Modin DataFrame Platform¶

Tags intermediate guide modin dataframe-platform

Modin is a drop-in replacement for Pandas that enables parallel and distributed DataFrame operations. BenchBox supports benchmarking Modin using its Pandas-compatible API through the modin-df platform.

Overview¶

Attribute	Value
CLI Name	`modin-df`
Family	Pandas
Execution	Eager (parallel)
Best For	Large datasets that don’t fit in single-core Pandas
Min Version	0.32.0

Features¶

Drop-in Pandas replacement - Same API as Pandas with automatic parallelization
Multiple backends - Ray (default), Dask, or Unidist execution engines
Automatic parallelization - No code changes needed for parallel execution
Full TPC-H support - All 22 queries implemented via Pandas family

How Modin Works¶

Modin partitions DataFrames across CPU cores and executes operations in parallel:

                  ┌─────────┐
                  │  Modin  │
                  │DataFrame│
                  └────┬────┘
                       │
         ┌─────────────┼─────────────┐
         ▼             ▼             ▼
    ┌─────────┐   ┌─────────┐   ┌─────────┐
    │Partition│   │Partition│   │Partition│
    │   1     │   │   2     │   │   3     │
    └────┬────┘   └────┬────┘   └────┬────┘
         │             │             │
         ▼             ▼             ▼
    ┌─────────┐   ┌─────────┐   ┌─────────┐
    │  Core   │   │  Core   │   │  Core   │
    │   1     │   │   2     │   │   3     │
    └─────────┘   └─────────┘   └─────────┘

Installation¶

# Install Modin with Ray backend (recommended)
uv add benchbox --extra dataframe-modin

# Or with pip
pip install "benchbox[dataframe-modin]"

# For Dask backend
pip install "modin[dask]"

# For Unidist backend (experimental)
pip install "modin[unidist]"

Verify Installation¶

python -c "import modin.pandas as pd; print(f'Modin ready')"

Quick Start¶

# Run TPC-H on Modin DataFrame platform (uses Ray backend by default)
benchbox run --platform modin-df --benchmark tpch --scale 0.1

# Specify the Ray backend explicitly
benchbox run --platform modin-df --benchmark tpch --scale 1 \
  --platform-option engine=ray

# Use Dask backend
benchbox run --platform modin-df --benchmark tpch --scale 1 \
  --platform-option engine=dask

Configuration Options¶

Option	Default	Choices	Description
`engine`	`ray`	`ray`, `dask`, `unidist`	Execution backend

Backend Comparison¶

Backend	Best For	Notes
Ray (default)	Most use cases	Best single-machine performance
Dask	Distributed clusters	Integrates with existing Dask infrastructure
Unidist	MPI environments	Experimental, for HPC clusters

Scale Factor Guidelines¶

Modin can handle larger datasets than Pandas by utilizing multiple cores:

Scale Factor	Data Size	Memory Required	Cores Used
0.01	~10 MB	~500 MB	1-2
0.1	~100 MB	~1 GB	2-4
1.0	~1 GB	~4 GB	4-8
10.0	~10 GB	~40 GB	8-16
100.0	~100 GB	~400 GB	16-32+

Recommendation: For SF > 10 with limited memory, consider using Dask DataFrame (dask-df) for out-of-core processing.

Performance Characteristics¶

Strengths¶

Easy migration - Change import pandas as pd to import modin.pandas as pd
Automatic parallelization - Operations scale across available CPU cores
Memory efficiency - Partitioned execution reduces peak memory
Familiar API - No learning curve for Pandas users

Considerations¶

Overhead - Small datasets may be slower due to parallelization overhead
API coverage - Not all Pandas operations are equally optimized
Backend dependency - Requires Ray, Dask, or Unidist

When to Use Modin¶

Use Case	Recommendation
Pandas code with large data	Use `modin-df`
Small data (< 1GB)	Use `pandas-df` (less overhead)
Distributed cluster	Use `dask-df`
GPU available	Use `cudf-df`
Production analytics	Use `polars-df` (optimized)

Parallelization Behavior¶

Modin can utilize multiple CPU cores for parallel execution. The actual performance improvement depends on:

Operation type (some operations parallelize better than others)
Dataset size (larger datasets benefit more from parallelization)
Data distribution and partition characteristics
Available CPU cores and memory bandwidth
Backend engine (Ray, Dask, or Unidist)

Run benchmarks with your specific workloads to measure actual performance characteristics.

Query Implementation¶

Modin uses the same Pandas-compatible API:

# TPC-H Q1: Pricing Summary Report (Modin)
def q1_pandas_impl(ctx: DataFrameContext) -> Any:
    lineitem = ctx.get_table("lineitem")  # Modin DataFrame

    cutoff = date(1998, 12, 1) - timedelta(days=90)
    filtered = lineitem[lineitem["l_shipdate"] <= cutoff]

    filtered = filtered.copy()
    filtered["disc_price"] = filtered["l_extendedprice"] * (1 - filtered["l_discount"])
    filtered["charge"] = filtered["disc_price"] * (1 + filtered["l_tax"])

    result = (
        filtered
        .groupby(["l_returnflag", "l_linestatus"], as_index=False)
        .agg({
            "l_quantity": ["sum", "mean"],
            "l_extendedprice": ["sum", "mean"],
            "disc_price": "sum",
            "charge": "sum",
            "l_discount": "mean",
            "l_orderkey": "count"
        })
        .sort_values(["l_returnflag", "l_linestatus"])
    )

    return result

Python API¶

from benchbox.platforms.dataframe import ModinDataFrameAdapter

# Create adapter with Ray backend
adapter = ModinDataFrameAdapter(
    working_dir="./benchmark_data",
    engine="ray"
)

# Create context and load tables
ctx = adapter.create_context()
adapter.load_tables(ctx, data_dir="./tpch_data")

# Execute query
from benchbox.core.tpch.dataframe_queries import TPCH_DATAFRAME_QUERIES
query = TPCH_DATAFRAME_QUERIES.get_query("Q1")
result = adapter.execute_query(ctx, query)
print(result)

Troubleshooting¶

Ray Initialization Errors¶

ray.exceptions.RaySystemError: System error: ...

Solutions:

Ensure Ray is properly installed: pip install ray
Initialize Ray manually: ray.init()
Check for port conflicts on default Ray ports

Slow Performance¶

If Modin is slower than expected:

Check dataset size - Small datasets have parallelization overhead
Check operation - Some operations fall back to Pandas
Check memory - Insufficient memory causes swapping

# Check if operation fell back to Pandas
import modin.config as cfg
cfg.IsExperimental.put(False)  # Disable fallback warnings

Memory Issues¶

MemoryError: ...