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"" ) 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"" ) 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)