def _load_agginfo_db(ds_path):
return {
# paths in DB on disk are always relative
# make absolute to ease processing during aggregation
op.normpath(op.join(ds_path, p)): {
k: op.normpath(op.join(ds_path, op.dirname(agginfo_relpath), v))
if k in location_keys else v
for k, v in props.items()
}
for p, props in _load_json_object(opj(ds_path,
agginfo_relpath)).items()
}
def _get_dsinfo_from_aggmetadata(ds_path, path, recursive, db):
"""Grab info on aggregated metadata for a path from a given dataset.
The actual info is stored in a `db` dict under the absolute path
of the dataset that contains the query path, plus any subdataset
in case of recursion (with their own DB entries).
Parameters
----------
ds : Dataset
source dataset
path : str
absolute path for which to obtain metadata
recursive : bool
Returns
-------
str or list
A string is an error message, a list contains all absolute paths for
all datasets on which info was put into the DB.
"""
info_fpath = opj(ds_path, agginfo_relpath)
info_basepath = dirname(info_fpath)
# TODO cache these
agginfos = _load_json_object(info_fpath)
def _ensure_abs_obj_location(rec):
# object location in the DB must be absolute so we can copy easily
# to all relevant datasets
for key in location_keys:
if key in rec and not isabs(rec[key]):
rec[key] = opj(info_basepath, rec[key])
return rec
rpath = relpath(path, start=ds_path)
seed_ds = _get_containingds_from_agginfo(agginfos, rpath)
if seed_ds is None:
# nothing found
# this will be the message in the result for the query path
# and could be a tuple
return ("No matching aggregated metadata in Dataset at %s", ds_path)
# easy peasy
seed_abs = opj(ds_path, seed_ds)
db[seed_abs] = _ensure_abs_obj_location(agginfos[seed_ds])
hits = [seed_abs]
if not recursive:
return hits
# a little more complicated: we need to loop over all subdataset
# records an pick the ones that are underneath the seed
for agginfo_path in agginfos:
if agginfo_path.startswith(_with_sep(seed_ds)):
absp = opj(ds_path, agginfo_path)
db[absp] = _ensure_abs_obj_location(agginfos[agginfo_path])
hits.append(absp)
# TODO we must keep the info on these recursively discovered datasets
# somewhere, because we cannot rediscover them on the filesystem
# when updating the datasets later on
return hits
def _update_ds_agginfo(refds_path, ds_path, subds_paths, agginfo_db, to_save):
"""Perform metadata aggregation for ds and a given list of subdataset paths
Parameters
----------
refds_path : str
Absolute path to the reference dataset that aggregate_metadata() was
called on.
ds_path : str
Absolute path to the dataset to have its aggregate info updates
subds_paths : list(str)
Sequence of absolute paths of subdatasets of the to-be-updated dataset,
whose agginfo shall be updated within the to-be-updated dataset
agginfo_db : dict
Dictionary with all information on aggregate metadata on all datasets.
Keys are absolute paths of datasets.
to_save : list
List of paths to save eventually. This function will add new paths as
necessary.
"""
ds = Dataset(ds_path)
# location info of aggregate metadata
# aggregate.json
agginfo_fpath = opj(ds.path, agginfo_relpath)
# base path in which aggregate.json and objects is located
agg_base_path = dirname(agginfo_fpath)
# load existing aggregate info dict
# TODO take from cache, once used in _get_dsinfo_from_aggmetadata()
ds_agginfos = _load_json_object(agginfo_fpath)
# object locations referenced initially
objlocs_was = set(ai[k] for ai in ds_agginfos.values()
for k in location_keys if k in ai)
# TODO look for datasets that are no longer registered and remove all
# info about them
# track which objects need to be copied
objs2copy = []
# for each subdataset (any depth level)
for dpath in [ds_path] + subds_paths:
# relative path of the currect dataset within the dataset we are updating
drelpath = relpath(dpath, start=ds.path)
# TODO figure out why `None` could be a value in the DB
## build aggregate info for the current subdataset
#ds_dbinfo = agginfo_db.get(dpath, {})
#if not ds_dbinfo:
# # we got nothing new, keep what we had
# continue
#ds_dbinfo = ds_dbinfo.copy()
ds_dbinfo = agginfo_db.get(dpath, {}).copy()
for loclabel in location_keys:
if loclabel == 'filepath_info' and drelpath == curdir:
# do not write a file list into the dataset it is from
if 'filepath_info' in ds_dbinfo:
del ds_dbinfo['filepath_info']
continue
# abspath to object
objloc = ds_dbinfo.get(loclabel, None)
if objloc is None:
continue
# XXX needs to change when layout of object store is changed
# current is ./datalad/metadata/objects/{hash}/{hash}
target_objrelpath = opj(*objloc.split(os.sep)[-3:])
# make sure we copy the file from its current location to where it is
# needed in this dataset
target_objpath = opj(agg_base_path, target_objrelpath)
objs2copy.append((objloc, target_objpath))
# now build needed local relpath
ds_dbinfo[loclabel] = target_objrelpath
# (re)assign in case record is new
ds_agginfos[drelpath] = ds_dbinfo
# set of metadata objects now referenced
objlocs_is = set(ai[k] for ai in ds_agginfos.values()
for k in location_keys if k in ai)
objs2remove = objlocs_was.difference(objlocs_is)
# TODO do we need to (double?) check if all object files exist?
#objs2add = [o for o in objlocs_is if exists(opj(ds_path, o))]
objs2add = objlocs_is
# secretly remove obsolete object files, not really a result from a
# user's perspective
if objs2remove:
ds.remove(
[opj(agg_base_path, p) for p in objs2remove],
# Don't use the misleading default commit message of `remove`:
message='[DATALAD] Remove obsolete metadata object files',
# we do not want to drop these files by default, because we would
# loose them for other branches, and earlier tags
# TODO evaluate whether this should be exposed as a switch
# to run an explicit force-drop prior to calling remove()
check=False,
result_renderer=None,
return_type=list)
if not objs2add and not refds_path == ds_path:
# this is not the base dataset, make sure to save removal in the
# parentds -- not needed when objects get added, as removal itself
# is already committed
to_save(dict(path=ds_path, type='dataset', staged=True))
# must copy object files to local target destination
for copy_from, copy_to in objs2copy:
if copy_to == copy_from:
continue
target_dir = dirname(copy_to)
if not exists(target_dir):
makedirs(target_dir)
# TODO we could be more clever (later) and maybe `addurl` (or similar)
# the file from another dataset
if exists(copy_to):
# no need to unlock, just wipe out and replace
os.remove(copy_to)
shutil.copy(copy_from, copy_to)
# TODO is there any chance that this file could be gone at the end?
#if exists(agginfo_fpath):
to_save.append(dict(path=agginfo_fpath, type='file', staged=True))
if objs2add:
# they are added standard way, depending on the repo type
ds.add([opj(agg_base_path, p) for p in objs2add],
save=False,
result_renderer=None,
return_type=list)
# queue for save, and mark as staged
to_save.extend([
dict(path=opj(agg_base_path, p), type='file', staged=True)
for p in objs2add
])
# write aggregate info file
if not ds_agginfos:
return
json_py.dump(ds_agginfos, agginfo_fpath)
ds.add(agginfo_fpath,
save=False,
to_git=True,
result_renderer=None,
return_type=list)
# queue for save, and mark as staged
to_save.append(dict(path=agginfo_fpath, type='file', staged=True))
def _update_ds_agginfo(refds_path, ds_path, subds_paths, agginfo_db, to_save):
"""Perform metadata aggregation for ds and a given list of subdataset paths
Parameters
----------
refds_path : str
Absolute path to the reference dataset that aggregate_metadata() was
called on.
ds_path : str
Absolute path to the dataset to have its aggregate info updates
subds_paths : list(str)
Sequence of absolute paths of subdatasets of the to-be-updated dataset,
whose agginfo shall be updated within the to-be-updated dataset.
Any subdataset that is not listed here is assumed to be gone (i.e. no longer
a subdataset at all, not just not locally installed)
agginfo_db : dict
Dictionary with all information on aggregate metadata on all datasets.
Keys are absolute paths of datasets.
to_save : list
List of paths to save eventually. This function will add new paths as
necessary.
"""
ds = Dataset(ds_path)
# location info of aggregate metadata
# aggregate.json
agginfo_fpath = opj(ds.path, agginfo_relpath)
# base path in which aggregate.json and objects is located
agg_base_path = dirname(agginfo_fpath)
# load existing aggregate info dict
# TODO take from cache, once used in _get_dsinfo_from_aggmetadata()
ds_agginfos = _load_json_object(agginfo_fpath)
# object locations referenced initially
objlocs_was = set(ai[k]
for ai in ds_agginfos.values()
for k in location_keys
if k in ai)
# track which objects need to be copied (each item is a from/to tuple
objs2copy = []
# for each subdataset (any depth level)
procds_paths = [ds_path] + subds_paths
for dpath in procds_paths:
ds_dbinfo = agginfo_db.get(dpath, {}).copy()
# relative path of the currect dataset within the dataset we are updating
drelpath = relpath(dpath, start=ds.path)
for loclabel in location_keys:
# TODO filepath_info is obsolete
if loclabel == 'filepath_info' and drelpath == curdir:
# do not write a file list into the dataset it is from
if 'filepath_info' in ds_dbinfo:
del ds_dbinfo['filepath_info']
continue
# abspath to object
objloc = ds_dbinfo.get(loclabel, None)
if objloc is None:
continue
# XXX needs to change when layout of object store is changed
# current is ./datalad/metadata/objects/{hash}/{hash}
target_objrelpath = opj(*objloc.split(os.sep)[-3:])
# make sure we copy the file from its current location to where it is
# needed in this dataset
target_objpath = opj(agg_base_path, target_objrelpath)
objs2copy.append((objloc, target_objpath))
# now build needed local relpath
ds_dbinfo[loclabel] = target_objrelpath
# (re)assign in case record is new
ds_agginfos[drelpath] = ds_dbinfo
# remove all entries for which we did not (no longer) have a corresponding
# subdataset to take care of
ds_agginfos = {k: v
for k, v in ds_agginfos.items()
if normpath(opj(ds_path, k)) in procds_paths}
# set of metadata objects now referenced
objlocs_is = set(
ai[k]
for sdsrpath, ai in ds_agginfos.items()
for k in location_keys
if k in ai)
objs2add = objlocs_is
# yoh: we appanretly do need to filter the ones to remove - I did
# "git reset --hard HEAD^" and
# aggregate-metadata failed upon next run trying to remove
# an unknown to git file. I am yet to figure out why that
# mattered (hopefully not that reflog is used somehow)
objs2remove = []
for obj in objlocs_was.difference(objlocs_is):
obj_path = opj(agg_base_path, obj)
if lexists(obj_path):
objs2remove.append(obj_path)
else:
# not really a warning, we don't need it anymore, it is already gone
lgr.debug(
"To-be-deleted metadata object not found, skip deletion (%s)",
obj_path
)
# secretly remove obsolete object files, not really a result from a
# user's perspective
if objs2remove:
ds.remove(
objs2remove,
# Don't use the misleading default commit message of `remove`:
message='[DATALAD] Remove obsolete metadata object files',
# we do not want to drop these files by default, because we would
# loose them for other branches, and earlier tags
# TODO evaluate whether this should be exposed as a switch
# to run an explicit force-drop prior to calling remove()
check=False,
result_renderer=None, return_type=list)
if not objs2add and not refds_path == ds_path:
# this is not the base dataset, make sure to save removal in the
# parentds -- not needed when objects get added, as removal itself
# is already committed
to_save.append(dict(path=ds_path, type='dataset', staged=True))
# must copy object files to local target destination
# make sure those objects are present
ds.get([f for f, t in objs2copy], result_renderer='disabled')
for copy_from, copy_to in objs2copy:
if copy_to == copy_from:
continue
target_dir = dirname(copy_to)
if not exists(target_dir):
makedirs(target_dir)
# TODO we could be more clever (later) and maybe `addurl` (or similar)
# the file from another dataset
if lexists(copy_to):
# no need to unlock, just wipe out and replace
os.remove(copy_to)
shutil.copy(copy_from, copy_to)
to_save.append(
dict(path=agginfo_fpath, type='file', staged=True))
if objs2add:
# they are added standard way, depending on the repo type
ds.add(
[opj(agg_base_path, p) for p in objs2add],
save=False, result_renderer=None, return_type=list)
# queue for save, and mark as staged
to_save.extend(
[dict(path=opj(agg_base_path, p), type='file', staged=True)
for p in objs2add])
# write aggregate info file
if not ds_agginfos:
return
json_py.dump(ds_agginfos, agginfo_fpath)
ds.add(agginfo_fpath, save=False, to_git=True,
result_renderer=None, return_type=list)
# queue for save, and mark as staged
to_save.append(
dict(path=agginfo_fpath, type='file', staged=True))