.. include:: ../global.rst.inc .. highlight:: none Data structures and file formats ================================ .. _repository: Repository ---------- .. Some parts of this description were taken from the Repository docstring |project_name| stores its data in a `Repository`, which is a filesystem-based transactional key-value store. Thus the repository does not know about the concept of archives or items. Each repository has the following file structure: README simple text file telling that this is a |project_name| repository config repository configuration data/ directory where the actual data is stored hints.%d hints for repository compaction index.%d repository index lock.roster and lock.exclusive/* used by the locking system to manage shared and exclusive locks Transactionality is achieved by using a log (aka journal) to record changes. The log is a series of numbered files called segments_. Each segment is a series of log entries. The segment number together with the offset of each entry relative to its segment start establishes an ordering of the log entries. This is the "definition" of time for the purposes of the log. Config file ~~~~~~~~~~~ Each repository has a ``config`` file which which is a ``INI``-style file 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. Keys ~~~~ Repository keys are byte-strings of fixed length (32 bytes), they don't have a particular meaning (except for the Manifest_). Normally the keys are computed like this:: key = id = id_hash(unencrypted_data) The id_hash function depends on the :ref:`encryption mode `. Segments ~~~~~~~~ A |project_name| repository is a filesystem based transactional key/value store. It makes extensive use of msgpack_ to store data and, unless otherwise noted, data is stored in msgpack_ encoded files. Objects referenced by a key are stored inline in files (`segments`) of approx. 500 MB size in numbered subdirectories of ``repo/data``. A segment starts with a magic number (``BORG_SEG`` as an eight byte ASCII string), followed by a number of log entries. Each log entry consists of: * size of the entry * CRC32 of the entire entry (for a PUT this includes the data) * entry tag: PUT, DELETE or COMMIT * PUT and DELETE follow this with the 32 byte key * PUT follow the key with the data Those files are strictly append-only and modified only once. Tag is either ``PUT``, ``DELETE``, or ``COMMIT``. When an object is written to the repository a ``PUT`` entry is written to the file containing the object id and data. If an object is deleted a ``DELETE`` entry is appended with the object id. A ``COMMIT`` tag is written when a repository transaction is committed. When a repository is opened any ``PUT`` or ``DELETE`` operations not followed by a ``COMMIT`` tag are discarded since they are part of a partial/uncommitted transaction. Compaction ~~~~~~~~~~ For a given key only the last entry regarding the key, which is called current (all other entries are called superseded), is relevant: If there is no entry or the last entry is a DELETE then the key does not exist. Otherwise the last PUT defines the value of the key. By superseding a PUT (with either another PUT or a DELETE) the log entry becomes obsolete. A segment containing such obsolete entries is called sparse, while a segment containing no such entries is called compact. Since writing a ``DELETE`` tag does not actually delete any data and thus does not free disk space any log-based data store will need a compaction strategy. Borg tracks which segments are sparse and does a forward compaction when a commit is issued (unless the :ref:`append_only_mode` is active). Compaction processes sparse segments from oldest to newest; sparse segments which don't contain enough deleted data to justify compaction are skipped. This avoids doing e.g. 500 MB of writing current data to a new segment when only a couple kB were deleted in a segment. Segments that are compacted are read in entirety. Current entries are written to a new segment, while superseded entries are omitted. After each segment an intermediary commit is written to the new segment, data is synced and the old segment is deleted -- freeing disk space. (The actual algorithm is more complex to avoid various consistency issues, refer to the ``borg.repository`` module for more comments and documentation on these issues.) .. _manifest: The manifest ------------ The manifest is an object with an all-zero key that references all the archives. It contains: * Manifest version * A list of archive infos * timestamp * config Each archive info contains: * name * id * time It is the last object stored, in the last segment, and is replaced each time an archive is added, modified or deleted. .. _archive: Archives -------- 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: * version * name * list of chunks containing item metadata (size: count * ~40B) * cmdline * hostname * username * time .. _archive_limitation: Note about archive limitations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The archive is currently stored as a single object in the repository and thus limited in size to MAX_OBJECT_SIZE (20MiB). As one chunk list entry is ~40B, that means we can reference ~500.000 item metadata stream chunks per archive. Each item metadata stream chunk is ~128kiB (see hardcoded ITEMS_CHUNKER_PARAMS). So that means the whole item metadata stream is limited to ~64GiB chunks. If compression is used, the amount of storable metadata is bigger - by the compression factor. If the medium size of an item entry is 100B (small size file, no ACLs/xattrs), that means a limit of ~640 million files/directories per archive. If the medium size of an item entry is 2kB (~100MB size files or more ACLs/xattrs), the limit will be ~32 million files/directories per archive. If one tries to create an archive object bigger than MAX_OBJECT_SIZE, a fatal IntegrityError will be raised. A workaround is to create multiple archives with less items each, see also :issue:`1452`. .. _item: Items ----- Each item represents a file, directory or other fs item and is stored as an ``item`` dictionary that contains: * path * list of data chunks (size: count * ~40B) * user * group * uid * gid * mode (item type + permissions) * source (for links) * rdev (for devices) * mtime, atime, ctime in nanoseconds * xattrs * acl * bsdfiles 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. .. _chunks: .. _chunker_details: Chunks ------ The |project_name| 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 = 19 (minimum chunk size = 2^19 B = 512 kiB) - CHUNK_MAX_EXP = 23 (maximum chunk size = 2^23 B = 8 MiB) - HASH_MASK_BITS = 21 (statistical medium chunk size ~= 2^21 B = 2 MiB) - 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 ``--chunker-params``. .. _cache: Indexes / Caches ---------------- The **files cache** is stored in ``cache/files`` and is used at backup time to quickly determine whether a given file is unchanged and we have all its chunks. The files cache is a key -> value mapping and contains: * key: - full, absolute file path id_hash * value: - file inode number - file size - file mtime_ns - list of file content chunk id hashes - age (0 [newest], 1, 2, 3, ..., BORG_FILES_CACHE_TTL - 1) To determine whether a file has not changed, cached values are looked up via the key in the mapping and compared to the current file attribute values. If the file's size, mtime_ns and inode number is still the same, it is considered to not have changed. In that case, we check that all file content chunks are (still) present in the repository (we check that via the chunks cache). If everything is matching and all chunks are present, the file is not read / chunked / hashed again (but still a file metadata item is written to the archive, made from fresh file metadata read from the filesystem). This is what makes borg so fast when processing unchanged files. If there is a mismatch or a chunk is missing, the file is read / chunked / hashed. Chunks already present in repo won't be transferred to repo again. The inode number is stored and compared to make sure we distinguish between different files, as a single path may not be unique across different archives in different setups. Not all filesystems have stable inode numbers. If that is the case, borg can be told to ignore the inode number in the check via --ignore-inode. The age value is used for cache management. If a file is "seen" in a backup run, its age is reset to 0, otherwise its age is incremented by one. If a file was not seen in BORG_FILES_CACHE_TTL backups, its cache entry is removed. See also: :ref:`always_chunking` and :ref:`a_status_oddity` The files cache is a python dictionary, storing python objects, which generates a lot of overhead. Borg can also work without using the files cache (saves memory if you have a lot of files or not much RAM free), then all files are assumed to have changed. This is usually much slower than with files cache. The **chunks cache** is stored in ``cache/chunks`` and is used to determine whether we already have a specific chunk, to count references to it and also for statistics. The chunks cache is a key -> value mapping and contains: * key: - chunk id_hash * value: - reference count - size - encrypted/compressed size The chunks cache is a hashindex, a hash table implemented in C and tuned for memory efficiency. The **repository index** is stored in ``repo/index.%d`` and is used to determine a chunk's location in the repository. The repo index is a key -> value mapping and contains: * key: - chunk id_hash * value: - segment (that contains the chunk) - offset (where the chunk is located in the segment) The repo index is a hashindex, a hash table implemented in C and tuned for memory efficiency. Hints are stored in a file (``repo/hints.%d``). It contains: * version * list of segments * compact hints and index can be recreated if damaged or lost using ``check --repair``. 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 grown. When it's emptied to 25%, its size is shrinked. So operations on it have a variable complexity between constant and linear with low factor, and memory overhead varies between 33% and 300%. .. _cache-memory-usage: Indexes / Caches memory usage ----------------------------- Here is the estimated memory usage of |project_name| - it's complicated: 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 Due to the hashtables, the best/usual/worst cases for memory allocation can be estimated like that: mem_allocation = mem_usage / load_factor # l_f = 0.25 .. 0.75 mem_allocation_peak = mem_allocation * (1 + growth_factor) # g_f = 1.1 .. 2 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. The chunks cache, files cache and the repo index are all implemented as hash tables. A hash table must have a significant amount of unused entries to be fast - the so-called load factor gives the used/unused elements ratio. When a hash table gets full (load factor getting too high), it needs to be grown (allocate new, bigger hash table, copy all elements over to it, free old hash table) - this will lead to short-time peaks in memory usage each time this happens. Usually does not happen for all hashtables at the same time, though. For small hash tables, we start with a growth factor of 2, which comes down to ~1.1x for big hash tables. E.g. backing up a total count of 1 Mi (IEC binary prefix i.e. 2^20) files with a total size of 1TiB. a) with ``create --chunker-params 10,23,16,4095`` (custom, like borg < 1.0 or attic): mem_usage = 2.8GiB b) with ``create --chunker-params 19,23,21,4095`` (default): mem_usage = 0.31GiB .. note:: 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. Encryption ---------- .. seealso:: The :ref:`borgcrypto` section for an in-depth review. AES_-256 is used in CTR mode (so no need for padding). A 64 bit initialization vector is used, a MAC is computed on the encrypted chunk and both are stored in the chunk. The header of each chunk is: ``TYPE(1)`` + ``MAC(32)`` + ``NONCE(8)`` + ``CIPHERTEXT``. Encryption and MAC 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 |project_name| 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 (and other secrets) are kept either in a key file on the client ('keyfile' mode) or in the repository config on the server ('repokey' mode). In both cases, the secrets are generated from random and then encrypted by a key derived from your passphrase (this happens on the client before the key is stored into the keyfile or as repokey). The passphrase is passed through the ``BORG_PASSPHRASE`` environment variable or prompted for interactive usage. .. _key_files: Key files --------- .. seealso:: The :ref:`key_encryption` section for an in-depth review of the key encryption. When initialized with the ``init -e keyfile`` command, |project_name| needs an associated file in ``$HOME/.config/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 same data structure is also used in the "repokey" modes, which store it in the repository in the configuration file. The internal data structure is as follows: version currently always an integer, 1 repository_id the ``id`` field in the ``config`` ``INI`` file of the repository. enc_key the key used to encrypt data with AES (256 bits) enc_hmac_key the key used to HMAC the encrypted data (256 bits) id_key the key used to HMAC the plaintext chunk data to compute the chunk's id chunk_seed the seed for the buzhash chunking table (signed 32 bit integer) These fields are packed using msgpack_. The utf-8 encoded passphrase is processed with PBKDF2_ (SHA256_, 100000 iterations, random 256 bit salt) to derive a 256 bit key encryption key (KEK). A `HMAC-SHA256`_ checksum of the packed fields is generated with the KEK, then the KEK is also used to encrypt the same packed fields using AES-CTR. The result is stored in a another msgpack_ formatted as follows: version currently always an integer, 1 salt random 256 bits salt used to process the passphrase iterations number of iterations used to process the passphrase (currently 100000) algorithm the hashing algorithm used to process the passphrase and do the HMAC checksum (currently the string ``sha256``) hash HMAC-SHA256 of the *plaintext* of the packed fields. data The encrypted, packed fields. 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. Compression ----------- |project_name| 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. See ``borg create --help`` about how to specify the compression level and its default. Lock files ---------- |project_name| 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 indicating 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 use the ``borg break-lock`` command after you first have made sure that no |project_name| 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.