What is Vectorization?

Vectorization in the context of databases refers to the process of optimizing database operations to take advantage of modern CPU architectures. This involves processing multiple data elements in parallel within a single CPU instruction cycle, leveraging a method known as SIMD (Single Instruction, Multiple Data). This contrasts with the SISD (Single Instruction, Single Data) architecture, where each instruction handles a single data point. In SIMD, operations that would typically require multiple load, add, and store operations in SISD are condensed, significantly improving performance.


The Impact of Vectorization on Database Performance


Assessing CPU Performance in the Context of Vectorization

To grasp the impact of vectorization, it's essential to understand CPU performance measurement and influencing factors. The formula for CPU time is a starting point:
CPU time = (Instruction numbers) * CPI * (Clock cycle time)
Here, 'Instruction numbers' represent the total instructions the CPU processes, 'CPI' (cycles per instruction) signifies the CPU cycles needed for each instruction, and 'Clock cycle time' is the duration of one CPU clock cycle. Since altering the clock cycle time isn't feasible, focusing on instruction numbers and CPI is key to enhancing software performance.

CPU Instruction Execution and Vectorization

CPU instruction execution comprises five steps: fetching, decoding, execution, memory access, and result write-back. The first two steps are managed by the CPU front end, while the latter three are handled by the CPU back end. Performance issues primarily arise from retiring, bad speculation, frontend bound, and backend bound, often attributed to insufficient SIMD instruction optimization, branch prediction errors, and cache misses.

Vectorizing Programs: Methods and Verification

Vectorization can be implemented in several ways, each varying in complexity and programmer involvement:
  • Auto-vectorization by Compiler: Requires no code changes. The compiler automatically converts scalar code to vector code.
  • Hints to Compiler: Providing hints enhances the compiler's ability to generate SIMD code.
  • Parallel Programming APIs: Tools like OpenMP or Intel TBB allow developers to add annotations to generate vector code.
  • SIMD Class Libraries: These libraries facilitate the use of SIMD instructions.
  • SIMD Intrinsics: These are assembly-coded functions allowing the use of C++ function calls for SIMD.
  • Writing Assembly Code Directly: This method demands high expertise in programming.
  • To verify vectorization, one can either use compiler options to get feedback on vectorization or review the executed assembly code for SIMD registers and instructions.


Overcoming the Challenges of Implementing Database Vectorization

Database vectorization is not merely about activating SIMD capabilities. It entails a comprehensive overhaul of database architecture, including:
  • End-to-end Columnar Data: Requires storing, transferring, and processing data in columnar format to eliminate impedance mismatch.
  • Vectorizing All Components: All database operators, expressions, and functions need to be vectorized.
  • Optimizing for SIMD Instruction Usage: This involves detailed optimization for invoking SIMD instructions.
  • Memory Management: Re-thinking memory management to fully leverage SIMD's parallel processing capabilities.
  • Developing New Data Structures: Core operators like join, aggregate, and sort need to be designed from the ground up to support vectorization.
  • Systematic Optimization: Comprehensive optimization of all database system components is necessary for significant performance improvements.

Strategies for Maximizing Database Performance with Vectorization

Vectorizing databases is an extensive engineering process, and StarRocks serves as a prime example. In recent years, numerous optimizations have been applied in the development of StarRocks. The key areas of focus for these optimizations include:
  • Utilization of High-Performance Third-Party Libraries: Leveraging powerful open-source libraries for data structures and algorithms is crucial. Libraries such as Parallel Hashmap, Fmt, SIMD Json, and Hyper Scan have been instrumental in this regard.
  • Optimization of Data Structures and Algorithms: Efficient data structures and algorithms play a significant role in reducing CPU cycles substantially. For instance, the introduction of a low-cardinality global dictionary in newer database versions has enabled the transformation of string-based operations into more efficient integer-based operations, leading to over 300% improvement in query performance.
  • Self-Adaptive Optimization: This involves optimizing query execution based on the specific context of each query, which often isn't fully known until execution time. The query engine dynamically adjusts its strategy based on real-time context information, thereby enhancing performance.
  • Strategic SIMD Optimization: Implementing a range of SIMD optimizations in database operators and expressions is a critical step. This process involves fine-tuning various database functions to align with SIMD capabilities.
  • Low-Level C++ Optimization: Different C++ implementations, even with identical data structures and algorithms, can yield varying performance outcomes. Optimizations may include tweaking move or copy operations, reserving vectors, or inlining function calls.
  • Memory Management Enhancement: As batch sizes increase and operations become more concurrent, efficient memory allocation and management become more critical. Innovative solutions, such as the use of a column pool data structure, have been developed to improve memory utilization, significantly boosting query performance.
  • CPU Cache Optimization: CPU cache misses significantly affect performance, measured in the increased CPU cycles required for memory access at different cache levels. After implementing SIMD optimization, addressing CPU cache misses through methods like prefetching becomes essential, although it's challenging to control their timing and distance effectively.
In summary, database vectorization is a multifaceted endeavor, requiring a combination of advanced techniques and optimizations across various aspects of database architecture and programming. These improvements collectively contribute to significantly enhanced database performance, demonstrating the power of vectorization in modern database systems


Vectorization: The Future of Database Performance

  • A Holistic Approach to Performance: Achieving high-performance databases through vectorization requires a blend of well-thought-out architecture and detailed engineering. This balance is crucial for maximizing the benefits of database vectorization.
  • Exploring Beyond Traditional CPUs: The future of database performance may lie in exploring new hardware frontiers, such as GPUs and FPGAs, to push the limits of database vectorization further.
  • The Power of Community and Innovation: Projects like StarRocks showcase the immense potential of community-driven development in enhancing database performance. The constant push towards innovation and challenging the status quo has led to significant strides in the field of database vectorization.



Vectorization is a key driver in the ongoing evolution of database technology, offering a pathway to dramatic performance improvements. As data volumes and processing demands grow, understanding and implementing vectorization becomes increasingly critical. Embracing this technology is essential for anyone seeking to stay at the cutting edge of database performance and efficiency.