Tahoe-LAFS Architecture

  1. Overview
  2. The Key-Value Store
  3. File Encoding
  4. Capabilities
  5. Server Selection
  6. Swarming Download, Trickling Upload
  7. The Filesystem Layer
  8. Leases, Refreshing, Garbage Collection
  9. File Repairer
  10. Security
  11. Reliability


(See the docs/specifications directory for more details.)

There are three layers: the key-value store, the filesystem, and the application.

The lowest layer is the key-value store. The keys are “capabilities” – short ASCII strings – and the values are sequences of data bytes. This data is encrypted and distributed across a number of nodes, such that it will survive the loss of most of the nodes. There are no hard limits on the size of the values, but there may be performance issues with extremely large values (just due to the limitation of network bandwidth). In practice, values as small as a few bytes and as large as tens of gigabytes are in common use.

The middle layer is the decentralized filesystem: a directed graph in which the intermediate nodes are directories and the leaf nodes are files. The leaf nodes contain only the data – they contain no metadata other than the length in bytes. The edges leading to leaf nodes have metadata attached to them about the file they point to. Therefore, the same file may be associated with different metadata if it is referred to through different edges.

The top layer consists of the applications using the filesystem. Allmydata.com used it for a backup service: the application periodically copies files from the local disk onto the decentralized filesystem. We later provide read-only access to those files, allowing users to recover them. There are several other applications built on top of the Tahoe-LAFS filesystem (see the RelatedProjects page of the wiki for a list).

The Key-Value Store

The key-value store is implemented by a grid of Tahoe-LAFS storage servers – user-space processes. Tahoe-LAFS storage clients communicate with the storage servers over TCP.

Storage servers hold data in the form of “shares”. Shares are encoded pieces of files. There are a configurable number of shares for each file, 10 by default. Normally, each share is stored on a separate server, but in some cases a single server can hold multiple shares of a file.

Nodes learn about each other through an “introducer”. Each server connects to the introducer at startup and announces its presence. Each client connects to the introducer at startup, and receives a list of all servers from it. Each client then connects to every server, creating a “bi-clique” topology. In the current release, nodes behind NAT boxes will connect to all nodes that they can open connections to, but they cannot open connections to other nodes behind NAT boxes. Therefore, the more nodes behind NAT boxes, the less the topology resembles the intended bi-clique topology.

The introducer is a Single Point of Failure (“SPoF”), in that clients who never connect to the introducer will be unable to connect to any storage servers, but once a client has been introduced to everybody, it does not need the introducer again until it is restarted. The danger of a SPoF is further reduced in two ways. First, the introducer is defined by a hostname and a private key, which are easy to move to a new host in case the original one suffers an unrecoverable hardware problem. Second, even if the private key is lost, clients can be reconfigured to use a new introducer.

For future releases, we have plans to decentralize introduction, allowing any server to tell a new client about all the others.

File Encoding

When a client stores a file on the grid, it first encrypts the file. It then breaks the encrypted file into small segments, in order to reduce the memory footprint, and to decrease the lag between initiating a download and receiving the first part of the file; for example the lag between hitting “play” and a movie actually starting.

The client then erasure-codes each segment, producing blocks of which only a subset are needed to reconstruct the segment (3 out of 10, with the default settings).

It sends one block from each segment to a given server. The set of blocks on a given server constitutes a “share”. Therefore a subset f the shares (3 out of 10, by default) are needed to reconstruct the file.

A hash of the encryption key is used to form the “storage index”, which is used for both server selection (described below) and to index shares within the Storage Servers on the selected nodes.

The client computes secure hashes of the ciphertext and of the shares. It uses Merkle Trees so that it is possible to verify the correctness of a subset of the data without requiring all of the data. For example, this allows you to verify the correctness of the first segment of a movie file and then begin playing the movie file in your movie viewer before the entire movie file has been downloaded.

These hashes are stored in a small datastructure named the Capability Extension Block which is stored on the storage servers alongside each share.

The capability contains the encryption key, the hash of the Capability Extension Block, and any encoding parameters necessary to perform the eventual decoding process. For convenience, it also contains the size of the file being stored.

To download, the client that wishes to turn a capability into a sequence of bytes will obtain the blocks from storage servers, use erasure-decoding to turn them into segments of ciphertext, use the decryption key to convert that into plaintext, then emit the plaintext bytes to the output target.


Capabilities to immutable files represent a specific set of bytes. Think of it like a hash function: you feed in a bunch of bytes, and you get out a capability, which is deterministically derived from the input data: changing even one bit of the input data will result in a completely different capability.

Read-only capabilities to mutable files represent the ability to get a set of bytes representing some version of the file, most likely the latest version. Each read-only capability is unique. In fact, each mutable file has a unique public/private key pair created when the mutable file is created, and the read-only capability to that file includes a secure hash of the public key.

Read-write capabilities to mutable files represent the ability to read the file (just like a read-only capability) and also to write a new version of the file, overwriting any extant version. Read-write capabilities are unique – each one includes the secure hash of the private key associated with that mutable file.

The capability provides both “location” and “identification”: you can use it to retrieve a set of bytes, and then you can use it to validate (“identify”) that these potential bytes are indeed the ones that you were looking for.

The “key-value store” layer doesn’t include human-meaningful names. Capabilities sit on the “global+secure” edge of Zooko’s Triangle. They are self-authenticating, meaning that nobody can trick you into accepting a file that doesn’t match the capability you used to refer to that file. The filesystem layer (described below) adds human-meaningful names atop the key-value layer.

Server Selection

When a file is uploaded, the encoded shares are sent to some servers. But to which ones? The “server selection” algorithm is used to make this choice.

The storage index is used to consistently-permute the set of all servers nodes (by sorting them by HASH(storage_index+nodeid)). Each file gets a different permutation, which (on average) will evenly distribute shares among the grid and avoid hotspots. Each server has announced its available space when it connected to the introducer, and we use that available space information to remove any servers that cannot hold an encoded share for our file. Then we ask some of the servers thus removed if they are already holding any encoded shares for our file; we use this information later. (We ask any servers which are in the first 2*``N`` elements of the permuted list.)

We then use the permuted list of servers to ask each server, in turn, if it will hold a share for us (a share that was not reported as being already present when we talked to the full servers earlier, and that we have not already planned to upload to a different server). We plan to send a share to a server by sending an ‘allocate_buckets() query’ to the server with the number of that share. Some will say yes they can hold that share, others (those who have become full since they announced their available space) will say no; when a server refuses our request, we take that share to the next server on the list. In the response to allocate_buckets() the server will also inform us of any shares of that file that it already has. We keep going until we run out of shares that need to be stored. At the end of the process, we’ll have a table that maps each share number to a server, and then we can begin the encode and push phase, using the table to decide where each share should be sent.

Most of the time, this will result in one share per server, which gives us maximum reliability. If there are fewer writable servers than there are unstored shares, we’ll be forced to loop around, eventually giving multiple shares to a single server.

If we have to loop through the node list a second time, we accelerate the query process, by asking each node to hold multiple shares on the second pass. In most cases, this means we’ll never send more than two queries to any given node.

If a server is unreachable, or has an error, or refuses to accept any of our shares, we remove it from the permuted list, so we won’t query it again for this file. If a server already has shares for the file we’re uploading, we add that information to the share-to-server table. This lets us do less work for files which have been uploaded once before, while making sure we still wind up with as many shares as we desire.

Before a file upload is called successful, it has to pass an upload health check. For immutable files, we check to see that a condition called ‘servers-of-happiness’ is satisfied. When satisfied, ‘servers-of-happiness’ assures us that enough pieces of the file are distributed across enough servers on the grid to ensure that the availability of the file will not be affected if a few of those servers later fail. For mutable files and directories, we check to see that all of the encoded shares generated during the upload process were successfully placed on the grid. This is a weaker check than ‘servers-of-happiness’; it does not consider any information about how the encoded shares are placed on the grid, and cannot detect situations in which all or a majority of the encoded shares generated during the upload process reside on only one storage server. We hope to extend ‘servers-of-happiness’ to mutable files in a future release of Tahoe-LAFS. If, at the end of the upload process, the appropriate upload health check fails, the upload is considered a failure.

The current defaults use k = 3, servers_of_happiness = 7, and N = 10. N = 10 means that we’ll try to place 10 shares. k = 3 means that we need any three shares to recover the file. servers_of_happiness = 7 means that we’ll consider an immutable file upload to be successful if we can place shares on enough servers that there are 7 different servers, the correct functioning of any k of which guarantee the availability of the immutable file.

N = 10 and k = 3 means there is a 3.3x expansion factor. On a small grid, you should set N about equal to the number of storage servers in your grid; on a large grid, you might set it to something smaller to avoid the overhead of contacting every server to place a file. In either case, you should then set k such that N/k reflects your desired availability goals. The best value for servers_of_happiness will depend on how you use Tahoe-LAFS. In a friendnet with a variable number of servers, it might make sense to set it to the smallest number of servers that you expect to have online and accepting shares at any given time. In a stable environment without much server churn, it may make sense to set servers_of_happiness = N.

When downloading a file, the current version just asks all known servers for any shares they might have. Once it has received enough responses that it knows where to find the needed k shares, it downloads at least the first segment from those servers. This means that it tends to download shares from the fastest servers. If some servers had more than one share, it will continue sending “Do You Have Block” requests to other servers, so that it can download subsequent segments from distinct servers (sorted by their DYHB round-trip times), if possible.

future work

A future release will use the server selection algorithm to reduce the number of queries that must be sent out.

Other peer-node selection algorithms are possible. One earlier version (known as “Tahoe 3”) used the permutation to place the nodes around a large ring, distributed the shares evenly around the same ring, then walked clockwise from 0 with a basket. Each time it encountered a share, it put it in the basket, each time it encountered a server, give it as many shares from the basket as they’d accept. This reduced the number of queries (usually to 1) for small grids (where N is larger than the number of nodes), but resulted in extremely non-uniform share distribution, which significantly hurt reliability (sometimes the permutation resulted in most of the shares being dumped on a single node).

Another algorithm (known as “denver airport” [1]) uses the permuted hash to decide on an approximate target for each share, then sends lease requests via Chord routing. The request includes the contact information of the uploading node, and asks that the node which eventually accepts the lease should contact the uploader directly. The shares are then transferred over direct connections rather than through multiple Chord hops. Download uses the same approach. This allows nodes to avoid maintaining a large number of long-term connections, at the expense of complexity and latency.

[1]all of these names are derived from the location where they were concocted, in this case in a car ride from Boulder to DEN. To be precise, “Tahoe 1” was an unworkable scheme in which everyone who holds shares for a given file would form a sort of cabal which kept track of all the others, “Tahoe 2” is the first-100-nodes in the permuted hash described in this document, and “Tahoe 3” (or perhaps “Potrero hill 1”) was the abandoned ring-with-many-hands approach.

Swarming Download, Trickling Upload

Because the shares being downloaded are distributed across a large number of nodes, the download process will pull from many of them at the same time. The current encoding parameters require 3 shares to be retrieved for each segment, which means that up to 3 nodes will be used simultaneously. For larger networks, 8-of-22 encoding could be used, meaning 8 nodes can be used simultaneously. This allows the download process to use the sum of the available nodes’ upload bandwidths, resulting in downloads that take full advantage of the common 8x disparity between download and upload bandwith on modern ADSL lines.

On the other hand, uploads are hampered by the need to upload encoded shares that are larger than the original data (3.3x larger with the current default encoding parameters), through the slow end of the asymmetric connection. This means that on a typical 8x ADSL line, uploading a file will take about 32 times longer than downloading it again later.

Smaller expansion ratios can reduce this upload penalty, at the expense of reliability (see Reliability, below). By using an “upload helper”, this penalty is eliminated: the client does a 1x upload of encrypted data to the helper, then the helper performs encoding and pushes the shares to the storage servers. This is an improvement if the helper has significantly higher upload bandwidth than the client, so it makes the most sense for a commercially-run grid for which all of the storage servers are in a colo facility with high interconnect bandwidth. In this case, the helper is placed in the same facility, so the helper-to-storage-server bandwidth is huge.

See The Tahoe Upload Helper for details about the upload helper.

The Filesystem Layer

The “filesystem” layer is responsible for mapping human-meaningful pathnames (directories and filenames) to pieces of data. The actual bytes inside these files are referenced by capability, but the filesystem layer is where the directory names, file names, and metadata are kept.

The filesystem layer is a graph of directories. Each directory contains a table of named children. These children are either other directories or files. All children are referenced by their capability.

A directory has two forms of capability: read-write caps and read-only caps. The table of children inside the directory has a read-write and read-only capability for each child. If you have a read-only capability for a given directory, you will not be able to access the read-write capability of its children. This results in “transitively read-only” directory access.

By having two different capabilities, you can choose which you want to share with someone else. If you create a new directory and share the read-write capability for it with a friend, then you will both be able to modify its contents. If instead you give them the read-only capability, then they will not be able to modify the contents. Any capability that you receive can be linked in to any directory that you can modify, so very powerful shared+published directory structures can be built from these components.

This structure enable individual users to have their own personal space, with links to spaces that are shared with specific other users, and other spaces that are globally visible.

Leases, Refreshing, Garbage Collection

When a file or directory in the virtual filesystem is no longer referenced, the space that its shares occupied on each storage server can be freed, making room for other shares. Tahoe-LAFS uses a garbage collection (“GC”) mechanism to implement this space-reclamation process. Each share has one or more “leases”, which are managed by clients who want the file/directory to be retained. The storage server accepts each share for a pre-defined period of time, and is allowed to delete the share if all of the leases are cancelled or allowed to expire.

Garbage collection is not enabled by default: storage servers will not delete shares without being explicitly configured to do so. When GC is enabled, clients are responsible for renewing their leases on a periodic basis at least frequently enough to prevent any of the leases from expiring before the next renewal pass.

See Garbage Collection in Tahoe for further information, and for how to configure garbage collection.

File Repairer

Shares may go away because the storage server hosting them has suffered a failure: either temporary downtime (affecting availability of the file), or a permanent data loss (affecting the preservation of the file). Hard drives crash, power supplies explode, coffee spills, and asteroids strike. The goal of a robust distributed filesystem is to survive these setbacks.

To work against this slow, continual loss of shares, a File Checker is used to periodically count the number of shares still available for any given file. A more extensive form of checking known as the File Verifier can download the ciphertext of the target file and perform integrity checks (using strong hashes) to make sure the data is still intact. When the file is found to have decayed below some threshold, the File Repairer can be used to regenerate and re-upload the missing shares. These processes are conceptually distinct (the repairer is only run if the checker/verifier decides it is necessary), but in practice they will be closely related, and may run in the same process.

The repairer process does not get the full capability of the file to be maintained: it merely gets the “repairer capability” subset, which does not include the decryption key. The File Verifier uses that data to find out which nodes ought to hold shares for this file, and to see if those nodes are still around and willing to provide the data. If the file is not healthy enough, the File Repairer is invoked to download the ciphertext, regenerate any missing shares, and upload them to new nodes. The goal of the File Repairer is to finish up with a full set of N shares.

There are a number of engineering issues to be resolved here. The bandwidth, disk IO, and CPU time consumed by the verification/repair process must be balanced against the robustness that it provides to the grid. The nodes involved in repair will have very different access patterns than normal nodes, such that these processes may need to be run on hosts with more memory or network connectivity than usual. The frequency of repair will directly affect the resources consumed. In some cases, verification of multiple files can be performed at the same time, and repair of files can be delegated off to other nodes.

future work

Currently there are two modes of checking on the health of your file: “Checker” simply asks storage servers which shares they have and does nothing to try to verify that they aren’t lying. “Verifier” downloads and cryptographically verifies every bit of every share of the file from every server, which costs a lot of network and CPU. A future improvement would be to make a random-sampling verifier which downloads and cryptographically verifies only a few randomly-chosen blocks from each server. This would require much less network and CPU but it could make it extremely unlikely that any sort of corruption – even malicious corruption intended to evade detection – would evade detection. This would be an instance of a cryptographic notion called “Proof of Retrievability”. Note that to implement this requires no change to the server or to the cryptographic data structure – with the current data structure and the current protocol it is up to the client which blocks they choose to download, so this would be solely a change in client behavior.


The design goal for this project is that an attacker may be able to deny service (i.e. prevent you from recovering a file that was uploaded earlier) but can accomplish none of the following three attacks:

  1. violate confidentiality: the attacker gets to view data to which you have not granted them access
  2. violate integrity: the attacker convinces you that the wrong data is actually the data you were intending to retrieve
  3. violate unforgeability: the attacker gets to modify a mutable file or directory (either the pathnames or the file contents) to which you have not given them write permission

Integrity (the promise that the downloaded data will match the uploaded data) is provided by the hashes embedded in the capability (for immutable files) or the digital signature (for mutable files). Confidentiality (the promise that the data is only readable by people with the capability) is provided by the encryption key embedded in the capability (for both immutable and mutable files). Data availability (the hope that data which has been uploaded in the past will be downloadable in the future) is provided by the grid, which distributes failures in a way that reduces the correlation between individual node failure and overall file recovery failure, and by the erasure-coding technique used to generate shares.

Many of these security properties depend upon the usual cryptographic assumptions: the resistance of AES and RSA to attack, the resistance of SHA-256 to collision attacks and pre-image attacks, and upon the proximity of 2^-128 and 2^-256 to zero. A break in AES would allow a confidentiality violation, a collision break in SHA-256 would allow a consistency violation, and a break in RSA would allow a mutability violation.

There is no attempt made to provide anonymity, neither of the origin of a piece of data nor the identity of the subsequent downloaders. In general, anyone who already knows the contents of a file will be in a strong position to determine who else is uploading or downloading it. Also, it is quite easy for a sufficiently large coalition of nodes to correlate the set of nodes who are all uploading or downloading the same file, even if the attacker does not know the contents of the file in question.

Also note that the file size and (when convergence is being used) a keyed hash of the plaintext are not protected. Many people can determine the size of the file you are accessing, and if they already know the contents of a given file, they will be able to determine that you are uploading or downloading the same one.

The capability-based security model is used throughout this project. Directory operations are expressed in terms of distinct read- and write- capabilities. Knowing the read-capability of a file is equivalent to the ability to read the corresponding data. The capability to validate the correctness of a file is strictly weaker than the read-capability (possession of read-capability automatically grants you possession of validate-capability, but not vice versa). These capabilities may be expressly delegated (irrevocably) by simply transferring the relevant secrets.

The application layer can provide whatever access model is desired, built on top of this capability access model. The first big user of this system so far is allmydata.com. The allmydata.com access model currently works like a normal web site, using username and password to give a user access to her “virtual drive”. In addition, allmydata.com users can share individual files (using a file sharing interface built on top of the immutable file read capabilities).


File encoding and peer-node selection parameters can be adjusted to achieve different goals. Each choice results in a number of properties; there are many tradeoffs.

First, some terms: the erasure-coding algorithm is described as k-out-of-N (for this release, the default values are k = 3 and N = 10). Each grid will have some number of nodes; this number will rise and fall over time as nodes join, drop out, come back, and leave forever. Files are of various sizes, some are popular, others are unpopular. Nodes have various capacities, variable upload/download bandwidths, and network latency. Most of the mathematical models that look at node failure assume some average (and independent) probability ‘P’ of a given node being available: this can be high (servers tend to be online and available >90% of the time) or low (laptops tend to be turned on for an hour then disappear for several days). Files are encoded in segments of a given maximum size, which affects memory usage.

The ratio of N/k is the “expansion factor”. Higher expansion factors improve reliability very quickly (the binomial distribution curve is very sharp), but consumes much more grid capacity. When P=50%, the absolute value of k affects the granularity of the binomial curve (1-out-of-2 is much worse than 50-out-of-100), but high values asymptotically approach a constant (i.e. 500-of-1000 is not much better than 50-of-100). When P is high and the expansion factor is held at a constant, higher values of k and N give much better reliability (for P=99%, 50-out-of-100 is much much better than 5-of-10, roughly 10^50 times better), because there are more shares that can be lost without losing the file.

Likewise, the total number of nodes in the network affects the same granularity: having only one node means a single point of failure, no matter how many copies of the file you make. Independent nodes (with uncorrelated failures) are necessary to hit the mathematical ideals: if you have 100 nodes but they are all in the same office building, then a single power failure will take out all of them at once. Pseudospoofing, also called a “Sybil Attack”, is where a single attacker convinces you that they are actually multiple servers, so that you think you are using a large number of independent nodes, but in fact you have a single point of failure (where the attacker turns off all their machines at once). Large grids, with lots of truly independent nodes, will enable the use of lower expansion factors to achieve the same reliability, but will increase overhead because each node needs to know something about every other, and the rate at which nodes come and go will be higher (requiring network maintenance traffic). Also, the File Repairer work will increase with larger grids, although then the job can be distributed out to more nodes.

Higher values of N increase overhead: more shares means more Merkle hashes that must be included with the data, and more nodes to contact to retrieve the shares. Smaller segment sizes reduce memory usage (since each segment must be held in memory while erasure coding runs) and improves “alacrity” (since downloading can validate a smaller piece of data faster, delivering it to the target sooner), but also increase overhead (because more blocks means more Merkle hashes to validate them).

In general, small private grids should work well, but the participants will have to decide between storage overhead and reliability. Large stable grids will be able to reduce the expansion factor down to a bare minimum while still retaining high reliability, but large unstable grids (where nodes are coming and going very quickly) may require more repair/verification bandwidth than actual upload/download traffic.