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json.py
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json.py
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import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class JsonConfig(datasets.BuilderConfig):
"""BuilderConfig for JSON."""
features: Optional[datasets.Features] = None
field: Optional[str] = None
use_threads: bool = True # deprecated
block_size: Optional[int] = None # deprecated
chunksize: int = 10 << 20 # 10MB
newlines_in_values: Optional[bool] = None
class Json(datasets.ArrowBasedBuilder):
BUILDER_CONFIG_CLASS = JsonConfig
def _info(self):
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead")
self.config.chunksize = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."
)
if self.config.newlines_in_values is not None:
raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported")
return datasets.DatasetInfo(features=self.config.features)
def _split_generators(self, dl_manager):
"""We handle string, list and dicts in datafiles"""
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
data_files = dl_manager.download_and_extract(self.config.data_files)
if isinstance(data_files, (str, list, tuple)):
files = data_files
if isinstance(files, str):
files = [files]
files = [dl_manager.iter_files(file) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files})]
splits = []
for split_name, files in data_files.items():
if isinstance(files, str):
files = [files]
files = [dl_manager.iter_files(file) for file in files]
splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
return splits
def _cast_table(self, pa_table: pa.Table) -> pa.Table:
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features) - set(pa_table.column_names):
type = self.config.features.arrow_schema.field(column_name).type
pa_table = pa_table.append_column(column_name, pa.array([None] * len(pa_table), type=type))
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
pa_table = table_cast(pa_table, self.config.features.arrow_schema)
return pa_table
def _generate_tables(self, files):
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
# We keep only the field we are interested in
dataset = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(dataset, (list, tuple)):
mapping = {col: [dataset[i][col] for i in range(len(dataset))] for col in dataset[0].keys()}
else:
mapping = dataset
pa_table = pa.Table.from_pydict(mapping=mapping)
yield file_idx, self._cast_table(pa_table)
# If the file has one json object per line
else:
with open(file, "rb") as f:
batch_idx = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
block_size = max(self.config.chunksize // 32, 16 << 10)
while True:
batch = f.read(self.config.chunksize)
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(f)
try:
while True:
try:
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(e, pa.ArrowInvalid)
and "straddling" not in str(e)
or block_size > len(batch)
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f"Batch of {len(batch)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."
)
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
except json.JSONDecodeError:
logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(dataset, list): # list is the only sequence type supported in JSON
try:
pa_table = pa.Table.from_pylist(dataset)
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
yield file_idx, self._cast_table(pa_table)
break
else:
logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "
) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(pa_table)
batch_idx += 1