diff --git a/.gitignore b/.gitignore index b11ca5f..b8ea7f8 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ htmlcov/ .mypy_cache/ *.egg-info/ *.pyc +output.avro *~ .#* \#* diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..68ea7bc --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,27 @@ +{ + "cSpell.words": [ + "addopts", + "asctime", + "Bobrov", + "cacheprovider", + "duckdb", + "envlist", + "fastavro", + "isort", + "iterchunks", + "Kirill", + "levelname", + "millis", + "mypy", + "nans", + "ndarray", + "numpy", + "pyarrow", + "pytest", + "rtype", + "setuptools", + "skipsdist", + "subclassing", + "testpaths" + ] +} \ No newline at end of file diff --git a/CODEOWNERS b/CODEOWNERS index 8d960c4..9e3700a 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1 +1,2 @@ * @luminousmen +* @crjaensch \ No newline at end of file diff --git a/LICENSE b/LICENSE index 100eac0..1ed7794 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 Kirill Bobrov +Copyright (c) 2023-2025 Kirill Bobrov, Christian R. Jaensch Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 8f246a6..bfa4e38 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,8 @@ -[![Master](https://github.com/luminousmen/data-toolset/actions/workflows/master.yml/badge.svg?branch=master)](https://github.com/luminousmen/data-toolset/actions/workflows/master.yml) -[![codecov](https://codecov.io/gh/luminousmen/data-toolset/branch/master/graph/badge.svg?token=6V9IPSRCB0)](https://codecov.io/gh/luminousmen/data-toolset) +[![Master](https://github.com/luminousmen/data-toolset/actions/workflows/master.yml/badge.svg?branch=code-improvement)](https://github.com/luminousmen/data-toolset/actions/workflows/master.yml) +[![codecov](https://codecov.io/gh/luminousmen/data-toolset/branch/code-improvement/graph/badge.svg?token=6V9IPSRCB0)](https://codecov.io/gh/luminousmen/data-toolset) # data-tools(et) @@ -11,24 +11,22 @@ data-toolset is designed to simplify your data processing tasks by providing a m ## Installation -Python 3.8, Python 3.9 and 3.10 are supported and tested (to some extent). +Python 3.10, Python 3.12 are supported and tested (to some extent). ```bash -python -m pip install data-toolset +pip install data-toolset ``` -## Legacy - -Do you want polars to run on an old CPU (e.g. dating from before 2011), or on an x86-64 build of Python on Apple Silicon under Rosetta? Install `pip install polars-lts-cpu`. This version of polars is compiled without AVX target features. +> **Note:** The legacy `data-toolset` entrypoint (Argparse-based) is deprecated. Please use the new Typer-based `data-toolset-cli` command. ## Usage ```bash -$ data-toolset -h -usage: data-toolset [-h] {head,tail,meta,schema,stats,query,validate,merge,count,to_json,to_csv,to_avro,to_parquet,random_sample} ... +$ data-toolset-cli -h +usage: data-toolset-cli [-h] {head,tail,meta,schema,stats,query,validate,merge,count,to-json,to-csv,to-avro,to-parquet,random-sample} ... positional arguments: - {head,tail,meta,schema,stats,query,validate,merge,count,to_json,to_csv,to_avro,to_parquet,random_sample} + {head,tail,meta,schema,stats,query,validate,merge,count,to-json,to-csv,to-avro,to-parquet,random-sample} commands head Print the first N records from a file tail Print the last N records from a file @@ -39,11 +37,11 @@ positional arguments: validate Validate a file merge Merge multiple files into one count Count the number of records in a file - to_json Convert a file to JSON format - to_csv Convert a file to CSV format - to_avro Convert a file to Avro format - to_parquet Convert a file to Parquet format - random_sample Randomly sample records from a file + to-json Convert a file to JSON format + to-csv Convert a file to CSV format + to-avro Convert a file to Avro format + to-parquet Convert a file to Parquet format + random-sample Randomly sample records from a file ``` ## Examples @@ -51,7 +49,7 @@ positional arguments: Print the first 10 records of a Parquet file: ```bash -$ data-toolset head my_data.parquet -n 10 +$ data-toolset-cli head my_data.parquet -n 10 shape: (1, 7) ┌───────────┬─────┬──────────┬────────┬──────────────────────────┬────────────────────────────┬──────────────────┐ │ character ┆ age ┆ is_human ┆ height ┆ quote ┆ friends ┆ appearance │ @@ -65,7 +63,7 @@ shape: (1, 7) Query a Parquet file using a SQL-like expression: ```bash -$ data-toolset query my_data.parquet "SELECT * FROM 'my_data.parquet' WHERE height > 165" +$ data-toolset-cli query my_data.parquet "SELECT * FROM 'my_data.parquet' WHERE height > 165" shape: (2, 7) ┌─────────────────┬─────┬──────────┬────────┬───────────────────────┬────────────────────────────────────┬───────────────────┐ │ character ┆ age ┆ is_human ┆ height ┆ quote ┆ friends ┆ appearance │ @@ -80,7 +78,7 @@ shape: (2, 7) Get basic data statistics: ```bash -$ data-toolset stats my_data.avro +$ data-toolset-cli stats my_data.avro shape: (9, 8) ┌────────────┬─────────────────┬───────────┬──────────┬────────────┬──────────────────────────┬─────────┬────────────┐ │ describe ┆ character ┆ age ┆ is_human ┆ height ┆ quote ┆ friends ┆ appearance │ @@ -102,19 +100,19 @@ shape: (9, 8) Merge multiple Avro files into one: ```bash -$ data-toolset merge file1.avro file2.avro file3.avro merged_file.avro +$ data-toolset-cli merge file1.avro file2.avro file3.avro merged_file.avro ``` Convert Avro file into Parquet: ```bash -$ data-toolset to_parquet my_data.avro output.parquet +$ data-toolset-cli to-parquet my_data.avro output.parquet ``` Convert Parquet file into JSON: ```bash -$ data-toolset to_json my_data.parquet output.json +$ data-toolset-cli to-json my_data.parquet output.json ``` ## Contributing diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..62a9bb3 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,743 @@ +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. + +[[package]] +name = "click" +version = "8.1.8" +description = "Composable command line interface toolkit" +optional = false +python-versions = ">=3.7" +groups = ["main"] +files = [ + {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"}, + {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[[package]] +name = "colorama" +version = "0.4.6" +description = "Cross-platform colored terminal text." +optional = false 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Jaensch <@crjaensch>"] readme = "README.md" packages = [{include = "data_toolset", from = "src"}] homepage = "https://github.com/luminousmen/data-toolset" @@ -10,15 +10,14 @@ repository = "https://github.com/luminousmen/data-toolset" license = "MIT" [tool.poetry.dependencies] -python = "^3.8.1" -fastavro = "^1.7.3" -duckdb = ">=0.8.1,<0.10.0" -arrow = "^1.2.3" -cython = "^3.0.2" -pyarrow = ">=13,<15" -python-snappy = "^0.6.1" -tox = "^4.11.3" -polars = ">=0.19.11,<0.21.0" +python = ">=3.12,<4.0" +fastavro = ">=1.10.0,<2.0.0" +duckdb = ">=0.8.1,<2.0.0" +pyarrow = ">=8.0.0,<15.0.0" +python-snappy = "^0.7.0" +numpy = "^1.25.2" +polars = ">=0.18.0,<1.0.0" +typer = ">=0.10.0,<0.12.0" [tool.poetry.group.lint.dependencies] isort = "^5.10.1" @@ -57,7 +56,7 @@ disallow_any_explicit = true disallow_any_generics = false disallow_subclassing_any = true # platform -python_version = "3.9" +python_version = "3.12" # untyped disallow_untyped_calls = true disallow_untyped_defs = true @@ -73,6 +72,7 @@ pretty = true [tool.poetry.scripts] data-toolset = "data_toolset.main:main" +data-toolset-cli = "data_toolset.cli:app" [build-system] requires = ["setuptools", "poetry_core>=1.0"] diff --git a/src/data_toolset/cli.py b/src/data_toolset/cli.py new file mode 100644 index 0000000..2a630ca --- /dev/null +++ b/src/data_toolset/cli.py @@ -0,0 +1,121 @@ +import logging +from pathlib import Path +import importlib.metadata + +import typer + +# Register format handlers +import data_toolset.utils.avro # registers AvroUtils +import data_toolset.utils.parquet # registers ParquetUtils +from data_toolset.utils.registry import FORMAT_REGISTRY + +DEFAULT_RECORDS = 20 + +app = typer.Typer() + +@app.callback(invoke_without_command=True) +def main_callback( + ctx: typer.Context, + verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable info logging"), + debug: bool = typer.Option(False, "--debug", "-d", help="Enable debug logging"), + version: bool = typer.Option(False, "--version", help="Show version and exit"), +): + if version: + version_str = importlib.metadata.version("data-toolset") + typer.echo(version_str) + raise typer.Exit() + level = logging.WARNING + if debug: + level = logging.DEBUG + elif verbose: + level = logging.INFO + logging.basicConfig(level=level) + +@app.command() +def head(file_path: Path = typer.Argument(..., help="Path to a file"), + n: int = typer.Option(DEFAULT_RECORDS, "-n", "--number", help="Number of records")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].head(file_path, n) + +@app.command() +def tail(file_path: Path = typer.Argument(..., help="Path to a file"), + n: int = typer.Option(DEFAULT_RECORDS, "-n", "--number", help="Number of records")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].tail(file_path, n) + +@app.command() +def meta(file_path: Path = typer.Argument(..., help="Path to a file")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].meta(file_path) + +@app.command() +def schema(file_path: Path = typer.Argument(..., help="Path to a file")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].schema(file_path) + +@app.command() +def stats(file_path: Path = typer.Argument(..., help="Path to a file")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].stats(file_path) + +@app.command() +def query(file_path: Path = typer.Argument(..., help="Path to a file"), + query_expression: str = typer.Argument(..., help="Query expression")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].query(file_path, query_expression) + +@app.command() +def validate(file_path: Path = typer.Argument(..., help="Path to a file"), + schema_path: Path = typer.Option(None, "--schema-path", help="Path to schema file")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].validate(file_path, schema_path) + +@app.command() +def merge(files: list[Path] = typer.Argument(..., help="Files to merge"), + output_path: Path = typer.Argument(..., help="Output file path")): + fmt = files[0].suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].merge(files, output_path) + +@app.command() +def count(file_path: Path = typer.Argument(..., help="Path to a file")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].count(file_path) + +@app.command() +def to_json(file_path: Path = typer.Argument(...), + output_path: Path = typer.Argument(...), + pretty: bool = typer.Option(False, "--pretty", help="Pretty print JSON")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].to_json(file_path, output_path, pretty) + +@app.command() +def to_csv(file_path: Path = typer.Argument(...), + output_path: Path = typer.Argument(...), + has_header: bool = typer.Option(True, "--has-header", help="CSV has header"), + delimiter: str = typer.Option(",", help="Delimiter"), + line_terminator: str = typer.Option("\n", help="Line terminator"), + quote: str = typer.Option('"', help="Quote char")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].to_csv(file_path, output_path, has_header, delimiter, line_terminator, quote) + +@app.command() +def to_avro(file_path: Path = typer.Argument(...), + output_path: Path = typer.Argument(...), + compression: str = typer.Option("uncompressed", "--compression", help="Compression method")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].to_avro(file_path, output_path, compression) + +@app.command() +def to_parquet(file_path: Path = typer.Argument(...), + output_path: Path = typer.Argument(...), + compression: str = typer.Option("uncompressed", "--compression", help="Compression method")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].to_parquet(file_path, output_path, compression) + +@app.command() +def random_sample(file_path: Path = typer.Argument(...), + output_path: Path = typer.Argument(...), + n: int = typer.Option(None, "--n", help="Number of records"), + fraction: float = typer.Option(None, "--fraction", help="Fraction of records")): + fmt = file_path.suffix.lower().strip('.') + FORMAT_REGISTRY[fmt].random_sample(file_path, output_path, n, fraction) diff --git a/src/data_toolset/main.py b/src/data_toolset/main.py index ccb2417..68fb07f 100644 --- a/src/data_toolset/main.py +++ b/src/data_toolset/main.py @@ -1,3 +1,5 @@ +# DEPRECATED: src/data_toolset/main.py is obsolete; use data_toolset.cli instead. + import logging from argparse import ArgumentParser, Namespace from pathlib import Path diff --git a/src/data_toolset/utils/avro.py b/src/data_toolset/utils/avro.py index 31f3a03..8747f54 100644 --- a/src/data_toolset/utils/avro.py +++ b/src/data_toolset/utils/avro.py @@ -5,12 +5,12 @@ from pathlib import Path import fastavro -import polars import pyarrow as pa from data_toolset.utils.base import BaseUtils +from data_toolset.utils.registry import register_format - +@register_format("avro") class AvroUtils(BaseUtils): @classmethod def to_arrow_table(cls, file_path: Path) -> pa.Table: @@ -29,20 +29,11 @@ def to_arrow_table(cls, file_path: Path) -> pa.Table: @classmethod def validate_format(cls, file_path: Path) -> None: - """ - Validate a Avro file. - - :param file_path: Path to the Avro file to validate. - :type file_path: Path - :raises Exception: If the file is invalid or doesn't meet the Avro format requirements. - """ - # Check that the file exists and is not empty - if not file_path.exists() or file_path.stat().st_size == 0: - raise Exception() - # Check that the file starts with the Avro magic bytes + """Validate an Avro file (existence + magic header).""" + super().validate_format(file_path) with open(file_path, "rb") as f: if f.read(4) != b"Obj\x01": - raise Exception() + raise ValueError(f"{file_path!r} is not a valid Avro file (magic header mismatch)") @classmethod def meta(cls, file_path: Path) -> T.Tuple: @@ -101,8 +92,8 @@ def stats(cls, file_path: Path) -> T.Tuple[int, T.Dict]: elif column_stat["max"] is None or cls.has_comparison_methods(v) and v > column_stat["max"]: column_stat["max"] = v column_stats[k] = column_stat - print(json.dumps(column_stats, indent=4)) - return num_rows, column_stats + print(json.dumps(column_stats, indent=4)) + return num_rows, column_stats @classmethod def merge(cls, file_paths: T.List[Path], output_path: Path) -> None: @@ -156,44 +147,4 @@ def validate(cls, file_path: Path, schema_path: T.Optional[Path] = None) -> None print("File is a valid Avro file.") logging.info("File is a valid Avro file.") - @classmethod - def to_parquet(cls, file_path: Path, output_path: Path, - compression: T.Literal[ - "lz4", "uncompressed", "snappy", "gzip", "lzo", "brotli", "zstd"] = "uncompressed") -> None: - """ - Convert an Avro file to a Parquet file. - - :param file_path: Path to the Avro file to convert. - :type file_path: Path - :param output_path: Path to the output Parquet file. - :type output_path: Path - :param compression: The compression method to use for the Parquet file (default is 'uncompressed'). - :type compression: str - """ - table = cls.to_arrow_table(file_path) - df = polars.from_arrow(table) - df.write_parquet(file=output_path) - - @classmethod - def random_sample(cls, file_path: Path, output_path: Path, n: T.Optional[int] = None, fraction: T.Optional[float] = None, - with_replacement: bool = False, shuffle: bool = False) -> None: - """ - Create a random sample from an Avro file and save it as an Avro file. - - :param file_path: Path to the Avro file to sample from. - :type file_path: Path - :param output_path: Path to the output Avro file for the random sample. - :type output_path: Path - :param n: The number of records to include in the random sample. - :type n: int - :param fraction: The fraction of records to include in the random sample (alternative to 'n'). - :type fraction: float - :param with_replacement: Whether to sample with replacement (default is False). - :type with_replacement: bool - :param shuffle: Whether to shuffle the input data before sampling (default is False). - :type shuffle: bool - """ - table = cls.to_arrow_table(file_path) - df = polars.from_arrow(table) - sample_df = df.sample(n=n, fraction=fraction, with_replacement=with_replacement, shuffle=shuffle) - sample_df.write_avro(output_path) + # conversion and sampling are inherited from BaseUtils diff --git a/src/data_toolset/utils/base.py b/src/data_toolset/utils/base.py index d5fd948..c3f7e17 100644 --- a/src/data_toolset/utils/base.py +++ b/src/data_toolset/utils/base.py @@ -5,6 +5,8 @@ import duckdb import polars import pyarrow as pa +import json +import csv class BaseUtils(ABC): @@ -29,9 +31,10 @@ def to_arrow_table(cls, file_path: Path) -> pa.Table: ... @classmethod - @abstractmethod def validate_format(cls, file_path: Path) -> None: - ... + """Check file exists and is non-empty.""" + if not file_path.exists() or file_path.stat().st_size == 0: + raise ValueError(f"File {file_path!r} not found or is empty") @classmethod @abstractmethod @@ -122,7 +125,12 @@ def to_json(cls, file_path: Path, output_path: Path, pretty: bool = False) -> No """ table = cls.to_arrow_table(file_path) df = polars.from_arrow(table) - df.write_json(file=output_path, pretty=pretty, row_oriented=True) + records = df.to_dicts() + with open(output_path, "w", encoding="utf-8") as f: + if pretty: + json.dump(records, f, indent=4, default=str) + else: + json.dump(records, f, default=str) @classmethod def to_csv(cls, file_path: Path, output_path: Path, has_header: bool = True, delimiter: str = ",", @@ -145,19 +153,74 @@ def to_csv(cls, file_path: Path, output_path: Path, has_header: bool = True, del """ table = cls.to_arrow_table(file_path) df = polars.from_arrow(table) - df.write_csv(file=output_path, has_header=has_header, separator=delimiter, line_terminator=line_terminator, - quote_char=quote) + records = df.to_dicts() + with open(output_path, "w", newline="", encoding="utf-8") as f: + fieldnames = list(records[0].keys()) if records else [] + writer = csv.DictWriter( + f, + fieldnames=fieldnames, + delimiter=delimiter, + quotechar=quote, + lineterminator=line_terminator, + ) + if has_header: + writer.writeheader() + for rec in records: + writer.writerow(rec) @classmethod def to_avro(cls, file_path: Path, output_path: Path, compression: T.Literal["uncompressed", "snappy", "deflate"] = "uncompressed") -> None: - pass + """Convert input file to Avro via Arrow→Polars pipeline.""" + table = cls.to_arrow_table(file_path) + df = polars.from_arrow(table) + df.write_avro(file=output_path, compression=compression) @classmethod def to_parquet(cls, file_path: Path, output_path: Path, compression: T.Literal[ "lz4", "uncompressed", "snappy", "gzip", "lzo", "brotli", "zstd"] = "uncompressed") -> None: - pass + """Convert input file to Parquet via Arrow→Polars pipeline.""" + table = cls.to_arrow_table(file_path) + df = polars.from_arrow(table) + if compression == "lzo": + # fallback to uncompressed parquet (LZO not supported reliably) + df.write_parquet(file=output_path, compression="uncompressed") + else: + df.write_parquet(file=output_path, compression=compression) + + @classmethod + def random_sample(cls, file_path: Path, output_path: Path, n: T.Optional[int] = None, + fraction: T.Optional[float] = None, with_replacement: bool = False, shuffle: bool = False) -> None: + """Randomly sample records and write in same or different format based on output extension.""" + table = cls.to_arrow_table(file_path) + df = polars.from_arrow(table) + sample_df = df.sample(n=n, fraction=fraction, with_replacement=with_replacement, shuffle=shuffle) + ext = output_path.suffix.lower() + if ext == ".avro": + sample_df.write_avro(file=output_path) + elif ext == ".parquet": + sample_df.write_parquet(file=output_path) + elif ext == ".csv": + records = sample_df.to_dicts() + with open(output_path, "w", newline="", encoding="utf-8") as f: + fieldnames = list(records[0].keys()) if records else [] + writer = csv.DictWriter( + f, + fieldnames=fieldnames, + delimiter=",", + quotechar='"', + lineterminator="\n", + ) + writer.writeheader() + for rec in records: + writer.writerow(rec) + elif ext == ".json": + records = sample_df.to_dicts() + with open(output_path, "w", encoding="utf-8") as f: + json.dump(records, f) + else: + raise ValueError(f"Unsupported sample output format: {ext}") @classmethod def query(cls, file_path: Path, query_expression: str, *, chunk_size: int = 1000000) -> T.Union[polars.DataFrame, polars.Series]: @@ -200,12 +263,17 @@ def query(cls, file_path: Path, query_expression: str, *, chunk_size: int = 1000 except StopIteration: break table = pa.Table.from_batches(batches=all_chunks) + df = polars.from_arrow(table) print(df) return df @classmethod @abstractmethod - def random_sample(cls, file_path: Path, output_path: Path, *, n: T.Optional[int] = None, - fraction: T.Optional[float] = None) -> None: + def merge(cls, file_paths: T.List[Path], output_path: Path) -> None: + ... + + @classmethod + @abstractmethod + def validate(cls, file_path: Path, schema_path: T.Optional[Path] = None) -> None: ... diff --git a/src/data_toolset/utils/parquet.py b/src/data_toolset/utils/parquet.py index 5197a1c..b17f0c5 100644 --- a/src/data_toolset/utils/parquet.py +++ b/src/data_toolset/utils/parquet.py @@ -6,11 +6,12 @@ import pyarrow as pa import pyarrow.parquet as pq import polars - from data_toolset.utils.base import BaseUtils +from data_toolset.utils.registry import register_format from data_toolset.utils.utils import NpEncoder +@register_format("parquet") class ParquetUtils(BaseUtils): @classmethod def to_arrow_table(cls, file_path: Path) -> pa.Table: @@ -22,25 +23,17 @@ def to_arrow_table(cls, file_path: Path) -> pa.Table: :return: Arrow Table containing the data from the Parquet file. :rtype: pa.Table """ - table = pa.parquet.read_table(file_path) - return table + # Lazy read via Polars for performance, then collect and convert to Arrow + df = polars.scan_parquet(str(file_path)).collect() + return df.to_arrow() @classmethod def validate_format(cls, file_path: Path) -> None: - """ - Validate a Parquet file. - - :param file_path: Path to the Parquet file to validate. - :type file_path: Path - :raises Exception: If the file is invalid or doesn't meet the Parquet format requirements. - """ - # Check that the file exists and is not empty - if not file_path.exists() or file_path.stat().st_size == 0: - raise Exception() - # Check that the file starts with the Parquet magic bytes + """Validate a Parquet file (existence + magic header).""" + super().validate_format(file_path) with open(file_path, "rb") as f: if f.read(4) != b"PAR1": - raise Exception() + raise ValueError(f"{file_path!r} is not a valid Parquet file (magic header mismatch)") @classmethod def meta(cls, file_path: Path) -> T.Tuple: @@ -78,39 +71,20 @@ def stats(cls, file_path: Path) -> T.Tuple[int, dict]: :return: A tuple containing the number of rows and column statistics. :rtype: Tuple[int, dict] """ - parquet_file = pq.ParquetFile(file_path) - num_rows = parquet_file.metadata.num_rows + # Use Polars native aggregations for stats + df = polars.scan_parquet(str(file_path)).collect() + num_rows = df.height + null_counts = df.null_count().to_dicts()[0] + mins = df.min().to_dicts()[0] + maxs = df.max().to_dicts()[0] column_stats = {} - for i in range(parquet_file.num_row_groups): - table = parquet_file.read_row_group(i) - for j, column_name in enumerate(table.schema.names): - column = table.column(j) - column_stat = column_stats.get(column_name, { - "count": 0, - "null_count": 0, - "min": None, - "max": None - }) - column_stat["count"] += len(column) - column_stat["null_count"] += column.null_count - # Process each chunk in the ChunkedArray - for chunk in column.iterchunks(): - if chunk.null_count == len(chunk): # Skip if all values are null - continue - non_null_values = polars.from_arrow(chunk).drop_nans() - - if len(non_null_values) > 0: - chunk_min = non_null_values[0] - chunk_max = non_null_values[-1] - - if column_stat["min"] is None or chunk_min < column_stat["min"]: - column_stat["min"] = chunk_min - - if column_stat["max"] is None or chunk_max > column_stat["max"]: - column_stat["max"] = chunk_max - - column_stats[column_name] = column_stat - + for col in df.columns: + column_stats[col] = { + "count": num_rows, + "null_count": null_counts[col], + "min": mins[col], + "max": maxs[col] + } print(json.dumps(column_stats, indent=4, cls=NpEncoder, default=str)) return num_rows, column_stats @@ -155,42 +129,4 @@ def validate(cls, file_path: Path, schema_path: T.Optional[Path] = None) -> None print("File is a valid Parquet file.") logging.info("File is a valid Parquet file.") - @classmethod - def to_avro(cls, file_path: Path, output_path: Path, - compression: T.Literal["uncompressed", "snappy", "deflate"] = "uncompressed") -> None: - """ - Convert an Parquet file to a Avro file. - - :param file_path: Path to the Avro file to convert. - :type file_path: Path - :param output_path: Path to the output Avro file. - :type output_path: Path - :param compression: The compression method to use for the Parquet file (default is 'uncompressed'). - :type compression: str - """ - # @TODO(kirillb): not supporting timestamps at the moment - df = polars.read_parquet(source=file_path) - df.write_avro(file=output_path, compression=compression) - - @classmethod - def random_sample(cls, file_path: Path, output_path: Path, n: T.Optional[int] = None, fraction: T.Optional[float] = None, - with_replacement: bool = False, shuffle: bool = False) -> None: - """ - Create a random sample from an Parquet file and save it as an Parquet file. - - :param file_path: Path to the Parquet file to sample from. - :type file_path: Path - :param output_path: Path to the output Avro file for the random sample. - :type output_path: Path - :param n: The number of records to include in the random sample. - :type n: int - :param fraction: The fraction of records to include in the random sample (alternative to 'n'). - :type fraction: float - :param with_replacement: Whether to sample with replacement (default is False). - :type with_replacement: bool - :param shuffle: Whether to shuffle the input data before sampling (default is False). - :type shuffle: bool - """ - df = polars.read_parquet(source=file_path) - sample_df = df.sample(n=n, fraction=fraction, with_replacement=with_replacement, shuffle=shuffle) - sample_df.write_parquet(output_path) + # conversion and sampling inherited from BaseUtils diff --git a/src/data_toolset/utils/registry.py b/src/data_toolset/utils/registry.py new file mode 100644 index 0000000..fe1d71f --- /dev/null +++ b/src/data_toolset/utils/registry.py @@ -0,0 +1,14 @@ +from typing import Type, Dict +from data_toolset.utils.base import BaseUtils + +# Registry for format handlers (e.g., avro, parquet) +FORMAT_REGISTRY: Dict[str, Type[BaseUtils]] = {} + +def register_format(fmt: str): + """ + Decorator to register a BaseUtils subclass for a given file format. + """ + def decorator(cls: Type[BaseUtils]): + FORMAT_REGISTRY[fmt] = cls + return cls + return decorator diff --git a/tests/unit/test_cli.py b/tests/unit/test_cli.py new file mode 100644 index 0000000..85bab68 --- /dev/null +++ b/tests/unit/test_cli.py @@ -0,0 +1,134 @@ +import pytest +from typer.testing import CliRunner +from pathlib import Path +from unittest.mock import MagicMock + +from utils import TEST_DATA_DIR +from data_toolset.cli import app, FORMAT_REGISTRY, DEFAULT_RECORDS + +runner = CliRunner() + +@pytest.mark.parametrize( + "command, fmt, file_name, extra_args, expected_args", + [ + ("head", "avro", "test.avro", [], lambda p: (p, DEFAULT_RECORDS)), + ("tail", "avro", "test.avro", [], lambda p: (p, DEFAULT_RECORDS)), + ("count", "avro", "test.avro", [], lambda p: (p,)), + ("stats", "avro", "test.avro", [], lambda p: (p,)), + ("schema", "avro", "test.avro", [], lambda p: (p,)), + ("meta", "avro", "test.avro", [], lambda p: (p,)), + ("validate", "avro", "test.avro", [], lambda p: (p, None)), + ("head", "parquet", "test.parquet", [], lambda p: (p, DEFAULT_RECORDS)), + ("tail", "parquet", "test.parquet", [], lambda p: (p, DEFAULT_RECORDS)), + ("count", "parquet", "test.parquet", [], lambda p: (p,)), + ("stats", "parquet", "test.parquet", [], lambda p: (p,)), + ("schema", "parquet", "test.parquet", [], lambda p: (p,)), + ("meta", "parquet", "test.parquet", [], lambda p: (p,)), + ("validate", "parquet", "test.parquet", [], lambda p: (p, None)), + ], +) +def test_cli_core_commands(command, fmt, file_name, extra_args, expected_args): + file_path = TEST_DATA_DIR / "data" / fmt / file_name + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + args = [command, str(file_path)] + extra_args + result = runner.invoke(app, args) + assert result.exit_code == 0, result.output + call_args = expected_args(file_path) + getattr(mock, command).assert_called_once_with(*call_args) + + +def test_cli_invalid_command(): + file_path = TEST_DATA_DIR / "data" / "avro" / "test.avro" + result = runner.invoke(app, ["invalid", str(file_path)]) + assert result.exit_code != 0 + assert "No such command" in result.output + + +def test_cli_unsupported_format(): + file_path = TEST_DATA_DIR / "data" / "avro" / "test.txt" + FORMAT_REGISTRY.pop("txt", None) + result = runner.invoke(app, ["head", str(file_path)]) + assert result.exit_code != 0 + assert isinstance(result.exception, KeyError) + + +# Additional tests for non-core commands + +def test_cli_query(): + file_path = TEST_DATA_DIR / "data" / "avro" / "test.avro" + fmt = "avro" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + query_expr = "field>10" + result = runner.invoke(app, ["query", str(file_path), query_expr]) + assert result.exit_code == 0 + mock.query.assert_called_once_with(file_path, query_expr) + + +def test_cli_merge(): + fmt = "parquet" + f1 = TEST_DATA_DIR / "data" / fmt / "test.parquet" + f2 = TEST_DATA_DIR / "data" / fmt / "test-snappy.parquet" + out = TEST_DATA_DIR / "data" / fmt / "merged.parquet" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + result = runner.invoke(app, ["merge", str(f1), str(f2), str(out)]) + assert result.exit_code == 0 + mock.merge.assert_called_once_with([f1, f2], out) + + +def test_cli_to_json(): + file_path = TEST_DATA_DIR / "data" / "parquet" / "test.parquet" + out = TEST_DATA_DIR / "data" / "parquet" / "out.json" + fmt = "parquet" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + # default pretty=False + result = runner.invoke(app, ["to-json", str(file_path), str(out)]) + assert result.exit_code == 0 + mock.to_json.assert_called_once_with(file_path, out, False) + + +def test_cli_to_csv(): + file_path = TEST_DATA_DIR / "data" / "parquet" / "test.parquet" + out = TEST_DATA_DIR / "data" / "parquet" / "out.csv" + fmt = "parquet" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + result = runner.invoke(app, ["to-csv", str(file_path), str(out)]) + assert result.exit_code == 0 + mock.to_csv.assert_called_once_with(file_path, out, True, ",", "\n", '"') + + +def test_cli_to_avro_and_to_parquet(): + # to-avro + file_path = TEST_DATA_DIR / "data" / "parquet" / "test.parquet" + out_avro = TEST_DATA_DIR / "data" / "parquet" / "out.avro" + fmt = "parquet" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + result = runner.invoke(app, ["to-avro", str(file_path), str(out_avro)]) + assert result.exit_code == 0 + mock.to_avro.assert_called_once_with(file_path, out_avro, "uncompressed") + # to-parquet + file_path2 = TEST_DATA_DIR / "data" / "avro" / "test.avro" + out_parquet = TEST_DATA_DIR / "data" / "avro" / "out.parquet" + fmt2 = "avro" + mock2 = MagicMock() + FORMAT_REGISTRY[fmt2] = mock2 + result2 = runner.invoke(app, ["to-parquet", str(file_path2), str(out_parquet)]) + assert result2.exit_code == 0 + mock2.to_parquet.assert_called_once_with(file_path2, out_parquet, "uncompressed") + + +def test_cli_random_sample(): + file_path = TEST_DATA_DIR / "data" / "avro" / "test.avro" + out = TEST_DATA_DIR / "data" / "avro" / "sample.avro" + fmt = "avro" + mock = MagicMock() + FORMAT_REGISTRY[fmt] = mock + # default n and fraction + result = runner.invoke(app, ["random-sample", str(file_path), str(out)]) + assert result.exit_code == 0 + mock.random_sample.assert_called_once_with(file_path, out, None, None)