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Tuning Commands

Commands for creating and managing tuning configurations for SQL and DataFrame platforms.

(cli-tuning-init)=

tuning init - Create Tuning Configuration

Generate sample unified tuning configurations for specific platforms.

Options

  • --platform TEXT: Target platform (required) - duckdb, databricks, snowflake, etc.
  • --output TEXT: Output file path (default: tuning_config.yaml)

Usage Examples

# Create sample tuning for Databricks
benchbox tuning init --platform databricks

# Create with custom output path
benchbox tuning init --platform snowflake \
  --output ./configs/snowflake-tuning.yaml

(cli-tuning-dataframe)=

DataFrame Tuning via tuning Commands

The unified tuning command group handles both SQL and DataFrame platforms. Use --mode dataframe (or let it auto-detect from the platform name).

Create DataFrame Tuning Configuration

# Create sample Polars tuning config (auto-detects DataFrame mode)
benchbox tuning init --platform polars

# Create with smart defaults based on your system
benchbox tuning init --platform polars --smart-defaults

# Explicit DataFrame mode with profile
benchbox tuning init --platform pandas --mode dataframe --profile memory-constrained

# Custom output path
benchbox tuning init --platform dask --output ./configs/dask_tuning.yaml

Options for DataFrame mode:

  • --platform TEXT: Target platform (polars, pandas, dask, modin, cudf)
  • --mode [sql|dataframe|auto]: Tuning mode (default: auto)
  • --profile [default|optimized|streaming|memory-constrained|gpu]: Configuration profile
  • --output TEXT: Output file path
  • --smart-defaults: Use auto-detected system-optimal settings

Validate Configuration

benchbox tuning validate polars_tuning.yaml --platform polars

Show Smart Defaults

benchbox tuning defaults --platform polars

Configuration Categories

DataFrame tuning supports these configuration sections:

Category Settings Description
parallelism thread_count, worker_count, threads_per_worker CPU resource allocation
memory memory_limit, chunk_size, spill_to_disk, rechunk_after_filter Memory management
execution streaming_mode, lazy_evaluation, engine_affinity Execution behavior
data_types dtype_backend, enable_string_cache, auto_categorize_strings Type handling
io memory_pool, memory_map, pre_buffer, row_group_size I/O optimization
gpu enabled, device_id, spill_to_host, pool_type GPU settings (cuDF)
write sort_by, partition_by, row_group_size, target_file_size_mb, repartition_count, compression, compression_level, dictionary_columns, skip_dictionary_columns, data_page_version Write-time physical layout and Parquet encoding

For complete configuration reference, see DataFrame Platforms - Tuning.

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