badger-batcher 0.4.0

Badger Batcher contains useful utilities for batching a sequence on records

Badger Batcher

Badger Batcher contains useful utilities for batching a sequence on records

• Free software: MIT license

• Documentation: https://badger-batcher.readthedocs.io.

Installation

$ pip install badger_batcher

Features

Import Batcher:

>>> from badger_batcher import Batcher

Split records based max limit for batch len:

>>> records = [f"record: {rec}" for rec in range(5)]
>>> batcher = Batcher(records, max_batch_size=2)
>>> batcher.batches()
[['record: 0', 'record: 1'], ['record: 2', 'record: 3'], ['record: 4']]

Split records with max limit for batch len and max limit for record size:

>>> records = [b"aaaa", b"bb", b"ccccc", b"d"]
>>> batcher = Batcher(
... records,
... max_batch_size=2,
... max_record_size=4,
... size_calc_fn=len,
... when_record_size_exceeded="skip",
... )
>>> batcher.batches()
[[b'aaaa', b'bb'], [b'd']]

Split records with max batch len and size:

>>> records = [b"a", b"a", b"a", b"b", b"ccc", b"toolargeforbatch", b"dd", b"e"]
>>> batcher = Batcher(
... records,
... max_batch_len=3,
... max_batch_size=5,
... size_calc_fn=len,
... when_record_size_exceeded="skip",
... )
>>> batcher.batches()
[[b'a', b'a', b'a'], [b'b', b'ccc'], [b'dd', b'e']]

When processing big chunks of data, consider iterating instead:

>>> import sys

>>> records = (f"record: {rec}" for rec in range(sys.maxsize))
>>> batcher = Batcher(records, max_batch_size=2)
>>> for batch in batcher:
...       # do something for each batch
...       some_fancy_fn(batch)

If you need to encode records before applying the batcher, just encode it before applying. Batcher will not eagerly realize the whole iterable, so use a generator for bigger iterables.

>>> records = ["a", "a", "a", "b", "ccc", "bbbb", "dd", "e"]
>>> encoded_records_gen = (record.encode("utf-16-le") for record in records)

>>> batcher = Batcher(
... encoded_records_gen,
... max_batch_len=3,
... max_record_size=6,
... max_batch_size=10,
... size_calc_fn=len,
... when_record_size_exceeded="skip",
... )

>>> batched_records = batcher.batches()
[
    [b"a\x00", b"a\x00", b"a\x00"],
    [b"b\x00", b"c\x00c\x00c\x00"],
    [b"d\x00d\x00", b"e\x00"],
]

Full example for e.g. Kinesis Streams like processing

import random
from badger_batcher import Batcher


def get_records():
    records = (
        f"""{{'id': '{i}', 'body': {('x' * random.randint(100_000, 7_000_000))}}}"""
        for i in range(10_000)
    )
    return records


records = get_records()
encoded_records = (record.encode("utf-8") for record in records)

batcher = Batcher(
    encoded_records,
    max_batch_len=500,
    max_record_size=1000 * 1000,
    max_batch_size=5 * 1000 * 1000,
    size_calc_fn=len,
    when_record_size_exceeded="skip",
)

for i, batch in enumerate(batcher):
    # do something

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2021-04-09)

• First release on PyPI.