This page documents the internal data structures and storage mechanisms of Borg. It is partly based on mailing list discussion about internals and also on static code analysis.
Repository and Archives¶
Borg stores its data in a Repository. Each repository can hold multiple Archives, which represent individual backups that contain a full archive of the files specified when the backup was performed. Deduplication is performed across multiple backups, both on data and metadata, using Chunks created by the chunker using the Buzhash algorithm.
Each repository has the following file structure:
- simple text file telling that this is a Borg repository
- repository configuration
- directory where the actual data is stored
- hints for repository compaction
- repository index
- lock.roster and lock.exclusive/*
- used by the locking system to manage shared and exclusive locks
Borg uses locks to get (exclusive or shared) access to the cache and the repository.
The locking system is based on creating a directory lock.exclusive (for exclusive locks). Inside the lock directory, there is a file indication hostname, process id and thread id of the lock holder.
There is also a json file lock.roster that keeps a directory of all shared and exclusive lockers.
If the process can create the lock.exclusive directory for a resource, it has the lock for it. If creation fails (because the directory has already been created by some other process), lock acquisition fails.
The cache lock is usually in ~/.cache/borg/REPOID/lock.*. The repository lock is in repository/lock.*.
In case you run into troubles with the locks, you can just delete the lock.* directory and file IF you first make sure that no Borg process is running on any machine that accesses this resource. Be very careful, the cache or repository might get damaged if multiple processes use it at the same time.
Each repository has a
config file which which is a
and looks like this:
[repository] version = 1 segments_per_dir = 10000 max_segment_size = 5242880 id = 57d6c1d52ce76a836b532b0e42e677dec6af9fca3673db511279358828a21ed6
This is where the
repository.id is stored. It is a unique
identifier for repositories. It will not change if you move the
repository around so you can make a local transfer then decide to move
the repository to another (even remote) location at a later time.
The key to address the key/value store is usually computed like this:
key = id = id_hash(unencrypted_data)
The id_hash function is:
- sha256 (no encryption keys available)
- hmac-sha256 (encryption keys available)
Segments and archives¶
Objects referenced by a key are stored inline in files (segments) of approx.
5MB size in numbered subdirectories of
- header size
Segments are built locally, and then uploaded. Those files are strictly append-only and modified only once.
Tag is either
COMMIT. A segment file is
basically a transaction log where each repository operation is
appended to the file. So if an object is written to the repository a
PUT tag is written to the file followed by the object id and
data. If an object is deleted a
DELETE tag is appended
followed by the object id. A
COMMIT tag is written when a
repository transaction is committed. When a repository is opened any
DELETE operations not followed by a
COMMIT tag are
discarded since they are part of a partial/uncommitted transaction.
The manifest is an object with an all-zero key that references all the archives. It contains:
- list of archive infos
Each archive info contains:
It is the last object stored, in the last segment, and is replaced each time.
The archive metadata does not contain the file items directly. Only references to other objects that contain that data. An archive is an object that contains:
- list of chunks containing item metadata
Each item represents a file, directory or other fs item and is stored as an
item dictionary that contains:
- list of data chunks
- mode (item type + permissions)
- source (for links)
- rdev (for devices)
ctime (change time) is not stored because there is no API to set
it and it is reset every time an inode’s metadata is changed.
All items are serialized using msgpack and the resulting byte stream is fed into the same chunker algorithm as used for regular file data and turned into deduplicated chunks. The reference to these chunks is then added to the archive metadata. To achieve a finer granularity on this metadata stream, we use different chunker params for this chunker, which result in smaller chunks.
A chunk is stored as an object as well, of course.
The Borg chunker uses a rolling hash computed by the Buzhash algorithm. It triggers (chunks) when the last HASH_MASK_BITS bits of the hash are zero, producing chunks of 2^HASH_MASK_BITS Bytes on average.
borg create --chunker-params CHUNK_MIN_EXP,CHUNK_MAX_EXP,HASH_MASK_BITS,HASH_WINDOW_SIZE
can be used to tune the chunker parameters, the default is:
- CHUNK_MIN_EXP = 10 (minimum chunk size = 2^10 B = 1 kiB)
- CHUNK_MAX_EXP = 23 (maximum chunk size = 2^23 B = 8 MiB)
- HASH_MASK_BITS = 16 (statistical medium chunk size ~= 2^16 B = 64 kiB)
- HASH_WINDOW_SIZE = 4095 [B] (0xFFF)
The buzhash table is altered by XORing it with a seed randomly generated once for the archive, and stored encrypted in the keyfile. This is to prevent chunk size based fingerprinting attacks on your encrypted repo contents (to guess what files you have based on a specific set of chunk sizes).
For some more general usage hints see also
Indexes / Caches¶
The files cache is stored in
cache/files and is indexed on the
file path hash. At backup time, it is used to quickly determine whether we
need to chunk a given file (or whether it is unchanged and we already have all
- file inode number
- file size
- file mtime_ns
- file content chunk hashes
The inode number is stored to make sure we distinguish between different files, as a single path may not be unique across different archives in different setups.
The files cache is stored as a python associative array storing python objects, which generates a lot of overhead.
The chunks cache is stored in
cache/chunks and is indexed on the
chunk id_hash. It is used to determine whether we already have a specific
chunk, to count references to it and also for statistics.
- reference count
- encrypted/compressed size
The repository index is stored in
repo/index.%d and is indexed on the
chunk id_hash. It is used to determine a chunk’s location in the repository.
- segment (that contains the chunk)
- offset (where the chunk is located in the segment)
The repository index file is random access.
Hints are stored in a file (
- list of segments
hints and index can be recreated if damaged or lost using
The chunks cache and the repository index are stored as hash tables, with only one slot per bucket, but that spreads the collisions to the following buckets. As a consequence the hash is just a start position for a linear search, and if the element is not in the table the index is linearly crossed until an empty bucket is found.
When the hash table is filled to 75%, its size is doubled. When it’s emptied to 25%, its size is halved. So operations on it have a variable complexity between constant and linear with low factor, and memory overhead varies between 33% and 300%.
Indexes / Caches memory usage¶
Here is the estimated memory usage of Borg:
chunk_count ~= total_file_size / 2 ^ HASH_MASK_BITS
repo_index_usage = chunk_count * 40
chunks_cache_usage = chunk_count * 44
files_cache_usage = total_file_count * 240 + chunk_count * 80
- mem_usage ~= repo_index_usage + chunks_cache_usage + files_cache_usage
- = chunk_count * 164 + total_file_count * 240
All units are Bytes.
It is assuming every chunk is referenced exactly once (if you have a lot of duplicate chunks, you will have less chunks than estimated above).
It is also assuming that typical chunk size is 2^HASH_MASK_BITS (if you have a lot of files smaller than this statistical medium chunk size, you will have more chunks than estimated above, because 1 file is at least 1 chunk).
If a remote repository is used the repo index will be allocated on the remote side.
E.g. backing up a total count of 1 Mi (IEC binary prefix e.g. 2^20) files with a total size of 1TiB.
- with create
mem_usage = 2.8GiB
- with create
mem_usage = 0.4GiB
There is also the
--no-files-cache option to switch off the files cache.
You’ll save some memory, but it will need to read / chunk all the files as
it can not skip unmodified files then.
AES-256 is used in CTR mode (so no need for padding). A 64bit initialization
vector is used, a HMAC-SHA256 is computed on the encrypted chunk with a
random 64bit nonce and both are stored in the chunk.
The header of each chunk is:
Encryption and HMAC use two different keys.
In AES CTR mode you can think of the IV as the start value for the counter. The counter itself is incremented by one after each 16 byte block. The IV/counter is not required to be random but it must NEVER be reused. So to accomplish this Borg initializes the encryption counter to be higher than any previously used counter value before encrypting new data.
To reduce payload size, only 8 bytes of the 16 bytes nonce is saved in the payload, the first 8 bytes are always zeros. This does not affect security but limits the maximum repository capacity to only 295 exabytes (2**64 * 16 bytes).
Encryption keys are either derived from a passphrase or kept in a key file.
The passphrase is passed through the
BORG_PASSPHRASE environment variable
or prompted for interactive usage.
When initialized with the
init -e keyfile command, Borg
needs an associated file in
$HOME/.borg/keys to read and write
the repository. The format is based on msgpack, base64 encoding and
PBKDF2 SHA256 hashing, which is then encoded again in a msgpack.
The internal data structure is as follows:
- currently always an integer, 1
idfield in the
INIfile of the repository.
- the key used to encrypt data with AES (256 bits)
- the key used to HMAC the encrypted data (256 bits)
- the key used to HMAC the plaintext chunk data to compute the chunk’s id
- the seed for the buzhash chunking table (signed 32 bit integer)
Those fields are processed using msgpack. The utf-8 encoded passphrase is processed with PBKDF2 (SHA256, 100000 iterations, random 256 bit salt) to give us a derived key. The derived key is 256 bits long. A HMAC-SHA256 checksum of the above fields is generated with the derived key, then the derived key is also used to encrypt the above pack of fields. Then the result is stored in a another msgpack formatted as follows:
- currently always an integer, 1
- random 256 bits salt used to process the passphrase
- number of iterations used to process the passphrase (currently 100000)
- the hashing algorithm used to process the passphrase and do the HMAC
checksum (currently the string
- the HMAC of the encrypted derived key
- the derived key, encrypted with AES over a PBKDF2 SHA256 key described above
The resulting msgpack is then encoded using base64 and written to the
key file, wrapped using the standard
textwrap module with a header.
The header is a single line with a MAGIC string, a space and a hexadecimal
representation of the repository id.
Borg supports the following compression methods:
- none (no compression, pass through data 1:1)
- lz4 (low compression, but super fast)
- zlib (level 0-9, level 0 is no compression [but still adding zlib overhead], level 1 is low, level 9 is high compression)
- lzma (level 0-9, level 0 is low, level 9 is high compression).
Speed: none > lz4 > zlib > lzma Compression: lzma > zlib > lz4 > none
Be careful, higher zlib and especially lzma compression levels might take a lot of resources (CPU and memory).
The overall speed of course also depends on the speed of your target storage. If that is slow, using a higher compression level might yield better overall performance. You need to experiment a bit. Maybe just watch your CPU load, if that is relatively low, increase compression until 1 core is 70-100% loaded.
Even if your target storage is rather fast, you might see interesting effects: while doing no compression at all (none) is a operation that takes no time, it likely will need to store more data to the storage compared to using lz4. The time needed to transfer and store the additional data might be much more than if you had used lz4 (which is super fast, but still might compress your data about 2:1). This is assuming your data is compressible (if you backup already compressed data, trying to compress them at backup time is usually pointless).
Compression is applied after deduplication, thus using different compression methods in one repo does not influence deduplication.
borg create --help about how to specify the compression level and its default.