Boosting DataFusion: Preserving Scalar Values For Faster Queries

DataFusion, a powerful query engine, is constantly evolving to optimize performance. One area ripe for improvement involves how scalar information, values that are the same across an entire batch of data, is handled as data moves between different ExecutionPlan stages. This article dives into a proposed API change designed to preserve scalar information, leading to significant performance gains in operations like sorting and aggregation. Let's explore why this change is necessary, how it works, and the benefits it unlocks. DataFusion's current architecture, while robust, can lose track of scalar values as data flows through various ExecutionPlan steps. This means that optimizations that could be applied when dealing with scalar columns are missed, leading to slower query execution times. The proposed solution aims to rectify this by allowing ExecutionPlan to return a stream that retains scalar knowledge, allowing DataFusion to make more informed decisions.

The Core Problem: Loss of Scalar Information

Currently, DataFusion's architecture doesn't inherently preserve information about scalar values as data is processed between ExecutionPlan components. This is a crucial area for optimization because scalar columns offer unique opportunities to improve query performance. Preserving scalar information allows the query engine to make smarter decisions, skipping unnecessary operations and streamlining data processing. When a column contains a scalar value, it means every row in the batch has the same value for that column. However, the current system treats these scalar columns the same way it treats columns with varying data, missing out on potential performance advantages. For instance, when sorting data, if a column being sorted on contains a scalar value, there's no need to perform a full sort operation because all values are already effectively in order. Similarly, in aggregation operations like grouping, scalar columns can bypass the computationally expensive hashing and comparison steps. The proposed API change directly addresses this by introducing a mechanism to retain scalar knowledge throughout the data processing pipeline. This change will require modifying how ExecutionPlan returns data, ensuring that scalar information is carried along with the RecordBatches.

Proposed Solution: Introducing ReturnedValue and RecordBatchWithScalars

The heart of the proposed solution is a new approach to how ExecutionPlan returns data. Instead of simply returning a stream of RecordBatches, the idea introduces a new enum, ReturnedValue, which can represent either a standard RecordBatch or a RecordBatchWithScalars. This design allows DataFusion to differentiate between regular data batches and batches that contain scalar information. This is a strategic enhancement that allows DataFusion to identify and act upon scalar columns effectively. The ReturnedValue enum is designed to encapsulate two distinct data representations: Batch(RecordBatch) for standard data and BatchWithScalars(RecordBatchWithScalars) for batches that include scalar information. This approach is fundamental to the proposed API change, providing a clear way to distinguish between data batches that contain scalar information and those that do not. The RecordBatchWithScalars structure is designed to be very similar to RecordBatch but with an important addition. In this structure, the columns are represented by ColumnarValue. This enum can be either an Array (for regular column data) or a Scalar (for scalar values). This flexibility is what allows the system to efficiently handle both standard columns and scalar columns. This enables the engine to handle scalar values more efficiently, avoiding unnecessary data copying or computations. The introduction of ColumnarValue allows the engine to distinguish between regular column data and scalar values. The Scalar variant uses an Arc-wrapped ScalarValue to avoid unnecessary copying of scalar values, making it efficient for use throughout the system.

    enum ReturnedValue {
        Batch(RecordBatch),
        BatchWithScalars(RecordBatchWithScalars)
    }

    enum ColumnarValue {
       Array(ArrayRef),
       Scalar(Arc<ScalarValue>),
    }

    struct RecordBatchWithScalars {
        schema: SchemaRef,
        columns: Vec<ColumnarValue>,
        row_count: usize,
    }

Benefits Unveiled: Optimizing Sort and Aggregation Operations

The primary benefits of this proposed change manifest in two key areas: sort operations and aggregation. Let's explore how DataFusion can leverage scalar information to improve performance in these critical query processing steps. Optimizing sort operations is a major advantage. When sorting, the engine can skip sorting columns containing scalar values, copying the scalar columns more efficiently, or only partially copying them. This is because, by definition, a scalar column is already sorted. This reduces the computational load and time required for the sort operation, leading to faster query execution. Sort operations can also output scalars for quicker downstream operations, further streamlining the data flow. This proactive approach ensures that DataFusion can make informed decisions based on the nature of the data it's processing. It is designed to recognize and exploit the characteristics of scalar values, leading to significant efficiency gains during sort operations. The ability to identify and utilize scalar information during sorting is a substantial performance optimization. Aggregation operations also see significant improvements. When grouping by a scalar column, the expensive hashing and comparison steps can be entirely avoided. This is because, since the grouping column contains only one unique value, all rows automatically belong to the same group. Aggregation operations, like GROUP BY, become significantly faster. The engine can directly compute the aggregated value without going through the usual hashing and comparison phases. This optimization is a direct result of the enhanced scalar awareness in the proposed API change. By skipping these operations, DataFusion can dramatically reduce the processing time, making queries more efficient and responsive. This can have a profound impact on the overall performance of the query engine, especially when dealing with large datasets and complex queries.

Potential Downsides and Complexity

While the advantages are clear, it's important to acknowledge the potential downsides and complexities associated with this proposed API change. These considerations are vital to evaluate the feasibility and impact of the changes. The primary downside is the introduction of a significant breaking change. This modification to the core API could require substantial code adjustments across various parts of the DataFusion codebase, along with potential compatibility issues with existing code that depends on the current API. This is a crucial factor, and careful planning and execution would be needed to ensure a smooth transition. The increase in code complexity is another factor. The introduction of new data structures like ReturnedValue, ColumnarValue, and RecordBatchWithScalars adds to the overall complexity of the code. This could potentially increase the effort required for development, testing, and maintenance. However, the benefits in terms of performance improvements could offset the increased complexity. It's a trade-off that requires careful consideration. Thorough testing and documentation will be necessary to mitigate these risks. Despite these challenges, the performance gains offered by the proposed API change are compelling. The decision to move forward with the change would depend on a careful balance between the increased complexity and the performance improvements. The development team would need to evaluate the impact on existing code and ensure a smooth migration process. This includes thorough testing and comprehensive documentation to facilitate adoption. The breaking change requires a well-planned migration strategy.

Potential Extensions: Row-Based Encoding

To further expand the capabilities, a potential extension involves adding another variant for row-based encoding. This could allow for even greater flexibility and efficiency in specific use cases. An additional ReturnedValue variant, EncodedRows(Rows), could be introduced. This would allow operators that process data as rows to pass encoded rows directly to the next operator. This avoids unnecessary conversions between columnar and row formats. The receiving operator can then decide whether to use row-based input directly or convert to columnar format. This approach provides flexibility and efficiency. The receiving operator can choose the best approach based on its own needs. It avoids unnecessary format conversions when possible, which could provide additional performance benefits, particularly for operators that natively work with rows. This extension could further optimize data processing in scenarios where row-based processing is beneficial, enhancing the overall performance of DataFusion. This can lead to significant gains in specialized data processing tasks. The ability to directly pass encoded rows can eliminate overhead in format conversions. This extension would involve adding a new variant to ReturnedValue that allows for row-based encoding.

Conclusion: A Step Towards Enhanced Data Processing

This proposed API change represents a significant step towards optimizing DataFusion's performance, particularly in sort and aggregation operations. By preserving scalar information throughout the data processing pipeline, the query engine can make more informed decisions, leading to faster query execution times and improved overall efficiency. Although the change introduces complexities and breaking changes, the performance gains and potential extensions make it a worthwhile consideration for the DataFusion project. The benefits of optimized sort and aggregation operations, coupled with the potential for row-based encoding, suggest a promising future for DataFusion. This enhancement showcases a commitment to optimizing query execution and improving overall efficiency. It is a vital step in enhancing its capabilities and maintaining its competitive edge in the data processing landscape. The implementation would require careful planning. The implementation would also require meticulous testing and thorough documentation. This will facilitate adoption and ensure a smooth transition for users. It underscores the ongoing dedication to innovation. This is done to provide a powerful and efficient query engine. The proposed API change is a strategic investment in the long-term performance and capabilities of the DataFusion query engine.

For further details on DataFusion and its development, you can explore the official Apache DataFusion website:

• Apache DataFusion