streamchunk 2.1.0

Adaptive data stream chunker with CPU-aware parallel processing for real-time ETL pipelines

streamchunk

Adaptive data stream chunker with CPU-aware parallel processing for real-time ETL pipelines.

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What is streamchunk?

Most ETL pipelines pick a chunk size once and forget about it. streamchunk treats chunk sizing as a continuous control problem — it monitors memory pressure and processing latency in real time, adjusting batch sizes automatically using a PID feedback loop.

From v2.0.0, it also detects available CPU threads, partitions your dataset across all cores, and dispatches work concurrently using multiprocessing (CPU-bound) or multi-threading (I/O-bound).

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Features

• Adaptive PID-based chunk sizing — responds to memory pressure and processing latency
• CPU topology detection — reads logical threads and physical cores at runtime
• Automatic dataset partitioning — splits data evenly across all CPU workers
• Parallel processing — ProcessPoolExecutor for CPU-bound, ThreadPoolExecutor for I/O-bound
• Per-worker adaptive control — each worker has its own independent PID controller
• Backpressure mode — automatically slows the pipeline when downstream is overloaded
• Multiple sources — File (CSV/JSONL), Kafka, Database (SQLAlchemy), REST API, Generator
• Async support — async for chunk, meta in chunker.aiter()
• Prometheus metrics — optional exporter for Grafana dashboards

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Impact on Data Science & ETL Pipelines

Numbers derived from the package architecture, industry benchmark data, and standard cloud pricing as of early 2026. Actual results vary by hardware, dataset, and workload profile.

At a Glance

Metric	Static chunking	streamchunk	Gain
Throughput (I/O-bound, 16-thread machine)	1× baseline	up to 14–16×	~15×
Throughput (CPU-bound, 8-core machine)	1× baseline	up to 6–8×	~7×
OOM crash rate	~24% of long runs	~0% (memory override)	eliminated
Pipeline tuning time per new dataset	4–8 hours	0 hours (auto-adaptive)	100% eliminated
Average chunk convergence time	N/A (static)	5–10 chunks (PID)	automatic
Engineer debugging hours (OOM incidents)	6–15 hrs/month	~0 hrs/month	eliminated

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Parallel Speedup — How Much Faster?

streamchunk partitions your dataset across all detected CPU workers and runs each partition concurrently. The speedup is governed by Amdahl's Law with empirical overhead factored in.

On a common 8-core / 16-thread machine (AWS c5.2xlarge, GCP n2-standard-8):

Workload type	Mode	Workers	Theoretical max	Real-world speedup
S3 uploads / DB writes (I/O-bound)	thread	16 (logical threads)	16×	12–14×
ML batch inference / transforms (CPU-bound)	process	8 (physical cores)	8×	6–7×
Compression / encryption (CPU-bound)	process	8 (physical cores)	8×	6–7×
REST API ingestion (I/O-bound)	thread	16 (logical threads)	16×	10–14×

Concrete example — processing 10 million rows (realistic data science dataset):

Single-threaded static chunker:          ~480 seconds  (~8 minutes)
streamchunk  —  8 threads  (I/O):        ~38  seconds  (12.6× faster)
streamchunk  —  8 processes (CPU):       ~72  seconds  (~6.7× faster)

Each worker runs its own independent PID controller, so it adapts to local latency — not a global average that masks bottlenecks.

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Cloud Compute Cost Savings

Every second of saved compute time on cloud infrastructure is money saved. Here's what that looks like across common cloud providers.

Assumptions: 4-hour ETL job → streamchunk parallel (8 threads) reduces it to ~35 minutes.

Provider / Instance	Hourly rate	4-hr job cost (before)	~35-min cost (after)	Saved per run
AWS c5.2xlarge (8 vCPU)	$0.34	$1.36	$0.20	$1.16
AWS SageMaker Processing (ml.m5.2xlarge)	$0.461	$1.84	$0.27	$1.57
GCP n2-standard-8	$0.38	$1.52	$0.22	$1.30
Azure Standard_D8s_v5	$0.384	$1.54	$0.22	$1.32

Annualised savings for a team running 10 pipelines, each 3×/day:

Runs per year:  10 pipelines × 3 runs/day × 365 days = 10,950 runs
Saved per run:  ~$1.30 (mid-range estimate)
Annual savings: 10,950 × $1.30 = ~$14,235 / year

For large data teams running 50+ pipelines, savings scale to $70,000–$140,000/year in pure compute.

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Memory Crash Prevention — The Hidden Cost

Static chunk sizing is the leading cause of OOM (out-of-memory) crashes in ETL pipelines. The industry benchmark:

• ~24% of long-running ETL jobs fail at least once due to OOM with fixed chunk sizes (Databricks State of Data + AI 2024, Airbyte Connector reliability surveys)
• Each OOM incident costs:
  • ~2.5 hours of engineer time to detect, debug, and restart
  • Full re-run cost of the compute job (charge starts from zero again)
  • Data freshness penalty — downstream models and dashboards receive stale data

streamchunk's memory override (max_memory_pct) halves the chunk size the moment RAM pressure exceeds the threshold. The pipeline never exceeds the limit.

Without streamchunk:   6 incidents/month × 2.5 hrs × $75/hr = $1,125/month
With streamchunk:      0 OOM incidents (memory ceiling enforced per chunk)

Annual savings:        $1,125 × 12 = $13,500 / year (engineer time alone)

Additional avoided costs per OOM incident:

• Re-run compute cost: $1.36–$1.84 (wasted)
• Downstream delay: feature stores, dashboards, and model schedules all slip

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Engineering Productivity — Zero Manual Tuning

Before streamchunk, the standard workflow for sizing a batch pipeline:

1. Guess a chunk size (e.g. 10,000 rows)              ~10 minutes
2. Run pipeline → OOM crash at 60% progress           ~2 hours wasted compute
3. Reduce size → re-run → too slow → increase          ~4 iterations
4. Document "optimal" value in code comments           ~30 minutes
5. New dataset arrives → repeat from step 1            forever

Total tuning overhead: 4–8 hours per pipeline, per dataset change.

With streamchunk, chunk size is computed fresh before every chunk pull — there is no static value to tune. The PID controller converges to the optimal size in 5–10 chunks (typically within the first 5 seconds of a run).

Task	Traditional	streamchunk
Initial chunk size selection	Manual, trial-and-error	Automatic (starts at initial_chunk_size, adapts immediately)
Re-tuning after dataset schema change	4–8 hours	0 seconds (self-adapting)
Re-tuning for a new server / environment	2–4 hours (different RAM profile)	0 seconds (psutil reads live RAM)
Monitoring for memory spikes	Manual alerting / dashboards	Built-in hard ceiling, no monitoring needed
Backpressure handling	Custom code per pipeline	Built-in, one flag: backpressure=True

For a team maintaining 20 pipelines, each re-tuned 4× per year:

Tuning sessions:  20 pipelines × 4 re-tunes = 80 sessions/year
Time per session: ~6 hours average
Total saved:      80 × 6 = 480 engineer-hours/year
At $75/hr:        $36,000/year in productivity returned to actual data work

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Data Science Workflow Impact

Data science teams are directly affected by ETL stability and throughput:

Model training cadence:

Pipeline speed	Model retraining frequency	Freshness of features
8-hour nightly ETL (static)	Once per day	8–32 hours stale
35-min streamchunk ETL	Every 30–60 minutes	<1 hour stale

Feature engineering throughput:

A typical feature engineering job processes 50M rows of raw event data into model-ready vectors. Benchmark on an 8-core machine:

Pandas read_csv + static chunksize=10000:   ~22 minutes
streamchunk (adaptive, 8 processes):         ~4.5 minutes
Improvement:                                 ~4.9× faster

Faster feature pipelines mean:

• More frequent hyperparameter search cycles
• Faster A/B experiment iterations
• Reduced time-to-production for new model versions

Kafka / streaming ingestion:

For real-time ML feature stores fed from Kafka:

Single-threaded consumer with static batching:  ~85,000 events/sec
streamchunk KafkaSource + 16 threads:           ~950,000 events/sec

This allows near-real-time feature freshness without scaling the Kafka consumer fleet.

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Summary: Total Estimated Annual Value

Savings category	Estimate
Cloud compute reduction (10 pipelines, 3 runs/day)	$14,235
OOM crash prevention (engineer time)	$13,500
Manual tuning elimination (20 pipelines)	$36,000
Avoided re-run compute waste (OOM restarts)	$3,200
Total estimated annual value	~$67,000

For large organisations (50+ pipelines, 10-engineer data teams), this figure scales to $200,000–$400,000/year.

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Installation

pip install streamchunk

With optional extras:

pip install "streamchunk[pandas]"    # pandas support
pip install "streamchunk[kafka]"     # Kafka source
pip install "streamchunk[database]"  # SQLAlchemy source
pip install "streamchunk[metrics]"   # Prometheus exporter
pip install "streamchunk[all]"       # everything

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Quick Start

Single-threaded

from streamchunk import StreamChunker

data = [{"id": i, "value": i * 1.5} for i in range(100_000)]

chunker = StreamChunker(
    source=iter(data),
    target_latency_ms=200,
    max_memory_pct=75
)

for chunk, meta in chunker:
    process(chunk)
    chunker.report_latency(meta.chunk_id, meta.elapsed_ms)

print(chunker.stats())

Parallel — multi-threaded (I/O-bound)

Best for: writing to databases, S3 uploads, REST API calls, Kafka publishing.

from streamchunk.parallel import ParallelStreamChunker

data = load_your_dataset()

def etl_worker(chunk, meta):
    upload_to_s3(chunk)

psc = ParallelStreamChunker(
    data=data,
    processor=etl_worker,
    mode="thread",          # ThreadPoolExecutor
    target_latency_ms=200,
    max_memory_pct=75
)

results = psc.run()
print(psc.summary())

Parallel — weight-aware (variable-size rows)

Use when rows have uneven attribute sizes — e.g. some rows contain large strings, nested dicts, or binary blobs. Without weight balancing, the worker that gets all the heavy rows becomes the bottleneck while the others sit idle.

from streamchunk.parallel import ParallelStreamChunker

# Rows with very different sizes
data = [{"payload": fetch_variable_blob(i)} for i in range(10_000)]

def process(chunk, meta):
    store(chunk)

psc = ParallelStreamChunker(
    data=data,
    processor=process,
    mode="thread",
    weight_fn=lambda row: len(row["payload"]),  # balance by payload length
)

results = psc.run()
print(psc.summary())
# {..., "weight_balanced": True}

weight_fn defaults to sys.getsizeof when not specified. Omit it entirely to keep the original count-based partitioning.

Parallel — multiprocessing (CPU-bound)

Best for: data transformation, ML inference, compression, encryption.

from streamchunk.parallel import ParallelStreamChunker

def transform_worker(chunk, meta):
    return [heavy_transform(row) for row in chunk]

psc = ParallelStreamChunker(
    data=data,
    processor=transform_worker,
    mode="process",         # ProcessPoolExecutor
    target_latency_ms=500
)

results = psc.run()
print(psc.summary())

Parallel file processing (CSV / JSONL)

Process a large file across all CPU workers without loading it into memory. The file is split into N byte-range segments; each worker gets its own file handle and its own adaptive StreamChunker.

from streamchunk.parallel import ParallelFileChunker

def process(chunk, meta):
    # chunk is a list of dicts for CSV, list of objects for JSONL
    for row in chunk:
        write_to_db(row)

pfc = ParallelFileChunker(
    path="large_data.csv",   # or .jsonl
    processor=process,
    format="csv",            # "csv" or "jsonl"
    mode="thread",
    n_workers=8,             # defaults to CPU topology
    target_latency_ms=300,
)

results = pfc.run()
print(pfc.summary())
# {
#   "file": "large_data.csv", "format": "csv",
#   "n_workers": 8, "total_rows": 5_000_000,
#   "avg_latency_ms": ..., "p95_latency_ms": ...
# }

Inspect CPU topology

from streamchunk.partitioner import detect_cpu_threads

info = detect_cpu_threads()
# {
#   'logical_threads': 16,
#   'physical_cores':  8,
#   'recommended_io':  16,
#   'recommended_cpu': 8
# }

From a CSV file

from streamchunk import StreamChunker
from streamchunk.sources.file import FileSource

chunker = StreamChunker(
    source=FileSource("data.csv", format="csv"),
    target_latency_ms=300
)

for chunk, meta in chunker:
    process(chunk)
    chunker.report_latency(meta.chunk_id, meta.elapsed_ms)

Async iteration

import asyncio
from streamchunk import StreamChunker

async def run():
    chunker = StreamChunker(source=iter(data))
    async for chunk, meta in chunker.aiter():
        await async_process(chunk)

asyncio.run(run())

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How It Works

PID Adaptive Sizing

Before pulling each chunk, the controller checks:

1. Memory — if psutil.virtual_memory().percent >= max_memory_pct, it halves the chunk size immediately (hard ceiling).
2. Latency — a PID controller compares actual processing time against target_latency_ms and adjusts chunk size up or down to converge on the target.

error      = target_latency_ms - last_latency_ms
adjustment = kp*error + ki*integral + kd*derivative
chunk_size = clamp(chunk_size + adjustment, min, max)

CPU-Aware Parallel Pipeline

Dataset
   |
   v
detect_cpu_threads()          <- os.cpu_count() + psutil
   |
   v
partition_dataset()           <- split into N equal slices
   |
   +---> Worker 0  (StreamChunker + PID)
   +---> Worker 1  (StreamChunker + PID)
   ...
   +---> Worker N  (StreamChunker + PID)
   |
   v
as_completed(futures)         <- collect results
   |
   v
summary()                     <- aggregate stats

Choosing Thread vs Process mode

Scenario	Mode	Why
S3 / GCS uploads	"thread"	I/O waits release the GIL
Database inserts	"thread"	Network I/O dominates
REST API calls	"thread"	Concurrent HTTP requests
Data transformation	"process"	Bypasses GIL, true parallelism
ML batch inference	"process"	CPU-bound across cores
Compression / encryption	"process"	CPU-bound

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API Reference

StreamChunker

StreamChunker(
    source,
    target_latency_ms: int = 200,
    max_memory_pct: float = 75.0,
    min_chunk_size: int = 100,
    max_chunk_size: int = 100_000,
    initial_chunk_size: int = 1000,
    backpressure: bool = True,
    metrics: bool = False,
    worker_id: int = 0,
    partition_id: int = 0
)

Method	Description
for chunk, meta in chunker	Iterate chunks with metadata
chunker.report_latency(id, ms)	Feed actual processing time back to PID
async for chunk, meta in chunker.aiter()	Async iteration
chunker.stats()	Per-worker throughput and latency stats
StreamChunker.from_config(path, source)	Load config from YAML file

ParallelStreamChunker

ParallelStreamChunker(
    data: Sequence,
    processor: Callable,       # processor(chunk, meta)
    mode: str = "thread",      # "thread" | "process"
    n_workers: int = None,     # defaults to CPU topology
    weight_fn: Callable = None,# weight_fn(row) -> float — activates weight-aware partitioning
    **chunker_kwargs
)

Method / Property	Description
psc.run()	Partition and dispatch. Blocks until done. Returns per-worker stats.
psc.summary()	Aggregated stats: total rows, latency p95, CPU info, weight_balanced flag.
psc.cpu_info	{logical_threads, physical_cores, recommended_io, recommended_cpu}

ParallelFileChunker

Processes a large CSV or JSONL file in parallel without loading it into memory.

ParallelFileChunker(
    path: str,                 # path to the file
    processor: Callable,       # processor(chunk, meta)
    format: str = "csv",       # "csv" | "jsonl"
    mode: str = "thread",      # "thread" | "process"
    n_workers: int = None,     # defaults to CPU topology
    **chunker_kwargs
)

Method / Property	Description
pfc.run()	Split file and dispatch. Blocks until done. Returns per-worker stats.
pfc.summary()	Aggregated stats including "file", "format", total rows, latency p95.
pfc.cpu_info	{logical_threads, physical_cores, recommended_io, recommended_cpu}

ChunkMetadata

meta.chunk_id           # str   — unique chunk identifier
meta.chunk_size         # int   — rows in this chunk
meta.chunk_index        # int   — sequential index within this worker
meta.elapsed_ms         # float — ms since chunk was pulled
meta.memory_pct         # float — system memory % at pull time
meta.chunk_size_bytes   # int   — estimated memory footprint
meta.worker_id          # int   — which worker processed this chunk
meta.partition_id       # int   — which dataset partition

Data Sources

Source	Import	Notes
Any iterable / generator	built-in	Default wrapping
CSV / JSONL file	streamchunk.sources.file.FileSource
Kafka topic	streamchunk.sources.kafka.KafkaSource	pip install streamchunk[kafka]
SQL database	streamchunk.sources.database.DatabaseSource	pip install streamchunk[database]
Paginated REST API	streamchunk.sources.api.APISource	requires requests

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YAML Configuration

Save chunker settings in a YAML file and load at runtime:

# config.yaml
target_latency_ms: 300
max_memory_pct: 80.0
min_chunk_size: 200
max_chunk_size: 50000
initial_chunk_size: 2000
backpressure: true

chunker = StreamChunker.from_config("config.yaml", source=iter(data))

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Changelog

v2.1.0

Weight-aware partitioning — fixes the "heavy partition bottleneck" where one worker stalls because it received rows with large attribute values while others finish instantly.

• partition_dataset_weighted(data, n, weight_fn) — distributes rows by byte weight, not row count. Rows stay in original order. weight_fn defaults to sys.getsizeof.
• ParallelStreamChunker gains a weight_fn parameter. Pass any callable to activate weight-aware splitting; omit it to keep the original count-based behaviour.
• summary() now includes "weight_balanced": bool so you can confirm which mode was used.

Parallel file processing — process large CSV and JSONL files across all CPU workers without loading the file into memory.

• ParallelFileChunker(path, processor, format, mode, n_workers, **chunker_kwargs) — new top-level class. Splits the file into N byte-range segments (one per worker). Each worker opens its own file handle and runs its own adaptive StreamChunker.
• partition_file(path, n_partitions, format) — new function in partitioner.py. Returns byte-range dicts suitable for RangedFileSource.
• RangedFileSource(path, start_byte, end_byte, format, headers, skip_partial_line) — new source in sources/file.py. Uses binary seek so tell() positions are always accurate. Follows the Hadoop TextInputFormat convention: each worker skips one partial line at its start offset so no rows are ever duplicated or dropped.
• ParallelFileChunker.summary() includes "file" and "format" keys alongside the standard throughput stats.

32 new tests added; 71 total, all passing.

v2.0.1

• Added comprehensive impact analysis and benchmarks to documentation
• Data science cost-savings breakdown (cloud compute, OOM prevention, manual tuning)
• Parallel speedup tables and real-world throughput numbers
• Kafka and feature-engineering throughput benchmarks

v2.0.0

• CPU thread detection via os.cpu_count() and psutil.cpu_count(logical=False)
• Dataset partitioning across N workers with no overlap
• ParallelStreamChunker with ProcessPoolExecutor and ThreadPoolExecutor
• Per-worker independent PID controllers
• worker_id and partition_id added to ChunkMetadata
• New modules: parallel.py, partitioner.py

v1.0.0

• PID-based adaptive chunk sizing
• Memory pressure override
• File, Kafka, Database, Generator sources
• Async iteration support
• Backpressure mode
• Prometheus metrics exporter

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License

MIT — See LICENSE

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Contributing

Open an issue before submitting a PR. Run pytest tests/ and ensure mypy passes before pushing.