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)): keys = set().union(*[row.keys() for row in dataset]) mapping = {col: [row.get(col) for row in dataset] for col in keys} else: mapping = dataset pa_table = pa.Table.from_pydict(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: keys = set().union(*[row.keys() for row in dataset]) mapping = {col: [row.get(col) for row in dataset] for col in keys} pa_table = pa.Table.from_pydict(mapping) 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