The file key and file cookie are both coded in hex. You can store the <volume id, file key, file cookie> tuple in your own format, or simply store the fid as a string.
If stored as a string, in theory, you would need 8+1+16+8=33 bytes. A char(33) would be enough, if not more than enough, since most uses will not need 2^32 volumes.
If space is really a concern, you can store the file id in the binary format. You would need one 4-byte integer for volume id, 8-byte long number for file key, and a 4-byte integer for the file cookie. So 16 bytes are more than enough.
Read a Blob
Here is an example of how to render the URL.
First look up the volume server's URLs by the file's volumeId:
> curl http://localhost:9333/dir/lookup?volumeId=3
{"volumeId":"3","locations":[{"publicUrl":"localhost:8080","url":"localhost:8080"}]}
Since (usually) there are not too many volume servers, and volumes don't move often, you can cache the results most of the time. Depending on the replication type, one volume can have multiple replica locations. Just randomly pick one location to read.
Now you can take the public URL, render the URL or directly read from the volume server via URL:
http://localhost:8080/3,01637037d6.jpg
Notice we add a file extension ".jpg" here. It's optional and just one way for the client to specify the file content type.
If you want a nicer URL, you can use one of these alternative URL formats:
http://localhost:8080/3/01637037d6/my_preferred_name.jpg
http://localhost:8080/3/01637037d6.jpg
http://localhost:8080/3,01637037d6.jpg
http://localhost:8080/3/01637037d6
http://localhost:8080/3,01637037d6
If you want to get a scaled version of an image, you can add some params:
http://localhost:8080/3/01637037d6.jpg?height=200&width=200
http://localhost:8080/3/01637037d6.jpg?height=200&width=200&mode=fit
http://localhost:8080/3/01637037d6.jpg?height=200&width=200&mode=fill
Rack-Aware and Data Center-Aware Replication
SeaweedFS applies the replication strategy at a volume level. So, when you are getting a blob id, you can specify the replication strategy. For example:
curl http://localhost:9333/dir/assign?replication=001
The replication parameter options are:
000: no replication
001: replicate once on the same rack
010: replicate once on a different rack, but same data center
100: replicate once on a different data center
200: replicate twice on two different data center
110: replicate once on a different rack, and once on a different data center
More details about replication can be found on the wiki.
You can also set the default replication strategy when starting the master server.
Allocate Blob Key on Specific Data Center
Volume servers can be started with a specific data center name:
weed volume -dir=/tmp/1 -port=8080 -dataCenter=dc1
weed volume -dir=/tmp/2 -port=8081 -dataCenter=dc2
When requesting a blob key, an optional "dataCenter" parameter can limit the assigned volume to the specific data center. For example, this specifies that the assigned volume should be limited to 'dc1':
http://localhost:9333/dir/assign?dataCenter=dc1
Other Features
- No Single Point of Failure
- Insert with your own keys
- Chunking large files
- Collection as a Simple Name Space
Blob Store Architecture
Usually distributed file systems split each file into chunks. A central server keeps a mapping of filenames to chunks, and also which chunks each chunk server has.
The main drawback is that the central server can't handle many small files efficiently, and since all read requests need to go through the central master, so it might not scale well for many concurrent users.
Instead of managing chunks, SeaweedFS manages data volumes in the master server. Each data volume is 32GB in size, and can hold a lot of blobs. And each storage node can have many data volumes. So the master node only needs to store the metadata about the volumes, which is a fairly small amount of data and is generally stable.
The actual blob metadata, which are the blob volume, offset, and size, is stored in each volume on volume servers. Since each volume server only manages metadata of blobs on its own disk, with only 16 bytes for each blob, all access can read the metadata just from memory and only needs one disk operation to actually read file data.
For comparison, consider that an xfs inode structure in Linux is 536 bytes.
Master Server and Volume Server
The architecture is fairly simple. The actual data is stored in volumes on storage nodes. One volume server can have multiple volumes, and can both support read and write access with basic authentication.
All volumes are managed by a master server. The master server contains the volume id to volume server mapping. This is fairly static information, and can be easily cached.
On each write request, the master server also generates a file key, which is a growing 64-bit unsigned integer. Since write requests are not generally as frequent as read requests, one master server should be able to handle the concurrency well.
Write and Read files
When a client sends a write request, the master server returns (volume id, file key, file cookie, volume node URL) for the blob. The client then contacts the volume node and POSTs the blob content.
When a client needs to read a blob based on (volume id, file key, file cookie), it asks the master server by the volume id for the (volume node URL, volume node public URL), or retrieves this from a cache. Then the client can GET the content, or just render the URL on web pages and let browsers fetch the content.
Saving memory
All blob metadata stored on a volume server is readable from memory without disk access. Each file takes just a 16-byte map entry of <64bit key, 32bit offset, 32bit size>. Of course, each map entry has its own space cost for the map. But usually the disk space runs out before the memory does.
Tiered Storage to the cloud
The local volume servers are much faster, while cloud storages have elastic capacity and are actually more cost-efficient if not accessed often (usually free to upload, but relatively costly to access). With the append-only structure and O(1) access time, SeaweedFS can take advantage of both local and cloud storage by offloading the warm data to the cloud.
Usually hot data are fresh and warm data are old. SeaweedFS puts the newly created volumes on local servers, and optionally upload the older volumes on the cloud. If the older data are accessed less often, this literally gives you unlimited capacity with limited local servers, and still fast for new data.
With the O(1) access time, the network latency cost is kept at minimum.
If the hot/warm data is split as 20/80, with 20 servers, you can achieve storage capacity of 100 servers. That's a cost saving of 80%! Or you can repurpose the 80 servers to store new data also, and get 5X storage throughput.
SeaweedFS Filer
Built on top of the blob store, SeaweedFS Filer adds directory structure to create a file system. The directory structure is an interface that is implemented in many key-value stores or databases.
The content of a file is mapped to one or many blobs, distributed to multiple volumes on multiple volume servers.
Compared to Other File Systems
Most other distributed file systems seem more complicated than necessary.
SeaweedFS is meant to be fast and simple, in both setup and operation. If you do not understand how it works when you reach here, we've failed! Please raise an issue with any questions or update this file with clarifications.
SeaweedFS is constantly moving forward. Same with other systems. These comparisons can be outdated quickly. Please help to keep them updated.
Compared to HDFS
HDFS uses the chunk approach for each file, and is ideal for storing large files.
SeaweedFS is ideal for serving relatively smaller files quickly and concurrently.
SeaweedFS can also store extra large files by splitting them into manageable data chunks, and store the file ids of the data chunks into a meta chunk. This is managed by "weed upload/download" tool, and the weed master or volume servers are agnostic about it.
Compared to GlusterFS, Ceph
The architectures are mostly the same. SeaweedFS aims to store and read files fast, with a simple and flat architecture. The main differences are
- SeaweedFS optimizes for small files, ensuring O(1) disk seek operation, and can also handle large files.
- SeaweedFS statically assigns a volume id for a file. Locating file content becomes just a lookup of the volume id, which can be easily cached.
- SeaweedFS Filer metadata store can be any well-known and proven data store, e.g., Redis, Cassandra, HBase, Mongodb, Elastic Search, MySql, Postgres, Sqlite, MemSql, TiDB, CockroachDB, Etcd, YDB etc, and is easy to customize.
- SeaweedFS Volume server also communicates directly with clients via HTTP, supporting range queries, direct uploads, etc.
| System | File Metadata | File Content Read | POSIX | REST API | Optimized for large number of small files |
|---|---|---|---|---|---|
| SeaweedFS | lookup volume id, cacheable | O(1) disk seek | Yes | Yes | |
| SeaweedFS Filer | Linearly Scalable, Customizable | O(1) disk seek | FUSE | Yes | Yes |
| GlusterFS | hashing | FUSE, NFS | |||
| Ceph | hashing + rules | FUSE | Yes | ||
| MooseFS | in memory | FUSE | No | ||
| MinIO | separate meta file for each file | Yes | No |
Compared to GlusterFS
GlusterFS stores files, both directories and content, in configurable volumes called "bricks".
GlusterFS hashes the path and filename into ids, and assigned to virtual volumes, and then mapped to "bricks".
Compared to MooseFS
MooseFS chooses to neglect small file issue. From moosefs 3.0 manual, "even a small file will occupy 64KiB plus additionally 4KiB of checksums and 1KiB for the header", because it "was initially designed for keeping large amounts (like several thousands) of very big files"
MooseFS Master Server keeps all meta data in memory. Same issue as HDFS namenode.
Compared to Ceph
Ceph can be setup similar to SeaweedFS as a key->blob store. It is much more complicated, with the need to support layers on top of it. Here is a more detailed comparison
SeaweedFS has a centralized master group to look up free volumes, while Ceph uses hashing and metadata servers to locate its objects. Having a centralized master makes it easy to code and manage.
Ceph, like SeaweedFS, is based on the object store RADOS. Ceph is rather complicated with mixed reviews.
Ceph uses CRUSH hashing to automatically manage data placement, which is efficient to locate the data. But the data has to be placed according to the CRUSH algorithm. Any wrong configuration would cause data loss. Topology changes, such as adding new servers to increase capacity, will cause data migration with high IO cost to fit the CRUSH algorithm. SeaweedFS places data by assigning them to any writable volumes. If writes to one volume failed, just pick another volume to write. Adding more volumes is also as simple as it can be.
SeaweedFS is optimized for small files. Small files are stored as one continuous block of content, with at most 8 unused bytes between files. Small file access is O(1) disk read.
SeaweedFS Filer uses off-the-shelf stores, such as MySql, Postgres, Sqlite, Mongodb, Redis, Elastic Search, Cassandra, HBase, MemSql, TiDB, CockroachCB, Etcd, YDB, to manage file directories. These stores are proven, scalable, and easier to manage.
| SeaweedFS | comparable to Ceph | advantage |
|---|---|---|
| Master | MDS | simpler |
| Volume | OSD | optimized for small files |
| Filer | Ceph FS | linearly scalable, Customizable, O(1) or O(logN) |
Compared to MinIO
Please note, as Apr 25, 2026 MinIO ceased development. It's strongly discouraged to use that unmaintained software with multiple security bugs.
MinIO followed AWS S3 closely and was ideal for testing for S3 API. It had good UI, policies, versionings, etc. SeaweedFS is trying to catch up here.
MinIO metadata were in simple files. Each file write will incur extra writes to corresponding meta file.
MinIO did not have optimization for lots of small files. The files were simply stored as is to local disks. Plus the extra meta file and shards for erasure coding, it only amplifies the LOSF problem.
MinIO had multiple disk IO to read one file. SeaweedFS has O(1) disk reads, even for erasure coded files.
MinIO had full-time erasure coding. SeaweedFS uses replication on hot data for faster speed and optionally applies erasure coding on warm data.
MinIO did not have POSIX-like API support.
MinIO had specific requirements on storage layout. It is not flexible to adjust capacity. In SeaweedFS, just start one volume server pointing to the master. That's all.
Dev Plan
- More tools and documentation, on how to manage and scale the system.
- Read and write stream data.
- Support structured data.
This is a super exciting project! And we need helpers and support!
Installation Guide
Installation guide for users who are not familiar with golang
Step 1: install go on your machine and setup the environment by following the instructions at:
https://golang.org/doc/install
make sure to define your $GOPATH
Step 2: checkout this repo:
git clone https://github.com/seaweedfs/seaweedfs.git
Step 3: download, compile, and install the project by executing the following command
cd seaweedfs/weed && make install
Once this is done, you will find the executable "weed" in your $GOPATH/bin directory
For more installation options, including how to run with Docker, see the Getting Started guide.
Disk Related Topics
Hard Drive Performance
When testing read performance on SeaweedFS, it basically becomes a performance test of your hard drive's random read speed. Hard drives usually get 100MB/s~200MB/s.
Solid State Disk
To modify or delete small files, SSD must delete a whole block at a time, and move content in existing blocks to a new block. SSD is fast when brand new, but will get fragmented over time and you have to garbage collect, compacting blocks. SeaweedFS is friendly to SSD since it is append-only. Deletion and compaction are done on volume level in the background, not slowing reading and not causing fragmentation.
Benchmark
My Own Unscientific Single Machine Results on Mac Book with Solid State Disk, CPU: 1 Intel Core i7 2.6GHz.
Write 1 million 1KB file:
Concurrency Level: 16
Time taken for tests: 66.753 seconds
Completed requests: 1048576
Failed requests: 0
Total transferred: 1106789009 bytes
Requests per second: 15708.23 [#/sec]
Transfer rate: 16191.69 [Kbytes/sec]
Connection Times (ms)
min avg max std
Total: 0.3 1.0 84.3 0.9
Percentage of the requests served within a certain time (ms)
50% 0.8 ms
66% 1.0 ms
75% 1.1 ms
80% 1.2 ms
90% 1.4 ms
95% 1.7 ms
98% 2.1 ms
99% 2.6 ms
100% 84.3 ms
Randomly read 1 million files:
Concurrency Level: 16
Time taken for tests: 22.301 seconds
Completed requests: 1048576
Failed requests: 0
Total transferred: 1106812873 bytes
Requests per second: 47019.38 [#/sec]
Transfer rate: 48467.57 [Kbytes/sec]
Connection Times (ms)
min avg max std
Total: 0.0 0.3 54.1 0.2
Percentage of the requests served within a certain time (ms)
50% 0.3 ms
90% 0.4 ms
98% 0.6 ms
99% 0.7 ms
100% 54.1 ms
Run WARP and launch a mixed benchmark.
make benchmark
warp: Benchmark data written to "warp-mixed-2025-12-05[194844]-kBpU.csv.zst"
Mixed operations.
Operation: DELETE, 10%, Concurrency: 20, Ran 42s.
* Throughput: 55.13 obj/s
Operation: GET, 45%, Concurrency: 20, Ran 42s.
* Throughput: 2477.45 MiB/s, 247.75 obj/s
Operation: PUT, 15%, Concurrency: 20, Ran 42s.
* Throughput: 825.85 MiB/s, 82.59 obj/s
Operation: STAT, 30%, Concurrency: 20, Ran 42s.
* Throughput: 165.27 obj/s
Cluster Total: 3302.88 MiB/s, 550.51 obj/s over 43s.
Enterprise
For enterprise users, please visit seaweedfs.com for the SeaweedFS Enterprise Edition, which has advanced features, including data recovery, self-healing storage, customizable erasure coding, EC vacuum and repair, etc.
License
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
The text of this page is available for modification and reuse under the terms of the Creative Commons Attribution-Sharealike 3.0 Unported License and the GNU Free Documentation License (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Stargazers over time
