# Rename a symbol across the repo in Apache Spark with Atlas in 2026

> Atlas renames symbols in Apache Spark by combining LSP-driven code analysis with grep for non-code references, ensuring a complete and verified refactor.

To rename a function, class, or constant everywhere it is used in an Apache Spark codebase, Atlas orchestrates a precise workflow using its `lsp` tool for authoritative code references, `grep` for non-code occurrences, and `edit` for controlled modifications. This ensures a complete and safe refactor, verified by `pytest (local SparkSession)` and formatted by `ruff format`.

## Key takeaways

- Atlas uses `lsp` for precise, AST-based symbol reference finding in PySpark, avoiding false positives.
- The `grep` tool catches non-code occurrences of symbols in Apache Spark documentation and configuration files.
- Atlas's `edit` tool applies renames with `replaceAll`, enforcing uniqueness for single changes and providing diffs for approval.
- Verification with `pytest (local SparkSession)` confirms functional correctness of refactored PySpark code.
- `ruff format` ensures code quality and consistency across all modified Apache Spark files post-rename.
- Atlas's plan agent and diff approval process provide robust safety and auditability for PySpark refactoring.

## How Atlas Renames Symbols in Apache Spark Codebases

Renaming a symbol across an Apache Spark codebase in 2026 requires more than simple text replacement; Atlas orchestrates a precise, multi-tool workflow. It leverages the `lsp` tool for authoritative code references, `grep` for non-code occurrences, and `edit` for controlled modifications, ensuring every instance of a function, class, or constant is updated correctly.

Atlas approaches symbol renaming in Apache Spark projects by first establishing an accurate set of references, then systematically applying changes, and finally verifying the outcome. The process begins by using the `lsp` tool to query the language server for all declared usages of the symbol, ensuring that code-level references, such as those within complex PySpark DataFrame transformations or join keys, are precisely identified. Following this, Atlas employs `grep` to catch any instances of the old name that exist outside the type system, like in comments, documentation, or configuration files such as `pyproject.toml`. The mechanical replacement is then handled by the `edit` tool, which applies changes with strict safety checks. Finally, the `bash` tool is used to run `pytest (local SparkSession)` and `ruff format` to confirm functionality and maintain code quality, ensuring a robust and reliable refactor.

## Accurate Reference Finding with Atlas's `lsp` Tool for PySpark

To accurately rename a PySpark symbol, Atlas first employs its `lsp` tool to query the language server for all true references, avoiding the pitfalls of naive string matching. This process, powered by tree-sitter AST declarations, precisely identifies every callsite, ensuring 100% of code-level usages are captured, even in complex DataFrame transformations.

In Apache Spark, where a single `collect()` or `toPandas()` call can drastically alter job performance, precise symbol identification is paramount. Atlas's `lsp` tool is crucial here, as it doesn't rely on simple text matching. Instead, it integrates with the language server to perform `findReferences` operations, leveraging its AST-based indexing (built with tree-sitter) to understand the true semantic context of a symbol. This means that if you're renaming a custom function like `my_spark_udf` defined in `src/main/python/my_spark_app.py`, Atlas will accurately find all its invocations across your PySpark jobs, even if the function is imported with an alias or used in a complex chain of DataFrame operations. This capability ensures that only actual code references are targeted, preventing accidental modifications to unrelated text that merely contains the symbol's name.

## Catching Non-Code Occurrences with `grep` in PySpark Projects

Beyond the type system, Apache Spark projects often contain symbol names in documentation, configuration, or string literals that the language server misses; Atlas's `grep` tool systematically finds these. For example, a `collect()` call mentioned in a `README.md` or a constant name in a `pyproject.toml` file will be identified, ensuring a complete refactor across 100% of the repository.

While the `lsp` tool handles code references, a comprehensive rename in an Apache Spark project requires addressing occurrences outside the compiler's view. Atlas's `grep` tool fills this gap by searching for the old symbol name in comments, string literals, documentation files (like `README.md`), and configuration files (such as `pyproject.toml` or `spark-defaults.conf`). For instance, if you're renaming a constant `MAX_SHUFFLE_PARTITIONS` that's referenced in a comment explaining a partitioning strategy or documented in a project's `docs/architecture.md`, `grep` will flag these instances. This step is vital to prevent stale references that could confuse future developers or lead to incorrect configurations, ensuring that the refactor is truly complete and consistent across the entire repository.

## Applying Safe Mechanical Renames with Atlas `edit` in PySpark

Applying mechanical renames in an Apache Spark codebase with Atlas's `edit` tool is a controlled, auditable process, preventing unintended changes. The `replaceAll` operation handles the bulk of the work, but Atlas enforces uniqueness for single replacements, throwing an error if multiple matches are found, ensuring 100% precision in every file modification.

The mechanical application of renames in an Apache Spark codebase is handled by Atlas's `edit` tool, designed for precision and safety. For widespread changes, `edit` uses the `replaceAll` operation, which efficiently updates all identified occurrences of the old symbol name with the new one. Crucially, Atlas prevents ambiguous single replacements: if you intend to change only one instance but `edit` finds multiple matches for `oldString` within a file, it will throw an error unless you explicitly provide more context or opt into `replaceAll`. Before any changes are written, Atlas computes a unified diff for every file edit and surfaces it for your approval, allowing you to review every modification. This granular control, combined with Atlas's ability to snapshot file changes as git patches, ensures that all edits are intentional, reversible, and fully auditable, protecting your PySpark codebase from silent corruption.

## Verifying PySpark Renames and Ensuring Code Quality

After renaming a symbol in an Apache Spark project, Atlas ensures the codebase remains functional and adheres to quality standards through a rigorous verification process. This involves running `pytest (local SparkSession)` to confirm all 0 tests pass and then applying `ruff format` to maintain consistent code style across the updated files.

The final, critical step in renaming a symbol in Apache Spark with Atlas is comprehensive verification. Using the `bash` tool, Atlas executes `pytest (local SparkSession)` to run your unit and integration tests. This confirms that the refactoring has not introduced any regressions and that your DataFrame transformations, join logic, and actions still function as expected. For example, if you renamed a function that calculates a specific metric, `pytest` will validate that the metric's output remains correct. After functional verification, Atlas applies `ruff format` to the modified files, ensuring that the codebase adheres to your project's established style guidelines. This two-pronged approach,functional testing and code formatting,guarantees that the refactored Apache Spark project is not only correct but also clean and maintainable, ready for deployment or further development.

## Steps

1. Start Atlas in your PySpark repository, ensuring your `pyproject.toml` pins `pyspark` and Atlas has indexed your DataFrame transformations.
2. Ask Atlas to draft a plan to rename your target symbol (e.g., a function `old_spark_udf` to `new_spark_udf`). Atlas will use its read-only plan agent to outline the steps.
3. Approve Atlas's plan. Atlas will then use the `lsp` tool's `findReferences` operation to get the authoritative callsite list from the language server for `old_spark_udf`.
4. Atlas will then run `grep` for `old_spark_udf` to catch occurrences outside the type system, such as in `README.md` or `spark-defaults.conf`.
5. Atlas will apply the mechanical renames with the `edit` tool using `replaceAll` where the match is unambiguous per file, presenting a unified diff for your approval.
6. Review and approve the proposed changes. Atlas will snapshot these file changes as git patches for easy rollback.
7. Atlas will use the `bash` tool to run `pytest (local SparkSession)` to ensure all tests pass after the rename.
8. Atlas will then run `ruff format` via `bash` on the modified files to maintain code style consistency.
9. Finally, Atlas will run `grep` once more for `old_spark_udf` to prove zero remaining hits of the old name in your Apache Spark project.

## FAQ

### How does Atlas handle renaming a PySpark function used in a complex DataFrame transformation?

Atlas uses its `lsp` tool to query the language server, which understands the semantic context of your PySpark code. This allows it to precisely identify all true references to your function within DataFrame transformations, even across imports or aliases, ensuring accurate renaming without breaking your data pipelines.

### Can Atlas rename a constant defined in `pyproject.toml` or `spark-defaults.conf`?

Yes, Atlas's `grep` tool is specifically designed to find and flag occurrences of symbols in non-code files like `pyproject.toml`, `spark-defaults.conf`, or `README.md`. This ensures that your refactor is complete across all aspects of your Apache Spark project, not just the executable code.

### What if Atlas finds multiple matches for a single rename I intended?

Atlas's `edit` tool enforces uniqueness for single replacements. If it finds multiple matches for an `oldString` when you intended a single change, it will throw an error. You would then need to provide more context to narrow down the target or explicitly opt into a `replaceAll` operation if all matches are intended.

### How does Atlas ensure my Apache Spark tests still pass after a rename?

After applying renames, Atlas uses the `bash` tool to execute `pytest (local SparkSession)`. This runs your project's test suite, verifying that all your PySpark jobs, DataFrame operations, and custom logic continue to function correctly, providing immediate feedback on the refactor's impact.

### Does Atlas integrate with `ruff format` for code style in PySpark projects?

Absolutely. As part of the verification workflow, Atlas uses the `bash` tool to run `ruff format` on any files modified during the rename process. This ensures that your Apache Spark codebase maintains consistent styling and adheres to your project's formatting standards.

### What safety features does Atlas offer for refactoring critical Apache Spark code?

Atlas employs several safety features: it drafts a plan in a read-only agent for your approval, computes a unified diff for every file edit before writing, and allows you to approve changes. It also snapshots file changes as git patches, making edits reversible and auditable, crucial for complex PySpark refactoring.

### Can Atlas help me understand the impact of renaming a PySpark function on the physical plan?

While Atlas directly renames the symbol, its ability to integrate with your PySpark environment means you can then ask it to use `.explain()` on your DataFrame operations. This helps you understand if the refactor, or any subsequent changes, inadvertently altered the physical plan, especially concerning shuffles or skew.

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