Open-Source Testing · MrPowers

chispa

Chispa is a Python library that simplifies testing PySpark applications by providing assertion methods that compare DataFrames and columns with readable error messages. It helps developers quickly identify data mismatches during testing without manually inspecting raw output.

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Source: GitHub — github.com/MrPowers/chispa

771

GitHub stars

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	MrPowers/chispa
Owner	MrPowers
Primary language	Python
License	MIT — OSI-approved
Stars	771
Forks	79
Open issues	37
Latest release	v0.12.0 (2026-03-24)
Last updated	2026-05-20
Source	https://github.com/MrPowers/chispa

What chispa is

Chispa offers assertion utilities (assert_df_equality, assert_column_equality) for PySpark DataFrames with configurable comparison modes (ignore row/column order, nullable properties, specific columns). It integrates with pytest and provides formatted diff output for debugging test failures.

Quickstart

Get the chispa source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/MrPowers/chispa.git2cd chispa3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

PySpark ETL validation testing

Validate data transformations in ETL pipelines by comparing source and output DataFrames with detailed error messages highlighting mismatched rows.

Data quality test suites

Build comprehensive test suites for data processing logic where column-level assertions catch data type or value mismatches early in the development cycle.

Data engineering CI/CD pipelines

Integrate into automated testing workflows to catch data regressions before deploying PySpark jobs to production environments.

Implementation considerations

Requires PySpark installation and a working SparkSession; local mode sufficient for unit tests but performance scaling behavior for larger datasets is not documented.
Assertions are primarily equality-based; custom comparison logic beyond row/column order, nullability, and column exclusion requires extending the library.
Error messages are formatted for visual inspection; no machine-readable error output documented for programmatic test result aggregation.
Test execution time depends on DataFrame size and whether row/column sorting is enabled; ignore_row_order=True adds overhead via sorting.
Python version support tracked via PyPI badge but specific minimum/maximum versions not stated in provided data.

When to avoid it — and what to weigh

Non-PySpark testing needs — Chispa is PySpark-specific; standard pytest assertions or pandas testing libraries are more appropriate for non-Spark workloads.
Large-scale distributed testing — No evidence of optimization for massive datasets; sorting/comparison operations may not scale efficiently for terabyte-scale DataFrames.
Real-time or streaming validation — Library is designed for batch DataFrame testing; not applicable for Spark Streaming or real-time data validation scenarios.
Cross-platform data comparison — Limited to PySpark; if you need to compare Spark DataFrames with data in other systems (SQL databases, cloud storage formats), additional tooling required.

License & commercial use

MIT License: permissive OSI-approved license allowing use, modification, and distribution (commercial and proprietary) with attribution and no warranty.

MIT license permits commercial use. However, MIT provides no warranty or liability protection. Review your risk tolerance and legal requirements before deploying in production. No SLA, support contract, or commercial backing mentioned.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Chispa is a test-time library; security risk primarily from dependency supply chain (PySpark, pytest). No secrets handling, encryption, or authentication in scope. Test data exposure depends on test environment isolation.

Alternatives to consider

pytest-spark

Alternative pytest plugin for Spark testing; lighter-weight but fewer built-in assertion helpers and less detailed error formatting than Chispa.

Spark Testing Base (Scala/Java)

Scala-based testing library for Spark; more mature ecosystem but requires JVM development and Scala language knowledge; not applicable to PySpark teams.

pandas DataFrame comparison (pandas testing module)

If converting Spark DataFrames to pandas for testing; trades distributed scale for simpler test execution; suitable only for small-to-medium datasets.

Software development agency

Build on chispa with DEV.co software developers

Review the README examples and check PyPI for detailed API docs. Verify Python/Spark version compatibility and test on a sample dataset before committing to production test suites.

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chispa FAQ

Does Chispa work with Spark 3.x and Python 3.9+?

Not explicitly stated in provided data. PyPI badge indicates supported Python versions are tracked. Requires direct review of repository or PyPI page for version compatibility matrix.

Can I use Chispa with Databricks or other Spark cloud platforms?

Likely yes (Chispa works with standard PySpark), but no Databricks, AWS EMR, or GCP Dataproc-specific documentation provided. Integration is at test-script level; cloud deployment depends on your notebook or job runtime.

How does Chispa handle very large DataFrames in tests?

No performance benchmarks or scalability guidance in provided data. Sorting operations (ignore_row_order=True) may be slow on large datasets; recommend testing locally on production-scale data subsets.

Is there commercial support available?

Not mentioned. Chispa is community-maintained under MIT license. No official support contract, SLA, or vendor backing documented.

Software development & web development with DEV.co

DEV.co helps companies turn open-source tools like chispa into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source testing stack.

Evaluate Chispa for Your PySpark Testing Strategy

Review the README examples and check PyPI for detailed API docs. Verify Python/Spark version compatibility and test on a sample dataset before committing to production test suites.

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