Tuning Commands

Tags reference cli tuning

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

tuning init - Create Tuning Configuration

Generate sample unified tuning configurations for specific platforms.

Note

The create-sample-tuning command is deprecated. Use tuning init instead.

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

df-tuning - DataFrame Tuning Configuration

Manage tuning configurations for DataFrame platforms. Create sample configurations, validate existing configs, and view smart defaults for your system.

Subcommands

df-tuning create-sample - Create Sample Configuration

Generate a sample tuning configuration file for a specific DataFrame platform.

Options:

  • --platform TEXT: Target DataFrame platform (required): polars, pandas, dask, modin, cudf

  • --output TEXT: Output file path (default: {platform}_tuning.yaml)

  • --smart-defaults: Include auto-detected system-optimal settings

Usage Examples:

# Create sample Polars tuning config
benchbox df-tuning create-sample --platform polars

# Create with smart defaults based on your system
benchbox df-tuning create-sample --platform polars --smart-defaults

# Custom output path
benchbox df-tuning create-sample --platform pandas --output ./configs/pandas_tuning.yaml

df-tuning validate - Validate Configuration

Validate a DataFrame tuning configuration file for errors and warnings.

Options:

  • CONFIG_FILE: Path to configuration file to validate (required)

  • --platform TEXT: Target platform for validation (auto-detected from config if not specified)

Usage Examples:

# Validate a configuration file
benchbox df-tuning validate polars_tuning.yaml

# Validate with explicit platform
benchbox df-tuning validate my_config.yaml --platform polars

df-tuning show-defaults - Show Smart Defaults

Display recommended tuning settings based on your system profile.

Options:

  • --platform TEXT: Target DataFrame platform (required)

Usage Examples:

# Show defaults for Polars
benchbox df-tuning show-defaults --platform polars

# Show defaults for Dask
benchbox df-tuning show-defaults --platform dask

df-tuning list-platforms - List Supported Platforms

List all DataFrame platforms that support tuning configuration.

Usage:

benchbox df-tuning list-platforms

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)

For complete configuration reference, see DataFrame Platforms - Tuning.