import base64 import collections import io import itertools import logging import math import os from functools import lru_cache from itertools import chain from typing import TYPE_CHECKING, Literal import fsspec.core from fsspec.spec import AbstractBufferedFile try: import ujson as json except ImportError: if not TYPE_CHECKING: import json from fsspec.asyn import AsyncFileSystem from fsspec.callbacks import DEFAULT_CALLBACK from fsspec.core import filesystem, open, split_protocol from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper from fsspec.utils import isfilelike, merge_offset_ranges, other_paths logger = logging.getLogger("fsspec.reference") class ReferenceNotReachable(RuntimeError): def __init__(self, reference, target, *args): super().__init__(*args) self.reference = reference self.target = target def __str__(self): return f'Reference "{self.reference}" failed to fetch target {self.target}' def _first(d): return next(iter(d.values())) def _prot_in_references(path, references): ref = references.get(path) if isinstance(ref, (list, tuple)) and isinstance(ref[0], str): return split_protocol(ref[0])[0] if ref[0] else ref[0] def _protocol_groups(paths, references): if isinstance(paths, str): return {_prot_in_references(paths, references): [paths]} out = {} for path in paths: protocol = _prot_in_references(path, references) out.setdefault(protocol, []).append(path) return out class RefsValuesView(collections.abc.ValuesView): def __iter__(self): for val in self._mapping.zmetadata.values(): yield json.dumps(val).encode() yield from self._mapping._items.values() for field in self._mapping.listdir(): chunk_sizes = self._mapping._get_chunk_sizes(field) if len(chunk_sizes) == 0: yield self._mapping[field + "/0"] continue yield from self._mapping._generate_all_records(field) class RefsItemsView(collections.abc.ItemsView): def __iter__(self): return zip(self._mapping.keys(), self._mapping.values()) def ravel_multi_index(idx, sizes): val = 0 mult = 1 for i, s in zip(idx[::-1], sizes[::-1]): val += i * mult mult *= s return val class LazyReferenceMapper(collections.abc.MutableMapping): """This interface can be used to read/write references from Parquet stores. It is not intended for other types of references. It can be used with Kerchunk's MultiZarrToZarr method to combine references into a parquet store. Examples of this use-case can be found here: https://fsspec.github.io/kerchunk/advanced.html?highlight=parquet#parquet-storage""" # import is class level to prevent numpy dep requirement for fsspec @property def np(self): import numpy as np return np @property def pd(self): import pandas as pd return pd def __init__( self, root, fs=None, out_root=None, cache_size=128, categorical_threshold=10, engine: Literal["fastparquet", "pyarrow"] = "fastparquet", ): """ This instance will be writable, storing changes in memory until full partitions are accumulated or .flush() is called. To create an empty lazy store, use .create() Parameters ---------- root : str Root of parquet store fs : fsspec.AbstractFileSystem fsspec filesystem object, default is local filesystem. cache_size : int, default=128 Maximum size of LRU cache, where cache_size*record_size denotes the total number of references that can be loaded in memory at once. categorical_threshold : int Encode urls as pandas.Categorical to reduce memory footprint if the ratio of the number of unique urls to total number of refs for each variable is greater than or equal to this number. (default 10) engine: Literal["fastparquet","pyarrow"] Engine choice for reading parquet files. (default is "fastparquet") """ self.root = root self.chunk_sizes = {} self.out_root = out_root or self.root self.cat_thresh = categorical_threshold self.engine = engine self.cache_size = cache_size self.url = self.root + "/{field}/refs.{record}.parq" # TODO: derive fs from `root` self.fs = fsspec.filesystem("file") if fs is None else fs from importlib.util import find_spec if self.engine == "pyarrow" and find_spec("pyarrow") is None: raise ImportError("engine choice `pyarrow` is not installed.") def __getattr__(self, item): if item in ("_items", "record_size", "zmetadata"): self.setup() # avoid possible recursion if setup fails somehow return self.__dict__[item] raise AttributeError(item) def setup(self): self._items = {} self._items[".zmetadata"] = self.fs.cat_file( "/".join([self.root, ".zmetadata"]) ) met = json.loads(self._items[".zmetadata"]) self.record_size = met["record_size"] self.zmetadata = met["metadata"] # Define function to open and decompress refs @lru_cache(maxsize=self.cache_size) def open_refs(field, record): """cached parquet file loader""" path = self.url.format(field=field, record=record) data = io.BytesIO(self.fs.cat_file(path)) try: df = self.pd.read_parquet(data, engine=self.engine) refs = {c: df[c].to_numpy() for c in df.columns} except OSError: refs = None return refs self.open_refs = open_refs @staticmethod def create(root, storage_options=None, fs=None, record_size=10000, **kwargs): """Make empty parquet reference set First deletes the contents of the given directory, if it exists. Parameters ---------- root: str Directory to contain the output; will be created storage_options: dict | None For making the filesystem to use for writing is fs is None fs: FileSystem | None Filesystem for writing record_size: int Number of references per parquet file kwargs: passed to __init__ Returns ------- LazyReferenceMapper instance """ met = {"metadata": {}, "record_size": record_size} if fs is None: fs, root = fsspec.core.url_to_fs(root, **(storage_options or {})) if fs.exists(root): fs.rm(root, recursive=True) fs.makedirs(root, exist_ok=True) fs.pipe("/".join([root, ".zmetadata"]), json.dumps(met).encode()) return LazyReferenceMapper(root, fs, **kwargs) @lru_cache() def listdir(self): """List top-level directories""" dirs = (p.rsplit("/", 1)[0] for p in self.zmetadata if not p.startswith(".z")) return set(dirs) def ls(self, path="", detail=True): """Shortcut file listings""" path = path.rstrip("/") pathdash = path + "/" if path else "" dirnames = self.listdir() dirs = [ d for d in dirnames if d.startswith(pathdash) and "/" not in d.lstrip(pathdash) ] if dirs: others = { f for f in chain( [".zmetadata"], (name for name in self.zmetadata), (name for name in self._items), ) if f.startswith(pathdash) and "/" not in f.lstrip(pathdash) } if detail is False: others.update(dirs) return sorted(others) dirinfo = [{"name": name, "type": "directory", "size": 0} for name in dirs] fileinfo = [ { "name": name, "type": "file", "size": len( json.dumps(self.zmetadata[name]) if name in self.zmetadata else self._items[name] ), } for name in others ] return sorted(dirinfo + fileinfo, key=lambda s: s["name"]) field = path others = set( [name for name in self.zmetadata if name.startswith(f"{path}/")] + [name for name in self._items if name.startswith(f"{path}/")] ) fileinfo = [ { "name": name, "type": "file", "size": len( json.dumps(self.zmetadata[name]) if name in self.zmetadata else self._items[name] ), } for name in others ] keys = self._keys_in_field(field) if detail is False: return list(others) + list(keys) recs = self._generate_all_records(field) recinfo = [ {"name": name, "type": "file", "size": rec[-1]} for name, rec in zip(keys, recs) if rec[0] # filters out path==None, deleted/missing ] return fileinfo + recinfo def _load_one_key(self, key): """Get the reference for one key Returns bytes, one-element list or three-element list. """ if key in self._items: return self._items[key] elif key in self.zmetadata: return json.dumps(self.zmetadata[key]).encode() elif "/" not in key or self._is_meta(key): raise KeyError(key) field, _ = key.rsplit("/", 1) record, ri, chunk_size = self._key_to_record(key) maybe = self._items.get((field, record), {}).get(ri, False) if maybe is None: # explicitly deleted raise KeyError elif maybe: return maybe elif chunk_size == 0: return b"" # Chunk keys can be loaded from row group and cached in LRU cache try: refs = self.open_refs(field, record) except (ValueError, TypeError, FileNotFoundError) as exc: raise KeyError(key) from exc columns = ["path", "offset", "size", "raw"] selection = [refs[c][ri] if c in refs else None for c in columns] raw = selection[-1] if raw is not None: return raw if selection[0] is None: raise KeyError("This reference does not exist or has been deleted") if selection[1:3] == [0, 0]: # URL only return selection[:1] # URL, offset, size return selection[:3] @lru_cache(4096) def _key_to_record(self, key): """Details needed to construct a reference for one key""" field, chunk = key.rsplit("/", 1) chunk_sizes = self._get_chunk_sizes(field) if len(chunk_sizes) == 0: return 0, 0, 0 chunk_idx = [int(c) for c in chunk.split(".")] chunk_number = ravel_multi_index(chunk_idx, chunk_sizes) record = chunk_number // self.record_size ri = chunk_number % self.record_size return record, ri, len(chunk_sizes) def _get_chunk_sizes(self, field): """The number of chunks along each axis for a given field""" if field not in self.chunk_sizes: zarray = self.zmetadata[f"{field}/.zarray"] size_ratio = [ math.ceil(s / c) for s, c in zip(zarray["shape"], zarray["chunks"]) ] self.chunk_sizes[field] = size_ratio or [1] return self.chunk_sizes[field] def _generate_record(self, field, record): """The references for a given parquet file of a given field""" refs = self.open_refs(field, record) it = iter(zip(*refs.values())) if len(refs) == 3: # All urls return (list(t) for t in it) elif len(refs) == 1: # All raws return refs["raw"] else: # Mix of urls and raws return (list(t[:3]) if not t[3] else t[3] for t in it) def _generate_all_records(self, field): """Load all the references within a field by iterating over the parquet files""" nrec = 1 for ch in self._get_chunk_sizes(field): nrec *= ch nrec = math.ceil(nrec / self.record_size) for record in range(nrec): yield from self._generate_record(field, record) def values(self): return RefsValuesView(self) def items(self): return RefsItemsView(self) def __hash__(self): return id(self) def __getitem__(self, key): return self._load_one_key(key) def __setitem__(self, key, value): if "/" in key and not self._is_meta(key): field, chunk = key.rsplit("/", 1) record, i, _ = self._key_to_record(key) subdict = self._items.setdefault((field, record), {}) subdict[i] = value if len(subdict) == self.record_size: self.write(field, record) else: # metadata or top-level if hasattr(value, "to_bytes"): val = value.to_bytes().decode() elif isinstance(value, bytes): val = value.decode() else: val = value self._items[key] = val new_value = json.loads(val) self.zmetadata[key] = {**self.zmetadata.get(key, {}), **new_value} @staticmethod def _is_meta(key): return key.startswith(".z") or "/.z" in key def __delitem__(self, key): if key in self._items: del self._items[key] elif key in self.zmetadata: del self.zmetadata[key] else: if "/" in key and not self._is_meta(key): field, _ = key.rsplit("/", 1) record, i, _ = self._key_to_record(key) subdict = self._items.setdefault((field, record), {}) subdict[i] = None if len(subdict) == self.record_size: self.write(field, record) else: # metadata or top-level self._items[key] = None def write(self, field, record, base_url=None, storage_options=None): # extra requirements if writing import kerchunk.df import numpy as np import pandas as pd partition = self._items[(field, record)] original = False if len(partition) < self.record_size: try: original = self.open_refs(field, record) except OSError: pass if original: paths = original["path"] offsets = original["offset"] sizes = original["size"] raws = original["raw"] else: paths = np.full(self.record_size, np.nan, dtype="O") offsets = np.zeros(self.record_size, dtype="int64") sizes = np.zeros(self.record_size, dtype="int64") raws = np.full(self.record_size, np.nan, dtype="O") for j, data in partition.items(): if isinstance(data, list): if ( str(paths.dtype) == "category" and data[0] not in paths.dtype.categories ): paths = paths.add_categories(data[0]) paths[j] = data[0] if len(data) > 1: offsets[j] = data[1] sizes[j] = data[2] elif data is None: # delete paths[j] = None offsets[j] = 0 sizes[j] = 0 raws[j] = None else: # this is the only call into kerchunk, could remove raws[j] = kerchunk.df._proc_raw(data) # TODO: only save needed columns df = pd.DataFrame( { "path": paths, "offset": offsets, "size": sizes, "raw": raws, }, copy=False, ) if df.path.count() / (df.path.nunique() or 1) > self.cat_thresh: df["path"] = df["path"].astype("category") object_encoding = {"raw": "bytes", "path": "utf8"} has_nulls = ["path", "raw"] fn = f"{base_url or self.out_root}/{field}/refs.{record}.parq" self.fs.mkdirs(f"{base_url or self.out_root}/{field}", exist_ok=True) if self.engine == "pyarrow": df_backend_kwargs = {"write_statistics": False} elif self.engine == "fastparquet": df_backend_kwargs = { "stats": False, "object_encoding": object_encoding, "has_nulls": has_nulls, } else: raise NotImplementedError(f"{self.engine} not supported") df.to_parquet( fn, engine=self.engine, storage_options=storage_options or getattr(self.fs, "storage_options", None), compression="zstd", index=False, **df_backend_kwargs, ) partition.clear() self._items.pop((field, record)) def flush(self, base_url=None, storage_options=None): """Output any modified or deleted keys Parameters ---------- base_url: str Location of the output """ # write what we have so far and clear sub chunks for thing in list(self._items): if isinstance(thing, tuple): field, record = thing self.write( field, record, base_url=base_url, storage_options=storage_options, ) # gather .zmetadata from self._items and write that too for k in list(self._items): if k != ".zmetadata" and ".z" in k: self.zmetadata[k] = json.loads(self._items.pop(k)) met = {"metadata": self.zmetadata, "record_size": self.record_size} self._items.clear() self._items[".zmetadata"] = json.dumps(met).encode() self.fs.pipe( "/".join([base_url or self.out_root, ".zmetadata"]), self._items[".zmetadata"], ) # TODO: only clear those that we wrote to? self.open_refs.cache_clear() def __len__(self): # Caveat: This counts expected references, not actual - but is fast count = 0 for field in self.listdir(): if field.startswith("."): count += 1 else: count += math.prod(self._get_chunk_sizes(field)) count += len(self.zmetadata) # all metadata keys # any other files not in reference partitions count += sum(1 for _ in self._items if not isinstance(_, tuple)) return count def __iter__(self): # Caveat: returns only existing keys, so the number of these does not # match len(self) metas = set(self.zmetadata) metas.update(self._items) for bit in metas: if isinstance(bit, str): yield bit for field in self.listdir(): for k in self._keys_in_field(field): if k in self: yield k def __contains__(self, item): try: self._load_one_key(item) return True except KeyError: return False def _keys_in_field(self, field): """List key names in given field Produces strings like "field/x.y" appropriate from the chunking of the array """ chunk_sizes = self._get_chunk_sizes(field) if len(chunk_sizes) == 0: yield field + "/0" return inds = itertools.product(*(range(i) for i in chunk_sizes)) for ind in inds: yield field + "/" + ".".join([str(c) for c in ind]) class ReferenceFileSystem(AsyncFileSystem): """View byte ranges of some other file as a file system Initial version: single file system target, which must support async, and must allow start and end args in _cat_file. Later versions may allow multiple arbitrary URLs for the targets. This FileSystem is read-only. It is designed to be used with async targets (for now). We do not get original file details from the target FS. Configuration is by passing a dict of references at init, or a URL to a JSON file containing the same; this dict can also contain concrete data for some set of paths. Reference dict format: {path0: bytes_data, path1: (target_url, offset, size)} https://github.com/fsspec/kerchunk/blob/main/README.md """ protocol = "reference" cachable = False def __init__( self, fo, target=None, ref_storage_args=None, target_protocol=None, target_options=None, remote_protocol=None, remote_options=None, fs=None, template_overrides=None, simple_templates=True, max_gap=64_000, max_block=256_000_000, cache_size=128, **kwargs, ): """ Parameters ---------- fo : dict or str The set of references to use for this instance, with a structure as above. If str referencing a JSON file, will use fsspec.open, in conjunction with target_options and target_protocol to open and parse JSON at this location. If a directory, then assume references are a set of parquet files to be loaded lazily. target : str For any references having target_url as None, this is the default file target to use ref_storage_args : dict If references is a str, use these kwargs for loading the JSON file. Deprecated: use target_options instead. target_protocol : str Used for loading the reference file, if it is a path. If None, protocol will be derived from the given path target_options : dict Extra FS options for loading the reference file ``fo``, if given as a path remote_protocol : str The protocol of the filesystem on which the references will be evaluated (unless fs is provided). If not given, will be derived from the first URL that has a protocol in the templates or in the references, in that order. remote_options : dict kwargs to go with remote_protocol fs : AbstractFileSystem | dict(str, (AbstractFileSystem | dict)) Directly provide a file system(s): - a single filesystem instance - a dict of protocol:filesystem, where each value is either a filesystem instance, or a dict of kwargs that can be used to create in instance for the given protocol If this is given, remote_options and remote_protocol are ignored. template_overrides : dict Swap out any templates in the references file with these - useful for testing. simple_templates: bool Whether templates can be processed with simple replace (True) or if jinja is needed (False, much slower). All reference sets produced by ``kerchunk`` are simple in this sense, but the spec allows for complex. max_gap, max_block: int For merging multiple concurrent requests to the same remote file. Neighboring byte ranges will only be merged when their inter-range gap is <= ``max_gap``. Default is 64KB. Set to 0 to only merge when it requires no extra bytes. Pass a negative number to disable merging, appropriate for local target files. Neighboring byte ranges will only be merged when the size of the aggregated range is <= ``max_block``. Default is 256MB. cache_size : int Maximum size of LRU cache, where cache_size*record_size denotes the total number of references that can be loaded in memory at once. Only used for lazily loaded references. kwargs : passed to parent class """ super().__init__(**kwargs) self.target = target self.template_overrides = template_overrides self.simple_templates = simple_templates self.templates = {} self.fss = {} self._dircache = {} self.max_gap = max_gap self.max_block = max_block if isinstance(fo, str): dic = dict( **(ref_storage_args or target_options or {}), protocol=target_protocol ) ref_fs, fo2 = fsspec.core.url_to_fs(fo, **dic) if ref_fs.isfile(fo2): # text JSON with fsspec.open(fo, "rb", **dic) as f: logger.info("Read reference from URL %s", fo) text = json.load(f) self._process_references(text, template_overrides) else: # Lazy parquet refs logger.info("Open lazy reference dict from URL %s", fo) self.references = LazyReferenceMapper( fo2, fs=ref_fs, cache_size=cache_size, ) else: # dictionaries self._process_references(fo, template_overrides) if isinstance(fs, dict): self.fss = { k: ( fsspec.filesystem(k.split(":", 1)[0], **opts) if isinstance(opts, dict) else opts ) for k, opts in fs.items() } if None not in self.fss: self.fss[None] = filesystem("file") return if fs is not None: # single remote FS remote_protocol = ( fs.protocol[0] if isinstance(fs.protocol, tuple) else fs.protocol ) self.fss[remote_protocol] = fs if remote_protocol is None: # get single protocol from any templates for ref in self.templates.values(): if callable(ref): ref = ref() protocol, _ = fsspec.core.split_protocol(ref) if protocol and protocol not in self.fss: fs = filesystem(protocol, **(remote_options or {})) self.fss[protocol] = fs if remote_protocol is None: # get single protocol from references # TODO: warning here, since this can be very expensive? for ref in self.references.values(): if callable(ref): ref = ref() if isinstance(ref, list) and ref[0]: protocol, _ = fsspec.core.split_protocol(ref[0]) if protocol not in self.fss: fs = filesystem(protocol, **(remote_options or {})) self.fss[protocol] = fs # only use first remote URL break if remote_protocol and remote_protocol not in self.fss: fs = filesystem(remote_protocol, **(remote_options or {})) self.fss[remote_protocol] = fs self.fss[None] = fs or filesystem("file") # default one # Wrap any non-async filesystems to ensure async methods are available below for k, f in self.fss.items(): if not f.async_impl: self.fss[k] = AsyncFileSystemWrapper(f) elif self.asynchronous ^ f.asynchronous: raise ValueError( "Reference-FS's target filesystem must have same value" "of asynchronous" ) def _cat_common(self, path, start=None, end=None): path = self._strip_protocol(path) logger.debug(f"cat: {path}") try: part = self.references[path] except KeyError as exc: raise FileNotFoundError(path) from exc if isinstance(part, str): part = part.encode() if hasattr(part, "to_bytes"): part = part.to_bytes() if isinstance(part, bytes): logger.debug(f"Reference: {path}, type bytes") if part.startswith(b"base64:"): part = base64.b64decode(part[7:]) return part, None, None if len(part) == 1: logger.debug(f"Reference: {path}, whole file => {part}") url = part[0] start1, end1 = start, end else: url, start0, size = part logger.debug(f"Reference: {path} => {url}, offset {start0}, size {size}") end0 = start0 + size if start is not None: if start >= 0: start1 = start0 + start else: start1 = end0 + start else: start1 = start0 if end is not None: if end >= 0: end1 = start0 + end else: end1 = end0 + end else: end1 = end0 if url is None: url = self.target return url, start1, end1 async def _cat_file(self, path, start=None, end=None, **kwargs): part_or_url, start0, end0 = self._cat_common(path, start=start, end=end) if isinstance(part_or_url, bytes): return part_or_url[start:end] protocol, _ = split_protocol(part_or_url) try: return await self.fss[protocol]._cat_file( part_or_url, start=start0, end=end0 ) except Exception as e: raise ReferenceNotReachable(path, part_or_url) from e def cat_file(self, path, start=None, end=None, **kwargs): part_or_url, start0, end0 = self._cat_common(path, start=start, end=end) if isinstance(part_or_url, bytes): return part_or_url[start:end] protocol, _ = split_protocol(part_or_url) try: return self.fss[protocol].cat_file(part_or_url, start=start0, end=end0) except Exception as e: raise ReferenceNotReachable(path, part_or_url) from e def pipe_file(self, path, value, **_): """Temporarily add binary data or reference as a file""" self.references[path] = value async def _get_file(self, rpath, lpath, **kwargs): if self.isdir(rpath): return os.makedirs(lpath, exist_ok=True) data = await self._cat_file(rpath) with open(lpath, "wb") as f: f.write(data) def get_file(self, rpath, lpath, callback=DEFAULT_CALLBACK, **kwargs): if self.isdir(rpath): return os.makedirs(lpath, exist_ok=True) data = self.cat_file(rpath, **kwargs) callback.set_size(len(data)) if isfilelike(lpath): lpath.write(data) else: with open(lpath, "wb") as f: f.write(data) callback.absolute_update(len(data)) def get(self, rpath, lpath, recursive=False, **kwargs): if recursive: # trigger directory build self.ls("") rpath = self.expand_path(rpath, recursive=recursive) fs = fsspec.filesystem("file", auto_mkdir=True) targets = other_paths(rpath, lpath) if recursive: data = self.cat([r for r in rpath if not self.isdir(r)]) else: data = self.cat(rpath) for remote, local in zip(rpath, targets): if remote in data: fs.pipe_file(local, data[remote]) def cat(self, path, recursive=False, on_error="raise", **kwargs): if isinstance(path, str) and recursive: raise NotImplementedError if isinstance(path, list) and (recursive or any("*" in p for p in path)): raise NotImplementedError # TODO: if references is lazy, pre-fetch all paths in batch before access proto_dict = _protocol_groups(path, self.references) out = {} for proto, paths in proto_dict.items(): fs = self.fss[proto] urls, starts, ends, valid_paths = [], [], [], [] for p in paths: # find references or label not-found. Early exit if any not # found and on_error is "raise" try: u, s, e = self._cat_common(p) if not isinstance(u, (bytes, str)): # nan/None from parquet continue except FileNotFoundError as err: if on_error == "raise": raise if on_error != "omit": out[p] = err else: urls.append(u) starts.append(s) ends.append(e) valid_paths.append(p) # process references into form for merging urls2 = [] starts2 = [] ends2 = [] paths2 = [] whole_files = set() for u, s, e, p in zip(urls, starts, ends, valid_paths): if isinstance(u, bytes): # data out[p] = u elif s is None: # whole file - limits are None, None, but no further # entries take for this file whole_files.add(u) urls2.append(u) starts2.append(s) ends2.append(e) paths2.append(p) for u, s, e, p in zip(urls, starts, ends, valid_paths): # second run to account for files that are to be loaded whole if s is not None and u not in whole_files: urls2.append(u) starts2.append(s) ends2.append(e) paths2.append(p) # merge and fetch consolidated ranges new_paths, new_starts, new_ends = merge_offset_ranges( list(urls2), list(starts2), list(ends2), sort=True, max_gap=self.max_gap, max_block=self.max_block, ) bytes_out = fs.cat_ranges(new_paths, new_starts, new_ends) # unbundle from merged bytes - simple approach for u, s, e, p in zip(urls, starts, ends, valid_paths): if p in out: continue # was bytes, already handled for np, ns, ne, b in zip(new_paths, new_starts, new_ends, bytes_out): if np == u and (ns is None or ne is None): if isinstance(b, Exception): out[p] = b else: out[p] = b[s:e] elif np == u and s >= ns and e <= ne: if isinstance(b, Exception): out[p] = b else: out[p] = b[s - ns : (e - ne) or None] for k, v in out.copy().items(): # these were valid references, but fetch failed, so transform exc if isinstance(v, Exception) and k in self.references: ex = out[k] new_ex = ReferenceNotReachable(k, self.references[k]) new_ex.__cause__ = ex if on_error == "raise": raise new_ex elif on_error != "omit": out[k] = new_ex if len(out) == 1 and isinstance(path, str) and "*" not in path: return _first(out) return out def _process_references(self, references, template_overrides=None): vers = references.get("version", None) if vers is None: self._process_references0(references) elif vers == 1: self._process_references1(references, template_overrides=template_overrides) else: raise ValueError(f"Unknown reference spec version: {vers}") # TODO: we make dircache by iterating over all entries, but for Spec >= 1, # can replace with programmatic. Is it even needed for mapper interface? def _process_references0(self, references): """Make reference dict for Spec Version 0""" if isinstance(references, dict): # do not do this for lazy/parquet backend, which will not make dicts, # but must remain writable in the original object references = { key: json.dumps(val) if isinstance(val, dict) else val for key, val in references.items() } self.references = references def _process_references1(self, references, template_overrides=None): if not self.simple_templates or self.templates: import jinja2 self.references = {} self._process_templates(references.get("templates", {})) @lru_cache(1000) def _render_jinja(u): return jinja2.Template(u).render(**self.templates) for k, v in references.get("refs", {}).items(): if isinstance(v, str): if v.startswith("base64:"): self.references[k] = base64.b64decode(v[7:]) self.references[k] = v elif isinstance(v, dict): self.references[k] = json.dumps(v) elif self.templates: u = v[0] if "{{" in u: if self.simple_templates: u = ( u.replace("{{", "{") .replace("}}", "}") .format(**self.templates) ) else: u = _render_jinja(u) self.references[k] = [u] if len(v) == 1 else [u, v[1], v[2]] else: self.references[k] = v self.references.update(self._process_gen(references.get("gen", []))) def _process_templates(self, tmp): self.templates = {} if self.template_overrides is not None: tmp.update(self.template_overrides) for k, v in tmp.items(): if "{{" in v: import jinja2 self.templates[k] = lambda temp=v, **kwargs: jinja2.Template( temp ).render(**kwargs) else: self.templates[k] = v def _process_gen(self, gens): out = {} for gen in gens: dimension = { k: ( v if isinstance(v, list) else range(v.get("start", 0), v["stop"], v.get("step", 1)) ) for k, v in gen["dimensions"].items() } products = ( dict(zip(dimension.keys(), values)) for values in itertools.product(*dimension.values()) ) for pr in products: import jinja2 key = jinja2.Template(gen["key"]).render(**pr, **self.templates) url = jinja2.Template(gen["url"]).render(**pr, **self.templates) if ("offset" in gen) and ("length" in gen): offset = int( jinja2.Template(gen["offset"]).render(**pr, **self.templates) ) length = int( jinja2.Template(gen["length"]).render(**pr, **self.templates) ) out[key] = [url, offset, length] elif ("offset" in gen) ^ ("length" in gen): raise ValueError( "Both 'offset' and 'length' are required for a " "reference generator entry if either is provided." ) else: out[key] = [url] return out def _dircache_from_items(self): self.dircache = {"": []} it = self.references.items() for path, part in it: if isinstance(part, (bytes, str)) or hasattr(part, "to_bytes"): size = len(part) elif len(part) == 1: size = None else: _, _, size = part par = path.rsplit("/", 1)[0] if "/" in path else "" par0 = par subdirs = [par0] while par0 and par0 not in self.dircache: # collect parent directories par0 = self._parent(par0) subdirs.append(par0) subdirs.reverse() for parent, child in zip(subdirs, subdirs[1:]): # register newly discovered directories assert child not in self.dircache assert parent in self.dircache self.dircache[parent].append( {"name": child, "type": "directory", "size": 0} ) self.dircache[child] = [] self.dircache[par].append({"name": path, "type": "file", "size": size}) def _open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs): part_or_url, start0, end0 = self._cat_common(path) # This logic is kept outside `ReferenceFile` to avoid unnecessary redirection. # That does mean `_cat_common` gets called twice if it eventually reaches `ReferenceFile`. if isinstance(part_or_url, bytes): return io.BytesIO(part_or_url[start0:end0]) protocol, _ = split_protocol(part_or_url) if start0 is None and end0 is None: return self.fss[protocol]._open( part_or_url, mode, block_size=block_size, cache_options=cache_options, **kwargs, ) return ReferenceFile( self, path, mode, block_size=block_size, cache_options=cache_options, **kwargs, ) def ls(self, path, detail=True, **kwargs): logger.debug("list %s", path) path = self._strip_protocol(path) if isinstance(self.references, LazyReferenceMapper): try: return self.references.ls(path, detail) except KeyError: pass raise FileNotFoundError(f"'{path}' is not a known key") if not self.dircache: self._dircache_from_items() out = self._ls_from_cache(path) if out is None: raise FileNotFoundError(path) if detail: return out return [o["name"] for o in out] def exists(self, path, **kwargs): # overwrite auto-sync version return self.isdir(path) or self.isfile(path) def isdir(self, path): # overwrite auto-sync version if self.dircache: return path in self.dircache elif isinstance(self.references, LazyReferenceMapper): return path in self.references.listdir() else: # this may be faster than building dircache for single calls, but # by looping will be slow for many calls; could cache it? return any(_.startswith(f"{path}/") for _ in self.references) def isfile(self, path): # overwrite auto-sync version return path in self.references async def _ls(self, path, detail=True, **kwargs): # calls fast sync code return self.ls(path, detail, **kwargs) def find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs): if withdirs: return super().find( path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs ) if path: path = self._strip_protocol(path) r = sorted(k for k in self.references if k.startswith(path)) else: r = sorted(self.references) if detail: if not self.dircache: self._dircache_from_items() return {k: self._ls_from_cache(k)[0] for k in r} else: return r def info(self, path, **kwargs): out = self.references.get(path) if out is not None: if isinstance(out, (str, bytes)): # decode base64 here return {"name": path, "type": "file", "size": len(out)} elif len(out) > 1: return {"name": path, "type": "file", "size": out[2]} else: out0 = [{"name": path, "type": "file", "size": None}] else: out = self.ls(path, True) out0 = [o for o in out if o["name"] == path] if not out0: return {"name": path, "type": "directory", "size": 0} if out0[0]["size"] is None: # if this is a whole remote file, update size using remote FS prot, _ = split_protocol(self.references[path][0]) out0[0]["size"] = self.fss[prot].size(self.references[path][0]) return out0[0] async def _info(self, path, **kwargs): # calls fast sync code return self.info(path) async def _rm_file(self, path, **kwargs): self.references.pop( path, None ) # ignores FileNotFound, just as well for directories self.dircache.clear() # this is a bit heavy handed async def _pipe_file(self, path, data, mode="overwrite", **kwargs): if mode == "create" and self.exists(path): raise FileExistsError # can be str or bytes self.references[path] = data self.dircache.clear() # this is a bit heavy handed async def _put_file(self, lpath, rpath, mode="overwrite", **kwargs): # puts binary if mode == "create" and self.exists(rpath): raise FileExistsError with open(lpath, "rb") as f: self.references[rpath] = f.read() self.dircache.clear() # this is a bit heavy handed def save_json(self, url, **storage_options): """Write modified references into new location""" out = {} for k, v in self.references.items(): if isinstance(v, bytes): try: out[k] = v.decode("ascii") except UnicodeDecodeError: out[k] = (b"base64:" + base64.b64encode(v)).decode() else: out[k] = v with fsspec.open(url, "wb", **storage_options) as f: f.write(json.dumps({"version": 1, "refs": out}).encode()) class ReferenceFile(AbstractBufferedFile): def __init__( self, fs, path, mode="rb", block_size="default", autocommit=True, cache_type="readahead", cache_options=None, size=None, **kwargs, ): super().__init__( fs, path, mode=mode, block_size=block_size, autocommit=autocommit, size=size, cache_type=cache_type, cache_options=cache_options, **kwargs, ) part_or_url, self.start, self.end = self.fs._cat_common(self.path) protocol, _ = split_protocol(part_or_url) self.src_fs = self.fs.fss[protocol] self.src_path = part_or_url self._f = None @property def f(self): if self._f is None or self._f.closed: self._f = self.src_fs._open( self.src_path, mode=self.mode, block_size=self.blocksize, autocommit=self.autocommit, cache_type="none", **self.kwargs, ) return self._f def close(self): if self._f is not None: self._f.close() return super().close() def _fetch_range(self, start, end): start = start + self.start end = min(end + self.start, self.end) self.f.seek(start) return self.f.read(end - start)