Add `MktPair` handling block for when a backend delivers
a `mkt_info`-field containing init msg. Adjust the original
`Symbol`-style `'symbol_info'` msg processing to do `Decimal` defaults
and convert to `MktPair` including slapping in a hacky `_atype: str`
field XD
General initial name changes to `bs_mktid` and `_fqme` throughout!
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.
There's been way too many issues when trying to calculate this
dynamically from the input array, so just expect the caller to know what
it's doing and don't bother with ever hitting the error case of
calculating and incorrect value internally.
Not sure why this was ever allowed but, for slicing to the sample
*before* whatever target time stamp is passed in we should definitely
not return the prior index as for the slice start since that might
include a very large gap prior to whatever sample is scanned to have
the earliest matching time stamp.
This was essential to fixing overlay intersect points searching in our
``ui.view_mode`` machinery..
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.
Not really sure there's much we can do besides dump Grpc stuff when we
detect an "error" `str` for the moment..
Either way leave a buncha complaints (como siempre) and do linting
fixups..
Previously we would make the `ahabd` supervisor-actor sync to docker
container startup using pseudo-blocking log message processing.
This has issues,
- we're forced to do a hacky "yield back to `trio`" in order to be
"fake async" when reading the log stream and further,
- blocking on a message is fragile and often slow.
Instead, run the log processor in a background task and in the parent
task poll for the container to be in the client list using a similar
pseudo-async poll pattern. This allows the super to `Context.started()`
sooner (when the container is actually registered as "up") and thus
unblock its (remote) caller faster whilst still doing full log msg
proxying!
Deatz:
- adds `Container.cuid: str` a unique container id for logging.
- correctly proxy through the `loglevel: str` from `pikerd` caller task.
- shield around `Container.cancel()` in the teardown block and use
cancel level logging in that method.
With the addition of a new `elastixsearch` docker support in
https://github.com/pikers/piker/pull/464, adjustments were made
to container startup sync logic (particularly the `trio` checkpoint
sleep period - which itself is a hack around a sync client api) which
caused a regression in upstream startup logic wherein container error
logs were not being bubbled up correctly causing a silent failure mode:
- `marketstore` container started with corrupt input config
- `ahabd` super code timed out on startup phase due to a larger log
polling period, skipped processing startup logs from the container,
and continued on as though the container was started
- history client fails on grpc connection with no clear error on why the
connection failed.
Here we revert to the old poll period (1ms) to avoid any more silent
failures and further extend supervisor control through a configuration
override mechanism. To address the underlying design issue, this patch
adds support for container-endpoint-callbacks to override supervisor
startup configuration parameters via the 2nd value in their returned
tuple: the already delivered configuration `dict` value.
The current exposed values include:
{
'startup_timeout': 1.0,
'startup_query_period': 0.001,
'log_msg_key': 'msg',
},
This allows for container specific control over the startup-sync query
period (the hack mentioned above) as well as the expected log msg key
and of course the startup timeout.
Adds a `piker storage` subcmd with a `-d` flag to wipe a particular
fqsn's time series (both 1s and 60s). Obviously this needs to be
extended much more but provides a start point.
In order to support existing `pps.toml` files in the wild which don't
have the `asset_type, price_tick_size, lot_tick_size` fields, we need to
only optionally read them and instead expect that backends will write
the fields going forward (coming in follow patches).
Further this makes some small asset-size (vlm accounting) quantization
related adjustments:
- rename `Symbol.decimal_quant()` -> `.quantize_size()` since that is
explicitly what this method is doing.
- and expect an input `size: float` which we cast to decimal instead of
doing it inside the `.calc_size()` caller code.
- drop `Symbol.iterfqsns()` which wasn't being used anywhere at all..
Additionally, this drafts out a new replacement market-trading-pair data
type to eventually replace `.data._source.Symbol` -> `MktPair` which we
aren't using yet, but serves as the documentation-driven motivator ;)
and, it relates to https://github.com/pikers/piker/issues/467.
Add decimal quantize API to Symbol to simplify by-broker truncation
Add symbol info to `pps.toml`
Move _assert call to outside the _async_main context manager
Minor indentation and styling changes, also convert a few prints to log calls
Fix multi write / race condition on open_pps call
Switch open_pps to not write by default
Fix integer math kraken syminfo _tick_size initialization
For the purposes of avoiding another full format call we can stash the
last rendered 1d xy pre-graphics formats as
`IncrementalFormatter.x/y_1d: np.ndarray`s and allow readers in the viz
and render machinery to use this data easily for things like "only
drawing the last uppx's worth of data as a line". Also add
a `.flat_index_ratio: float` which can be used similarly as a scalar
applied to indexes into the src array but instead when indexing
(flattened) 1d xy formatted outputs. Finally, this drops the way
overdone/noisy `.__repr__()` meth we had XD
We obviously don't want to be debugging a sample-index issue if/when the
market for the asset is closed (since we'll be guaranteed to have
a mismatch, lul). Pass in the `feed_is_live: trio.Event` throughout the
backfilling routines to allow first checking for the live feed being active
so as to avoid breakpointing on false +ves. Also, add a detailed warning
log message for when *actually* investigating a mismatch.
This should never really happen but when it does it appears to be a race
with writing startup pre-graphics-formatter array data where we get
`x_end` epoch value subtracting some really small offset value (like
`-/+0.5`) or the opposite where the `x_start` is epoch and `x_end` is
small.
This adds a warning msg and `breakpoint()` as well as guards around the
entire code downsampling code path so that when resumed the downsampling
cycle should just be skipped and avoid a crash.
Whenever the last datum is in view `slice_from_time()` need to always
spec the final array index (i.e. the len - 1 value we set as
`read_i_max`) to avoid a uniform-step arithmetic error where gaps in the
underlying time series causes an index that's too low to be returned.
Doesn't seem like we really need to handle the situation where the start
or stop input time stamps are outside the index range of the data since
the new binary search handling via `numpy.searchsorted()` covers this
case at minimal runtime cost and with an equally correct output. Allows
us to drop some other indexing endpoint internal variables as well.
Define the x-domain coords "offset" (determining the curve graphics
per-datum placement) for each formatter such that there's only on place
to change it when needed. Obviously each graphics type has it's own
dimensionality and this is reflected by the array shapes on each
subtype.
Previously we were drawing with the middle of the bar on each index with
arms to either side: +/- some arm length. Instead this changes so that
each bar is drawn *after* each index/timestamp such that in graphics
coords the bar span more correctly matches the time span in the
x-domain. This makes the linked region between slow and fast chart
directly match (without any transform) for epoch-time indexing such that
the last x-coord in view on the fast chart is no more then the
next time step in (downsampled) slow view.
Deats:
- adjust in `._pathops.path_arrays_from_ohlc()` and take an `bar_w` bar
width input (normally taken from the data step size).
- change `.ui._ohlc.bar_from_ohlc_row()` and
`BarItems.draw_last_datum()` to match.
Allows easily switching between normal array `int` indexing and time
indexing by just flipping the `Viz._index_field: str`.
Also, guard all the x-data audit breakpoints with a time indexing
condition.
First allocation vs. first "prepend" of source data to an xy `ndarray`
format **must be mutex** in order to avoid a double prepend.
Previously when both blocks were executed we'd end up with
a `.xy_nd_start` that was decremented (at least) twice as much as it
should be on the first `.format_to_1d()` call which is obviously
incorrect (and causes problems for m4 downsampling as discussed below).
Further, since the underlying `ShmArray` buffer indexing is managed
(i.e. write-updated) completely independently from the incremental
formatter updates and internal xy indexing, we can't use
`ShmArray._first.value` and instead need to use the particular `.diff()`
output's prepend length value to decrement the `.xy_nd_start` on updates
after initial alloc.
Problems this resolves with m4:
- m4 uses a x-domain diff to calculate the number of "frames" to
downsample to, this is normally based on the ratio of pixel columns on
screen vs. the size of the input xy data.
- previously using an int-index (not epoch time) the max diff between
first and last index would be the size of the input buffer and thus
would never cause a large mem allocation issue (though it may have
been inefficient in terms of needed size).
- with an epoch time index this max diff could explode if you had some
near-now epoch time stamp **minus** an x-allocation value: generally
some value in `[0.5, -0.5]` which would result in a massive frames and
thus internal `np.ndarray()` allocation causing either a crash in
`numba` code or actual system mem over allocation.
Further, put in some more x value checks that trigger breakpoints if we
detect values that caused this issue - we'll remove em after this has
been tested enough.
If we presume that time indexing using a uniform step we can calculate
the exact index (using `//`) for the input time presuming the data
set has zero gaps. This gives a massive speedup over `numpy` fancy
indexing and (naive) `numba` iteration. Further in the case where time
gaps are detected, we can use `numpy.searchsorted()` to binary search
for the nearest expected index at lower latency.
Deatz,
- comment-disable the call to the naive `numba` scan impl.
- add a optional `step: int` input (calced if not provided).
- add todos for caching binary search results in the gap detection
cases.
- drop returning the "absolute buffer indexing" slice since the caller
can always just use the read-relative slice to acquire it.
Gives approx a 3-4x speedup using plain old iterate-with-for-loop style
though still not really happy with this .5 to 1 ms latency..
Move the core `@njit` part to a `_slice_from_time()` with a pure python
func with orig name around it. Also, drop the output `mask` array since
we can generally just use the slices in the caller to accomplish the
same input array slicing, duh..
We need to subtract the first index in the array segment read, not the
first index value in the time-sliced output, to get the correct offset
into the non-absolute (`ShmArray.array` read) array..
Further we **do** need the `&` between the advance indexing conditions
and this adds profiling to see that it is indeed real slow (like 20ms
ish even when using `np.where()`).
Planning to put the formatters into a new mod and aggregate all path
gen/op helpers into this module.
Further tweak include:
- moving `path_arrays_from_ohlc()` back to module level
- slice out the last xy datum for `OHLCBarsAsCurveFmtr` 1d formatting
- always copy the new x-value from the source to `.x_nd`
This was a major cause of error (particularly trying to get epoch
indexing working) and really isn't necessary; instead just have
`.diff()` always read from the underlying source array for current
index-step diffing and append/prepend slice construction.
Allows us to,
- drop `._last_read` state management and thus usage.
- better handle startup indexing by setting `.xy_nd_start/stop` to
`None` initially so that the first update can be done in one large
prepend.
- better understand and document the step curve "slice back to previous
level" logic which is now heavily commented B)
- drop all the `slice_to_head` stuff from and instead allow each
formatter to choose it's 1d segmenting.
Don't expect values (array + slice) to be returned and applied by
`.incr_update_xy_nd()` and instead presume this will implemented
internally in each (sub)formatter.
Attempt to simplify some incr-update routines, (particularly in the step
curve formatter, though most of it was reverted to just a simpler form
of the original implementation XD) including:
- dropping the need for the `slice_to_head: int` control.
- using the `xy_nd_start/stop` index counters over custom lookups.
Remove harcoded `'index'` field refs from all formatters in a first
attempt at moving towards epoch-time alignment (though don't actually
use it it yet).
Adjustments to the formatter interface:
- property for `.xy_nd` the x/y nd arrays.
- property for and `.xy_slice` the nd format array(s) start->stop index
slice.
Internal routine tweaks:
- drop `read_src_from_key` and always pass full source array on updates
and adjust handlers to expect to have to index the data field of
interest.
- set `.last_read` right after update calls instead of after 1d
conversion.
- drop `slice_to_head` array read slicing.
- add some debug points for testing 'time' indexing (though not used
here yet).
- add `.x_nd` array update logic for when the `.index_field` is not
'index' - i.e. when we begin to try and support epoch time.
- simplify some new y_nd updates to not require use of `np.broadcast()`
where possible.
Probably means it doesn't need to be a `Flume` method but it's
convenient to expect the caller to pass in the `np.ndarray` with
a `'time'` field instead of a `timeframe: str` arg; also, return the
slice mask instead of the sliced array as output (again allowing the
caller to do any slicing). Also, handle the slice-outside-time-range
case by just returning the entire index range with a `None` mask.
Adjust `Viz.view_data()` to instead do timeframe (for rt vs. hist shm
array) lookup and equiv array slicing with the returned mask.
Since these modules no longer contain Qt specific code we might
as well include them in the data sub-package.
Also, add `IncrementalFormatter.index_field` as single point to def the
indexing field that should be used for all x-domain graphics-data
rendering.
Since higher level charting and fsp management need access to the
new `Flume` indexing apis this adjusts some func sigs to pass through
(and/or create) flume instances:
- `LinkedSplits.add_plot()` and dependents.
- `ChartPlotWidget.draw_curve()` and deps, and it now returns a `Flow`.
- `.ui._fsp.open_fsp_admin()` and `FspAdmin.open_fsp_ui()` related
methods => now we wrap the destination fsp shm in a flume on the admin
side and is returned from `.start_engine_method()`.
Drop a bunch of (unused) chart widget methods including some already
moved to flume methods: `.get_index()`, `.in_view()`,
`.last_bar_in_view()`, `.is_valid_index()`.
Allows running simultaneous data feed services on the same (linux) host
by avoiding file-name collisions instead keying shm buffer sets by the
given `brokerd` instance. This allows, for example, either multiple dev
versions of the data layer to run side-by-side or for the test suite to
be seamlessly run alongside a production instance.
Always use `open_sample_stream()` to register fast and slow quote feed
buffers and get a sampler stream which we use to trigger
`Sampler.broadcast_all()` calls on the service side after backfill
events.
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.
When we see multiple history frames that are duplicate to the request
set, bail re-trying after a number of tries (6 just cuz) and return
early from the tsdb backfill loop; presume that this many duplicates
means we've hit the beginning of history. Use a `collections.Counter`
for the duplicate counts. Make sure and warn log in such cases.
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
Add some (untested) data slicing util methods for mapping time ranges to
source data indices:
- `.get_index()` which maps a single input epoch time to an equiv array
(int) index.
- add `slice_from_time()` which returns a view of the shm data from an
input epoch range presuming the underlying struct array contains
a `'time'` field with epoch stamps.
- `.view_data()` which slices out the "in view" data according to the
current state of the passed in `pg.PlotItem`'s view box.
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.
Set each quote-stream by matching the provider for each `Flume` and thus
results in some flumes mapping to the same (multiplexed) stream.
Monkey-patch the equivalent `tractor.MsgStream._ctx: tractor.Context` on
each broadcast-receiver subscription to allow use by feed bus methods as
well as other internals which need to reference IPC channel/portal info.
Start a `_FeedsBus` subscription management API:
- add `.get_subs()` which returns the list of tuples registered for the
given key (normally the fqsn).
- add `.remove_sub()` which allows removing by key and tuple value and
provides encapsulation for sampler task(s) which deal with dropped
connections/subscribers.
Adds provider-list-filtered (quote) stream multiplexing support allowing
for merged real-time `tractor.MsgStream`s using an `@acm` interface.
Behind the scenes we are just doing a classic multi-task push to common
mem chan approach.
Details to make it work on `Feed`:
- add `Feed.mods: dict[str, Moduletype]` and
`Feed.portals[ModuleType, tractor.Portal]` which are both populated
during init in `open_feed()`
- drop `Feed.portal` and `Feed.name`
Also fix a final lingering tsdb history loading loop termination bug.
A slight facepalm but, the main issue was a simple indexing logic error:
we need to slice with `tsdb_history[-shm._first.value:]` to push most
recent history not oldest.. This allows cleanup of tsdb backfill loop as
well.
Further, greatly simply `diff_history()` time slicing by using the
classic `numpy` conditional slice on the epoch field.
This had a bug prior where the end of a frame (a partial) wasn't being
sliced correctly and we'd get odd gaps showing up in the backfilled from
`brokerd` vs. tsdb end index. Repair this by doing timeframe aware index
diffing in `diff_history()` which seems to resolve it. Also, use the
frame-result's `end_dt: datetime` for the loop exit condition.
Sync per-symbol sampler loop start to subscription registers such that
the loop can't start until the consumer's stream subscription is added;
the task-sync uses a `trio.Event`. This patch also drops a ton of
commented cruft.
Further adjustments needed to get parity with prior functionality:
- pass init msg 'symbol_info' field to the `Symbol.broker_info: dict`.
- ensure the `_FeedsBus._subscriptions` table uses the broker specific
(without brokername suffix) as keys for lookup so that the sampler
loop doesn't have to append in the brokername as a suffix.
- ensure the `open_feed_bus()` flumes-table-msg returned sent by
`tractor.Context.started()` uses the `.to_msg()` form of all flume
structs.
- ensure `maybe_open_feed()` uses `tractor.MsgStream.subscribe()` on all
`Flume.stream`s on cache hits using the
`tractor.trionics.gather_contexts()` helper.
Orient shm-flow-arrays around the new idea of a `Flume` which provides
access, mgmt and basic measure of real-time data flow sets (see water
flow management semantics).
- We discard the previous idea of a "init message" which contained all
the shm attachment info and instead send a startup message full of
`Flume.to_msg()`s which are symmetrically loaded on the caller actor
side.
- Create data-flows "entries" for every passed in fqsn such that the consumer gets back
streams and shm for each, now all wrapped in `Flume` types. For now we
allocate `brokermod.stream_quotes()` tasks 1-to-1 for each fqsn
(instead of expecting each backend to do multi-plexing, though we
might want that eventually) as well a `_FeedsBus._subscriber` entry
for each. The pause/resume management loop is adjusted to match.
Previously `Feed`s were allocated 1-to-1 with each fqsn.
- Make `Feed` a `Struct` subtype instead of a `@dataclass` and move all
flow specific attrs to the new `Flume`:
- move `.index_stream()`, `.get_ds_info()` to `Flume`.
- drop `.receive()`: each fqsn entry will now require knowledge of
separate streams by feed users.
- add multi-fqsn tables: `.flumes`, `.streams` which point to the
appropriate per-symbol entries.
- Async load all `Flume`s from all contexts and all quote streams using
`tractor.trionics.gather_contexts()` on the client `open_feed()` side.
- Update feeds test to include streaming 2 symbols on the same (binance)
backend.
Tyler Goodlet
jaredgoldman
Guillermo Rodriguez
algorandpa
Guillermo Rodriguez
Esmeralda Gallardo