seaweedfs

SeaweedFS

SeaweedFS is a simple and highly scalable distributed file system. There are two objectives:

1. to store billions of files!
2. to serve the files fast!

One weed binary serves an S3 object store, a POSIX file system, and a lakehouse with S3 Tables, all over the same data. Each blob is one disk read away, capacity grows by starting another volume server, and cloud storage can be cached or tiered transparently.

• Download Binaries for different platforms
• Wiki Documentation
• Community: Slack, Twitter, Telegram, Reddit, Mailing List
• SeaweedFS White Paper and introduction slides: 2025.5, 2021.5, 2019.3

Table of Contents

• Quick Start
  • One command
  • Docker
  • Docker Compose
  • Kubernetes with Helm
  • Build from source
  • Scale out
• Why SeaweedFS
  • Fast
  • Scalable
  • The most complete S3 API
  • A data warehouse with S3 Tables
  • A fast cache for cloud storage
  • Active-active replication and more
• Architecture
• Compared to Other Systems
  • Compared to HDFS
  • Compared to GlusterFS, Ceph
  • Compared to MooseFS
  • Compared to Ceph
  • Compared to MinIO, RustFS
• Benchmark
• Enterprise
• License
• Sponsors

Quick Start

One command

Download the latest binary from the releases page and unzip the single weed (or weed.exe) file, or let the install script put it in /usr/local/bin:

curl -fsSL https://raw.githubusercontent.com/seaweedfs/seaweedfs/master/install.sh | bash

Then start a ready-to-use S3 object store:

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 exists, and admin/secret are valid credentials:

AWS_ACCESS_KEY_ID=admin AWS_SECRET_ACCESS_KEY=secret \
  aws --endpoint-url http://localhost:8333 s3 cp README.md s3://my-bucket/

The same process also runs the master, a volume server, the filer, WebDAV, the Iceberg REST catalog, and the Admin UI. Add S3_TABLE_BUCKET=warehouse to also create an Iceberg table bucket, or warehouse:LANCE for a Lance one. Drop the AWS keys to run without authentication for development.

macOS: if the binary is quarantined, run xattr -d com.apple.quarantine ./weed first.

weed mini is auto-tuned for one node and is fine for single-node production, such as an S3 gateway that issues presigned URLs. See [Quick Start with weed mini][WeedMini].

Docker

docker run -p 8333:8333 -v weed-data:/data \
  -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.

Docker Compose

To run master, volume server, filer, S3, and WebDAV as separate services:

wget https://raw.githubusercontent.com/seaweedfs/seaweedfs/master/docker/seaweedfs-compose.yml
wget -P prometheus https://raw.githubusercontent.com/seaweedfs/seaweedfs/master/docker/prometheus/prometheus.yml
docker compose -f seaweedfs-compose.yml -p seaweedfs up

[Docker Compose for S3][DockerComposeS3] adds credentials, and the docker/compose folder has variants for replication, mounts, message queues, and more.

Kubernetes with Helm

helm repo add seaweedfs https://seaweedfs.github.io/seaweedfs/helm
helm install seaweedfs seaweedfs/seaweedfs -n seaweedfs --create-namespace -f values.yaml

A production-shaped values.yaml for a three-node cluster: two copies of every write, three masters, and an S3 endpoint with credentials and a bucket.

…

The S3 endpoint is the seaweedfs-s3 service on port 8333. [Helm Chart Recipes][HelmRecipes] has values for a development cluster, a lakehouse with the Iceberg catalog exposed, filer metadata on PostgreSQL, and node-local disks. The [SeaweedFS Operator][Operator] and the [CSI driver][SeaweedFsCsiDriver] are the other Kubernetes paths.

Build from source

git clone https://github.com/seaweedfs/seaweedfs.git
cd seaweedfs/weed && make install

weed lands in $GOPATH/bin. [Getting Started][GettingStarted] covers running master, volume, filer, and S3 as separate processes.

Scale out

Capacity is a volume server. Start one on any machine with disk and point it at the master:

weed volume -dir=/data -master=<master_host>:9333

Nothing rebalances until you ask it to. Throughput is a filer or S3 gateway; they are stateless, so run as many as you need behind a load balancer. [Production Setup][ProductionSetup] walks through a multi-node cluster.

Back to TOC

Why SeaweedFS

Fast

• One disk read per blob. A small file is one blob; a large file is split into chunks of a few MB, each its own blob. A volume server keeps a 16-byte index entry per blob in memory and reads it in a single seek, also for erasure-coded data.
• The master is not in the read path. Clients cache the volume-to-server mapping and talk to volume servers directly.
• 40 bytes of metadata per file on disk. Small files are packed into append-only volume files, so there is no per-file inode, no per-file metadata file, no fragmentation, and writes are SSD friendly.
• Hot data is replicated; [erasure coding][ErasureCoding] is applied to warm data in the background, so writes never pay the encoding cost.
• The [Rust volume server][RustVolume] is a drop-in for higher throughput and lower tail latency on the same on-disk format.

On one laptop, [weed benchmark][Benchmarks] writes 1KB files at 15,700 per second and reads them back at 47,000 per second, and a mixed S3 [warp][S3Benchmark] run totals 3.2 GiB/s. Numbers are in the Benchmark section; throughput grows with volume servers and gateways.

Scalable

• The master tracks volumes, not files. A cluster with billions of files has a few thousand volumes, so the master stays small. One master is enough for most clusters; run three for [Raft failover][FailoverMaster].
• Adding a server adds capacity with no data reshuffle. Balancing, vacuum, erasure coding, and repair run on demand from [weed shell][WeedShell] or the [maintenance worker][Worker].
• Filer and S3 gateways are stateless and scale linearly. Directory metadata lives in a [store you already run][FilerStores]: LevelDB, RocksDB, SQLite, MySQL, PostgreSQL, Cassandra, HBase, MongoDB, Redis, Elasticsearch, etcd, TiKV, FoundationDB, YDB, ArangoDB, Tarantool, and MySQL or PostgreSQL compatible databases such as TiDB, CockroachDB, and MemSQL.
• Rack and data center aware [replication][Replication], [tiered storage][TieredStorage] across disk types, and [transparent cloud tiering][CloudTier] for unlimited capacity.
• Files from a byte to [tens of TB][SuperLargeFiles]. Volumes up to 8TB with the large-disk build.

The most complete S3 API

The S3 gateway implements the object, bucket, S3 Tables, IAM, and STS APIs on one endpoint, so the AWS SDKs and CLI, rclone, restic, Spark, and Trino work unchanged.

• [Versioning][Versioning], [Object Lock][ObjectLock] with retention and legal hold, [lifecycle][Lifecycle] rules, tagging, [CORS][CORS], [conditional reads and writes][ConditionalOps], checksums, presigned URLs, browser POST uploads, multipart uploads, and an atomic [RenameObject][RenameObject].
• [Bucket policies][BucketPolicies] with [conditions][PolicyConditions] and [variables][PolicyVariables]; IAM users, groups, and policies; STS with [OIDC][OIDC], LDAP, and [Kubernetes service accounts][K8sSA].
• [SSE-S3, SSE-KMS, and SSE-C][SSE] server-side encryption, with OpenBao and Vault, AWS KMS, Azure Key Vault, and GCP KMS as key providers.
• [Audit log][AuditLog], [bucket quota][BucketQuota], and [rate limiting][RateLimiting].
• Each bucket is its own collection, so deleting a bucket is instant.

The full operation list is in [Amazon S3 API][AmazonS3API], and [Supported APIs vs MinIO][S3vsMinio] compares. The S3 compatibility suite and the SDK, IAM, SSE, policy, and Spark integration tests run in CI on every change.

A data warehouse with S3 Tables

SeaweedFS is a lakehouse in one system. [S3 Table Buckets][S3TableBucket] hold Apache Iceberg tables by default, or [Lance][LanceCatalog] tables for vectors and multimodal data, and the built-in [Iceberg REST Catalog][IcebergCatalog] and Lance namespace serve them directly. There is no Hive Metastore, Glue, or separate catalog service to deploy, secure, and back up.

• Query engines operate on the same tables at the same time: [Spark][SparkIceberg], [Trino][TrinoIceberg], [Dremio][DremioIceberg], [DuckDB][DuckDBIceberg], [Apache Doris][DorisIceberg], [RisingWave][RisingWaveIceberg], ClickHouse, and [LanceDB][LanceDB]. Catalog commits are atomic compare-and-swap, so concurrent writers are safe. [Lakekeeper][Lakekeeper] can front the same storage with STS-vended credentials.
• [Automated table maintenance][IcebergMaintenance]: compaction, snapshot expiration, orphan file removal, and manifest rewriting, configured per bucket or table through the S3 Tables maintenance APIs, and the same for [Lance][LanceMaintenance].
• IAM at the bucket, namespace, and table level with standard bucket policies, see [S3 Tables Security][S3TablesSecurity].
• A [Hadoop compatible file system][Hadoop] for Spark, Flink, and HBase.

S3_TABLE_BUCKET=warehouse ./weed mini -dir=./data brings the whole stack up on a laptop.

A fast cache for cloud storage

[Cloud Drive][CloudDrive] mounts a bucket from S3, Google Cloud Storage, Azure, Backblaze B2, Wasabi, Storj, or any S3-compatible store into SeaweedFS and serves it at local speed:

• Metadata is pulled once, so listing, stat, and directory walks cost no cloud API calls.
• File content is downloaded once, on first read or [warmed][CacheRemote] by folder, name pattern, size, or age, and cached with the capacity of the whole cluster: cache everything, no churn.
• Local writes complete at local latency and are written back to the cloud asynchronously in the cloud's native layout, so other tools keep reading the bucket directly.
• Uncache by the same rules to free local disk while keeping the metadata.

[Cloud Tier][CloudTier] goes the other direction, moving whole warm volumes to cloud storage while keeping one-read access, and the [Gateway to Remote Object Storage][GatewayToRemoteObjectStore] mirrors every bucket to a remote store. Faster and cheaper than reading the cloud directly.

Active-active replication and more

• [Active-active or active-p