~bzr-pqm/bzr/bzr.dev

===========

Inventories

===========

.. contents::

Overview

========

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.

Serialization

=============

There are several variants of serialised tree shape in use by bzr. To date

these have been mostly xml based, though plugins have offered non-xml versions.

dirstate

--------

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.

xml

---

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 bzr 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 bzr 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, bzr-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.

Layering

--------

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 bzrlib 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

=============================

Overview

--------

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.

Issues

------

 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

map_node: INTERNAL_NODE or LEAF_NODE

INTERNAL_NODE:

INTERNAL_NODE_SIGNATURE

hash_prefix: PREFIX

prefix_width: INT

PREFIX CHK TYPE SIZE

PREFIX CHK TYPE SIZE ...

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

leaf_node:

LEAF_NODE_SIGNATURE

hash_prefix: PREFIX

HASH\x00KEY\x00 VALUE

For path maps, VALUE is::

  fileid

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. 

Apply

-----

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.

Delta

-----

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

============================

Overview

--------

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.

Issues

------

 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.

radix_node: INTERNAL_NODE or LEAF_NODE

INTERNAL_NODE:

INTERNAL_NODE_SIGNATURE

prefix: PREFIX

suffix CHK TYPE SIZE

suffix CHK TYPE SIZE ...

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

LEAF_NODE:

LEAF_NODE_SIGNATURE

prefix: PREFIX

suffix\x00VALUE

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

-----

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

-----

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.

Insertion

---------

#. 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.

#. 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.

#. 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.

#. 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.

#. 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 propogation 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.