Inventories provide an abstraction for talking about the shape of a tree. Generally only tree object implementors should be concerned about entire inventory objects and their implementation. Other common exceptions are full-tree operations such as ‘checkout’, ‘export’ and ‘import’.

In memory inventories

In memory inventories are often used in diff and status operations between trees. We are working to reduce the number of times this occurs with ‘full tree’ inventory objects, and instead use more custom tailored data structures that allow operations on only a small amount of data regardless of the size of the tree.


There are several variants of serialised tree shape in use by Breezy. To date these have been mostly XML-based, though plugins have offered non-XML versions.


The dirstate file in a working tree includes many different tree shapes - one for the working tree and one for each parent tree, interleaved to allow efficient diff and status operations.


All the XML serialized forms write to and read from a single byte string, whose hash is then the inventory validator for the commit object.

Serialization scaling and future designs

Overall efficiency and scaling is constrained by the bottom level structure that an inventory is stored as. We have a number of goals we want to achieve:

  1. Allow commit to write less than the full tree’s data in to the repository in the general case.

  2. Allow the data that is written to be calculated without examining every versioned path in the tree.

  3. Generate the exact same representation for a given inventory regardless of the amount of history available.

  4. Allow in memory deltas to be generated directly from the serialised form without upcasting to a full in-memory representation or examining every path in the tree. Ideally the work performed will be proportional to the amount of changes between the trees being compared.

  5. Allow fetch to determine the file texts that need to be pulled to ensure that the entire tree can be reconstructed without having to probe every path in the tree.

  6. Allow Breezy to map paths to file ids without reading the entire serialised form. This is something that is used by commands such as merge PATH and diff -r X PATH.

  7. Let Breezy map file ids to paths without reading the entire serialised form. This is used by commands that are presenting output to the user such as loggerhead, brz-search, log FILENAME.

  8. We want a strong validator for inventories which is cheap to generate. Specifically we should be able to create the generator for a new commit without processing all the data of the basis commit.

  9. Testaments generation is currently size(tree), we would like to create a new testament standard which requires less work so that signed commits are not significantly slower than regular commits.

We have current performance and memory bugs in log -v, merge, commit, diff -r, loggerhead and status -r which can be addressed by an inventory system meeting these goals.

Current situation

The XML-based implementation we use today layers the inventory as a bytestring which is stored under a single key; the bytestring is then compressed as a delta against the bytestring of its left hand parent by the knit code.

Gap analysis:

  1. Succeeds

  2. Fails - generating a new XML representation needs full tree data.

  3. Succeeds - the inventory layer accesses the bytestring, which is deterministic

  4. Fails - we have to reconstruct both inventories as trees and then delta the resulting in memory objects.

  5. Partial success - the revision field in the inventory can be scanned for in both text-delta and full-bytestring form; other revision values than those revisions which are being pulled are by definition absent.

  6. Partially succeeds - with appropriate logic a path<->id map can be generated just-in-time, but it is complex and still requires reconstructing the entire byte-string.

  7. As for 6.

  8. Fails - we have to hash the entire tree in serialised form to generate validators.

  9. Fails.

Long term work

Some things are likely harder to fix incrementally than others. In particular, goal 3 (constant canonical form) is arguably only achieved if we remove all derived data such as the last-modified revision from the inventory itself. That said, the last-modified appears to be in a higher level than raw serialization. So in the medium term we will not alter the contents of inventories, only the way that the current contents are mapped to and from disk.


We desire clear and clean layers. Each layer should be as simple as we can make it to aid in debugging and performance tuning. So where we can choose to either write a complex layer and something simple on top of it, or two layers with neither being as complex - then we should consider the latter choice better in the absence of compelling reasons not to.

Some key layers we have today and can look at using or tweaking are:

  • Tree objects - the abstract interface breezy code works in

  • VersionedFiles - the optionally delta compressing key->bytes storage interface.

  • Inventory - the abstract interface that many tree operations are written in.

These layers are probably sufficient with minor tweaking. We may want to add additional modules/implementations of one or more layers, but that doesn’t really require new layers to be exposed.

Design elements to achieve the goals in a future inventory implementation

  • Split up the logical document into smaller serialised fragements. For instance hash buckets or nodes in a tree of some sort. By serialising in smaller units, we can increase the number of smaller units rather than their size as the tree grows; as long as two similar trees have similar serialised forms, the amount of different content should be quite high.

  • Use fragment identifiers that are independent of revision id, so that serialisation of two related trees generates overlap in the keyspace for fragments without requiring explicit delta logic. Content Hash Keys (e.g. (‘sha1:ABCDEF0123456789…’,) are useful here because of the ability to assign them without reference to history.)

  • Store the fragments in our existing VersionedFiles store. Adding an index for them. Have the serialised form be uncompressed utf8, so that delta logic in the VersionedFiles layer can be used. We may need to provide some sort of hinting mechanism to get good compression - but the trivially available zlib compression of knits-with-no-deltas is probably a good start.

  • Item_keys_introduced_by is innately a history-using function; we can reproduce the text-key finding logic by doing a tree diff between any tree and an older tree - that will limit the amount of data we need to process to something proportional to the difference and the size of each fragment. When checking many versions we can track which fragments we have examined and only look at new unique ones as each version is examined in turn.

  • Working tree to arbitrary history revision deltas/comparisons can be scaled up by doing a two-step (fixed at two!) delta combining - delta(tree, basis) and then combine that with delta(basis, arbitrary_revision) using the repositories ability to get a delta cheaply.

  • The key primitives we need seem to be: * canonical_form(inventory) -> fragments * delta(inventory, inventory) -> inventory_delta * apply(inventory_delta, canonical_form) -> fragments

  • Having very many small fragments is likely to cause a high latency multiplier unless we are careful.

  • Possible designs to investigate - a hash bucket approach, radix trees, B+ trees, directory trees (with splits inside a directory?).

Hash bucket based inventories


We store two maps - fileid:inventory_entry and path:fileid, in a stable hash trie, stored in densly packed fragments. We pack keys into the map densely up the tree, with a single canonical form for any given tree. This is more stable than simple fixed size buckets, which prevents corner cases where the tree size varies right on a bucket size border. (Note that such cases are not a fatal flaw - the two forms would both be present in the repository, so only a small amount of data would be written at each transition - but a full tree reprocess would be needed at each tree operation across the boundary, and thats undesirable.)

Goal satisfaction

  1. Success

  2. Success

  3. Success

  4. Success, though each change will need its parents looked up as well so it will be proportional to the changes + the directories above the changed path.

  5. Success - looking at the difference against all parents we can determine new keys without reference to the repository content will be inserted into.

  6. This probably needs a path->id map, allowing a 2-step lookup.

  7. If we allocate buckets by hashing the id, then this is succeed, though, as per 4 it will need recursive lookups.

  8. Success

  9. Fail - data beyond that currently included in testaments is included in the strong validator.


1. Tuning the fragment size needs doing. 1. Testing. 1. Writing code. 1. Separate root node, or inline into revision? 1. Cannot do ‘ls’ efficiently in the current design. 1. Cannot detect invalid deltas easily. 1. What about LCA merge of inventories?

Canonical form

There are three fragment types for the canonical form. Each fragment is addressed using a Content Hash Key (CHK) - for instance “sha1:12345678901234567890”.

root_node: (Perhaps this should be inlined into the revision object). HASH_INVENTORY_SIGNATURE path_map: CHK to root of path to id map content_map: CHK to root of id to entry map


(Where TYPE is I for internal or L for leaf).

leaf_node: LEAF_NODE_SIGNATURE hash_prefix: PREFIX HASHx00KEYx00 VALUE

For path maps, VALUE is::


For content maps, VALUE::

fileid basename kind last-changed kind-specific-details

The path and content maps are populated simply by serialising every inventory entry and inserting them into both the path map and the content map. The maps start with just a single leaf node with an empty prefix.


Given an inventory delta - a list of (old_path, new_path, InventoryEntry) items, with a None in new_path indicating a delete operation, and recursive deletes not being permitted - all entries to be deleted must be explicitly listed, we can transform a current inventory directly. We can’t trivially detect an invalid delta though.

To perform an application, naively we can just update both maps. For the path map we would remove all entries where the paths in the delta do not match, then insert those with a new_path again. For the content map we would just remove all the fileids in the delta, then insert those with a new_path that is not None.


To generate a delta between two inventories, we first generate a list of altered fileids, and then recursively look up their parents to generate their old and new file paths.

To generate the list of altered file ids, we do an entry by entry comparison of the full contents of every leaf node that the two inventories do not have in common. To do this, we start at the root node, and follow every CHK pointer that is only in one tree. We can then bring in all the values from the leaf nodes and do a set difference to get the altered ones, which we would then parse.

Radix tree based inventories


We store two maps - fileid:path and path:inventory_entry. The fileid:path map is a hash trie (as file ids have no useful locality of reference). The path:inventory_entry map is stored as a regular trie. As for hash tries we define a single canonical representation for regular tries similar to that defined above for hash tries.

Goal satisfaction

  1. Success

  2. Success

  3. Success

  4. Success

  5. Success - looking at the difference against all parents we can determine new keys without reference to the repository content will be inserted into.

  6. Success

  7. Success

  8. Success

  9. Fail - data beyond that currently included in testaments is included in the strong validator.


1. Tuning the fragment size needs doing. 1. Testing. 1. Writing code. 1. Separate root node, or inline into revision? 1. What about LCA merge of inventories?

Canonical form

There are five fragment types for the canonical form:

The root node, hash trie internal and leaf nodes as previous.

Then we have two more, the internal and leaf node for the radix tree.



(Where TYPE is I for internal or L for leaf).


For the content map we use the same value as for hashtrie inventories.

Node splitting and joining in the radix tree are managed in the same fashion as as for the internal nodes of the hashtries.


Apply is implemented as for hashtries - we just remove and reinsert the fileid:paths map entries, and likewise for the path:entry map. We can however cheaply detect invalid deltas where a delete fails to include its children.


Delta generation is very similar to that with hash tries, except we get the path of nodes as part of the lookup process.

Hash Trie details

The canonical form for a hash trie is a tree of internal nodes leading down to leaf nodes, with no node exceeding some threshold size, and every node containing as much content as it can, but no leaf node containing less than its lower size threshold. (In the event that an imbalance in the hash function causes a tree where an internal node is needed, but any prefix generates a child with less than the lower threshold, the smallest prefix should be taken). An internal node holds some number of key prefixes, all with the same bit-width. A leaf node holds the actual values. As trees do not spring fully-formed, the canonical form is defined iteratively - by taking every item in a tree and inserting it into a new tree in order you can determine what canonical form would look like. As that is an expensive operation, it should only be done rarely.

Updates to a tree that is in canonical form can be done preserving canonical form if we can prove that our rules for insertion are order-independent, and that our rules for deletion generate the same tree as if we never inserted those nodes.

Our hash tries are balanced vertically but not horizontally. That is, one leg of a tree can be arbitrarily deeper than adjacent legs. We require that each node along a path within the tree be densely packed, with the densest nodes near the top of the tree, and the least dense at the bottom. Except where the tree cannot support it, no node is smaller than a minimum_size, and none larger than maximum_size. The minimum size constraint is only applied when there are enough entries under a prefix to meet that minimum. The maximum size constraint is always applied except when a node with a single entry is larger than the maximum size. Loosely, the maximum size constraint wins over the minimum size constraint, and if the minimum size contraint is to be ignored, a deeper prefix can be chosen to pack the containing node more densely, as long as no additional minimum sizes checks on child nodes are violated.


  1. Hash the entry, and insert the entry in the leaf node with a matching prefix, creating that node and linking it from the internal node containing that prefix if there is no appropriate leaf node.

  2. Starting at the highest node altered, for all altered nodes, check if it has transitioned across either size boundary - 0 < min_size < max_size. If it has not, proceed to update the CHK pointers.

  3. If it increased above min_size, check the node above to see if it can be more densely packed. To be below the min_size the node’s parent must have hit the max size constraint and been forced to split even though this child did not have enough content to support a min_size node - so the prefix chosen in the parent may be shorter than desirable and we may now be able to more densely pack the parent by splitting the child nodes more. So if the parent node can support a deeper prefix without hitting max_size, and the count of under min_size nodes cannot be reduced, the parent should be given a deeper prefix.

  4. If it increased above max_size, shrink the prefix width used to split out new nodes until the node is below max_size (unless the prefix width is already 1 - the minimum). To shrink the prefix of an internal node, create new internal nodes for each new prefix, and populate them with the content of the nodes which were formerly linked. (This will normally bubble down due to keeping densely packed nodes). To shrink the prefix of a leaf node, create an internal node with the same prefix, then choose a width for the internal node such that the contents of the leaf all fit into new leaves obeying the min_size and max_size rules. The largest prefix possible should be chosen, to obey the higher-nodes-are-denser rule. That rule also gives room in leaf nodes for growth without affecting the parent node packing.

  5. Update the CHK pointers - serialise every altered node to generate a CHK, and update the CHK placeholder in the nodes parent; then reserialise the parent. CHK pointer propagation can be done lazily when many updates are expected.

Multiple versions of nodes for the same PREFIX and internal prefix width should compress well for the same tree.

Inventory deltas

An inventory is a serialization of the in-memory inventory delta. To serialize an inventory delta, one takes an existing inventory delta and the revision_id of the revision it was created it against and the revision id of the inventory which should result by applying the delta to the parent. We then serialize every item in the delta in a simple format:

‘format: bzr inventory delta v1 (1.14)’ NL ‘parent:’ SP BASIS_INVENTORY NL ‘version:’ SP NULL_OR_REVISION NL ‘versioned_root:’ SP BOOL NL ‘tree_references:’ SP BOOL NL DELTA_LINES


DELTA_LINES is lexicographically sorted.

Some explanation is in order. When NEWPATH is ‘None’ a delete has been recorded, and because this inventory delta is not attempting to be a reversible delta, the only other valid fields are OLDPATH and ‘file-id’. PARENT_ID is ‘’ when a delete has been recorded or when recording a new root entry.

Delta consistency

Inventory deltas and more broadly changes between trees are a significant part of Breezy’s core operations: they are key components in status, diff, commit, and merge (although merge uses tree transform, deltas contain the changes that are applied to the transform). Our ability to perform a given operation depends on us creating consistent deltas between trees. Inconsistent deltas lead to errors and bugs, or even just unexpected conflicts.

An inventory delta is a transform to change an inventory A into another inventory B (in patch terms its a perfect patch). Sometimes, for instance in a regular commit, inventory B is known at the time we create the delta. Other times, B is not known because the user is requesting that some parts of the second inventory they have are masked out from consideration. When this happens we create a delta that when applied to A creates a B we haven’t seen in total before. In this situation we need to ensure that B will be internally consistent. Deltas are unidirectional, a delta(A, B) creates B from A, but cannot be used to create A from B.

Deltas are expressed as a list of (oldpath, newpath, fileid, entry) tuples. The fileid, entry elements are normative; the old and new paths are strong hints but not currently guaranteed to be accurate. (This is a shame and something we should tighten up). Deltas are required to list all removals explicitly - removing the parent of an entry doesn’t remove the entry.

Applying a delta to an inventory consists of:
  • removing all fileids for which entry is None

  • adding or replacing all other fileids

  • detecting consistency errors

An interesting aspect of delta inconsistencies is when we notice them:
  • Silent errors which our application logic misses

  • Visible errors we catch during application, so bad data isn’t stored in the system.

The minimum safe level for our application logic would be to catch all errors during application. Making generation never generate inconsistent deltas is a seperate but necessary condition for robust code.

An inconsistent delta is one which:
  • after application to an inventory the inventory is an impossible state.

  • has the same fileid, or oldpath(not-None), or newpath(not-None) multiple times.

  • has a fileid field different to the entry.fileid in the same item in the delta.

  • has an entry that is in an impossible state (e.g. a directory with a text size)

Forms of inventory inconsistency deltas can carry/cause:
  • An entry newly introduced to a path without also removing or relocating any existing entry at that path. (Duplicate paths)

  • An entry whose parent id isn’t present in the tree. (Missing parent).

  • Having oldpath or newpath not be actual original path or resulting path. (Wrong path)

  • An entry whose parent is not a directory. (Under non-directory).

  • An entry that is internally inconsistent.

  • An entry that is already present in the tree (Duplicate id)

Known causes of inconsistency:
  • A ‘new’ entry which the inventory already has - when this is a directory even arbitrary file ids under the ‘new’ entry are more likely to collide on paths.

  • Removing a directory without recursively removing its children - causes Missing parent.

  • Recording a change to an entry without including all changed entries found following its parents up to and includin the root - can cause duplicate paths, missing parents, wrong path, under non-directory.

Avoiding inconsistent deltas

The simplest thing is to never create partial deltas, as it is trivial to be consistent when all data is examined every time. However users sometimes want to specify a subset of the changes in their tree when they do an operation which needs to create a delta - such as commit.

We have a choice about handling user requests that can generate inconsistent deltas. We can alter or interpret the request in such a way that the delta will be consistent, but perhaps larger than the user had intended. Or we can identify problematic situations and abort, specifying to the user why we have aborted and likely things they can do to make their request generate a consistent delta.

Currently we attempt to expand/interpret the request so that the user is not required to understand all the internal constraints of the system: if they request ‘foo/bar’ we automatically include foo. This works but can surprise the user sometimes when things they didn’t explicitly request are committed.

Different trees can use different algorithms to expand the request as long as they produce consistent deltas. As part of getting a consistent UI we require that all trees expand the paths requested downwards. Beyond that as long as the delta is consistent it is up to the tree.

Given two trees, source and target, and a set of selected file ids to check for changes and if changed in a delta between them, we have to expand that set by the following rules, to get consistent deltas. The test for consistency is that if the resulting delta is applied to source, to create a third tree ‘output’, and the paths in the delta match the paths in source and output, only one file id is at each path in output, and no file ids are missing parents, then the delta is consistent.

Firstly, the parent ids to the root for all of the file ids that have actually changed must be considered. Unless they are all examined the paths in the delta may be wrong.

Secondly, when an item included in the delta has a new path which is the same as a path in source, the fileid of that path in source must be included. Failing to do this leads to multiple ids tryin to share a path in output.

Thirdly, when an item changes its kind from ‘directory’ to anything else in the delta, all of the direct children of the directory in source must be included.