Not sure how i missed this (and left in handling of `list.remove()` and
it ever worked for that?) after the `samplerd` impl in 5ec1a72 but, this
adjusts the remove-broken-subscriber loop to catch the correct
`set.remove()` exception type on a missing (likely already removed)
subscription entry.
In situations where clients are (dynamically) subscribing *while*
broadcasts are starting to taking place we need to handle the
`set`-modified-during-iteration case. This scenario seems to be more
common during races on concurrent startup of multiple symbols. The
solution here is to use another set to take note of subscribers which
are successfully sent-to and then skipping them on re-try.
This also contains an attempt to exception-handle throttled stream
overruns caused by higher frequency feeds (like binance) pushing more
quotes then can be handled during (UI) client startup.
Now spawned under the `pikerd` tree as a singleton-daemon-actor we offer
a slew of new routines in support of this micro-service:
- `maybe_open_samplerd()` and `spawn_samplerd()` which provide the
`._daemon.Services` integration to conduct service spawning.
- `open_sample_stream()` which is a client-side endpoint which does all
the work of (lazily) starting the `samplerd` service (if dne) and
registers shm buffers for update as well as connect a sample-index
stream for iterator by the caller.
- `register_with_sampler()` which is the `samplerd`-side service task
endpoint implementing all the shm buffer and index-stream registry
details as well as logic to ensure a lone service task runs
`Services.increment_ohlc_buffer()`; it increments at the shortest period
registered which, for now, is the default 1s duration.
Further impl notes:
- fixes to `Services.broadcast()` to ensure broken streams get discarded
gracefully.
- we use a `pikerd` side singleton mutex `trio.Lock()` to ensure
one-and-only-one `samplerd` is ever spawned per `pikerd` actor tree.
We're moving toward a single actor managing sampler work and distributed
independently of `brokerd` services such that a user can run samplers on
different hosts then real-time data feed infra. Most of the
implementation details include aggregating `.data._sampling` routines
into a new `Sampler` singleton type.
Move the following methods to class methods:
- `.increment_ohlc_buffer()` to allow a single task to increment all
registered shm buffers.
- `.broadcast()` for IPC relay to all registered clients/shms.
Further add a new `maybe_open_global_sampler()` which allocates
a service nursery and assigns it to the `Sampler.service_nursery`; this
is prep for putting the step incrementer in a singleton service task
higher up the data-layer actor tree.
Wow, turns out tick framing was totally borked since we weren't framing
on "greater then throttle period long waits" XD
This moves all the framing logic into a common func and calls it in
every case:
- every (normal) "pre throttle period expires" quote receive
- each "no new quote before throttle period expires" (slow case)
- each "no clearing tick yet received" / only burst on clears case
This has been an outstanding idea for a while and changes the framing
format of tick events into a `dict[str, list[dict]]` wherein for each
tick "type" (eg. 'bid', 'ask', 'trade', 'asize'..etc) we create an FIFO
ordered `list` of events (data) and then pack this table into each
(throttled) send. This gives an additional implied downsample reduction
(in terms of iteration on the consumer side) from `N` tick-events to
a (max) `T` tick-types presuming the rx side only needs the latest tick
event.
Drop the `types: set` and adjust clearing event test to use the new
`ticks_by_type` map's keys.
Instead of uniformly distributing the msg send rate for a given
aggregate subscription, choose to be more bursty around clearing ticks
so as to avoid saturating the consumer with L1 book updates and vs.
delivering real trade data as-fast-as-possible.
Presuming the consumer is in the "UI land of slow" (eg. modern display
frame rates) such an approach serves more useful for seeing "material
changes" in the market as-bursty-as-possible (i.e. more short lived fast
changes in last clearing price vs. many slower changes in the bid-ask
spread queues). Such an approach also lends better to multi-feed
overlays which in aggregate tend to scale linearly with the number of
feeds/overlays; centralization of bursty arrival rates allows for
a higher overall throttle rate if used cleverly with framing.
Allows using `set` ops for subscription management and guarantees no
duplicates per `brokerd` actor. New API is simpler for dynamic
pause/resume changes per `Feed`:
- `_FeedsBus.add_subs()`, `.get_subs()`, `.remove_subs()` all accept multi-sub
`set` inputs.
- `Feed.pause()` / `.resume()` encapsulates management of *only* sending
a msg on each unique underlying IPC msg stream.
Use new api in sampler task.
Previously we would only detect overruns and drop subscriptions on
non-throttled feed subs, however you can get the same issue with
a wrapping throttler task:
- the intermediate mem chan can be blocked either by the throttler task
being too slow, in which case we still want to warn about it
- the stream's IPC channel actually breaks and we still want to drop
the connection and subscription so it doesn't be come a source of
stale backpressure.
Instead of worrying about the increment period per shm subscription,
just use the value passed as input and presume the caller knows that
only one task is necessary and that the wakeup (sampling) period should
be the shortest that is needed.
It's very unlikely we don't want at least a 1s sampling (both in terms
of task switching cost and general usage) which will eventually ship as
the default "real-time" feed "timeframe". Further, this "fast" increment
sampling task can handle all lower sampling periods (eg. 1m, 5m, 1H)
based on the current implementation just the same.
Also, add a global default sample period as `_defaul_delay_s` for use in
other internal modules.
When the market isn't open the feed layer won't create a subscriber
entry in the sampler broadcast loop and so if a manual call to
``broadcast()`` is made (like when trying to update a chart from
a history prepend) we need to handle that case and just broadcast
a random `-1` for now..BD
This should in theory result in increased burstiness since we remove
the plain `trio.sleep()` and instead always wait on the receive channel
as much as possible until the `trio.move_on_after()` (+ time diffing
calcs) times out and signals the next throttled send cycle. This also is
slightly easier to grok code-wise instead of the `try, except` and
another tight while loop until a `trio.WouldBlock`. The only simpler
way i can think to do it is with 2 tasks: 1 to collect ticks and the
other to read and send at the throttle rate.
Comment out the log msg for now to avoid latency and add much more
detailed comments. Add an overrun log msg to the main sample loop.
If a client attaches to a quotes data feed and requests a throttle rate,
be sure to unsub that side-band memchan + task when it detaches and
especially so on any transport connection error.
Also, use an explicit `tractor.Context.cancel()` on the client feed
block exit since we removed the implicit cancel option from the
`tractor` api.
Adding binance's "hft" ws feeds has resulted in a lot of context
switching in our Qt charts, so much so it's chewin CPU and definitely
worth it to throttle to the detected display rate as per discussion in
issue #192.
This is a first very very naive attempt at throttling L1 tick feeds on
the `brokerd` end (producer side) using a constant and uniform delivery
rate by way of a `trio` task + mem chan. The new func is
`data._sampling.uniform_rate_send()`. Basically if a client request
a feed and provides a throttle rate we just spawn a task and queue up
ticks until approximately the next display rate's worth period of time
has passed before forwarding. It's definitely nothing fancy but does
provide fodder and a start point for an up and coming queueing eng to
start digging into both #107 and #109 ;)
Tyler Goodlet