palletjack 2.12.2

Faster parquet metadata reading

PalletJack

PalletJack was created as a workaround for apache/arrow#38149. The standard parquet reader is not efficient for files with numerous columns and row groups, as it requires parsing the entire metadata section each time the file is opened. The size of this metadata section is proportional to the number of columns and row groups in the file.

PalletJack reduces the amount of metadata bytes that need to be read and decoded by storing metadata in a different format. This approach enables reading only the essential subset of metadata as required.

Features

• Storing parquet metadata in an indexed format
• Reading parquet metadata for a subset of row groups and columns

Required:

• pyarrow ~= 24.0.0

PalletJack operates on top of pyarrow, making it an essential requirement for both building and using PalletJack. While our source package is compatible with recent versions of pyarrow, the binary distribution package specifically requires the latest major version of pyarrow.

Installation

pip install palletjack

How to use:

Generating a sample parquet file:

import palletjack as pj
import pyarrow.parquet as pq
import pyarrow.fs as fs
import pyarrow as pa
import numpy as np

row_groups = 200
columns = 200
chunk_size = 1000
rows = row_groups * chunk_size
path = "my.parquet"

data = np.random.rand(rows, columns)
pa_arrays = [pa.array(data[:, i]) for i in range(columns)]
column_names = [f'column_{i}' for i in range(columns)]
table = pa.Table.from_arrays(pa_arrays, names=column_names)
pq.write_table(table, path, row_group_size=chunk_size, use_dictionary=False, write_statistics=False, store_schema=False)

Generating the metadata index file:

index_path = path + '.index'
pj.generate_metadata_index(path, index_path)

Generating the in-memory metadata index:

index_data = pj.generate_metadata_index(path)

Writing the in-memory metadata index to a file using pyarrow's fs:

fs.LocalFileSystem().open_output_stream(index_path).write(index_data)

Reading the in-memory metadata index from a file using pyarrow's fs:

index_data = fs.LocalFileSystem().open_input_stream(index_path).readall()

Reading data with help of the index file:

metadata = pj.read_metadata(index_path, row_groups = [5, 7])
pr = pq.ParquetReader()
pr.open(path, metadata=metadata)
data = pr.read_all()

Reading data with help of the in-memory index:

metadata = pj.read_metadata(index_data = index_data, row_groups = [5, 7])
pr = pq.ParquetReader()
pr.open(path, metadata=metadata)
data = pr.read_all()

Reading a subset of columns using column indices:

metadata = pj.read_metadata(index_path, column_indices = [1, 3])
pr = pq.ParquetReader()
pr.open(path, metadata=metadata)
data = pr.read_all()

Reading a subset of columns using column names:

metadata = pj.read_metadata(index_path, column_names = ['column_1', 'column_3'])
pr = pq.ParquetReader()
pr.open(path, metadata=metadata)
data = pr.read_all()

Reading a subset of row groups and columns:

metadata = pj.read_metadata(index_path, row_groups = [5, 7], column_indices = [1, 3])
pr = pq.ParquetReader()
pr.open(path, metadata=metadata)

data = pr.read_all()

Reading the schema

schema = pj.read_schema(index_path)

Reading the schema for a subset of columns:

schema = pj.read_schema(index_path, column_indices = [1, 3])
schema = pj.read_schema(index_path, column_names = ['column_1', 'column_3'])