spark-data-test 0.1.0

A library for validating and comparing datasets in Spark using PySpark.

spark-data-test

Overview

spark-data-test provides utilities to compare two Spark DataFrames or datasets, generating detailed reports on matches, mismatches, and missing records. It is designed for data validation, ETL testing, and regression testing in Spark pipelines.

Configuration Dataclasses

Below are the main dataclasses used for configuration in spark-data-test. You can use these directly in Python or as a reference for your YAML/JSON configs.

DatasetParams

Defines parameters for a single dataset comparison.

from dataclasses import dataclass, field

@dataclass
class DatasetParams:
    dataset_name: str                  # Name of the dataset/table
    primary_keys: list                 # List of primary key column names
    select_cols: list = field(default_factory=lambda: ["*"])  # Columns to select (default: all)
    drop_cols: list = field(default_factory=list)             # Columns to drop (default: none)

DataframeConfig

Defines how to read a DataFrame from storage.

from dataclasses import dataclass, field

@dataclass
class DataframeConfig:
    path: str                          # Path to the data (e.g., file or table)
    file_format: str = "parquet"       # File format (parquet, csv, etc.)
    spark_options: dict = field(default_factory=dict)  # Spark read options (e.g., {"header": "true"})

OutputConfig

Defines output options for writing comparison results.

from dataclasses import dataclass, field

@dataclass
class OutputConfig:
    output_dir: str                    # Directory to write output files
    output_file_format: str = "parquet" # Output file format
    spark_options: dict = field(default_factory=dict)  # Spark write options
    no_of_partitions: int = -1         # Number of partitions for output (-1 for default)

DatasetConfig

Groups together the configs for a single dataset comparison.

from dataclasses import dataclass

@dataclass
class DatasetConfig:
    params: DatasetParams              # Dataset parameters
    source_config: DataframeConfig     # Source DataFrame config
    target_config: DataframeConfig     # Target DataFrame config

ComparisonJobConfig

Top-level config for a comparison job (can include multiple datasets).

from dataclasses import dataclass

@dataclass
class ComparisonJobConfig:
    job_name: str                      # Name of the comparison job
    dataset_configs: list[DatasetConfig] # List of dataset configs to compare
    output_config: OutputConfig        # Output config for all results

---

Usage

1. Compare DataFrames Directly

Use run_comparison_job_from_dfs to compare two Spark DataFrames directly.

Function Signature

run_comparison_job_from_dfs(
    spark: SparkSession,
    job_name: str,
    source_df: DataFrame,
    target_df: DataFrame,
    params: DatasetParams,
    output_config: OutputConfig
)

Parameters

• spark: The active SparkSession.
• job_name: Name for the comparison job (used in output paths).
• source_df: Source DataFrame.
• target_df: Target DataFrame.
• params: An instance of DatasetParams specifying dataset name, primary keys, columns to select/drop, etc.
• output_config: An instance of OutputConfig specifying output directory, file format, Spark write options, etc.

Example

from spark_data_test.jobs.comparison_job import run_comparison_job_from_dfs
from spark_data_test.entities.config import DatasetParams, OutputConfig

params = DatasetParams(
    dataset_name="my_table",
    primary_keys=["id"]
)
output_config = OutputConfig(
    output_dir="/tmp/comparison_results"
)

run_comparison_job_from_dfs(spark, "my_job", df1, df2, params, output_config)

---

2. Compare Using Config (YAML/JSON/dict)

Use run_comparison_job to compare multiple datasets using a configuration dictionary or object.

Function Signature

run_comparison_job(
    spark: SparkSession,
    config: dict or ComparisonJobConfig
)

Parameters

• spark: The active SparkSession.
• config: A dictionary or ComparisonJobConfig instance describing one or more datasets to compare, their source/target configs, and output config.

Example

from spark_data_test.jobs.comparison_job import run_comparison_job

config = {
    "job_name": "multi_dataset_job",
    "dataset_configs": [
        {
            "params": {
                "dataset_name": "table1",
                "primary_keys": ["id"]
            },
            "source_config": {
                "path": "/data/source/table1",
                "file_format": "parquet"
            },
            "target_config": {
                "path": "/data/target/table1",
                "file_format": "parquet"
            }
        }
    ],
    "output_config": {
        "output_dir": "/tmp/comparison_results"
    }
}

run_comparison_job(spark, config)

---

Output Files

After running a comparison job, the following files/directories are generated under the specified output_dir and job_name:

overall_test_report

Summary DataFrame with row counts, matched counts, duplicate counts, missing rows, and test status for each dataset.

dataset_name	count	matched_count	duplicate_count	missing_rows	test_status
table1	{"source": 100, "target": 98}	97	{"source": 0, "target": 1}	{"source": 1, "target": 3}	PASSED

---

col_lvl_test_report

Column-level report showing the count of unmatched values for each non-key column.

dataset_name	column_name	unmatched_rows_count
table1	colA	2
table1	colB	0

---

row_lvl_test_report

Row-level report with primary keys, duplicate count, missing row status, and match status for each row.

dataset_name	id	duplicate_count	missing_row_status	all_rows_matched
table1	1	0	PRESENT_IN_BOTH	true
table1	2	0	MISSING_AT_TARGET	false

---

unmatched_rows/

Directory containing one file per column with all rows where that column did not match between source and target.

Example for unmatched_rows/colA:

dataset_name	id	colA_src	colA_target
table1	5	foo	bar
table1	8	baz	qux

All outputs are written in the format specified by output_file_format (default: parquet).

---

Notes

• The package requires PySpark and is intended for use in Spark environments.
• For more details on configuration options, see the entities/config.py dataclasses.