standardized immutable objects in the spirit of datomic, especially suited for use in data pipelines
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standardized immutable objects in the spirit of datomic, especially suited for use in data pipelines
NOTE: Documentation is still fragmentary. WIP.
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.)
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.
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.
@freeze = ( d ) ->@thaw = ( d ) ->@lets = ( original, modifier ) ->@set = ( d, k, P... ) ->@unset = ( d, k ) ->@stamp = ( d, P... ) ->@unstamp = ( d ) ->@is_system = ( d ) ->@is_stamped = ( d ) ->@is_fresh = ( d ) ->@is_dirty = ( d ) ->@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 = ( $value, other... ) ->@new_end_datom = ->@new_warning = ( ref, message, d, other... ) ->@select = ( d, selector ) ->d.$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).The below copied from PipeDreams docs, to be updated
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
EventEmitterway 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
d1first, thend2and 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 nowAlthough there's really no telling whether step
s3will really process datomd1at the 'same point in time' that steps2processes datomd2and 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.
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.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.
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.
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().
Where a consistent relative ordering of streams of datoms is needed, especially if any number of datoms may get deleted and inserted at some mid-stream point, Vectorial Numbers (VNRs), which are implemented as lists of integers, can be used to avoid a re-numbering of elements and still be able to insert arbitrarily many new elements between any two given elements.
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.
implement Vectorial NumbeRs (VNRs)
document Vectorial NumbeRs (VNRs)
implement & document standard attributes, $-prefixed and otherwise (?), such as
^text—key for 'text datoms'text—the underlying source text where code, data is parsed$$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 rangesFAQs
standardized immutable objects in the spirit of datomic, especially suited for use in data pipelines
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