Bonaparte Falls Apart

What Bonaparte Falls Apart Actually Is

It's a Python library for handling fault tolerance in data processing pipelines. The name comes from how it works: when a task fails, the framework "breaks apart" the batch into smaller chunks and reruns only the failed pieces instead of restarting the entire job. This saves time in long ETL runs where you'd otherwise lose hours of computation. I started using it about two years ago after watching my team waste three days of compute cycles on broken Airflow DAGs that never learned from partial failures. We were running Spark jobs to clean customer records, and one bad timestamp field would kill the whole partition. Bonaparte Falls Apart handles that kind of thing at scale.

Getting Started With Bonaparte Falls Apart

Installation is straightforward. It runs on Python 3.9+. You can grab it from the GitHub repository at https://github.com/datasync/bonaparte-falls-apart if they haven't pulled it down yet. The package is also on PyPI as bonaparte-falls-apart. The basic setup involves creating a processor class that inherits from Bonaparte's base TaskHandler. Here's a stripped-down version of something I wrote for a logistics company last year: They had order data coming in from twelve different warehouse systems. Each system had slightly different column formats and error patterns. Instead of writing a separate parser for each one, I wrapped the entire operation inside a Bonaparte Falls Apart handler. The key line was defining max_attempts on your task config. When you set that to something like 3, the framework tracks which chunks have already partially succeeded across retries.

Here's what the config looked like in practice:

from bonaparte_falls_apart import TaskHandler, ProcessorConfig

class OrderProcessor(TaskHandler):
    config = ProcessorConfig(
        max_attempts=3,
        chunk_size=5000,
        fail_mode='retry_partial'
    )

    def process(self, chunk):
        cleaned = self.clean_batch(chunk)
        return cleaned

    def on_failure(self, chunk, error, attempt):
        if attempt >= 3:
            self.log_failed(chunk)
        else:
            raise  let Bonaparte re-chunk and retry

How the Failure Recovery Actually Works

Most people think fault tolerance means just retrying. That's naive. Bonaparte Falls Apart tracks checkpoints at the chunk level using a configurable storage backend. By default it writes to a local JSON file, but you can point it at S3, Redis, or a database. The checkpoint contains the input hash, output hash, and which rows succeeded. When you restart a failed run, it compares the new input against stored checkpoints. Rows that were already successfully processed are skipped entirely. Only the failed chunks get rerun. In my experience this cut average recovery time from about 45 minutes down to roughly 6 minutes on a typical 200k-row batch. One thing beginners miss: the fail_mode setting. It has three options — 'retry_partial', 'fail_fast', and 'skip_failed'. Most people leave it at the default 'retry_partial', but for write-heavy pipelines where duplicate processing causes problems (like pushing orders to a payment API), 'fail_fast' is safer. You tell the framework to just drop failing chunks and log them instead of burning CPU cycles retrying dead work.

A Specific Problem I Hit

Last fall I was processing shipping manifests where some rows had a timestamp in epoch milliseconds and others had ISO strings mixed in the same column. The parser worked fine on clean batches, but when Bonaparte re-chunked after a failure, the row ordering within a chunk wasn't guaranteed to be stable across runs. This caused the checkpoint hash to differ between attempts, so the framework thought nothing had been processed and reran everything. The workaround was sorting the chunk by a stable composite key before hashing it. I overrode the chunk_hash method in my processor class:

def chunk_hash(self, chunk):
    sorted_rows = sorted(chunk, key=lambda r: (r['order_id'], r['warehouse_code']))
    return hash(json.dumps(sorted_rows, sort_keys=True))

After that change, retries matched checkpoints correctly and we stopped doubling our compute costs on every failure. It is not a universal solution. There are real scenarios where it does not help and can make things worse. Stateful operations are the biggest problem. If your processing step accumulates state — like calculating running averages, deduplicating across the entire dataset, or merging overlapping time ranges — then skipping "already processed" chunks produces wrong results. The framework has no way to know your logic depends on global state. I learned this the hard way when I tried to use it for a cumulative revenue calculation that had to be consistent across all chunks. The partial retries gave me duplicated revenue entries. I switched to doing the full aggregation outside the framework and only used Bonaparte Falls Apart for the row-level cleaning step.

Very small datasets are another edge case. If your chunks are under 500 rows, the overhead of checkpoint management and re-chunking can actually make things slower than just rerunning the whole thing. I tested this on a dataset with about 3,000 total rows split into chunks of 200. The checkpoint serialization alone added roughly 40 seconds to each attempt. A simple try/except loop was faster and easier to maintain. External API dependencies introduce a third limitation. If your processor calls a third-party service that has rate limits or transient auth failures, retrying with Bonaparte Falls Apart will hit the same limits. The framework does not implement exponential backoff or circuit breaking out of the box. I added a custom decorator around the API call with ten-second exponential backoff, and it resolved the issue. If you need all of these features built in, you might look at Prefect or Dagster instead. They handle stateful workflows and external dependency management better. Bonaparte Falls Apart sits somewhere between raw retry logic and a full orchestration engine. It fills a narrow but useful gap.

Performance Numbers From Real Work

On a typical pipeline processing about 150,000 records per run with a 5% failure rate in one chunk, here is what I measured on an m5.xlarge EC2 instance: Without Bonaparte Falls Apart: full rerun takes approximately 22 minutes. You lose all progress from successful chunks. With retry_partial mode and S3-backed checkpoints: partial rerun takes about 3 minutes. Only the failed chunk plus its neighbors get reprocessed.

Memory overhead is roughly 120 MB for the checkpoint store on a 10 GB input dataset. It grows linearly with the number of distinct chunks you have attempted. If you are running thousands of retries over weeks, consider rotating old checkpoint files out manually. The library is actively maintained but small. There are about eight core contributors and the issue response time is usually within a few days. The documentation covers the happy path well but leaves some advanced configuration underspecified. The GitHub issues section is where you will find the actual answers to most problems. For most teams doing batch data cleaning with occasional failures, it is a solid choice. The tradeoff is that you have to think carefully about whether your processing logic is chunk-independent before you commit to it. If it is not, you will spend more time working around the framework than you save.