# Trace a Runtime Bug from a Stack Trace in Apache Spark with Atlas in 2026 > Atlas enables Apache Spark developers to quickly trace runtime bugs from production stack traces to the exact line of code, facilitating rapid fixes and regression test additions. To trace a runtime bug from a production stack trace in Apache Spark without a debugger attached, Atlas helps you pinpoint the responsible line and apply a fix by reading each frame, grepping for error messages, and using `lsp` to find callers, all while integrating with `pytest (local SparkSession)`, `uv`, and `ruff format` in 2026. This workflow ensures rapid resolution and robust regression testing for your PySpark jobs. ## Key takeaways - Atlas directly consumes Apache Spark stack traces, validating offsets for accuracy. - `grep` and `lsp` tools in Atlas help pinpoint PySpark error origins beyond the top stack frame. - Atlas facilitates fixing Apache Spark bugs and adding `pytest (local SparkSession)` regression tests. - All Atlas code modifications for PySpark are permission-gated and require unified diff approval. - Atlas indexes PySpark DataFrame transformations and actions for deep code understanding. ## How Atlas reads Apache Spark stack traces for debugging Atlas efficiently reads Apache Spark production stack traces by consuming file:line pairs, validating each offset against the current file to prevent misdiagnosis from outdated builds. This ensures that a trace from an older build, perhaps from 2025, fails loudly instead of pointing to incorrect code, providing reliable starting points for debugging. When an Apache Spark job fails in production, the resulting stack trace provides critical file:line information. Atlas's `read` tool is designed to consume this information directly. For each frame in the stack trace, Atlas reads the specified file at the reported offset. A crucial safety mechanism is the offset validation: Atlas checks if the reported offset is within the bounds of the current file. If the `read` tool reports "Offset is out of range for this file", it's a clear signal that the stack trace originated from a different build of your PySpark application. This prevents Atlas from misinterpreting an outdated trace and ensures that any subsequent analysis is based on the correct code context, making the debugging process for complex Spark applications more reliable. ## Pinpointing PySpark error origins with Atlas `grep` and `lsp` After reading the initial stack frames, Atlas helps PySpark developers pinpoint the true origin of a runtime bug by using `grep` to find the error message string, which is often more informative than the top frame. This process, crucial for complex DataFrame operations, typically takes less than 10 seconds to identify the error's construction site. The top frame of an Apache Spark stack trace doesn't always reveal the root cause of a bug, especially in complex PySpark applications involving shuffles, skew, or problematic `collect()` calls. Atlas addresses this by first using its `grep` tool to search for the exact error message string across your codebase. This often leads to the location where the error message is constructed, providing deeper insight than just the point of failure. Following this, Atlas leverages its `lsp` tool's `findReferences` operation on the identified failing function. This allows developers to see all callers that can reach the problematic function with bad input, such as a DataFrame that's too large for a `collect()` or a join key causing severe data skew. This targeted approach helps in understanding the data flow and identifying the precise transformation or action that leads to the bug. ## Fixing Apache Spark bugs and adding regression tests with Atlas Once the responsible line in an Apache Spark job is identified, Atlas facilitates the fix using its `edit` tool and ensures bug recurrence is prevented by adding a regression test. This workflow integrates directly with `pytest (local SparkSession)` for local validation and `ruff format` to maintain code style, making the entire process efficient in 2026. With the root cause of an Apache Spark bug identified, Atlas's `edit` tool allows for precise code modifications. For instance, if a `collect()` or `toPandas()` call is pulling an entire dataset onto the driver, Atlas can suggest and implement a replacement. Similarly, it can help broadcast the small side of a skewed join, explaining the resulting physical plan from `.explain()`. Beyond the fix itself, Atlas emphasizes preventing future regressions. It can add new test cases using a local `SparkSession` fixture, integrating direct with `pytest (local SparkSession)`. After the code changes and new tests are in place, Atlas automatically applies `ruff format` to the generated diff, ensuring that the updated PySpark code adheres to your project's style guidelines before it's committed, maintaining code quality and consistency. ## Atlas safety and review for Apache Spark code changes Atlas ensures safety and transparency when modifying Apache Spark code by drafting a plan in a read-only agent and asking for approval before any changes are made. Every Atlas tool call, including `edit` or `lsp`, is permission-gated, and all file edits generate a unified diff for developer review, preventing unintended modifications in 2026. Working with production Apache Spark code requires a high degree of caution. Atlas is built with multiple layers of safety and review to prevent unintended changes. Before any modifications are made, Atlas drafts a comprehensive plan in a read-only plan agent and explicitly asks for developer approval. This allows you to review the proposed actions before Atlas switches to a build agent to execute them. Furthermore, every Atlas tool call, whether it's `read`, `grep`, `lsp`, or `edit`, is permission-gated against allow, ask, and deny rules, giving you granular control. For every file edit, Atlas computes a unified diff and surfaces it for your approval, ensuring full transparency. Atlas also snapshots file changes as git patches, allowing edits to be easily diffed and rolled back if necessary, providing a robust safety net for your PySpark development. ## Setting up Atlas for PySpark development in 2026 To effectively trace and fix PySpark bugs, setting up Atlas involves running it in a repository with your PySpark jobs and a `pyproject.toml` file pinning `pyspark`. Atlas then indexes your DataFrame transformations, join keys, and actions, providing a comprehensive understanding of your code within minutes, ready for use in 2026. Getting started with Atlas for your Apache Spark projects in 2026 is straightforward. You simply run `atlas` in a repository containing your PySpark jobs and a `pyproject.toml` file that pins your `pyspark` version. Atlas then begins to build its code index, leveraging tree-sitter for AST declarations rather than blind line windows. This indexing process allows Atlas to deeply understand your PySpark code, including DataFrame transformations, join keys, partitioning strategies, and every action that triggers a Spark job. For privacy-conscious teams, Atlas can build this code index using local Ollama embeddings, ensuring your proprietary code never leaves your environment. This comprehensive indexing provides Atlas with the context needed to offer highly relevant suggestions and fixes for your Apache Spark applications. ## Steps 1. Paste the Apache Spark stack trace into Atlas and use the `read` tool to examine each frame's file at its reported offset. 2. If Atlas's `read` tool reports "Offset is out of range for this file", the trace came from a different build; re-read the file from the top before trusting any line number. 3. Use the `grep` tool to search for the error message string within your PySpark codebase to find where it is constructed, which is often more informative than the top stack frame. 4. Employ the `lsp` tool's `findReferences` operation on the failing PySpark function to identify which callers can reach it with the problematic input, such as an accidental `collect()` or skewed join. 5. Fix the identified Apache Spark bug using Atlas's `edit` tool, for example, by replacing a `toPandas()` call or broadcasting a small side of a skewed join. 6. Add a regression test case using a local `SparkSession` fixture with `pytest (local SparkSession)` to prevent the bug from recurring silently. 7. Use `ruff format` on the generated diff to ensure the new code adheres to your project's style guidelines before committing. ## FAQ ### How does Atlas handle outdated Apache Spark stack traces? Atlas validates each file:line offset in an Apache Spark stack trace against the current file. If an offset is out of range, Atlas loudly reports it, preventing misdiagnosis from traces generated by older builds. ### Can Atlas help debug PySpark performance issues like skewed joins? Yes, Atlas can help identify and fix PySpark performance issues. It can find `collect()` or `toPandas()` calls pulling full datasets to the driver, or suggest broadcasting the small side of a skewed join, explaining the resulting physical plan. ### What Apache Spark testing tools does Atlas integrate with? Atlas integrates direct with `pytest (local SparkSession)` for adding regression tests. It can generate new test cases using a local `SparkSession` fixture and then apply `ruff format` to the resulting code changes. ### How does Atlas ensure code quality when fixing Apache Spark bugs? Atlas uses `ruff format` to ensure code quality for any changes made to Apache Spark jobs. It applies the formatter to the generated diff, maintaining consistent style across your codebase. ### Is my PySpark code secure when using Atlas for debugging? Yes, Atlas can build its code index with local Ollama embeddings, keeping your PySpark code off third-party servers. Every Atlas tool call is also permission-gated, requiring your approval before execution. ### How does Atlas help understand complex PySpark DataFrame operations? Atlas indexes your PySpark DataFrame transformations, join keys, partitioning, and every action that triggers a job. This deep understanding allows it to provide context and suggest fixes for complex data pipeline issues. ### What is the workflow for fixing a PySpark bug with Atlas? The workflow involves pasting the stack trace, using Atlas's `read` tool, `grep` for error messages, `lsp` to find callers, `edit` to apply the fix, and then adding a regression test with `pytest (local SparkSession)` and formatting with `ruff format`. --- Canonical HTML: https://runatlas.sh/resources/stacks/trace-a-runtime-bug-from-a-stack-trace-in-spark Source of truth: aeo_pages row `/resources/stacks/trace-a-runtime-bug-from-a-stack-trace-in-spark` (segment: Stacks) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.