IKiChunk

Enterprise-portable Swiss-army-knife scripting toolkit for Data Engineers, Data Architects, Data Scientists, Software Engineers, and Systems Engineers.

One facade, zero required dependencies—tackle common data engineering tasks with minimal code.

from ikichunk import partition   # the only import most users need

parts = partition.smart_split("big.csv", goal="parallel")
results = partition.pmap(process, parts, workers=len(parts))

Every method documented below is real, production-tested code—not a spec. See IKiChunk-Codebase-v2.md for the full source and IKiChunk-Examples.md for worked examples against a 2,000,000-row dataset.

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Table of Contents

1. Installation
2. Quickstart
3. Core Concepts
4. Feature Reference
   • I/O
   • Inspect
   • Config & Secrets
   • Logging
   • Time
   • Retry
   • Parallel (pmap)
   • Partition & Chunk
   • Hash & Integrity
   • Compress
   • Archive
   • Platform & Portability
   • Process
   • Net
   • Watch
   • Template
   • Validate
5. Extensibility
6. CLI
7. Known Limitations
8. Project Structure

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Installation

pip install ikichunk

Requires Python ≥3.9. The core package has no required dependencies—everything below works out of the box on a bare Python install.

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Quickstart

from ikichunk import partition

# 1. Inspect unknown data — safe on config, files, DataFrames, lists, dicts
print(partition.inspect("data.csv"))

# 2. Safe, atomic write with a backup of the previous version
partition.write("out.json", {"status": "ok"}, backup=True)

# 3. Split a big file without loading it into memory
parts = partition.split_file("big.csv", by="rows", rows=100_000)

# 4. Or let it decide the split shape for you
parts = partition.smart_split("big.csv", goal="parallel")

# 5. Process each partition in parallel
results = partition.pmap(my_transform, parts, workers=len(parts))

# 6. Verify nothing got corrupted along the way
m = partition.manifest(parts)
ok = all(partition.verify(f["path"], f["hash"]) for f in m["files"])

---

Core Concepts

• The facade. partition is a ready-to-use singleton instance of the Partition class. Every feature is reachable as partition.<method>—you never import a submodule directly for normal use.
• No silent fallback. Ambiguous formats, unpicklable process-pool arguments, and shell-metacharacter strings raise clearly instead of silently guessing.
• Streaming-first. Anything that touches "big" data (stream, split_file, hash, download) is designed to avoid loading entire files into memory.
• Instantiable, not just a singleton. Need an isolated instance (e.g., for tests)?

from ikichunk import Partition

test_partition = Partition(log_level="DEBUG", env_prefix="TEST_")

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Feature Reference

I/O

partition.read(path, fmt=None, **kwargs) -> Any
partition.write(path, data, *, atomic=True, backup=False, fmt=None, **kwargs) -> str
partition.exists(path) -> bool
partition.ensure_dir(path) -> str
partition.list_files(path=".", pattern="*", recursive=False) -> list[str]
partition.ls(...)          # alias for list_files
partition.cat(path) -> str # alias for read(path, fmt="text")
partition.stream(path, fmt=None, chunk_size=None) -> Iterator[Any]

Supported formats: json, yaml (extra), csv, tsv, parquet (extra), excel (extra), feather (extra), avro (extra), pickle, text—auto-detected from the file extension or passed explicitly via fmt=.

parquet, excel, feather, and avro are read/written with polars (pl.read_parquet/write_parquet, etc.)—not pandas—so partition.read(...) on these formats returns a polars.DataFrame. Install the extra you need: pip install -e '.[parquet]' (polars) or pip install -e '.[excel]' (polars + fastexcel + xlsxwriter).

partition.write("report.json", {"rows": 42}, atomic=True, backup=True)
data = partition.read("report.json")

# Streaming: constant memory regardless of file size
for row in partition.stream("huge.csv"):
    process(row)

# Unknown extensions raise instead of silently guessing "text"
partition.read("data.xyz")
# → UnknownFormatError: Cannot determine format for 'data.xyz' ...

Extending formats at runtime:

from ikichunk.io.formats import FormatHandler

def read_upper(path, kwargs): return path.read_text().upper()
def write_upper(path, data, kwargs): path.write_text(str(data).upper())

partition.register_format("shout", FormatHandler("shout", read_upper, write_upper), extensions=[".shout"])
partition.write("out.shout", "hello")
partition.read("out.shout")  # "HELLO"

---

Inspect

partition.inspect(obj_or_path, sample=3, **kwargs) -> dict
partition.head(obj_or_path, n=5, **kwargs) -> Any

Works on file paths, lists, dicts, strings, and polars.DataFrame objects (if polars is installed—e.g. from reading parquet/excel/feather/avro). Automatically redacts values whose keys look like secrets (*_key, *_token, *_secret, *_password, etc.):

partition.inspect({"user": "alice", "api_key": "sk-live-..."})
# {'type': 'dict', 'keys': ['user', 'api_key'],
#  'sample': {'user': 'alice', 'api_key': '<redacted>'}}

sample= controls how many items are included—set it high enough (sample=len(obj)) to see all keys, not just the first few.

---

Config & Secrets

partition.config(*sources, secrets=None, env_prefix=None, **kwargs) -> dict
partition.env(key, default=None, cast=str) -> Any

Merge order (later values override earlier ones): files → secrets → environment variables.

cfg = partition.config("config.yaml", secrets=".env", env_prefix="APP_")
db_host = partition.env("DB_HOST", default="localhost")
port = partition.env("PORT", default=8080, cast=int)

Secrets loaded via secrets= are flagged for redaction whenever the config dict passes through inspect(). They are never cached to disk beyond the source file.

---

Logging

partition.log(name=None, level=None, **kwargs) -> logging.Logger

Zero-config, console-friendly, structured output.

log = partition.log("etl", level="DEBUG")
log.info("processed %d rows", 1000)

---

Time

partition.now(fmt="%Y-%m-%d %H:%M:%S") -> str
partition.timer(name="block")          # context manager
partition.duration(seconds) -> str     # human-readable

with partition.timer("load"):
    df = partition.read("big.csv")
# prints: [load] 3s

print(partition.duration(3725))  # "1h 2m 5s"

---

Retry

@partition.retry(tries=3, delay=1.0, backoff=2.0, exceptions=(Exception,))
def flaky_call(): ...

Exponential backoff decorator that composes cleanly with net.fetch for retry-on-failure HTTP calls:

@partition.retry(tries=3, delay=1.0, exceptions=(TimeoutError,))
def fetch_with_retry(url):
    return partition.fetch(url, timeout=5)

---

Parallel (pmap)

partition.pmap(func, items, *, workers=None, backend="thread"|"process",
                retries=0, progress=False, ordered=True) -> list

results = partition.pmap(transform, records, workers=8, retries=2, progress=True)

• backend="thread" (default)—no picklability constraint, best for I/O-bound work.
• backend="process"—for CPU-bound work. Both func and every item must be picklable (no lambdas or closures). A clear PartitionParallelError is raised at call time if they aren't, rather than a raw multiprocessing traceback.

def region_revenue(part_path):        # must be a top-level function for backend="process"
    ...

partition.pmap(region_revenue, parts, workers=4, backend="process")

---

Partition & Chunk (the namesake feature)

partition.split_file(path, *, by="size"|"rows"|"count", size=None, rows=None,
                      count=None, out_dir=None, fmt=None, prefix=None) -> list[str]
partition.smart_split(path, *, goal="parallel"|"memory-safe"|"storage",
                       workers=None, out_dir=None, fmt=None, explain=False) -> list[str]
partition.chunks(iterable, size) -> Iterator[list]
partition.manifest(paths, *, hash_algo="sha256") -> dict

Manual splitting—three strategies, all streaming (never load whole source files into memory):

partition.split_file("big.csv", by="size", size="256MB", out_dir="parts/")
partition.split_file("big.csv", by="rows", rows=100_000, out_dir="parts/")
partition.split_file("big.csv", by="count", count=8, out_dir="parts/")  # exact even split

by="size" is a raw byte-level split—fast and works on any file, but partitions may cut mid-row for row-based formats. by="rows"/by="count" are row-aware for csv/tsv and repeat the header in every partition by default.

Auto-strategy splitting—inspects the file and system, picks an optimal shape for you:

parts = partition.smart_split("big.csv", goal="parallel")       # count = CPU count
parts = partition.smart_split("big.csv", goal="memory-safe")    # size capped under available RAM
parts = partition.smart_split("big.csv", goal="storage")        # 100MB parts, object-storage sized

# See the reasoning, not just the result:
parts, why = partition.smart_split("big.csv", goal="parallel", explain=True)
print(why)  # {'goal': 'parallel', 'cpu_count': 8, 'chosen_strategy': {...}, 'partition_count': 8, ...}

Generic in-memory chunking—feeds pmap:

for batch in partition.chunks(records, size=500):
    partition.pmap(transform, batch, workers=8)

Manifests—an auditable record of a partition batch:

m = partition.manifest(parts)
# {'algo': 'sha256', 'files': [{'path': ..., 'size_bytes': ..., 'hash': ...}, ...]}
partition.write("manifest.json", m)

Extending goals at runtime:

def goal_tiny(file_size, info, workers):
    return {"by": "size", "size": 1024 * 1024}  # force 1MB partitions

partition.register_split_goal("tiny", goal_tiny)
partition.smart_split("big.csv", goal="tiny")

---

Hash & Integrity

partition.hash(path_or_bytes, algo="sha256") -> str
partition.verify(path, expected_hash, algo="sha256") -> bool

Streams the file in fixed-size blocks—no full-file memory load.

h = partition.hash("part1.csv")
assert partition.verify("part1.csv", h)   # True — unless the file changed

Typical use: verify a manifest() after copying or transferring partitions to another location.

manifest = partition.read("manifest.json")
for entry in manifest["files"]:
    if not partition.verify(entry["path"], entry["hash"]):
        raise RuntimeError(f"corrupted: {entry['path']}")

---

Compress

partition.compress(path, *, algo="gzip"|"zip"|"zstd", out=None, keep_original=True) -> str
partition.decompress(path, *, out=None) -> str

gzip and zip are in the standard library. algo="zstd" requires pip install ikichunk[zstd]—attempting it without the extra raises a MissingDependencyError with the exact install command.

gz_path = partition.compress("part1.csv", algo="gzip")
restored = partition.decompress(gz_path, out="restored.csv")

Extending codecs at runtime via partition.register_codec(name, codec) with a storage.codecs.Codec(name, default_ext, compress_fn, decompress_fn).

---

Archive

partition.archive(source, out_path, *, fmt="tar.gz"|"zip") -> str
partition.extract(archive_path, *, out_dir=None) -> str

Distinct from compress()—this bundles a directory (or multiple files) into a single archive, rather than shrinking one file.

partition.archive("parts/", "parts_batch.tar.gz")
partition.extract("parts_batch.tar.gz", out_dir="restored/")

extract() refuses to write outside out_dir (zip-slip guard)—this is a hard rule, not configurable.

---

Platform & Portability

partition.platform_info() -> dict
partition.which(cmd) -> str | None
partition.normalize_path(path) -> str

info = partition.platform_info()
# {'os': 'Linux', 'cpu_count': 8, 'available_memory_bytes': ..., 'ikichunk_version': '0.2.0', ...}

if partition.which("docker") is None:
    raise RuntimeError("docker not found on PATH")

---

Process

partition.is_running(pid) -> bool
partition.kill(pid, *, timeout=5, force=False) -> bool
partition.wait_for_port(host, port, *, timeout=30, interval=0.5) -> bool
partition.is_port_open(host, port, *, timeout=1) -> bool

Checks and signals processes—not a supervisor. It never restarts or daemonizes.

partition.run(["systemctl", "restart", "myapp"])
if partition.wait_for_port("localhost", 8080, timeout=30):
    log.info("service is up")

kill() sends SIGTERM first, polls up to timeout seconds, and only sends SIGKILL if force=True and the process is still alive.

---

Net

partition.fetch(url, *, timeout=10, headers=None) -> bytes | str
partition.download(url, path, *, timeout=30, progress=False) -> str
partition.reachable(host, port=None, *, timeout=2) -> bool

Built on stdlib urllib—not a requests replacement (no sessions, auth flows, or pagination). Covers the "I just need to fetch one thing" case.

if partition.reachable("api.example.com", 443):
    data = partition.fetch("https://api.example.com/status")

partition.download("https://example.com/dataset.csv", "dataset.csv", progress=True)

download() streams directly to a temp file and atomically renames into place—a failed download never leaves a corrupted partial file.

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Watch

partition.watch(path, *, on_change=None, interval=1.0, recursive=False) -> WatchHandle
partition.watch_once(path, *, since=None) -> bool

Poll-based (mtime + size), not OS-native filesystem events—portable across every OS with zero dependencies.

handle = partition.watch("config/", on_change=lambda p: reload_config(), interval=1.0)
# ... later
handle.stop()

---

Template

partition.render(template, variables, *, out=None, strict=True) -> str

Variable substitution only (stdlib string.Template, $var syntax)—not a templating engine. No loops or conditionals.

partition.render("app.env.tmpl", {"DB_HOST": partition.env("DB_HOST")}, out="app.env")

strict=True (default) raises on a missing variable instead of silently leaving it unrendered. Pass strict=False to allow partial renders.

---

Validate

partition.require(condition, msg="Requirement failed")
partition.not_none(value, name="value") -> value

partition.require(len(records) > 0, "no records loaded")
config_val = partition.not_none(cfg.get("api_key"), name="api_key")

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Extensibility

Every registry-backed feature above (register_format, register_codec, register_split_goal, register_inspector) adds new capabilities without editing the library source. The facade has one more general mechanism:

def double(x):
    return x * 2

partition.register("double", double)
partition.double(21)  # 42

# Refuses to silently clobber an existing method:
partition.register("read", lambda x: x)
# ValueError: 'read' is already a facade method/attribute

Independent instances for testing or multi-configuration use:

from ikichunk import Partition

worker_partition = Partition(log_level="DEBUG", env_prefix="WORKER_")

Pip-installable plugins register automatically via an ikichunk.plugins setuptools entry point, discovered at import time—no manual register() call needed for third-party packages.

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CLI

ikichunk inspect data.csv
ikichunk ls . -r -p "*.json"
ikichunk split big.csv --by size --size 256MB --out-dir parts/
ikichunk smart-split big.csv --goal parallel --out-dir parts/
ikichunk wait-for-port localhost 8080 --timeout 30
ikichunk archive parts/ parts.tar.gz
ikichunk platform
ikichunk version

Every subcommand maps 1:1 to a facade method—the CLI carries no logic the Python API doesn't also expose. Run ikichunk --help for the full list of available subcommands.

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Known Limitations

• split_file(by="count") on row-based formats (CSV/TSV) does two streaming passes—at most one partition's worth of rows is held in memory at a time, not the whole file. by="rows"/by="size" are single-pass throughout.
• net and template are deliberately minimal—reach for requests/httpx or Jinja2 if you need sessions, auth flows, or templating logic.
• watch() is poll-based, not OS-native events—suitable for config-reload use cases, not designed for high-frequency, low-latency file watching at scale.
• smart_split(goal="memory-safe") uses a heuristic safety margin (10% of available RAM, 64MB floor), not a guaranteed bound—tune it via register_split_goal if your workload needs stricter guarantees.

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Project Structure

ikichunk/
├── pyproject.toml
└── src/
    └── ikichunk/
        ├── __init__.py         # exports `partition` (singleton) and `Partition` (class)
        ├── facade.py           # Partition class — the single public entry point
        ├── exceptions.py       # centralized custom exceptions
        ├── io/                 # read/write/stream — Strategy-pattern format registry
        ├── inspection/         # inspect/head — type-dispatch registry
        ├── configuration/      # config/env
        ├── observability/      # log/now/timer/duration
        ├── resilience/         # retry
        ├── concurrency/        # pmap
        ├── partitioning/       # split_file/smart_split/chunks/manifest — the namesake package
        ├── integrity/          # hash/verify
        ├── storage/            # compress/decompress/archive/extract — Strategy-pattern codecs
        ├── system/             # platform_info/process/shell
        ├── net/                # fetch/download/reachable
        ├── automation/         # watch/render
        ├── validation/         # require/not_none
        ├── plugins/            # entry-point plugin discovery
        └── cli/                # Command-pattern CLI, thin wrapper over the facade

See IKiChunk-Blueprint.md for design rationale, IKiChunk-Codebase-v2.md for the full source, and IKiChunk-Examples.md for executed examples with output captured against a real 2,000,000-row dataset.