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PHASE_8_ANALYTICS.md

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Phase 8: Analytics Performance (Columnar & Vectorized)

Overview

Phase 8 introduced native columnar storage and a vectorized execution engine to drastically improve the performance of analytical workloads.

Key Components

1. Columnar Storage Layer (src/storage/columnar_table.cpp)

Implemented a high-performance column-oriented data store.

  • Binary Column Files: Stores data in contiguous binary files on disk, one per column.
  • Batch Read/Write: Optimized I/O paths for loading and retrieving large blocks of data efficiently.
  • Schema-Defined Layout: Automatically organizes data based on the table's schema definition.

2. Vectorized Data Structures (include/executor/types.hpp)

Developed SIMD-friendly contiguous memory buffers for batch processing.

  • ColumnVector & NumericVector: Specialized C++ templates for storing a "vector" of data for a single column.
  • VectorBatch: A collection of ColumnVector objects representing a chunk of rows (typically 1024 rows).

3. Vectorized Execution Engine (include/executor/vectorized_operator.hpp)

Built a batch-at-a-time physical execution model.

  • Vectorized Operators: Implemented Scan, Filter, Project, and Aggregate operators designed for chunk-based execution.
  • Batch-at-a-Time Interface: Operators pass entire VectorBatch objects between themselves, minimizing virtual function call overhead.

4. High-Performance Aggregation

Optimized global analytical queries (COUNT, SUM).

  • Vectorized Global Aggregate: Aggregates entire batches of data with minimal branching and high cache locality.
  • Type-Specific Aggregation: Leverages C++ templates to generate highly efficient aggregation logic for different data types.

Lessons Learned

  • Vectorized execution significantly outperforms the traditional Volcano model for large-scale analytical queries.
  • Columnar storage is essential for minimizing I/O overhead when only a subset of columns is accessed.

Status: 100% Test Pass

Successfully verified the end-to-end vectorized pipeline, including columnar data persistence and complex analytical query patterns, through dedicated integration tests.