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from __future__ import annotations
import collections
import functools
import logging
import math
import os
import threading
import warnings
from concurrent.futures import Future, ThreadPoolExecutor
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Generic,
NamedTuple,
OrderedDict,
TypeVar,
)
if TYPE_CHECKING:
import mmap
from typing_extensions import ParamSpec
P = ParamSpec("P")
else:
P = TypeVar("P")
T = TypeVar("T")
logger = logging.getLogger("fsspec")
Fetcher = Callable[[int, int], bytes] # Maps (start, end) to bytes
class BaseCache:
"""Pass-though cache: doesn't keep anything, calls every time
Acts as base class for other cachers
Parameters
----------
blocksize: int
How far to read ahead in numbers of bytes
fetcher: func
Function of the form f(start, end) which gets bytes from remote as
specified
size: int
How big this file is
"""
name: ClassVar[str] = "none"
def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
self.blocksize = blocksize
self.fetcher = fetcher
self.size = size
def _fetch(self, start: int | None, stop: int | None) -> bytes:
if start is None:
start = 0
if stop is None:
stop = self.size
if start >= self.size or start >= stop:
return b""
return self.fetcher(start, stop)
class MMapCache(BaseCache):
"""memory-mapped sparse file cache
Opens temporary file, which is filled blocks-wise when data is requested.
Ensure there is enough disc space in the temporary location.
This cache method might only work on posix
"""
name = "mmap"
def __init__(
self,
blocksize: int,
fetcher: Fetcher,
size: int,
location: str | None = None,
blocks: set[int] | None = None,
) -> None:
super().__init__(blocksize, fetcher, size)
self.blocks = set() if blocks is None else blocks
self.location = location
self.cache = self._makefile()
def _makefile(self) -> mmap.mmap | bytearray:
import mmap
import tempfile
if self.size == 0:
return bytearray()
# posix version
if self.location is None or not os.path.exists(self.location):
if self.location is None:
fd = tempfile.TemporaryFile()
self.blocks = set()
else:
fd = open(self.location, "wb+")
fd.seek(self.size - 1)
fd.write(b"1")
fd.flush()
else:
fd = open(self.location, "r+b")
return mmap.mmap(fd.fileno(), self.size)
def _fetch(self, start: int | None, end: int | None) -> bytes:
logger.debug(f"MMap cache fetching {start}-{end}")
if start is None:
start = 0
if end is None:
end = self.size
if start >= self.size or start >= end:
return b""
start_block = start // self.blocksize
end_block = end // self.blocksize
need = [i for i in range(start_block, end_block + 1) if i not in self.blocks]
while need:
# TODO: not a for loop so we can consolidate blocks later to
# make fewer fetch calls; this could be parallel
i = need.pop(0)
sstart = i * self.blocksize
send = min(sstart + self.blocksize, self.size)
logger.debug(f"MMap get block #{i} ({sstart}-{send}")
self.cache[sstart:send] = self.fetcher(sstart, send)
self.blocks.add(i)
return self.cache[start:end]
def __getstate__(self) -> dict[str, Any]:
state = self.__dict__.copy()
# Remove the unpicklable entries.
del state["cache"]
return state
def __setstate__(self, state: dict[str, Any]) -> None:
# Restore instance attributes
self.__dict__.update(state)
self.cache = self._makefile()
class ReadAheadCache(BaseCache):
"""Cache which reads only when we get beyond a block of data
This is a much simpler version of BytesCache, and does not attempt to
fill holes in the cache or keep fragments alive. It is best suited to
many small reads in a sequential order (e.g., reading lines from a file).
"""
name = "readahead"
def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
super().__init__(blocksize, fetcher, size)
self.cache = b""
self.start = 0
self.end = 0
def _fetch(self, start: int | None, end: int | None) -> bytes:
if start is None:
start = 0
if end is None or end > self.size:
end = self.size
if start >= self.size or start >= end:
return b""
l = end - start
if start >= self.start and end <= self.end:
# cache hit
return self.cache[start - self.start : end - self.start]
elif self.start <= start < self.end:
# partial hit
part = self.cache[start - self.start :]
l -= len(part)
start = self.end
else:
# miss
part = b""
end = min(self.size, end + self.blocksize)
self.cache = self.fetcher(start, end) # new block replaces old
self.start = start
self.end = self.start + len(self.cache)
return part + self.cache[:l]
class FirstChunkCache(BaseCache):
"""Caches the first block of a file only
This may be useful for file types where the metadata is stored in the header,
but is randomly accessed.
"""
name = "first"
def __init__(self, blocksize: int, fetcher: Fetcher, size: int) -> None:
super().__init__(blocksize, fetcher, size)
self.cache: bytes | None = None
def _fetch(self, start: int | None, end: int | None) -> bytes:
start = start or 0
end = end or self.size
if start < self.blocksize:
if self.cache is None:
if end > self.blocksize:
data = self.fetcher(0, end)
self.cache = data[: self.blocksize]
return data[start:]
self.cache = self.fetcher(0, self.blocksize)
part = self.cache[start:end]
if end > self.blocksize:
part += self.fetcher(self.blocksize, end)
return part
else:
return self.fetcher(start, end)
class BlockCache(BaseCache):
"""
Cache holding memory as a set of blocks.
Requests are only ever made ``blocksize`` at a time, and are
stored in an LRU cache. The least recently accessed block is
discarded when more than ``maxblocks`` are stored.
Parameters
----------
blocksize : int
The number of bytes to store in each block.
Requests are only ever made for ``blocksize``, so this
should balance the overhead of making a request against
the granularity of the blocks.
fetcher : Callable
size : int
The total size of the file being cached.
maxblocks : int
The maximum number of blocks to cache for. The maximum memory
use for this cache is then ``blocksize * maxblocks``.
"""
name = "blockcache"
def __init__(
self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32
) -> None:
super().__init__(blocksize, fetcher, size)
self.nblocks = math.ceil(size / blocksize)
self.maxblocks = maxblocks
self._fetch_block_cached = functools.lru_cache(maxblocks)(self._fetch_block)
def __repr__(self) -> str:
return (
f"<BlockCache blocksize={self.blocksize}, "
f"size={self.size}, nblocks={self.nblocks}>"
)
def cache_info(self):
"""
The statistics on the block cache.
Returns
-------
NamedTuple
Returned directly from the LRU Cache used internally.
"""
return self._fetch_block_cached.cache_info()
def __getstate__(self) -> dict[str, Any]:
state = self.__dict__
del state["_fetch_block_cached"]
return state
def __setstate__(self, state: dict[str, Any]) -> None:
self.__dict__.update(state)
self._fetch_block_cached = functools.lru_cache(state["maxblocks"])(
self._fetch_block
)
def _fetch(self, start: int | None, end: int | None) -> bytes:
if start is None:
start = 0
if end is None:
end = self.size
if start >= self.size or start >= end:
return b""
# byte position -> block numbers
start_block_number = start // self.blocksize
end_block_number = end // self.blocksize
# these are cached, so safe to do multiple calls for the same start and end.
for block_number in range(start_block_number, end_block_number + 1):
self._fetch_block_cached(block_number)
return self._read_cache(
start,
end,
start_block_number=start_block_number,
end_block_number=end_block_number,
)
def _fetch_block(self, block_number: int) -> bytes:
"""
Fetch the block of data for `block_number`.
"""
if block_number > self.nblocks:
raise ValueError(
f"'block_number={block_number}' is greater than "
f"the number of blocks ({self.nblocks})"
)
start = block_number * self.blocksize
end = start + self.blocksize
logger.info("BlockCache fetching block %d", block_number)
block_contents = super()._fetch(start, end)
return block_contents
def _read_cache(
self, start: int, end: int, start_block_number: int, end_block_number: int
) -> bytes:
"""
Read from our block cache.
Parameters
----------
start, end : int
The start and end byte positions.
start_block_number, end_block_number : int
The start and end block numbers.
"""
start_pos = start % self.blocksize
end_pos = end % self.blocksize
if start_block_number == end_block_number:
block: bytes = self._fetch_block_cached(start_block_number)
return block[start_pos:end_pos]
else:
# read from the initial
out = []
out.append(self._fetch_block_cached(start_block_number)[start_pos:])
# intermediate blocks
# Note: it'd be nice to combine these into one big request. However
# that doesn't play nicely with our LRU cache.
for block_number in range(start_block_number + 1, end_block_number):
out.append(self._fetch_block_cached(block_number))
# final block
out.append(self._fetch_block_cached(end_block_number)[:end_pos])
return b"".join(out)
class BytesCache(BaseCache):
"""Cache which holds data in a in-memory bytes object
Implements read-ahead by the block size, for semi-random reads progressing
through the file.
Parameters
----------
trim: bool
As we read more data, whether to discard the start of the buffer when
we are more than a blocksize ahead of it.
"""
name: ClassVar[str] = "bytes"
def __init__(
self, blocksize: int, fetcher: Fetcher, size: int, trim: bool = True
) -> None:
super().__init__(blocksize, fetcher, size)
self.cache = b""
self.start: int | None = None
self.end: int | None = None
self.trim = trim
def _fetch(self, start: int | None, end: int | None) -> bytes:
# TODO: only set start/end after fetch, in case it fails?
# is this where retry logic might go?
if start is None:
start = 0
if end is None:
end = self.size
if start >= self.size or start >= end:
return b""
if (
self.start is not None
and start >= self.start
and self.end is not None
and end < self.end
):
# cache hit: we have all the required data
offset = start - self.start
return self.cache[offset : offset + end - start]
if self.blocksize:
bend = min(self.size, end + self.blocksize)
else:
bend = end
if bend == start or start > self.size:
return b""
if (self.start is None or start < self.start) and (
self.end is None or end > self.end
):
# First read, or extending both before and after
self.cache = self.fetcher(start, bend)
self.start = start
else:
assert self.start is not None
assert self.end is not None
if start < self.start:
if self.end is None or self.end - end > self.blocksize:
self.cache = self.fetcher(start, bend)
self.start = start
else:
new = self.fetcher(start, self.start)
self.start = start
self.cache = new + self.cache
elif self.end is not None and bend > self.end:
if self.end > self.size:
pass
elif end - self.end > self.blocksize:
self.cache = self.fetcher(start, bend)
self.start = start
else:
new = self.fetcher(self.end, bend)
self.cache = self.cache + new
self.end = self.start + len(self.cache)
offset = start - self.start
out = self.cache[offset : offset + end - start]
if self.trim:
num = (self.end - self.start) // (self.blocksize + 1)
if num > 1:
self.start += self.blocksize * num
self.cache = self.cache[self.blocksize * num :]
return out
def __len__(self) -> int:
return len(self.cache)
class AllBytes(BaseCache):
"""Cache entire contents of the file"""
name: ClassVar[str] = "all"
def __init__(
self,
blocksize: int | None = None,
fetcher: Fetcher | None = None,
size: int | None = None,
data: bytes | None = None,
) -> None:
super().__init__(blocksize, fetcher, size) # type: ignore[arg-type]
if data is None:
data = self.fetcher(0, self.size)
self.data = data
def _fetch(self, start: int | None, stop: int | None) -> bytes:
return self.data[start:stop]
class KnownPartsOfAFile(BaseCache):
"""
Cache holding known file parts.
Parameters
----------
blocksize: int
How far to read ahead in numbers of bytes
fetcher: func
Function of the form f(start, end) which gets bytes from remote as
specified
size: int
How big this file is
data: dict
A dictionary mapping explicit `(start, stop)` file-offset tuples
with known bytes.
strict: bool, default True
Whether to fetch reads that go beyond a known byte-range boundary.
If `False`, any read that ends outside a known part will be zero
padded. Note that zero padding will not be used for reads that
begin outside a known byte-range.
"""
name: ClassVar[str] = "parts"
def __init__(
self,
blocksize: int,
fetcher: Fetcher,
size: int,
data: dict[tuple[int, int], bytes] = {},
strict: bool = True,
**_: Any,
):
super().__init__(blocksize, fetcher, size)
self.strict = strict
# simple consolidation of contiguous blocks
if data:
old_offsets = sorted(data.keys())
offsets = [old_offsets[0]]
blocks = [data.pop(old_offsets[0])]
for start, stop in old_offsets[1:]:
start0, stop0 = offsets[-1]
if start == stop0:
offsets[-1] = (start0, stop)
blocks[-1] += data.pop((start, stop))
else:
offsets.append((start, stop))
blocks.append(data.pop((start, stop)))
self.data = dict(zip(offsets, blocks))
else:
self.data = data
def _fetch(self, start: int | None, stop: int | None) -> bytes:
if start is None:
start = 0
if stop is None:
stop = self.size
out = b""
for (loc0, loc1), data in self.data.items():
# If self.strict=False, use zero-padded data
# for reads beyond the end of a "known" buffer
if loc0 <= start < loc1:
off = start - loc0
out = data[off : off + stop - start]
if not self.strict or loc0 <= stop <= loc1:
# The request is within a known range, or
# it begins within a known range, and we
# are allowed to pad reads beyond the
# buffer with zero
out += b"\x00" * (stop - start - len(out))
return out
else:
# The request ends outside a known range,
# and we are being "strict" about reads
# beyond the buffer
start = loc1
break
# We only get here if there is a request outside the
# known parts of the file. In an ideal world, this
# should never happen
if self.fetcher is None:
# We cannot fetch the data, so raise an error
raise ValueError(f"Read is outside the known file parts: {(start, stop)}. ")
# We can fetch the data, but should warn the user
# that this may be slow
warnings.warn(
f"Read is outside the known file parts: {(start, stop)}. "
f"IO/caching performance may be poor!"
)
logger.debug(f"KnownPartsOfAFile cache fetching {start}-{stop}")
return out + super()._fetch(start, stop)
class UpdatableLRU(Generic[P, T]):
"""
Custom implementation of LRU cache that allows updating keys
Used by BackgroudBlockCache
"""
class CacheInfo(NamedTuple):
hits: int
misses: int
maxsize: int
currsize: int
def __init__(self, func: Callable[P, T], max_size: int = 128) -> None:
self._cache: OrderedDict[Any, T] = collections.OrderedDict()
self._func = func
self._max_size = max_size
self._hits = 0
self._misses = 0
self._lock = threading.Lock()
def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T:
if kwargs:
raise TypeError(f"Got unexpected keyword argument {kwargs.keys()}")
with self._lock:
if args in self._cache:
self._cache.move_to_end(args)
self._hits += 1
return self._cache[args]
result = self._func(*args, **kwargs)
with self._lock:
self._cache[args] = result
self._misses += 1
if len(self._cache) > self._max_size:
self._cache.popitem(last=False)
return result
def is_key_cached(self, *args: Any) -> bool:
with self._lock:
return args in self._cache
def add_key(self, result: T, *args: Any) -> None:
with self._lock:
self._cache[args] = result
if len(self._cache) > self._max_size:
self._cache.popitem(last=False)
def cache_info(self) -> UpdatableLRU.CacheInfo:
with self._lock:
return self.CacheInfo(
maxsize=self._max_size,
currsize=len(self._cache),
hits=self._hits,
misses=self._misses,
)
class BackgroundBlockCache(BaseCache):
"""
Cache holding memory as a set of blocks with pre-loading of
the next block in the background.
Requests are only ever made ``blocksize`` at a time, and are
stored in an LRU cache. The least recently accessed block is
discarded when more than ``maxblocks`` are stored. If the
next block is not in cache, it is loaded in a separate thread
in non-blocking way.
Parameters
----------
blocksize : int
The number of bytes to store in each block.
Requests are only ever made for ``blocksize``, so this
should balance the overhead of making a request against
the granularity of the blocks.
fetcher : Callable
size : int
The total size of the file being cached.
maxblocks : int
The maximum number of blocks to cache for. The maximum memory
use for this cache is then ``blocksize * maxblocks``.
"""
name: ClassVar[str] = "background"
def __init__(
self, blocksize: int, fetcher: Fetcher, size: int, maxblocks: int = 32
) -> None:
super().__init__(blocksize, fetcher, size)
self.nblocks = math.ceil(size / blocksize)
self.maxblocks = maxblocks
self._fetch_block_cached = UpdatableLRU(self._fetch_block, maxblocks)
self._thread_executor = ThreadPoolExecutor(max_workers=1)
self._fetch_future_block_number: int | None = None
self._fetch_future: Future[bytes] | None = None
self._fetch_future_lock = threading.Lock()
def __repr__(self) -> str:
return (
f"<BackgroundBlockCache blocksize={self.blocksize}, "
f"size={self.size}, nblocks={self.nblocks}>"
)
def cache_info(self) -> UpdatableLRU.CacheInfo:
"""
The statistics on the block cache.
Returns
-------
NamedTuple
Returned directly from the LRU Cache used internally.
"""
return self._fetch_block_cached.cache_info()
def __getstate__(self) -> dict[str, Any]:
state = self.__dict__
del state["_fetch_block_cached"]
del state["_thread_executor"]
del state["_fetch_future_block_number"]
del state["_fetch_future"]
del state["_fetch_future_lock"]
return state
def __setstate__(self, state) -> None:
self.__dict__.update(state)
self._fetch_block_cached = UpdatableLRU(self._fetch_block, state["maxblocks"])
self._thread_executor = ThreadPoolExecutor(max_workers=1)
self._fetch_future_block_number = None
self._fetch_future = None
self._fetch_future_lock = threading.Lock()
def _fetch(self, start: int | None, end: int | None) -> bytes:
if start is None:
start = 0
if end is None:
end = self.size
if start >= self.size or start >= end:
return b""
# byte position -> block numbers
start_block_number = start // self.blocksize
end_block_number = end // self.blocksize
fetch_future_block_number = None
fetch_future = None
with self._fetch_future_lock:
# Background thread is running. Check we we can or must join it.
if self._fetch_future is not None:
assert self._fetch_future_block_number is not None
if self._fetch_future.done():
logger.info("BlockCache joined background fetch without waiting.")
self._fetch_block_cached.add_key(
self._fetch_future.result(), self._fetch_future_block_number
)
# Cleanup the fetch variables. Done with fetching the block.
self._fetch_future_block_number = None
self._fetch_future = None
else:
# Must join if we need the block for the current fetch
must_join = bool(
start_block_number
<= self._fetch_future_block_number
<= end_block_number
)
if must_join:
# Copy to the local variables to release lock
# before waiting for result
fetch_future_block_number = self._fetch_future_block_number
fetch_future = self._fetch_future
# Cleanup the fetch variables. Have a local copy.
self._fetch_future_block_number = None
self._fetch_future = None
# Need to wait for the future for the current read
if fetch_future is not None:
logger.info("BlockCache waiting for background fetch.")
# Wait until result and put it in cache
self._fetch_block_cached.add_key(
fetch_future.result(), fetch_future_block_number
)
# these are cached, so safe to do multiple calls for the same start and end.
for block_number in range(start_block_number, end_block_number + 1):
self._fetch_block_cached(block_number)
# fetch next block in the background if nothing is running in the background,
# the block is within file and it is not already cached
end_block_plus_1 = end_block_number + 1
with self._fetch_future_lock:
if (
self._fetch_future is None
and end_block_plus_1 <= self.nblocks
and not self._fetch_block_cached.is_key_cached(end_block_plus_1)
):
self._fetch_future_block_number = end_block_plus_1
self._fetch_future = self._thread_executor.submit(
self._fetch_block, end_block_plus_1, "async"
)
return self._read_cache(
start,
end,
start_block_number=start_block_number,
end_block_number=end_block_number,
)
def _fetch_block(self, block_number: int, log_info: str = "sync") -> bytes:
"""
Fetch the block of data for `block_number`.
"""
if block_number > self.nblocks:
raise ValueError(
f"'block_number={block_number}' is greater than "
f"the number of blocks ({self.nblocks})"
)
start = block_number * self.blocksize
end = start + self.blocksize
logger.info("BlockCache fetching block (%s) %d", log_info, block_number)
block_contents = super()._fetch(start, end)
return block_contents
def _read_cache(
self, start: int, end: int, start_block_number: int, end_block_number: int
) -> bytes:
"""
Read from our block cache.
Parameters
----------
start, end : int
The start and end byte positions.
start_block_number, end_block_number : int
The start and end block numbers.
"""
start_pos = start % self.blocksize
end_pos = end % self.blocksize
if start_block_number == end_block_number:
block = self._fetch_block_cached(start_block_number)
return block[start_pos:end_pos]
else:
# read from the initial
out = []
out.append(self._fetch_block_cached(start_block_number)[start_pos:])
# intermediate blocks
# Note: it'd be nice to combine these into one big request. However
# that doesn't play nicely with our LRU cache.
for block_number in range(start_block_number + 1, end_block_number):
out.append(self._fetch_block_cached(block_number))
# final block
out.append(self._fetch_block_cached(end_block_number)[:end_pos])
return b"".join(out)
caches: dict[str | None, type[BaseCache]] = {
# one custom case
None: BaseCache,
}
def register_cache(cls: type[BaseCache], clobber: bool = False) -> None:
"""'Register' cache implementation.
Parameters
----------
clobber: bool, optional
If set to True (default is False) - allow to overwrite existing
entry.
Raises
------
ValueError
"""
name = cls.name
if not clobber and name in caches:
raise ValueError(f"Cache with name {name!r} is already known: {caches[name]}")
caches[name] = cls
for c in (
BaseCache,
MMapCache,
BytesCache,
ReadAheadCache,
BlockCache,
FirstChunkCache,
AllBytes,
KnownPartsOfAFile,
BackgroundBlockCache,
):
register_cache(c)