FEAT-#5394: Reduce amount of remote calls for TreeReduce and GroupByReduce operators

[korbit-ai]

User description

Apply approaches from PR-7136 for TreeReduce and GroupByReduce operators

What do these changes do?

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• passes flake8 modin/ asv_bench/benchmarks scripts/doc_checker.py
• passes black --check modin/ asv_bench/benchmarks scripts/doc_checker.py
• signed commit with git commit -s
• Resolves #5394
• tests added and passing
• module layout described at docs/development/architecture.rst is up-to-date

Description by Korbit AI

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What change is being made?

Reduce the number of remote calls for TreeReduce and GroupByReduce operators by simplifying the map function logic in dataframe.py and partition_manager.py.

Why are these changes being made?

The previous implementation had complex logic to decide between block-wise and axis-wise mapping, which led to unnecessary remote calls. Simplifying this logic improves performance by reducing the overhead of remote calls, especially for smaller dataframes.

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PR Type

Enhancement

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Description

• Simplified the map function in dataframe.py to reduce unnecessary remote calls by removing complex logic for mapping decisions.
• Enhanced partition_manager.py to include logic for deciding between block-wise and axis-wise mapping based on partition size, improving performance for larger dataframes.

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Changes walkthrough 📝

Relevant files

Enhancement

dataframe.py Simplify map function logic in dataframe.py                            modin/core/dataframe/pandas/dataframe/dataframe.py Removed complex logic for deciding between block-wise and axis-wise mapping. Simplified map function to always use partition manager's map functions. +7/-41    partition_manager.py Enhance partition mapping logic in partition_manager.py    modin/core/dataframe/pandas/partitioning/partition_manager.py Added logic to decide between block-wise and axis-wise mapping based on partition size. Implemented axis-wise mapping for larger partition sizes. +46/-12

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Summary by CodeRabbit

• New Features

  • Enhanced partition mapping logic for improved performance based on CPU core availability.
  • Introduced flexibility in the mapping strategy, optimizing data processing for larger datasets.

• Bug Fixes

  • Simplified the mapping function to reduce complexity and potential overhead.

• Documentation

  • Updated guidance on utilizing the new mapping capabilities for better performance optimization.

[korbit-ai]

My review is in progress 📖 - I will have feedback for you in a few minutes!

[coderabbitai]

Walkthrough

The recent changes streamline the mapping logic in the Modin library, enhancing efficiency in handling data partitions. The map function in ModinDataframe has simplified its control flow by removing complex conditional checks tied to CPU counts, opting for direct assignment based on the lazy parameter. In contrast, the map_partitions method in the partition manager now adjusts its strategy based on CPU capabilities, improving performance during data processing tasks.

Changes

Files	Change Summary
modin/core/dataframe/pandas/dataframe.py, modin/core/dataframe/pandas/partitioning/partition_manager.py	The map function in ModinDataframe has been simplified by removing CPU count checks, while map_partitions now uses CPU counts to adapt mapping strategies for efficiency.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant ModinDataframe
    participant PartitionManager
    
    User->>ModinDataframe: Call map function
    ModinDataframe->>PartitionManager: Map partitions
    PartitionManager->>PartitionManager: Check CPU count
    alt If partitions <= 1.5 * CPUs
        PartitionManager->>PartitionManager: Apply block-wise mapping
    else
        PartitionManager->>PartitionManager: Apply axis-wise mapping
    end
    PartitionManager->>ModinDataframe: Return mapped result
    ModinDataframe->>User: Return final output

Loading

Poem

🐇 In fields of data where rabbits play,
New mapping magic brightens the day!
With CPU counts guiding the way,
Efficiency hops in a joyful ballet.
So let’s celebrate this wondrous change,
In Modin’s world, our dreams rearrange! 🌼

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[korbit-ai]

I have reviewed your code and did not find any issues!

---

Please note that I can make mistakes, and you should still encourage your team to review your code as well.

[furwellness]

/review

[codiumai-pr-agent-free]

PR Reviewer Guide 🔍

(Review updated until commit f1f0c42)

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Performance Optimization The PR simplifies the map function by removing the complex logic for choosing between block-wise and axis-wise mapping. This change may impact performance for different dataset sizes and should be carefully reviewed. Logic Relocation The complex mapping logic has been moved from the dataframe.py to the partition_manager.py. This change in responsibility should be reviewed to ensure it aligns with the overall architecture and doesn't introduce any unexpected side effects.

[furwellness]

@coderabbitai full review

[codiumai-pr-agent-free]

Persistent review updated to latest commit f1f0c42

[coderabbitai]

Actions performed

Full review triggered.

[codiumai-pr-agent-free]

PR Code Suggestions ✨

Category	Suggestion	Score
Best practice	Replace magic number with a named constant Consider using a constant for the magic number 1.5 in the condition. This would make the code more maintainable and easier to adjust if needed. modin/core/dataframe/pandas/partitioning/partition_manager.py [625] -if np.prod(partitions.shape) <= 1.5 * CpuCount.get(): +BLOCK_WISE_THRESHOLD = 1.5 +if np.prod(partitions.shape) <= BLOCK_WISE_THRESHOLD * CpuCount.get(): Apply this suggestion Suggestion importance[1-10]: 8 Why: Using a named constant instead of a magic number improves code clarity and maintainability, making it easier to adjust the threshold if needed.	8
Maintainability	Extract condition for choosing mapping strategy into a separate method Consider extracting the condition for choosing between block-wise and axis-wise mapping into a separate method. This would improve readability and make the logic more maintainable. modin/core/dataframe/pandas/partitioning/partition_manager.py [625-650] -if np.prod(partitions.shape) <= 1.5 * CpuCount.get(): +def should_use_block_wise_mapping(partitions): + return np.prod(partitions.shape) <= 1.5 * CpuCount.get() + +if should_use_block_wise_mapping(partitions): # block-wise map preprocessed_map_func = cls.preprocess_func(map_func) new_partitions = np.array( [ [ part.apply( preprocessed_map_func, *func_args if func_args is not None else (), **func_kwargs if func_kwargs is not None else {}, ) for part in row_of_parts ] for row_of_parts in partitions ] ) else: # axis-wise map - # we choose an axis for a combination of partitions - # whose size is closer to the number of CPUs - if abs(partitions.shape[0] - CpuCount.get()) < abs( - partitions.shape[1] - CpuCount.get() - ): - axis = 1 - else: - axis = 0 + axis = 1 if abs(partitions.shape[0] - CpuCount.get()) < abs(partitions.shape[1] - CpuCount.get()) else 0 Apply this suggestion Suggestion importance[1-10]: 7 Why: Extracting the condition into a separate method enhances code readability and maintainability, making it easier to understand and modify the logic.	7
Enhancement	Improve variable naming for better code readability Consider using a more descriptive variable name instead of map_fn. For example, partition_mapping_function would better describe its purpose and improve code readability. modin/core/dataframe/pandas/dataframe/dataframe.py [208-212] -map_fn = ( +partition_mapping_function = ( self._partition_mgr_cls.lazy_map_partitions if lazy else self._partition_mgr_cls.map_partitions ) Apply this suggestion Suggestion importance[1-10]: 6 Why: The suggestion to use a more descriptive variable name improves code readability, but it is not crucial for functionality.	6
Performance	Use list comprehension instead of np.array() for potential performance improvement Consider using a more efficient method to create the new_partitions array in the block-wise map case. Using a list comprehension instead of np.array() with nested loops could potentially improve performance. modin/core/dataframe/pandas/partitioning/partition_manager.py [628-639] -new_partitions = np.array( +new_partitions = [ [ - [ - part.apply( - preprocessed_map_func, - *func_args if func_args is not None else (), - **func_kwargs if func_kwargs is not None else {}, - ) - for part in row_of_parts - ] - for row_of_parts in partitions + part.apply( + preprocessed_map_func, + *func_args if func_args is not None else (), + **func_kwargs if func_kwargs is not None else {}, + ) + for part in row_of_parts ] -) + for row_of_parts in partitions +] Apply this suggestion Suggestion importance[1-10]: 5 Why: The suggestion to use list comprehension may improve performance slightly, but the impact is not guaranteed and depends on the specific use case.	5

[coderabbitai]

Actionable comments posted: 0

Review details

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Commits

Files that changed from the base of the PR and between b236b76 and f1f0c42.

Files selected for processing (2)

• modin/core/dataframe/pandas/dataframe/dataframe.py (2 hunks)
• modin/core/dataframe/pandas/partitioning/partition_manager.py (2 hunks)

Additional comments not posted (3)

modin/core/dataframe/pandas/partitioning/partition_manager.py (2)

625-641: Ensure clarity and correctness in CPU-based mapping logic.

The logic for choosing between block-wise and axis-wise mapping based on CPU count is a good optimization. However, ensure that the condition np.prod(partitions.shape) <= 1.5 * CpuCount.get() is appropriate for all expected use cases. Consider edge cases where the number of partitions might be very low or very high.

To verify the correctness of the CPU-based mapping logic, ensure that the condition is tested under various scenarios with different partition sizes and CPU counts. Consider adding unit tests to cover these scenarios.

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642-671: Review axis determination and partition splitting logic.

The logic for determining the axis based on CPU count and partition dimensions is sound. However, ensure that the calculation CpuCount.get() // partitions.shape[1] for column_splits is valid for all cases, especially when partitions.shape[1] is zero or very small.

Test the axis determination logic with different partition shapes and CPU counts to ensure robustness. Consider edge cases where the partition dimensions are close to or exceed the CPU count.

modin/core/dataframe/pandas/dataframe/dataframe.py (1)

2208-2212: Simplification of map function logic looks good!

The removal of conditional checks related to CPU count simplifies the code and improves maintainability. Ensure that this change does not negatively impact performance in scenarios where CPU count was previously a factor.

Consider verifying the performance impact of this change, especially in multi-core environments.

Run the following script to verify the performance impact:

[furwellness]

Zero comments from coderabbit