105 lines
2.6 KiB
Python
105 lines
2.6 KiB
Python
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"""Local reader of parquet files.
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1. Make sure you are initialized locally:
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```
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./images/init_venv_macos.sh
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```
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2. Activate
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```
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source ~/tml_venv/bin/activate
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```
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3. Use tool, e.g.
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`head` prints the first `--num` rows of the dataset.
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```
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python3 tools/pq.py \
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--num 5 --path "tweet_eng/small/edges/all/*" \
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head
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```
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`distinct` prints the observed values in the first `--num` rows for the specified columns.
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```
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python3 tools/pq.py \
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--num 1000000000 --columns '["rel"]' \
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--path "tweet_eng/small/edges/all/*" \
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distinct
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```
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"""
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from typing import List, Optional
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from tml.common.filesystem import infer_fs
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import fire
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import pandas as pd
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import pyarrow as pa
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import pyarrow.dataset as pads
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import pyarrow.parquet as pq
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def _create_dataset(path: str):
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fs = infer_fs(path)
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files = fs.glob(path)
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return pads.dataset(files, format="parquet", filesystem=fs)
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class PqReader:
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def __init__(
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self, path: str, num: int = 10, batch_size: int = 1024, columns: Optional[List[str]] = None
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):
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self._ds = _create_dataset(path)
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self._batch_size = batch_size
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self._num = num
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self._columns = columns
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def __iter__(self):
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batches = self._ds.to_batches(batch_size=self._batch_size, columns=self._columns)
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rows_seen = 0
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for count, record in enumerate(batches):
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if self._num and rows_seen >= self._num:
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break
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yield record
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rows_seen += record.data.num_rows
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def _head(self):
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total_read = self._num * self.bytes_per_row
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if total_read >= int(500e6):
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raise Exception(
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"Sorry you're trying to read more than 500 MB " f"into memory ({total_read} bytes)."
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)
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return self._ds.head(self._num, columns=self._columns)
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@property
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def bytes_per_row(self) -> int:
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nbits = 0
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for t in self._ds.schema.types:
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try:
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nbits += t.bit_width
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except:
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# Just estimate size if it is variable
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nbits += 8
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return nbits // 8
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def schema(self):
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print(f"\n# Schema\n{self._ds.schema}")
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def head(self):
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"""Displays first --num rows."""
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print(self._head().to_pandas())
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def distinct(self):
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"""Displays unique values seen in specified columns in the first `--num` rows.
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Useful for getting an approximate vocabulary for certain columns.
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"""
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for col_name, column in zip(self._head().column_names, self._head().columns):
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print(col_name)
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print("unique:", column.unique().to_pylist())
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if __name__ == "__main__":
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pd.set_option("display.max_columns", None)
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pd.set_option("display.max_rows", None)
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fire.Fire(PqReader)
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