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distributed read/write filesystem in go, bound to local mountpoint using go-fuse
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mrpc finished refactoring, tests passing again, hadoop xattrs next Mar 24, 2014


A fuse-mountable, almost-POSIX distributed filesystem.

Why MaggieFS?

Why a new distributed file system? In short, because the available DFSes are limited either by their feature-set or their architecture. The two most successful open-source distributed filesystems are Hadoop and GlusterFS.

Hadoop is a properly distributed filesystem with facility for dispatching computation to the node it's located and saving network load. However, it lacks a POSIX API or any sort of unix integration. It's a java library which takes strings that look a lot like paths, but is not usable by any non-hadoop application. Users and permissions must be configured separately from your basic unix users, and all interaction with the filesystem has to be done via API or via Hadoop's cumbersome CLI tool. Additionally, files are write-once, append-only (no random writes), and its I/O architecture is optimized for long streaming reads in a way which severely disadvantages random reads. MaggieFS borrows several ideas from the Hadoop architecture but is focused on a POSIX API, natively readable by any application on your server, and emphasizes random read speed (necessarily since kernels make sequential random reads for file streaming). OS read-ahead compensates for latency problems with stateless reads.

GlusterFS is more of a SAN than it is a properly distributed filesystem. It exposes volumes as block devices, and then it's up to the administrator to configure "translators" in front of these block devices which stripe and mirror data, which also serve as bottlenecks and points of failure in the system while giving administrators plenty of places to make mistakes. This means that your application is very rarely reading local data -- data must be funneled through one or many translators, separating your cluster into compute and storage nodes, rather than a single grid. MaggieFS by comparison has a single (soon to be multiple) master(s) and everything else is a peer. As a peer, you export your drives, you get access to the filesystem, and you don't have to worry about configuring your networked storage topology.

The goal of MaggieFS is to provide posix semantics and full read-write capability, to be faster than HDFS in some situations, and to take up fewer system resources.


Architecturally, MaggieFS is very similar to the Hadoop Distributed Filesystem (HDFS) with a model of one namenode (master in our parlance) and N datanodes (peers). Files are split into chunks of 128MB and then distributed and replicated across the peers.

The Master contains metadata about the filesystem, including the tree structure, file attributes (permissions, size, etc), and the list of peers containing the blocks for each file. It also manages happens-before relationships having to do with when the changes from writing, fsyncing and closing files are guaranteed to be visible to the rest of the cluster.

Peers do two things: They export local drives to the cluster, for use by the filesystem, and provide a mountpoint for access to the filesystem. A peer that exports 0 drives would be a pure client. The filesystem is mounted using the Fuse low-level API, which provides a tree model to the kernel and the kernel walks that tree to resolve paths, reading/writing data from files by inode id. In terms of performance, the kernel routines for managing readahead and VFS cache should benefit MaggieFS and likely make it more performant than Hadoop for random reads while equivalent for streaming reads, once MaggieFS itself is free of glaring performance holes.

To Install

The mfs binary is responsible for running the nameserver and dataserver processes, as well as the client.

To install, first set up your $GOPATH according to standard go project conventions.

mfs has a dependency on the leveldb library, version 1.9.0. If you're using ubuntu 13.04, it should be available using:

apt-get libleveldb-dev

If your package manager doesn't have a recent enough version of levelDB, follow the mfs levelDB installation instructions to set up go to build against a downloaded version of the library.

Finally, run:

go get  
go install 

And you'll have the mfs binary in $GOPATH/bin.

To Run

Singlenode mode

The mfs binary has 4 operation modes (and a couple utilities).

mfs singlenode [numDatanodes] [volumesPerDN] [replicationFactor] [baseDir for data] [mountPoint] 

mfs singlenode runs a mock cluster by building out directories under a temp directory and striping the relevant ports up from 11000. It's useful for testing or test-driving. If you wanted to run a mock cluster with 3 DNs, 1 volume each and a replicationFactor of 2, you could run:

mfs singlenode 3 1 2 /tmp/maggiefsData /tmp/maggiefsMount

Running a full cluster is a little (but only a little) more involved. You need to run a master on one server and peers on several others, using configuration files. These steps are outlined below:

mfs command-line options

mfs configuration files

Cluster setup instructions

Hadoop integration

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