Add a cache interface which supports expirable entries with a changeable
backend for the cache into which they are entered.
The default cache is a `dict` but could swapped for
`cachetools.LRUCache` or any other cache which supports `__get__`,
`__set__`, and `__del__`.
So that consumers can change the use of `CachedObjects` stored in a
cache from:
```
self._cache = {}
...
try:
obj = self._cache[key]
try:
return obj.unwrap(dt)
except Expired:
pass
except KeyError:
pass
...
self._cache[key] = CachedObject(value, new_expiration)
```
to:
```
self._cache = ExpiringCache(LRUCache(maxsize=6))
...
try:
return self._cache.get(key, dt)
except KeyError:
# Get fresh value
...
self._cache.set(key, value, new_expiration)
```
Instead of using the `remember_last` memoization on all calls to
`_find_position_of_minute`, add an instance local cache which is only
used by the `get_value` call. The `get_value` call is very hot, so any
extra overhead (e.g. creating the WeakArgs on every invocation) becomes
costly. The current usage `get_value` also has the property that it is
called with monotonically increasing, but with a high repeat count on
each value. (A further improvement could making a `get_value` which
supports being used by many sids, for use by the update portfolio
positions.)
The caching is not done at the `_find_position_of_minute_level` because
`unadjusted_window` always uses two positions on the tape (start and end
of range) which would cause the entries and removal into the cache which
would be invalidated both between the calls of start and end, and next
call of the function.
The argument was only needed for mapping the positions which need to be
removed on adjusted windows. The start and end position of each range
can be derived from the early closes' positions and the market open,
respectively.
Remove to reduce moving parts.
The cache in data portal was added before the change to using a
CachedObject to wrap the window_blocks in the USEquityHistoryLoader.
Removing this extra layer saves some cycles.
Does not fix current memory investigaton (since only one sids/dts pair
per column was cached in `_equity_daily_reader_array_data` at a time),
but removing should make it more clear where needed references are being
held.
The minute history loader caching was incorrectly mimicking the daily
history loader caching.
Where caching the adjusted array on the last dt helps an access pattern
of repeated calling history windows on the same day (which has an end_dt
of the previous day), with minute windows the end dt is always moving
forward, so the cached values are seldom used. (Would only be used if
`history` was called with same parameters twice on the same simulation time.)
The intervals are returned as a set, so order is not guaranteed,
which becomes exposed when reading windows which span multiple years.
The deletion of values from the regular sized minute array assumes that
intervals can be reversed to delete the array from the back.
When the dts and length of cols are mismatched the writer behaves in
unintended ways. e.g. in a case where a consumer passed dts which had
minutes with no trades removed, but regular (market minute for day)
sized arrays for the data with `0`'s on minutes without trades, the non
trade minutes from cols are written to slots in the output where a trade
is intended.
Protect against this misuse by checking that all lengths are equal when
using the `write_cols` method.
Make a separate `_write_cols` method for use by both `write_cols` and
`write`, since the `write` method which takes a DataFrame has the
matched input length enforced by the DataFrame.
Eddie Hebert