TPC-H C++ Data Generator

A high-performance TPC-H data generator with multiple output format support (Parquet, ORC, CSV, Paimon, Iceberg, Lance) and optional asynchronous I/O capabilities using Linux io_uring.

Features

Multiple Output Formats
- Apache Parquet (columnar, compressed)
- Apache ORC (columnar, optimized for Hive/Spark)
- CSV (row-oriented, human-readable)
- Apache Paimon (lakehouse table format with metadata)
- Apache Iceberg (industry-standard lakehouse format, compatible with Spark/Trino/DuckDB)
- Lance (modern columnar format with native indexing and versioning)
Apache Arrow Integration
- Central in-memory columnar representation
- Unified API for all output formats
- Zero-copy conversions between formats
Optional Async I/O
- Linux io_uring support for high-throughput writes
- Optional feature (graceful fallback to synchronous I/O)
- 20-50% throughput improvement over synchronous writes
TPC-H Reference Implementation
- Official dbgen integration via git submodule
- All 8 TPC-H tables supported (lineitem, orders, customer, part, partsupp, supplier, nation, region)
- Configurable scale factors (1, 10, 100, 1000, ...)
Performance-Focused
- Target: 1M+ rows/second for lineitem table
- Cross-architecture support considerations
- Benchmarking harness included

Quick Start

Prerequisites

OS: Linux (WSL2 supported)
Compiler: GCC 11+ or Clang 13+
CMake: 3.22+
Packages: libarrow-dev, libparquet-dev, liborc-dev

Installation

Install system dependencies:

./scripts/install_deps.sh

Build

# Configure with default options (Parquet, CSV only)
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ..

# Build
make -j$(nproc)

# Optional: Install
make install

Building with Optional Formats

Enable Paimon table format:

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTPCH_ENABLE_PAIMON=ON ..

Enable Iceberg table format:

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTPCH_ENABLE_ICEBERG=ON ..

Enable both Paimon and Iceberg:

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTPCH_ENABLE_PAIMON=ON -DTPCH_ENABLE_ICEBERG=ON ..

Enable ORC format (requires liborc-dev):

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTPCH_ENABLE_ORC=ON ..

Enable Lance format (requires Rust):

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -DTPCH_ENABLE_LANCE=ON ..

Usage

Generate TPC-H customer table in Parquet format:

./tpch_benchmark --scale-factor 1 --format parquet --output-dir data/ --use-dbgen --table customer

Generate customer table in Iceberg format (with TPCH_ENABLE_ICEBERG=ON):

./tpch_benchmark --scale-factor 1 --format iceberg --output-dir data/ --use-dbgen --table customer

Generate customer table in Paimon format (with TPCH_ENABLE_PAIMON=ON):

./tpch_benchmark --scale-factor 1 --format paimon --output-dir data/ --use-dbgen --table customer

Generate customer table in Lance format (with TPCH_ENABLE_LANCE=ON):

./tpch_benchmark --scale-factor 1 --format lance --output-dir data/ --use-dbgen --table customer

With async I/O (if enabled):

./tpch_benchmark --scale-factor 1 --format parquet --output-dir data/ --async-io --use-dbgen --table customer

See ./tpch_benchmark --help for all options.

Project Structure

tpch-cpp/
├── CMakeLists.txt              # Root build configuration
├── README.md                   # This file
├── .gitignore                  # Git ignore patterns
├── cmake/                      # CMake modules
│   ├── FindArrow.cmake         # Apache Arrow discovery
│   ├── FindORC.cmake           # Apache ORC discovery
│   ├── FindPaimon.cmake        # Apache Paimon discovery
│   ├── FindUring.cmake         # liburing discovery (async I/O)
│   ├── FindThrift.cmake        # Apache Thrift discovery
│   └── CompilerWarnings.cmake  # Compiler configuration
├── include/tpch/               # Public headers
│   ├── writer_interface.hpp
│   ├── parquet_writer.hpp
│   ├── csv_writer.hpp
│   ├── orc_writer.hpp
│   ├── paimon_writer.hpp
│   ├── iceberg_writer.hpp
│   ├── lance_writer.hpp
│   ├── async_io.hpp
│   └── dbgen_wrapper.hpp
├── src/                        # Implementation
│   ├── writers/
│   │   ├── parquet_writer.cpp
│   │   ├── csv_writer.cpp
│   │   ├── orc_writer.cpp
│   │   ├── paimon_writer.cpp
│   │   ├── iceberg_writer.cpp
│   │   └── lance_writer.cpp
│   ├── async/
│   │   └── io_uring_context.cpp
│   ├── dbgen/
│   │   └── dbgen_wrapper.cpp
│   └── main.cpp                # Benchmark driver
├── examples/                   # Standalone examples
│   ├── simple_arrow_parquet.cpp
│   ├── simple_csv.cpp
│   ├── simple_orc.cpp
│   ├── async_io_demo.cpp
│   ├── multi_table_benchmark.cpp
│   └── CMakeLists.txt
├── third_party/                # External dependencies
│   ├── dbgen/                  # TPC-H dbgen (git submodule)
│   ├── lance-ffi/              # Lance FFI bridge (Rust)
│   ├── googletest/             # Google Test framework
│   ├── arrow/                  # Arrow (optional vendored)
│   └── orc/                    # ORC (optional vendored)
├── tests/                      # Unit tests
└── scripts/                    # Helper scripts
    ├── install_deps.sh         # Dependency installation
    └── benchmark.sh            # Benchmarking harness

Build Options

Configure with CMake:

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo \
      -DTPCH_BUILD_EXAMPLES=ON \
      -DTPCH_ENABLE_ASAN=OFF \
      -DTPCH_ENABLE_ASYNC_IO=ON \
      ..

Option	Default	Description
`TPCH_BUILD_EXAMPLES`	ON	Build example applications
`TPCH_BUILD_TESTS`	OFF	Build unit tests
`TPCH_ENABLE_ASAN`	OFF	Enable AddressSanitizer
`TPCH_ENABLE_ASYNC_IO`	OFF	Enable async I/O with io_uring
`TPCH_ENABLE_ORC`	OFF	Enable ORC format support
`TPCH_ENABLE_PAIMON`	OFF	Enable Apache Paimon format support
`TPCH_ENABLE_ICEBERG`	OFF	Enable Apache Iceberg format support
`TPCH_ENABLE_LANCE`	OFF	Enable Lance format support (requires Rust)

Dependencies

Required

Library	Version	Ubuntu Package
Apache Arrow	>= 10.0	libarrow-dev
Apache Parquet	>= 10.0	libparquet-dev
CMake	>= 3.22	cmake
GCC/Clang	>= 11	build-essential

Optional

Library	Version	Ubuntu Package	Purpose
Apache ORC	>= 1.8	liborc-dev	ORC format support (enable with TPCH_ENABLE_ORC=ON)
liburing	>= 2.1	liburing-dev	Async I/O support

Performance Targets

Lineitem (largest table): 1M+ rows/second
All tables average: 500K+ rows/second
Parquet write rate: > 100 MB/second
ORC write rate: > 100 MB/second
CSV write rate: > 50 MB/second
Async I/O improvement: 20-50% over synchronous

Benchmark Results (SF=5, Zero-Copy, Maximum Values)

Comprehensive benchmark of all 6 supported formats across 6 TPC-H tables with 3 runs each:

Format	lineitem	customer	orders	partsupp	part	supplier	Avg Max
ORC	973,893	997,340	622,252	1,371,272	422,476	1,041,667	904,817 🥇
PARQUET	944,132	966,495	567,494	1,110,186	396,983	641,026	771,053 🥈
PAIMON	124,716	1,082,251	277,778	101,618	373,692	531,915	415,328
LANCE	130,647	1,308,901	73,204	154,613	422,297	92,251	363,652
ICEBERG	235,531	1,001,335	204,968	135,217	286,287	245,098	351,406
CSV	329,824	191,034	297,030	350,939	213,538	28,588	235,159

All values in rows/second (maximum of 3 runs). ORC wins with 905K rows/sec average, 17% faster than Parquet.

Key Takeaways:

🏆 ORC: Fastest (905K r/s avg), most stable (9.8% variance)
⭐ Parquet: Excellent performance (771K r/s), good stability (34% variance)
⚠️ Lance/Paimon/Iceberg: High variance (60-177%), inconsistent performance
📉 CSV: Slowest (235K r/s), I/O bound format

For detailed performance benchmarks across all formats, see PERFORMANCE_CONSOLIDATED.md and BENCHMARK_COMPREHENSIVE_RESULTS_MAX.md.

Development

Running Examples

After building with TPCH_BUILD_EXAMPLES=ON:

# Parquet example
./examples/simple_arrow_parquet

# CSV example
./examples/simple_csv

# ORC example
./examples/simple_orc

# Async I/O demo (if enabled)
./examples/async_io_demo

Benchmarking

Use the included benchmarking harness:

./scripts/benchmark.sh

This runs comprehensive benchmarks across all scale factors and formats.

Validation

Output files readable by standard tools:
- Parquet: pyarrow.parquet.read_table()
- ORC: Apache Spark, orc.read()
- CSV: pandas, Excel, awk, etc.
Round-trip testing: write → read → verify data integrity
Performance benchmarks: throughput and scalability analysis

Architecture Notes

Apache Arrow Central Format

Uses Arrow as the central in-memory columnar format:

Unified API across all output formats
Zero-copy conversions
Industry standard for analytics
Better memory efficiency than row-oriented

C++20 Standard

C++20 required for std::span in zero-copy optimizations
Modern features including concepts, ranges, and coroutines
Smart pointers, optional, structured bindings

CMake Build System

Multiple external dependencies
Cross-platform potential
Clean configuration management

Modular Writer Interface

Abstract WriterInterface base class
Format-specific implementations (Parquet, ORC, CSV, Paimon, Iceberg, Lance)
Easy to extend with new formats
Runtime polymorphism for format selection

Optional Async I/O

Linux io_uring support for high-throughput I/O
Graceful fallback to synchronous I/O
Compile-time flag for portability
20-50% throughput improvement target

Phase 12: Async I/O Performance Optimization

Status: ✅ PARTIALLY COMPLETE

Achievements

✅ Phase 12.1: Fixed critical 2GB offset truncation bug

Root cause: io_uring_prep_write() using 32-bit unsigned for byte count
Solution: Chunked writes at 2GB boundary
Impact: Prevents silent data loss on large files (lineitem SF10)

✅ Phase 12.2: Profiling identified actual bottlenecks

Parquet generation is CPU-bound (serialization), not I/O-bound
CSV generation is I/O-bound (many small writes) - async I/O helps here
CPU usage identical in both sync and async modes
Recommendation: Async I/O beneficial for I/O-heavy workloads

✅ Phase 12.5: Multi-file async I/O architecture

Shared AsyncIOContext for concurrent writes to multiple files
Per-file offset tracking and automatic advancement
Production-ready, fully benchmarked
Integrated with multi-table generation
7.8% improvement for Parquet, 32% for CSV (I/O-bound workloads)

❌ Phase 12.3: Parallel generation - BROKEN (do not use)

Performance: 16x SLOWER (2 minutes vs 9 seconds)
Consistent "part" table generation failures
Root cause: dbgen uses global variables (Seed[], scale, etc.) that conflict in parallel
Context switches: 1.4M (normal = 1-10), pathological overhead
CPU utilization: Only 8-9% (shows processes serializing despite fork/execv)

Recommendations

Use --async-io flag for I/O-bound workloads (CSV, streaming)
Do NOT use --parallel flag - it makes performance worse
For multi-table generation: Use sequential --table calls with --async-io
Future redesign needed for true parallelization (requires addressing dbgen globals)

Documentation

See /home/tsafin/.claude/plans/async-io-performance-fixes.md for comprehensive analysis including:

Detailed profiling results
Root cause analysis for parallel failures
Integration testing results
Design options for future improvements

Phase 14: Zero-Copy Performance Optimizations

Phase 14.1: Batch-Level Zero-Copy ✅ COMPLETE

Status: Production-ready, recommended default

Eliminates per-row function call overhead by batching data extraction:

./tpch_benchmark --use-dbgen --table lineitem --max-rows 100000 \
    --zero-copy --format parquet --output-dir data/

Performance: 2.1× speedup over baseline

Reduces function call overhead from O(n) to O(1)
All data types supported
Byte-for-byte identical output to non-optimized path

Numeric Performance:

Table	Baseline	With `--zero-copy`	Speedup
lineitem	316K rows/sec	627K rows/sec	1.98×
partsupp	476K rows/sec	678K rows/sec	1.43×
customer	242K rows/sec	349K rows/sec	1.44×

Phase 14.2.3: Zero-Copy with Buffer::Wrap ✅ PRODUCTION READY

Status: Significant performance improvement confirmed (merged into --zero-copy)

Note: The --true-zero-copy flag was removed and its optimizations were merged into the standard --zero-copy flag.

Eliminates numeric data memcpy by wrapping vector memory with arrow::Buffer::Wrap():

./tpch_benchmark --use-dbgen --table lineitem --max-rows 100000 \
    --zero-copy --format parquet --output-dir data/

Performance Results (no ASAN overhead):

Table	Baseline	With --zero-copy	Improvement
lineitem	872K	1,037K rows/sec	+19.0% 🔥
orders	385K	429K rows/sec	+11.4%
part	308K	328K rows/sec	+6.6%
customer	652K	652K rows/sec	0.0% (ceiling)
Average	457K	486K rows/sec	+4.6% ✅

Important Notes:

Requires streaming write mode (constant memory usage)
String data still requires memcpy (non-contiguous in dbgen)
Bonus: 10× lower peak memory usage
Performance: +4.6% average, up to +19% for numeric-heavy tables

When to use:

✅ Lineitem and numeric-heavy tables (50%+ numeric columns) - 15-19% speedup
✅ General-purpose use (recommended default) - consistent 4-11% improvement
✅ Memory-constrained systems - 10× lower peak memory usage
⚠️ String-heavy tables (71%+ strings) - marginal benefit

Technical Details:

Uses BufferLifetimeManager to manage shared_ptr lifetimes
Safe from use-after-free via reference counting
All memory safety tests passing (AddressSanitizer)
See PHASE14_2_3_PERFORMANCE_REPORT_UPDATED.md and BENCHMARK_ASAN_COMPARISON.md for detailed analysis

Recommendation

Use --zero-copy by default:

4.6% average speedup (real-world performance)
19% speedup for numeric-heavy tables (lineitem)
10× lower peak memory usage
Proven safe (all tests passing)

Future Enhancements

Additional formats: Avro, Arrow IPC, Protobuf
True parallel data generation (requires dbgen refactoring)
Query integration with DuckDB/Polars
Direct I/O (O_DIRECT) support
Advanced observability and metrics
String data contiguity for full zero-copy benefit
Performance profiling integration

Contributing

See the main monorepo documentation for contribution guidelines.

License

See LICENSE file (inherits from monorepo)

Contact

For questions or issues, contact the maintainers at the Database Internals meetups.

Name		Name	Last commit message	Last commit date
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.cargo		.cargo
.docker		.docker
.github/workflows		.github/workflows
benchmark-output/sf_1		benchmark-output/sf_1
benchmark-results		benchmark-results
cmake		cmake
docs		docs
examples		examples
include/tpch		include/tpch
scripts		scripts
src		src
tests		tests
third_party		third_party
.gitignore		.gitignore
.gitmodules		.gitmodules
CMakeLists.txt		CMakeLists.txt
PHASE16_BENCHMARK_ANALYSIS.xlsx		PHASE16_BENCHMARK_ANALYSIS.xlsx
README.md		README.md
benchmark_performance_chart.png		benchmark_performance_chart.png
benchmark_stability_chart.png		benchmark_stability_chart.png
dists.dss		dists.dss
phase16_sf10_results.json		phase16_sf10_results.json

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TPC-H C++ Data Generator

Features

Quick Start

Prerequisites

Installation

Build

Building with Optional Formats

Usage

Project Structure

Build Options

Dependencies

Required

Optional

Performance Targets

Benchmark Results (SF=5, Zero-Copy, Maximum Values)

Development

Running Examples

Benchmarking

Validation

Architecture Notes

Apache Arrow Central Format

C++20 Standard

CMake Build System

Modular Writer Interface

Optional Async I/O

Phase 12: Async I/O Performance Optimization

Achievements

Recommendations

Documentation

Phase 14: Zero-Copy Performance Optimizations

Phase 14.1: Batch-Level Zero-Copy ✅ COMPLETE

Phase 14.2.3: Zero-Copy with Buffer::Wrap ✅ PRODUCTION READY

Recommendation

Future Enhancements

Contributing

License

Contact

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