构建列式查询引擎

项目概述

本项目实现一个面向 analytical query processing 的完整系统，核心要解决的问题是 scanning billions of rows for aggregates without deserializing irrelevant columns。整体由 Column reader、Predicate pushdown、Vectorized aggregate、Spill to disk、Query planner 五个模块组成，每个模块单一职责、可独立测试，最后通过集成测试验证端到端正确性。设计上把 a wide select that forces the engine to materialize many columns and exhausts the row-group cache 作为第一类需求先行处理，而非事后再补。

架构与关键决策

数据模型为 columns stored as typed, compressed arrays with min/max statistics per chunk to skip blocks during scans。Column reader 定义其余阶段共同依赖的数据形状并在边界处校验输入；Predicate pushdown 承载 analytical query processing 的正确性逻辑；Vectorized aggregate 专门处理 a wide select that forces the engine to materialize many columns and exhausts the row-group cache，在失败路径上给出明确的拒绝、重试或降级策略；Spill to disk 提供可观测性，每次操作输出带关联 id 的结构化事件；Query planner 在过载时按既定方式降级，避免过载演变为数据损坏。配置与代码分离，使风险变更可经 diff 审阅。

实施与测试

按五个阶段依次实现 Column reader、Predicate pushdown、Vectorized aggregate、Spill to disk、Query planner，每阶段配套聚焦单测。集成测试组合各模块并驱动真实 analytical query processing 场景：先验证正常路径，再刻意触发 a wide select that forces the engine to materialize many columns and exhausts the row-group cache 并断言系统降级而非损坏。所有依赖均在进程内或被桩替换，用例在 CI 中确定可复现。

限制与后续

• 单区域设计，多区域复制超出范围。
• Column reader 默认进程内，未水平共享。
• 无鉴权层，假定部署在强制身份的边缘之后。

代码示例

# setup for columnar-query-engine
pnpm init
pnpm add apache-arrow duckdb-async
pnpm add -D typescript tsx vitest

columnar_query_engine/
├── src/index.ts
├── src/column-reader.ts
├── src/predicate-pushdown.ts
├── tests/columnar_query_engine.test.ts
└── package.json

// Column reader: analytical query processing core
export function columnreader(input: ColumnreaderInput): ColumnreaderResult {
  // Build the column reader that anchors analytical query processing and defines the core data shape.
  return processColumnreader(input);
}

// Predicate pushdown — handles analytical query processing Add the predicate pushdown so
export class Predicatepushdown {
  constructor(private opts: PredicatepushdownOptions) {}
  async run(ctx: Context): Promise<PredicatepushdownOutput> {
    return this.execute(ctx);
  }
}

// Vectorized aggregate: analytical query processing stage
export const vectorizedaggregate = (next: Next) => async (req: Request) => {
  const out = await transform(req);
  return next(out);
};

// Spill to disk for analytical query processing
export async function spilltodisk(stream: AsyncIterable<Chunk>): Promise<Aggregate> {
  let acc = init();
  for await (const c of stream) acc = merge(acc, c);
  return acc;
}

// Query planner: analytical query processing finalization
export function queryplanner(state: State): Result {
  validate(state);
  return finalize(state);
}

# run the analytical query processing test suite
pnpm vitest run