Optimizing AST: Memory Layout And Traversal Techniques

by Alex Johnson 55 views

In this article, we will discuss various optimization techniques for Abstract Syntax Trees (ASTs), focusing on memory layout and traversal efficiency. These optimizations are crucial for improving the performance of applications that heavily rely on ASTs, such as compilers, interpreters, and database systems. The discussion category includes rjwalters and vibesql, highlighting the collaborative nature of this optimization effort. The following sections delve into the current state of AST optimization, identify opportunities for improvement, and outline implementation phases.

Current State of AST Optimization

Currently, significant progress has been made in optimizing ASTs through arena allocation. Arena allocation is a memory management technique where memory is allocated from a large, contiguous block (the arena), reducing the overhead associated with individual allocations. This has led to substantial improvements in parsing performance. Let's break down the progress made:

  • Phase 1: Arena AST types for SELECT statements: This phase focused on implementing arena allocation for the AST nodes representing SELECT statements, a fundamental part of SQL queries.
  • Phase 2: DML parsing integration with PreparedStatementCache: This phase integrated arena allocation with the PreparedStatementCache, further optimizing the parsing of Data Manipulation Language (DML) statements.
  • Phase 3: Full SELECT execution using arena types: This phase extended arena allocation to the full execution of SELECT statements, ensuring that the benefits of arena allocation are realized throughout the query processing pipeline.
  • Phase 4: ArenaPreparedStatement cache integration: This phase integrated the arena-based AST representation with the PreparedStatement cache, optimizing the reuse of prepared statements.

Recent benchmark results from TPC-C (Transaction Processing Performance Council Benchmark C) demonstrate the effectiveness of these optimizations:

Parse time: 38,280 µs total, 1.37 µs avg per query
Execute time: 11,948,154 µs total, 429.02 µs avg per query
Parse %: 0.3%

The results indicate that parsing is now highly optimized, accounting for only 0.3% of the total time. This means that the primary areas for further optimization lie in reducing cloning during execution, improving memory layout for cache efficiency, and developing better traversal abstractions. The next sections will explore these opportunities in detail.

Optimization Opportunities for ASTs

While arena allocation has significantly improved parsing performance, there are still several opportunities to optimize ASTs further. These opportunities include string interning, expression enum size reduction, visitor pattern abstraction, clone elimination, and flattening deeply nested expressions. Let's examine each of these in detail.

1. String Interning (loom:architect)

String interning is a technique used to optimize memory usage and improve performance by storing only one copy of each unique string. In the context of ASTs, column and table names are often stored as separate allocations, leading to redundancy as the same names (e.g., `