LUMIERA.clone/doc/technical/library/DiffFramework.txt

Diff Handling Framework
=======================
:Date: 2015
:Toc:

Within the support library, in the namespace `lib::diff`, there is a collection of loosely coupled tools
known as »the diff framework«. It revolves around generic representation and handling of structural differences.
Beyond some rather general assumptions, to avoid stipulating the usage of specific data elements or containers,
the framework is kept _generic_, cast in terms of *elements*, *sequences* and *strategies*
for access, indexing and traversal.

.motivation
**********************
Diff handling is multi purpose within Lumiera:
Diff representation is seen as a meta language and abstraction mechanism;
it enables tight collaboration without the need to tie and tangle the involved implementation data structures.
Used this way, diff representation reduces coupling and helps to cut down overall complexity — so to justify
the considerable amount of complexity seen within the diff framework implementation.
**********************

Definitions
-----------
element::
  the atomic unit treated in diff detection, representation and application. +
  Elements are considered to be

  - lightweight copyable values
  - equality comparable
  - bearing distinct identity
  - unique _as far as concerned_

sequence::
  data is delivered in the form of a sequence, which might or might not be _ordered,_
  but in any case will be traversed once only.

diff::
  the changes necessary to transform an input sequence (``old sequence'') into a desired target sequence (``new sequence'')

diff language::
  differences are spelled out in linearised form: as a sequence of constant-size diff actions, called »diff verbs«

diff verb::
  a single element within a diff. Diff verbs are conceived as operations, which,
  when applied consuming the input sequence, will produce the target sequence of the diff.

diff application::
  the process of consuming a diff (sequence of diff verbs), with the goal to produce some effect at the 
  _target_ of diff application. Typically we want to apply a diff to a data sequence, to mutate it
  into a new shape, conforming with the shape of the diff's ``target sequence''

tree mutator::
  a customisable intermediary, allowing to apply a diff sequence against an opaque data structure.
  For the purpose of diff application, the TreeMutator serves as target object, while in fact it is itself
  attached by a _binding_ to the actual target data structure, which remains a hidden implementation detail.
  This allows to manipulate the target data structure without knowing its concrete representation. 

diff generator::
  a facility producing a diff, which is a sequence of diff verbs.
  Typically, a diff generator works lazily, demand driven.

diff detector::
  special kind of diff generator, which takes two data sequences as input:
  an ``old sequence'' and a ``new sequence''. The diff detector traverses and compares
  these sequences to produce a diff, which describes the steps necessary to transform
  the ``old'' shape into the ``new'' shape of the data.


List Diff Algorithm
-------------------
While in general this is a well studied subject, in Lumiera we'll confine ourselves to a very
specific flavour of diff handling: we rely on _elementary atomic units_ with well established
object identity. And in addition, within the scope of one coherent diff handling task,
we require those elements to be 'unique'. The purpose of this design decision is to segregate
the notorious matching problem and treat diff handling in isolation.

Effectively this means that, for any given element, there can be at most _one_ matching
counterpart in the other sequence, and the presence of such can be detected by using an *index*.
In fact, we retrieve an index for every sequence involved into the diff detection task;
this is our trade-off for simplicity in the diff detection algorithm.footnote:[traditionally,
diff detection schemes, especially those geared at text diff detection, engage into great lengths
of producing an ``optimal'' diff, which effectively means to build specifically tuned pattern
or decision tables, from which the final diff can then be pulled or interpreted.
We acknowledge that in our case building a lookup table index with additional annotations can be
as bad as O(n^2^) and worse; we _might_ be able to do better, but likely for the price of
turning the algorithm into some kind of mental challenge.]
In case this turns out as a performance problem, we might consider integrating the index
maintenance into the data structure to be diffed, which shifts the additional impact of
indexing onto the data population phase.footnote:[in the general tree diff case this is far
from trivial, since we need an self-contained element index for every node, and we need the
ability to take a snapshot of the ``old'' state before mutating a node into ``new'' shape]

Element classification
~~~~~~~~~~~~~~~~~~~~~~
By using the indices of the old and the new sequence, we are able to _classify_ each element:

- elements only present in the new sequence are treated as *inserts*
- elements only present in the old sequence are treated as *deletes*
- elements present in both sequences form the *permutation*

Processing pattern
~~~~~~~~~~~~~~~~~~
We _consume both the old and the new sequence synchronously, while emitting the diff sequence_.

The diff describes a sequence of operations, which, when applied, consume a sequence congruent
to the old sequence, while emitting a sequence congruent to the new sequence. We use the
following *list diff language* here:

verb `ins(elm)`::
  insert the given argument element `elm` at the _current processing position_
  into the target sequence. This operation allows to inject new data
verb `del(elm)`::
  delete the _next_ element `elm` at _current position._
  For sake of verification, the element to be deleted is also included as argument (redundancy).
verb `pick(elm)`::
  accepts the _next_ element at _current position_ into the resulting altered sequence.
  The element is given redundantly as argument.
verb `find(elm)`::
  effect a re-ordering of the target list contents. This verb requires to search
  for the (next respective single occurrence of the) given element in the remainder
  of the datastructure, to bring it forward and insert it as the _next_ element.
verb `skip(elm)`::
  processing hint, emitted at the position where an element previously fetched by
  some `find` verb happened to sit in the old order. This allows an optimising
  implementation to ``fetch'' a copy and just drop or skip the original,
  thereby avoiding to shift other elements.

Since _inserts_ and _deletes_ can be detected and emitted right at the processing frontier,
for the remaining theoretical discussion, we consider the insert / delete part filtered
away conceptually, and concentrate on generating the permutation part.

Handling sequence permutation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Permutation handling turns out to be the turning point and tricky part of diff generation;
the handling of new and missing members can be built on top, once we manage to describe a
permutation diff. If we thus consider -- in theory -- the _inserts_ and _deletes_ to be
filtered away, what remains is a permutation of index numbers to cause the re-ordering.
We may describe this re-ordering by the index numbers in the new sequence, given in the order
of the old sequence. For such a re-ordering permutation, the theoretically optimal result
can be achieved by http://wikipedia.org/wiki/Cycle_sort[Cycle Sort] in _linear time_.footnote:[
assuming random access by index is possible, *Cycle Sort* walks the sequence once. Whenever
encountering an element out-of order, i.e. new postion != current position, we leap to the
indicated new position, which necessarily will be out-of-order too, so we can leap from there
to the next indicated position, until we jump back to the original position eventually. Such
a closed cycle can then just be _rotated_ into proper position. Each permutation can be
decomposed into a finite number of closed cycles, which means, after rotating all cycles
out of order, the permutation will be sorted completely.] Starting from such ``cycle rotations'',
we possibly could work out a minimal set of moving operations.

But there is a twist: Our design avoids using index numbers, since we aim at _stream processing_
of diffs. We do not want to communicate index numbers to the consumer of the diff; rather we
want to communicate reference elements with our _diff verbs_. Thus we prefer the most simplistic
processing mechanism, which happens to be some variation of *Insertion Sort*.footnote:[to support
this choice, *Insertion Sort* -- in spite of being O(n^2^) -- turns out to be the best choice at
sorting small data sets for reasons of cache locality; even typical Quicksort implementations
switch to insertion sorting of small subsets for performance reasons]
This is the purpose of our `find` verb: to extract some element known to be out of order, and
insert it at the current position.


Implementation and Complexity
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Based on these choices, we're able to consume two permutations of the same sequence simultaneously,
while emitting `find` and `pick` verbs to describe the re-ordering. Consider the two sequences
split into an already-processed part, and a part still-to-be-processed.

.Invariant
Matters are arranged such, that, in the to-be-processed part, each element appearing at the
front of the ``new'' sequence _can be consumed right away_.

Now, to arrive at that invariant, we use indices to determine

- if the element at head of the old sequence is not present in the new sequence, which means
  it has to be deleted
- while an element appearing at head of the new sequence but not present in the old sequence
  needs to be inserted
- and especially an element known to be present in both sequences, appearing at the head
  of the new sequence but non-matching at the old sequence prompts us to fetch the right
  element from further down in the sequence and insert it a current position

after that, the invariant is (re)established and we can consume the element and move the
processing point forward.

For generating the diff description, we need index tables of the ``old'' and the ``new'' sequence,
which causes a O(n log n) complexity and storage in the order of the sequence length. Application
of the diff is quadratic, due to the find-and-insert passes.footnote:[In the theoretical treatment
of diff problems it is common to introduce a *distance metric* to describe how _far apart_ the
two sequences are in terms of atomic changes. This helps to make the quadratic (or worse) complexity
of such algorithms look better: if we know the sequences are close, the nested sub-scans will be
shorter than the whole sequence (with n·d < n^2^). +
However, since our goal is to support permutations and we have to deal with arbitrary sequences, such
an argument looks somewhat pointless. Let's face it, structural diff computation is expensive; the only
way to keep matters under control is to keep the local sequences short, which prompts us to exploit
structural knowledge instead of comparing the entire data as flat sequence]



Tree structure differences
--------------------------
The handling of list differences can be used as prototype to build a description of structural
changes in hierarchical data: traverse the structure and account for each element and each change.
Such a description of changes won't be _optimal_ though. What appears as a insertion or deletion locally,
might indeed be just the result of rearranging subtrees as a whole. The _tree diff problem_ in this general
form is known to be a rather tough challenge. But our goals are different here. Lumiera relies on a
link:{ldoc}/design/architecture/ExternalTreeDescription.html[»External Tree Description«] for
_symbolic representation_ of hierarchically structured elements, without actually implementing them.
The purpose of this ``external'' description is to largely remove the need for a central data model
to work against. A _symbolic diff message_ allows to propagate data and structure changes,
without even using the same data representation at both sides of the collaboration.

Generic Node Record
~~~~~~~~~~~~~~~~~~~
For this to work, we need some very generic meta representation. This can be a textual representation
(e.g. JSON) -- but within the application it seems more appropriate to use an abstracted and unspecific
typed data representation, akin to ``JSON with typed language data''. It can be considered _symbolic,_
insofar it isn't the data, it refers to it. To make such an approach work, we need the following parts:

- a _generic node_, which has an _identity_ and some payload data. This `GenNode` is treated as elementary value.
- a _record_ made from a collection of generic nodes, to take on the abstracted role of an object. Such a
  `Record<GenNode>` is a sequence of nodes, partitioned in two scopes: the (named) attributes and the children.
- together these elements form an essentially recursive structure: The record is comprised of nodes and the
  nodes might, besides elementary values, also carry records.
- and finally we need an _identification scheme_, allowing to produce named and unnamed yet unique identities,
  also including opaque type information.

Type information is deliberately kept opaque, to prevent switch-on-type.
We always presume synced (similar) data structures on both ends of the collaboration, where the partners
share common knowledge about types and structure. Changes are indicated and propagated, not probed.

Nested list differences
~~~~~~~~~~~~~~~~~~~~~~~
Exploiting the fact that `Record<GenNode>` is essentially a sequence, we're able to build the description
of structure changes as an extension layer on top of our _linearised diff language format._ We introduce
a *bracketing construct* to _open and close sub scopes._ Within each scope, the verbs of our list diff
language are deployed, just now with a `GenNode` as payload. This yields the following *tree diff language*

verb `ins(GenNode)`::
         prompts to insert the given argument element at the _current processing position_
         into the target sequence. This operation allows to inject new data
verb `del(ID)`::
         requires to delete the _next_ element at _current_ position.footnote:[The payload of this
         and all the following verbs is a `GenNode`, but only the ID part matters. This allows to
         send a special _ref element_ over the wire instead of having to send a full subtree, for
         obvious performance reasons.]
         For sake of verification, the ID of the argument payload is
         required to match the ID of the element about to be discarded.
verb `pick(ID)`::
         just accepts the _next_ element at _current_ position into
         the resulting altered sequence. Again, the ID of the argument
         has to match the ID of the element to be picked, for sake
         of verification.
verb `find(ID)`::
         change the order of the target scope contents: this verb requires
         to _search ahead_ for the (next respective single occurrence of the)
         given element further down into the remainder of the current
         record scope (but not into nested child scopes). The designated
         element is to be retrieved and inserted as the next element
         _at current position._
verb `skip(ID)`::
         this is a mere processing hint, emitted at the position where an element
         previously extracted by a `find(ID)` verb happened to sit within the old order.
verb `after(ID)`::
         shortcut to `pick` existing elements up to the designated point. +
         As a special notation, `after(Ref::ATTRIBUTES)` allows to fast forward
         to the first child element, while `after(Ref::END)` means to accept
         all of the existing data contents as-is (possibly to append further
         elements beyond that point).
verb `mut(ID)`::
         *bracketing construct* to open a nested sub scope.
         The element designated by the ID of the argument needs to be a record
         (``nested child object''). Moreover, this element must have been
         mentioned with the preceding diff verbs at that level, which means
         that the element as such must already be present in the altered
         target structure. The `mut(ID)` verb then opens the designated nested
         record for diff handling, and all subsequent diff verbs are to be
         interpreted relative to this scope, until the corresponding
         `emu(ID)` verb is encountered.
verb `emu(ID)`::
         closing bracketing construct and counterpart to `mut(ID)`. This verb
         must be given precisely at the end of the nested scope.footnote:[it is
         not allowed to ``return'' from the middle of a scope, for sake of sanity.
         The diff messages transport a certain degree of redundancy to detect when
         the data structure at target does no longer conform to the assumptions
         made at the generation side.] At this point, this child scope is left
         and the parent scope with all existing diff state is _popped from an
         internal stack._
verb `set(GenNode)`::
         assign a new value to the designated element. This is primarily intended
         for primitive data values and requires the payload type to be assignable,
         without changing the element's identity. The element is identified by the
         payload's ID and -- similar as with the `mut()` verb -- needs to be present
         already, i.e. it has to be mentioned by preceding order defining verbs.

In practical application, a diff thus is typically comprised of two parts or ``phases'':
In a first pass, we have to mention, confirm or supply all the elements, and as second step
we may assign new values or enter recursive mutation for some child elements. However, it is
possible to deviate from that general scheme, as long as all elements are _confirmed_ prior to
mutation. Some typical ``degenerated'' patterns of spelling out a diff are as follows:

- just a ``population diff'' without any `set` or `mut` verbs
- on the other hand, if we just want to mutate something, with the first verb `after(Ref::END)`
  we accept all of the content as-is, while the next verbs may mutate some selected elements
- however, single assignment- or mutation operations can be interspersed into the first phase
  of sequence confirmation (or re-ordering): because, whenever an element has just been mentioned,
  it can (in fact without further search) be mutated right away. Thus, e.g. a `pick(ID)` verb can
  immediately followed by a `set(GenNode)` to assign changed payload data without altering the
  position or the identity of the element.


representation of objects
~~~~~~~~~~~~~~~~~~~~~~~~~
While we are talking about _structured data,_ in fact the entities we are about to handle are *objects*, understood in
the standard flavour of object orientation, where an object is the instance of a type and offers a contract. Incidentally,
this is not the original, ``pure'' meaning of object orientation, but the one that became prolific to bring our daily
practice closer to the inherent structuring of modern human organisation. And in dealing with this kind of object, we
sometimes get into conflict with the essentially open and permissive nature of structured data. So we need to establish
a mapping rule, which translates into additional conventions about how to spell out matters in the diff language.

We choose to leave this largely on the level of stylistic rules, thus stressing the language nature of the diff.
Even when this bears the danger to produce an exception very late, when it comes to applying the diff to a target
data structure. The intention behind this whole diff approach is to transform tight coupling with strict rules
into a loose collaboration based on a common understanding. So generally we'll assume the diff is just right,
and if not, we'll get what we deserve.footnote:[This gives rise to a tricky discussion about loss of strictness
and the costs incurred by that happening. We should not treat this topic in isolation; we should recall that
loose coupling was chosen to avoid far more serious problems caused by tight coupling, and especially the
deteriorating and poisoning effect on architecture and the further dire consequences of a global fixed common
data model -- which become prdominant when used in a large, not homogeneous system.
And _when a system indeed is not homogeneous,_ we better try to make each part open-closed, open for change
but closed against extension. This is especially true in the case of the UI.]


object representation protocol
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A diff is created to tell some partner about our data, and the »protocol to describe an object« is as follows

- the ID is the object's identity and is once given, right at start, and never changed
- we spell out any metadata (esp. a type information) first, followed by all attributes,
  and then followed by contents of the object's scope (children).
- attributes are to be given in a way not in contradiction to the more stringent semantics
  of an object field or property

  * never attempt to re-order or delete such attributes, since their presence is fixed in the class definition
  * when a field is mandatory _by its nature,_ it shall be required in construction, and the corresponding data
    is to be given with the `ins` verb causing the constructor call
  * on the other hand, the data for an optional field, when present, shall be spelled out by `ins` verb
    after construction, with the first _population diff._
  * we do not support attribute map semantics (or extended ``object properties'' of any kind). +
    If necessary, treat them as nested entity with map semantics