Overview of the bupstash repository format.


The most important part of bupstash is the repository. It is where all data is stored in a mostly encrypted form. The bupstash client interacts via the repository over stdin/stdout of the bupstash-serve process. This may be locally, or via a protocol such as ssh.

Because most data is encrypted, the repository structure is quite simple.


├── bupstash.sqlite3
├── data
│   ├── 079ef643e50a060b9302258a6af745d90637b3ef34d79fa889f3fd8d90f207ce
│   └── ...
├── repo.lock
└── storage-engine.json


An sqlite repository, with the following schema:

RepositoryMeta(Key primary key, Value) without rowid;

The metadata table has the follows key/value pairs:

# Unique identifier for this repository.

# Version marker for future upgrades.

# Marker for client side cache invalidation after gc.

# Marker that a garbage collection was interrupted.

The ItemOpLog is an append only ledger where each OpData entry is a bare LogOp of the following format:

type Xid data<16>;
type Address data<32>;

type LogOp  (AddItem | RemoveItems);

type AddItem {
  metadata: VersionedItemMetadata 

type RemoveItems {
  items: []Xid

type VersionedItemMetadata = (V1VersionedItemMetadata | ...)

type V1VersionedItemMetadata {
  primary_key_id: Xid,
  tree_height: usize,
  address: Address,
  encryped_metadata: data

struct V1EncryptedItemMetadata {
  plain_text_hash: data<32>
  send_key_id: Xid,
  hash_key_part_2: data<32>,
  timestamp: String,
  tags: Map[String]String,

It is important to note, all metadata like search tags are stored encrypted and are not readable without a master key or metadata key.

The Items table is an aggregated view of current items which have not be marked for removal.

data directory

This directory contains a set of encrypted and deduplicated data chunks. The name of the file corresponds to the an HMAC hash of the unencrypted contents, as such if two chunks are added to the repository with the same hmac, they only need to be stored once.

This directory is not used when the repository is configured for storage engines other than "Dir" storage.


This lock is held exclusively during garbage collection.


Contains the the storage engine specification, which allows storage of data chunks in external or alternative storage formats. This file is human editable to assist manual data migrations between supported formats.

The hash tree structure

Bupstash stores arbitrary streams of data in the repository by splitting the stream into chunks, hmac addressing the chunks, then compressing and encrypting the chunks with the public key portion of a bupstash key. Each chunk is then stored in the data directory in a file named after the hmac hash of the contents. As we generate a sequence of chunks with a corresponding hmac addresses, we can build a tree structure out of these addresses. Leaf nodes of the tree are simply the encrypted data. Other nodes in the tree are simply unencrypted lists of hmac hashes, which may point to encrypted leaf nodes, or other subtrees. The key idea behind the hash tree, is we can convert an arbitrary stream of data into a single HMAC address with approximately equal sized chunks. When multiple hash trees are added to the repository, they share structure and enable deduplication.

This addressing and encryption scheme has some important properties:

  • The repository owner cannot guess chunk contents as the HMAC key is unknown to him.
  • The repository owner cannot decrypt leaves of the hash tree, as they are encrypted.
  • The repository owner can iterate the hash tree for garbage collection purposes.
  • The repository owner can run garbage collection without retrieving the leaf nodes from cold storage.
  • The repository owner can push stream a of hash tree nodes to a client with no network round trips.
  • A client can send data streams to a repository without sharing the encryption key.
  • A client can retrieve and verify a datastream by checking hmacs.

These properties are desirable for enabling high performance garbage collection and data streaming with prefetch on the repository side.

Chunking and deduplication

Data is deduplicated by splitting a data stream into small chunks, and never storing the same chunk twice. The performance of this deduplication is thus determined by how chunks split points are defined.

One way to chunk data would be to split the data stream every N bytes, this works in some cases, but you will find your data is not deduplicated when similar, but offset data streams are chunked. The chunks will often not match up as data insertion/removal quickly desynchronizes the chunk streams. A good example of this problem is inserting a file into the middle of a tarball. No deduplication will occur after that file, as the data streams have been shifted by an offset.

To avoid this problem we need to find a way to resync the chunk streams when they diverge from eachother but then reconverge. One way to do this is via content defined chunking. The most intuitive way to think about content defined chunking is splitting a tarball into a chunk representing every file or directory, this means storing the same file in multiple tarballs will only ever be stored in the repository once.

Another way to do content defined chunking might be to split every time you see the sequence 0xffff in your data stream. Your chunks streams will always resync on the 0xffff byte after diverging, but relies on your data containing 0xffff in evenly spaced places. What we really want is a way to pseudorandomly detect good split points, so the chunking does not really depend on byte values within the chunk. Luckily we have such functions, they are called hash functions. If we split a chunk whenever the hash of the last N bytes is 0xff, we might get a good enough pseudorandom set of chunks, which also resynchronize with mostly similar data.

So what does bupstash use? Bupstash uses a combination of tar splitting on directory boundaries and content defined chunking when uploading a directory directly, and purely content defined chunking with a hash function when chunking arbitrary data.

It should be noted the chunking algorithms can be changed and mixed at any time and will not affect the bupstash repository or reading data streams back.

Chunk formats

Chunks in the database are one of the following types, in general we know the type of a chunk based on the item metadata and the hash tree height.

Encrypted data chunk

These chunks form the roots of our hash trees, they contain encrypted data. They contain a key exchange packet, with enough information for the master key to derive the session key.


After decryption, the chunk is optionally compressed, so is either compressed data, or data with a null footer byte.



DATA[...] || 0x00

Valid compression flags are:

  • 1 << 0 == lz4 compression.

Hash tree node chunk

These chunks form non leaf nodes in our hash tree, and consist of an array of addresses.


These addresses must be recursively followed to read our data chunks, these addresses correspond to data chunks when the tree height is 0.

Format of key exchange bytes

Coming soon...