DuckDB Unveiled: Lightweight Analytical Engine

Recently, I discovered DuckDB, an open-source analytical database ideal for in-depth learning. Simply put, DuckDB is the OLAP (column-store) equivalent of SQLite. As a learning resource, DuckDB offers several advantages:

1. ​Zero external dependencies—compilation, linking, and execution are straightforward.
2. ​Concise, high-quality code—130k–140k lines (excluding tests).
3. ​Compact yet feature-rich—follows textbook-modular design for clarity.
4. ​Integrated OLAP knowledge—implementations reference academic papers for easy study.

​Comparison with LevelDB​
LevelDB (~20k lines) is a simple key-value store. DuckDB is a full database system, including:

• SQL Parser
• Optimizer
• Execution Engine
• Transaction Manager
• Storage Engine

​Key Modules​

1. ​SQL Parser​
   Adapted from PostgreSQL’s libpg_query. Converts SQL to a C parse tree, then transforms it into DuckDB’s C++ objects.
2. ​Logical Planner​
   • ​Binder: Links parse tree to schema (column names, types) via the catalog.
   • ​Plan Generator: Produces logical operator trees (scan, filter, project, etc.).
3. ​Optimizer​
   Implements rule-based and cost-based optimization:
   • Predicate pushdown
   • Expression rewriting
   • Join reordering
4. ​Execution Engine​
   Uses ​vectorized interpretation​ (SIMD acceleration). Avoids compilation (e.g., LLVM) to minimize binary size.
5. ​Transaction & Concurrency​
   Supports Serializable isolation.
6. ​Storage​
   Columnar storage engine for efficient I/O.

​Build & Run

git clone https://github.com/duckdb/duckdb
cd duckdb
BUILD_BENCHMARK=1 BUILD_TPCH=1 make

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Execute benchmarks:

# List benchmarks
build/release/benchmark/benchmark_runner --list

# Run specific benchmark
build/release/benchmark/benchmark_runner benchmark/tpch/sf1/q01.benchmark

# Run all benchmarks
build/release/benchmark/benchmark_runner

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