def update_dataset_from_partitions(
partition_list,
store_factory,
dataset_uuid,
ds_factory,
delete_scope,
metadata,
metadata_merger,
):
store = _instantiate_store(store_factory)
if ds_factory:
ds_factory = ds_factory.load_all_indices()
remove_partitions = _get_partitions(ds_factory, delete_scope)
index_columns = list(ds_factory.indices.keys())
for column in index_columns:
index = ds_factory.indices[column]
if isinstance(index, PartitionIndex):
del ds_factory.indices[column]
else:
# Dataset does not exist yet.
remove_partitions = []
new_dataset = store_dataset_from_partitions(
partition_list=partition_list,
store=store,
dataset_uuid=dataset_uuid,
dataset_metadata=metadata,
metadata_merger=metadata_merger,
update_dataset=ds_factory,
remove_partitions=remove_partitions,
)
return new_dataset
def raise_if_dataset_exists(dataset_uuid, store):
try:
store_instance = _instantiate_store(store)
for form in ["msgpack", "json"]:
key = naming.metadata_key_from_uuid(uuid=dataset_uuid, format=form)
if key in store_instance:
raise RuntimeError(
"Dataset `%s` already exists and overwrite is not permitted!",
dataset_uuid,
)
except KeyError:
pass
def persist_indices(store, dataset_uuid, indices):
store = _instantiate_store(store)
output_filenames = {}
for column, index in indices.items():
# backwards compat
if isinstance(index, dict):
legacy_storage_key = "{dataset_uuid}.{column}{suffix}".format(
dataset_uuid=dataset_uuid,
column=column,
suffix=naming.EXTERNAL_INDEX_SUFFIX,
)
index = ExplicitSecondaryIndex(
column=column,
index_dct=index,
index_storage_key=legacy_storage_key)
elif isinstance(index, PartitionIndex):
continue
output_filenames[column] = index.store(store=store,
dataset_uuid=dataset_uuid)
return output_filenames
def align_datasets(left_dataset_uuid, right_dataset_uuid, store, match_how="exact"):
"""
Determine dataset partition alignment
Parameters
----------
left_dataset_uuid : basestring
right_dataset_uuid : basestring
store : KeyValuestore or callable
match_how : basestring or callable, {exact, prefix, all, callable}
Yields
------
list
"""
store = _instantiate_store(store)
left_dataset = DatasetMetadata.load_from_store(uuid=left_dataset_uuid, store=store)
right_dataset = DatasetMetadata.load_from_store(
uuid=right_dataset_uuid, store=store
)
metadata_version = left_dataset.metadata_version
# Loop over the dataset with fewer partitions, treating its keys as
# partition label prefixes
if (
callable(match_how)
or match_how == "left"
or (
match_how == "prefix"
and len(list(left_dataset.partitions.keys())[0])
< len(list(right_dataset.partitions.keys())[0])
)
):
first_dataset = left_dataset
second_dataset = right_dataset
else:
first_dataset = right_dataset
second_dataset = left_dataset
# The del statements are here to reduce confusion below
del left_dataset
del right_dataset
# For every partition in the 'small' dataset, at least one partition match
# needs to be found in the larger dataset.
available_partitions = list(second_dataset.partitions.items())
partition_stack = available_partitions[:]
# TODO: write a test which protects against the following scenario!!
# Sort the partition labels by length of the labels, starting with the
# labels which are the longest. This way we prevent label matching for
# similar partitions, e.g. cluster_100 and cluster_1. This, of course,
# works only as long as the internal loop removes elements which were
# matched already (here improperly called stack)
for l_1 in sorted(first_dataset.partitions, key=len, reverse=True):
p_1 = first_dataset.partitions[l_1]
res = [
MetaPartition.from_partition(
partition=p_1, metadata_version=metadata_version
)
]
for parts in available_partitions:
l_2, p_2 = parts
if callable(match_how) and not match_how(l_1, l_2):
continue
if match_how == "exact" and l_1 != l_2:
continue
elif match_how == "prefix" and not l_2.startswith(l_1):
LOGGER.debug("rejecting (%s, %s)", l_1, l_2)
continue
LOGGER.debug(
"Found alignment between partitions " "(%s, %s) and" "(%s, %s)",
first_dataset.uuid,
p_1.label,
second_dataset.uuid,
p_2.label,
)
res.append(
MetaPartition.from_partition(
partition=p_2, metadata_version=metadata_version
)
)
# In exact or prefix matching schemes, it is expected to only
# find one partition alignment. in this case reduce the size of
# the inner loop
if match_how in ["exact", "prefix"]:
partition_stack.remove((l_2, p_2))
# Need to copy, otherwise remove will alter the loop iterator
available_partitions = partition_stack[:]
if len(res) == 1:
raise RuntimeError(
"No matching partition for {} in dataset {} "
"found".format(p_1, first_dataset)
)
yield res
def store_dataset_from_partitions(
partition_list,
store,
dataset_uuid,
dataset_metadata=None,
metadata_merger=None,
update_dataset=None,
remove_partitions=None,
metadata_storage_format=naming.DEFAULT_METADATA_STORAGE_FORMAT,
):
store = _instantiate_store(store)
if update_dataset:
dataset_builder = DatasetMetadataBuilder.from_dataset(update_dataset)
metadata_version = dataset_builder.metadata_version
else:
mp = next(iter(partition_list), None)
if mp is None:
raise ValueError(
"Cannot store empty datasets, partition_list must not be empty if in store mode."
)
metadata_version = mp.metadata_version
dataset_builder = DatasetMetadataBuilder(
uuid=dataset_uuid,
metadata_version=metadata_version,
partition_keys=mp.partition_keys,
)
dataset_builder.explicit_partitions = True
dataset_builder.table_meta = persist_common_metadata(
partition_list, update_dataset, store, dataset_uuid)
# We can only check for non unique partition labels here and if they occur we will
# fail hard. The resulting dataset may be corrupted or file may be left in the store
# without dataset metadata
partition_labels = partition_labels_from_mps(partition_list)
non_unique_labels = extract_duplicates(partition_labels)
if non_unique_labels:
raise ValueError(
"The labels {} are duplicated. Dataset metadata was not written.".
format(", ".join(non_unique_labels)))
if remove_partitions is None:
remove_partitions = []
if metadata_merger is None:
metadata_merger = combine_metadata
dataset_builder = update_metadata(dataset_builder, metadata_merger,
partition_list, dataset_metadata)
dataset_builder = update_partitions(dataset_builder, partition_list,
remove_partitions)
dataset_builder = update_indices(dataset_builder, store, partition_list,
remove_partitions)
if metadata_storage_format.lower() == "json":
store.put(*dataset_builder.to_json())
elif metadata_storage_format.lower() == "msgpack":
store.put(*dataset_builder.to_msgpack())
else:
raise ValueError(
"Unknown metadata storage format encountered: {}".format(
metadata_storage_format))
dataset = dataset_builder.to_dataset()
return dataset