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import base64
import collections
import io
import itertools
import logging
import math
import os
from functools import lru_cache
from typing import TYPE_CHECKING
import fsspec.core
try:
import ujson as json
except ImportError:
if not TYPE_CHECKING:
import json
from ..asyn import AsyncFileSystem
from ..callbacks import DEFAULT_CALLBACK
from ..core import filesystem, open, split_protocol
from ..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 list(d.values())[0]
def _prot_in_references(path, references):
ref = references.get(path)
if isinstance(ref, (list, tuple)):
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
):
"""
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)
"""
self.root = root
self.chunk_sizes = {}
self.out_root = out_root or self.root
self.cat_thresh = categorical_threshold
self.cache_size = cache_size
self.dirs = None
self.url = self.root + "/{field}/refs.{record}.parq"
# TODO: derive fs from `root`
self.fs = fsspec.filesystem("file") if fs is None else fs
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))
df = self.pd.read_parquet(data, engine="fastparquet")
refs = {c: df[c].values for c in df.columns}
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)
def listdir(self, basename=True):
"""List top-level directories"""
# cache me?
if self.dirs is None:
dirs = [p.split("/", 1)[0] for p in self.zmetadata]
self.dirs = {p for p in dirs if p and not p.startswith(".")}
listing = self.dirs
if basename:
listing = [os.path.basename(path) for path in listing]
return listing
def ls(self, path="", detail=True):
"""Shortcut file listings"""
if not path:
dirnames = self.listdir()
others = set(
[".zmetadata"]
+ [name for name in self.zmetadata if "/" not in name]
+ [name for name in self._items if "/" not in name]
)
if detail is False:
others.update(dirnames)
return sorted(others)
dirinfo = [
{"name": name, "type": "directory", "size": 0} for name in dirnames
]
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"])
parts = path.split("/", 1)
if len(parts) > 1:
raise FileNotFoundError("Cannot list within directories right now")
field = parts[0]
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):
raise KeyError(key)
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
self._items[key] = value
new_value = json.loads(
value.decode() if isinstance(value, bytes) else value
)
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 IOError:
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)
df.to_parquet(
fn,
engine="fastparquet",
storage_options=storage_options
or getattr(self.fs, "storage_options", None),
compression="zstd",
index=False,
stats=False,
object_encoding=object_encoding,
has_nulls=has_nulls,
# **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[".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). This FileSystem only allows whole-file access, no
``open``. 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"
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
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:
raise FileNotFoundError(path)
if isinstance(part, str):
part = part.encode()
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:
await self.fss[protocol]._cat_file(part_or_url, start=start, end=end)
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)
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)):
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):
data = self.cat_file(path) # load whole chunk into memory
return io.BytesIO(data)
def ls(self, path, detail=True, **kwargs):
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):
# 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, **kwargs):
# puts binary
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())
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