Datom ⚛

Table of Contents generated with DocToc

• Export Bound Methods
• Creation of Bespoke Library Instances
• Configuration Parameters
• Methods
  • Freezing & Thawing
  • Stamping
  • Type Testing
  • Value Creation
  • Selecting
• System Properties
• WIP
  • PipeDreams Datoms (Data Events)
  • select = ( d, selector ) ->
  • The XEmitter (XE) Sub-Module
    • XE Sending API
    • XE Receiving API
    • Sample
    • Managing Scope
• Cup Of Datom
• Benchmarks
• To Do

standardized immutable objects in the spirit of datomic, especially suited for use in data pipelines

NOTE: Documentation is still fragmentary. WIP.

Export Bound Methods

If you plan on using methods like new_datom() or select() a lot, consider using .export():

DATOM         = require 'datom'
{ new_datom
  select }    = DATOM.export()

Now new_datom() and select() are methods bound to DATOM. (Observe that because of the JavaScript 'tear-off' effect, when you do method = DATOM.method, then method() will likely fail as its reference to this has been lost.)

Creation of Bespoke Library Instances

In order to configure a copy of the library, pass in a settings object:

_DATOM        = require 'datom'
settings      = { merge_values: false, }
DATOM         = new _DATOM.Datom settings
{ new_datom
  select }    = DATOM.export()

Or, mode idiomatically:

DATOM         = new ( require 'datom' ).Datom { merge_values: false, }
{ new_datom
  select }    = DATOM.export()

The second form also helps to avoid accidental usage of the result of require 'datom', which is of course the same library with a different configuration.

Configuration Parameters

• merge_values (boolean, default: true)—Whether to merge attributes of the second argument to new_datom() into the resulting value. When set to false, new_datom '^somekey', somevalue will always result in a datom { $key: '^somekey', $value: somevalue, }; when left to the default, and if somevalue is an object, then its attributes will become attributes of the datom, which may result in name clashes in case any attribute name should start with a $ (dollar sign).

• freeze (boolean, default: true)—Whether to freeze datoms. When set to false, no freezing will be performed, which may entail slightly improved performance.

• dirty (boolean, default: true)—Whether to automatically set { $dirty: true, } when the copy of a datom has been treated with lets() and a modifyer function.

Methods

Freezing & Thawing

• @freeze = ( d ) ->
• @thaw = ( d ) ->
• @lets = ( original, modifier ) ->
• @set = ( d, k, P... ) ->
• @unset = ( d, k ) ->

Stamping

• @stamp = ( d, P... ) ->
• @unstamp = ( d ) ->

Type Testing

• @is_system = ( d ) ->
• @is_stamped = ( d ) ->
• @is_fresh = ( d ) ->
• @is_dirty = ( d ) ->

Value Creation

• @new_datom = ( $key, $value, other... ) ->
• @new_single_datom = ( $key, $value, other... ) ->
• @new_open_datom = ( $key, $value, other... ) ->
• @new_close_datom = ( $key, $value, other... ) ->
• @new_system_datom = ( $key, $value, other... ) ->
• @new_text_datom = ( $value, other... ) ->
• @new_end_datom = ->
• @new_warning = ( ref, message, d, other... ) ->

Selecting

• @select = ( d, selector ) ->

System Properties

• d.$key—key (i.e., type) of a datom.
• d.$value—'the' proper value of a datom. This is always used in case new_datom() was called with a non-object in the value slot (as in new_datom '^mykey', 123), or when the library was configured with { merge_values: false, }.—In case there is no d.$value, the datom's proper value is the object that would result from deleting all properties whose names start with a $ (dollar sign).
• d.$dirty—whether the object has been (thawed, then) changed (and then frozen again) since its $dirty property was last cleared or set to false.
• d.$stamped—whether the object has been marked as 'stamped' (i.e., processed).

WIP

The below copied from PipeDreams docs, to be updated

PipeDreams Datoms (Data Events)

Data streams—of which pull-streams, PipeStreams, and NodeJS Streams are examples—do their work by sending pieces of data (that originate from a data source) through a number of transforms (to finally end up in a data sink).note

(note) I will ignore here alternative ways of dealing with streams, especially the EventEmitter way of dealing with streamed data. When I say 'streams', I also implicitly mean 'pipelines'; when I say 'pipelines', I also implicitly mean 'pipelines to stream data' and 'streams' in general.

When NodeJS streams started out, the thinking about those streams was pretty much confined to saying that 'a stream is a series of bytes'. Already back then, an alternative view took hold (I'm slightly paraphrasing here):

The core interpretation was that stream could be buffers or strings - but the userland interpretation was that a stream could be anything that is serializeable [...] it was a sequence of buffers, bytes, strings or objects. Why not use the same api?

I will no repeat here what I've written about perceived shortcomings of NodeJS streams; instead, let me iterate a few observations:

• In streaming, data is just data. There's no need for having a separate 'Object Mode' or somesuch.

• There's a single exception to the above rule, and that is when the data item being sent down the line is null. This has historically—by both NodeJS streams and pull-streams—been interpreted as a termination signal, and I'm not going to change that (although at some point I might as well).

• When starting out with streams and building fairly simple-minded pipelines, sending down either raw pieces of business data or else null to indicate termination is enough to satisfy most needs. However, when one transitions to more complex environments, raw data is not sufficient any more: When processing text from one format to another, how could a downstream transform tell whether a given piece of text is raw data or the output of an upstream transform?

  Another case where raw data becomes insufficient are circular pipelines—pipelines that re-compute (some or all) output values in a recursive manner. An example which outputs the integer sequences of the Collatz Conjecture is in the tests folder. There, whenever we see an even number n, we send down that even number n alongside with half its value, n/2; whenever we see an odd number n, we send it on, followed by its value tripled plus one, 3*n+1. No matter whether you put the transform for even numbers in front of that for odd numbers or the other way round, there will be numbers that come out at the bottom that need to be re-input into the top of the pipeline, and since there's no telling in advance how long a Collatz sequence will be for a given integer, it is, in the general case, insufficient to build a pipeline made from a (necessarily finite) repetitive sequence of copies of those individual transforms. Thus, classical streams cannot easily model this kind of processing.

The idea of datoms—short for data atoms, a term borrowed from Rich Hickey's Datomic—is to simply to wrap each piece of raw data in a higher-level structure. This is of course an old idea, but not one that is very prevalent in NodeJS streams, the fundamental assumption (of classical stream processing) being that all stream transforms get to process each piece of data, and that all pieces of data are of equal status (with the exception of null).

The PipeDreams sample implementation of Collatz Sequences uses datoms to (1) wrap the numerical pieces of data, which allows to mark data as processed (a.k.a. 'stamped'), to (2) mark data as 'to be recycled', and to (3) inject system-level synchronization signals into the data stream to make sure that recycled data gets processed before new data is allowed into the stream.

In PipeDreams datoms, each piece of data is explicitly labelled for its type; each datom may have a different status: there are system-level datoms that serve to orchestrate the flow of data within the pipeline; there are user-level datoms which originate from the application; there are datoms to indicate the opening and closing of regions (phases) in the data stream; there are stream transforms that listen to and act on specific system-level events.

Datoms are JS objects that must minimally have a key property, a string that specifies the datom's category, namespace and name; in addition, they may have a value property with the payload (where desired), and any number of other attributes. The property $ is used to carry metadata (e.g. from which line in a source file a given datom was generated from). Thus, we may give the outline of a datom as (in a rather informal notation) d := { $key, ?$value, ?$stamped,..., ?$, }.

The key of a datom must be a string that consists of at least two parts, the sigil and the name. The sigil, a single punctuation character, indicates the 'category' of each datom; there are two levels and three elementary categories, giving six types of datoms:

• Application level:

  • ^ for data datoms (a.k.a. 'singletons'),
  • < for start-of-region datoms,
  • > for end-of-region datoms.

• System level:

  • ~ for data datoms,
  • [ for start-of-region datoms,
  • ] for end-of-region datoms.

Normally, one will probably want to send around business data inside (the value property of) application-level data datoms (hence their name, also shortened to D-datoms); however, one can also set other properties of datom objects, or send data around using properties of start- or end-of-region datoms.

Region events are intended to be used e.g. when parsing text with markup; say you want to turn a snippet of HTML like this:

<document><div>Helo <em>world!</em></div></document>

into another textual representation, you may want to turn that into a sequence of datoms similar to these, in the order of sending and regions symbolized by boxes:note

--------------------------------------------------------+
  { key: '<document',                   }   # d1        |
------------------------------------------------------+ |
  { key: '<div',                        }   # d2      | |
  { key: '^text',     value: "Helo ",   }   # d3      | |
----------------------------------------------------+ | |
  { key: '<em',                         }   # d4    | | |
  { key: '^text'      value: "world!",  }   # d5    | | |
  { key: '>em',                         }   # d6    | | |
----------------------------------------------------+ | |
  { key: '>div',                        }   # d7      | |
------------------------------------------------------+ |
  { key: '>document',                   }   # d8        |
--------------------------------------------------------+

note by 'in the order of sending' I mean you'd have to send datom d1 first, then d2 and so on. Trivial until you imagine you write a pipeline and then picture how the events will travel down that pipeline:

pipeline.push $do_this() # s1, might be processing d3 right now pipeline.push $do_that() # s2, might be processing d2 right now pipeline.push $do_something_else() # s3, might be processing d1 right now

Although there's really no telling whether step s3 will really process datom d1 at the 'same point in time' that step s2 processes datom d2 and so on (in the strict sense, this is hardly possible in a single-threaded language anyway), the visualization still holds a grain of truth: stream transforms that come 'later' (further down) in the pipeline will see events near the top of your to-do list first, and vice versa. This can be mildly confusing.

select = ( d, selector ) ->

The select method can be used to determine whether a given event d matches a set of conditions; typically, one will want to use select d, selector to decide whether a given event is suitable for processing by the stream transform at hand, or whether it should be passed on unchanged.

The current implementation of select() is much dumber and faster than its predecessors; where previously, it was possible to match datoms with multiple selectors that contained multiple sigils and so forth, the new version does little more than check wheter the single selector allowed equals the given datom's key value—that's about it, except that one can still select d, '^somekey#stamped' to match both unstamped and stamped datoms.

The XEmitter (XE) Sub-Module

XE Sending API

• XE.emit = ( key, d ) -> emit (a.k.a. 'publish', 'send to whom it may concern') an event. To be called either as XE.emit '^mykey', 'myvalue' or as XE.emit PD.new_event '^mykey', 'myvalue' (in which latter case the datom's key will become the channel key). When called with await as in return_values = await XE.emit '^foo', ..., return_values will be a list with all values returned by all listeners that got called for this event.

• XE.delegate = ( key, d ) -> like XE.emit() but will pick out and unwrap the event value from the event contractor (see below). If no event contractor was listening, an error will be raised.

XE Receiving API

• XE.listen_to_all = ( listener ) -> Register a listener for all events.

• XE.listen_to_unheard = ( listener ) -> Register a listener for all events that do not have a listener or a contractor.

• XE.listen_to = ( key, listener ) -> Register a listener for events that match key. No pattern matching is implemented atm, so you can only listen to all keys or a single key.

• XE.contract = ( key, listener ) -> Register a contractor (a.k.a. 'result producer') for events that match key.

Sample

PD                        = require 'pipedreams'
defer                     = setImmediate
XE                        = PD.XE.new_scope()

#-----------------------------------------------------------------------------------------------------------
### Register a 'contractor' (a.k.a. 'result producer') for `^plus-async` events; observe that asynchronous
contractors should return a promise: ###
XE.contract '^plus-async', ( d ) =>
  return new Promise ( resolve, reject ) =>
    defer => resolve d.value.a + d.value.b

############################################################################################################
do =>
  info 'µ28823-5', await XE.emit PD.new_event '^plus-async', { a: 42, b: 108, }
  # in case other listeners were registered that returned values like `'listener #1'` and so on, the
  # returned list of values might look like:
  # -> [ 'listener #4', { key: '~xemitter-preferred', value: 150 }, 'listener #1', 'listener #2' ]

  ### When using `delegate()` instead of `emit()`, the preferred value (a.k.a. '*the* event result')
  will be picked out of the list and unwrapped for you: ###
  info 'µ28823-6', await XE.delegate PD.new_event '^plus-async', { a: 42, b: 108, }
  # -> 150

For a demo with more coverage, have a look at experiments/demo-xemitter.coffee.

Managing Scope

Typically, you'll start using XEmitter with XE = PD.XE.new_scope(); this creates a new 'scope' for events. Only methods that emit and listen to the same scope can exchange messages. When used within an application, you will want to publish that scope to all participating modules; one way to do so is to write a dedicated module with a single line in it, module.exports = ( require 'pipedreams' ).XE.new_scope().

Cup Of Datom

Class Cupofdatom is a derivative of Cupofjoe that is geared towards easy declarative generation of nested sequences of datoms with a teacup-like syntax:

c = new DATOM.Cupofdatom()
c.cram 'helo', 'world'
c.cram 'foo', ->
  c.cram 'bold', ->
    c.cram null, 'content'
ds = c.expand()
# `ds` is now a list of datoms:
[
  { $key: '<helo' },
  { $key: '^text', text: 'world' },
  { $key: '>helo' },
  { $key: '<foo' },
  { $key: '<bold' },
  { $key: '^text', text: 'content' },
  { $key: '>bold' },
  { $key: '>foo' } ]

• First argument to cram() becomes key of datom
• therefore, must be a valid datom name
• sigil will be ^ if called with no further arguments
• or else two datoms with sigils < and > will be generated that surround their contents
• text arguments will be turned into ^text datoms
• as with Cupofjoe, functions will be called, may either call cram() method or return value
• return values will not be further analyzed but be kept as-is in the list returned by expand()
• also possible to provide (in non-initial positions) objects whose members will become attributes of the respective datom:

c = new DATOM.Cupofdatom { absorb: true, } # default value; Note: turn attributes off with { absorb: false, }
c.cram 'greeting', 'helo', 'world'
c.cram 'greeting', '早安', { lang: 'zh_CN', }
c.cram 'greeting', { lang: 'zh_CN', 问候: '早安', time_of_day: 'morning', }
c.cram 'text', { lang: 'hi', text: 'नमस्ते', }
c.cram 'greeting', ->
  c.cram 'language',    { $value: 'Japanese', }
  c.cram 'time_of_day', { $value: 'morning', }
  c.cram null, 'お早うございます'

gives

{ $key: '<greeting'                                                                 }
{ $key: '^text',          text:   'helo',                                           }
{ $key: '^text',          text:   'world',                                          }
{ $key: '>greeting'                                                                 }
{ $key: '<greeting',      lang:   'zh_CN',                                          }
{ $key: '^text',          text:   '早安',                                           }
{ $key: '>greeting'                                                                 }
{ $key: '^greeting',      lang:   'zh_CN', '问候': '早安', time_of_day: 'morning',  }
{ $key: '^text',          text:   'नमस्ते', lang: 'hi',                              }
{ $key: '<greeting'                                                                 }
{ $key: '^language',      $value: 'Japanese',                                       }
{ $key: '^time_of_day',   $value: 'morning',                                        }
{ $key: '^text',          text:   'お早うございます',                                 }
{ $key: '>greeting'                                                                 }

Call patterns:

• first argument is always:
  • the basic name (the $key of the datom minus the sigil) of the datom,
  • or else the comprehensive name, where implemented (for example in InterText CupOfHtml, this means one can give div#c59.draggable.hilite as first argument to produce elements with a tag name (div), an ID (c59), and HTML class attribute (draggable hilite) in one go
  • or else null to indicate absence of a specific name
  • when a name has been given
    • and there are content arguments, then a pair of { $key: '<name', }, { $key: '>name', } datoms will be produced, with the content arguments coming in between
    • in case no content has been given, a single { $key: '^name', } datom will be produced
• as for arguments in non-initial positions:
  • objects will be merged with Object.assign() and passed on to DATOM.new_datom(), so cram 'foo', { id: 'c221', frob: true, x: 1, }, ... { x: 2, } will produce { $key: '^foo', id: 'c221', frob: true, x: 2, }.
  • In case a key/value pair attributes argument conflicts with one set by an comprehensive name (as in cram 'foo#IDA', { id: 'IDB', }), the one in the attributes argument wins (as it would in a similar situation when using Object.assign())
  • functions will be called without arguments
    • if a function itself calls cram() from the same instance, its return value will be discarded;
    • in case it does not call cram(), its return value will be discarded if it is null or undefined, and otherwise become a content argument without being processed, so contained functions will not be called and text values will not be wrapped in { $key: '^text', } datoms
  • texts will be wrapped in { $key: '^text', 'text': ..., } datoms
  • other values will be wrapped in { $key: '^value', '$value': ..., } datoms, e.g. cram null, 42, 'some text', true will emit { $key: '^value', $value: 42, }, { $key: '^text', $text: 'some text', }, { $key: '^value', $value: true, }

cram name
cram name, content1, content2, ...
cram name, content1, ( -> function ), ...
cram name, content1, ( -> cram ... ), ...
cram name, { key: value, }, content1, ( -> cram ... ), ...

Benchmarks

Here is a speed comparison (code on GitHub) between Datom versions 7 and 8, using two methods of dealing with object freezing and two Datom configurations, f1 standing for the standard configuration (i.e. either DATOM = require 'datom' or DATOM = ( require 'datom' ).new { freeze: true, }) and f0 for the non-freezing configuration (obtained by DATOM = ( require 'datom' ).new { freeze: true, }). datom_v7_thaw_freeze_f0 is missing here because of a bug in the thaw method used in v7. Each run involved thawing 100 datoms with 5 key/value pairs each (ex.: { '$key': '^vapeurs', '𤭨': 447, '媑': true, escamote: false, auditionnerais: true, exacerbant: true, }), changing 3 values and freezing the object again. Tests marked ...thaw_freeze... use explicit calls to d = thaw d; ...; d = freeze d to do this, the ones marked ...lets... use a single call d = lets d, ( d ) -> ... to accomplish the same.

We see an overall improvement in the performance of v8 as compared to v7 which can be ascribed to the update of the letsfreezethat dependency which represents a complete overhaul of that library:

datom_v8_thaw_freeze_f0                          144,938 Hz   100.0 % │████████████▌│
datom_v8_lets_f0                                 128,930 Hz    89.0 % │███████████▏ │
datom_v8_thaw_freeze_f1                          126,920 Hz    87.6 % │███████████  │
datom_v7_lets_f0                                  92,669 Hz    63.9 % │████████     │
datom_v8_lets_f1                                  81,917 Hz    56.5 % │███████▏     │
datom_v7_lets_f1                                  40,063 Hz    27.6 % │███▌         │
datom_v7_thaw_freeze_f1                           39,334 Hz    27.1 % │███▍         │

For best performance, it is recommended to

• prefer d = thaw d; ...; d = freeze d over lets() although the latter is more elegant and prevents one from forgetting to freeze() a thaw()ed value, and to
• configure the DATOM library to forego actual freezing when moving from development to production, where appropriate, for a speed gain of around 10%.

To Do

• [ ] implement piecemeal structural validation such that on repeated calls to a validator instance's validate() method an error will be thrown as soon as unbalanced regions (delimeted by { $key: '<token', ..., } and { $key: '>token', ..., }) are encountered.

• [ ] VNRs:

  • [X] implement Vectorial NumbeRs (VNRs)
  • [ ] document Vectorial NumbeRs (VNRs)
  • [ ] remove either cmp_total() or cmp_partial() for simplification
  • [ ] assert and document that VNRs may be sorted element-wise lexicographically (e.g in Postgres, but also in JS) by appending a single zero element (or, for that matter, by padding as many zeroes as needed to make all VNRs the same length)
  • [ ] consider to disallow giving VNRs a final zero element
  • [ ] consider to store VNRs with an apended zero element

• [ ] implement & document standard attributes, $-prefixed and otherwise (?), such as

  • [ ] ^text—key for 'text datoms'
  • [ ] text—the underlying source text where code, data is parsed
  • [ ] $—'produced by', contains short label to point to source position, may be left-chained (most recent first) to obtain breadcrumbs path of responsible source locations
  • [ ] $vnr—for VNRs, the primary ordering criterium
  • [ ] $ref—do we still use this? See DataMill
  • [ ] $pos? $range? for [ start, stop, ] pairs, indices into a source; use inclusive or exclusive upper bound?
  • [ ] $loc? for [ line_nr, col_nr, ] pairs; NB might also want to use stop position of ranges

• [X] make { dirty: false, } the default setting (i.e. not marking changed datoms)

• [ ] consider to remove $dirty altogether; datoms-as-immutable-values can not be updated anyway, and whether an operation like d2 = lets d1, ( d ) -> ... has or has not caused any differences between d1 and d2 (short of a Turing-complete analysis of the function passed in to lets()) is only answerable by comparing all members of both datoms.

• [ ] Dependency [email protected] changed behavior: "Ensure .emit() doesn't return a value" which breaks contracts. The fix currently consists in not upgrading from 0.6.0 until a workaround has been implemented.

• [ ] Allow to instantiate with configuration for freezing (Object.freeze(), letsfreezethat.freeze()) and cloning (strcuturedClone(), Object.assign(), GUY.props.nonull_assign())