Execution Engine Configuration

DataStore can execute operations using different backends. This guide explains how to configure and optimize engine selection.

Available Engines

Engine	Description	Best For
auto	Automatically selects best engine per operation	General use (default)
chdb	Forces all operations through ClickHouse SQL	Large datasets, aggregations
pandas	Forces all operations through pandas	Compatibility testing, pandas-specific features

Setting the Engine

Global Configuration

from chdb.datastore.config import config

# Option 1: Using set method
config.set_execution_engine('auto')    # Default
config.set_execution_engine('chdb')    # Force ClickHouse
config.set_execution_engine('pandas')  # Force pandas

# Option 2: Using shortcuts
config.use_auto()     # Auto-select
config.use_chdb()     # Force ClickHouse
config.use_pandas()   # Force pandas

Checking Current Engine

print(config.execution_engine)  # 'auto', 'chdb', or 'pandas'

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Auto Mode

In auto mode (default), DataStore selects the optimal engine for each operation:

Operations Executed in chDB

• SQL-compatible filtering (filter(), where())
• Column selection (select())
• Sorting (sort(), orderby())
• Grouping and aggregation (groupby().agg())
• Joins (join(), merge())
• Distinct (distinct(), drop_duplicates())
• Limiting (limit(), head(), tail())

Operations Executed in pandas

• Custom apply functions (apply(custom_func))
• Complex pivot tables with custom aggregations
• Operations not expressible in SQL
• When input is already a pandas DataFrame

Example

from chdb import datastore as pd
from chdb.datastore.config import config

config.use_auto()  # Default

ds = pd.read_csv("data.csv")

# This uses chDB (SQL)
result = (ds
    .filter(ds['amount'] > 100)   # SQL: WHERE
    .groupby('region')            # SQL: GROUP BY
    .agg({'amount': 'sum'})       # SQL: SUM()
)

# This uses pandas (custom function)
result = ds.apply(lambda row: complex_calculation(row), axis=1)

---

chDB Mode

Force all operations through ClickHouse SQL:

config.use_chdb()

When to Use

• Processing large datasets (millions of rows)
• Heavy aggregation workloads
• When you want maximum SQL optimization
• Consistent behavior across all operations

Performance Characteristics

Operation Type	Performance
GroupBy/Aggregation	Excellent (up to 20x faster)
Complex Filtering	Excellent
Sorting	Very Good
Simple Single Filters	Good (slight overhead)

Limitations

• Custom Python functions may not be supported
• Some pandas-specific features require conversion

---

pandas Mode

Force all operations through pandas:

config.use_pandas()

When to Use

• Compatibility testing with pandas
• Using pandas-specific features
• Debugging pandas-related issues
• When data is already in pandas format

Performance Characteristics

Operation Type	Performance
Simple Single Operations	Good
Custom Functions	Excellent
Complex Aggregations	Slower than chDB
Large Datasets	Memory intensive

---

Cross-DataStore Engine

Configure the engine for operations that combine columns from different DataStores:

# Set cross-DataStore engine
config.set_cross_datastore_engine('auto')
config.set_cross_datastore_engine('chdb')
config.set_cross_datastore_engine('pandas')

Example

ds1 = pd.read_csv("sales.csv")
ds2 = pd.read_csv("inventory.csv")

# This operation involves two DataStores
result = ds1.join(ds2, on='product_id')
# Uses cross_datastore_engine setting

---

Engine Selection Logic

Auto Mode Decision Tree

Operation requested
    │
    ├─ Can be expressed in SQL?
    │      │
    │      ├─ Yes → Use chDB
    │      │
    │      └─ No → Use pandas
    │
    └─ Cross-DataStore operation?
           │
           └─ Use cross_datastore_engine setting

Function-Level Override

Some functions can have their engine explicitly configured:

from chdb.datastore.config import function_config

# Force specific functions to use specific engine
function_config.use_chdb('length', 'substring')
function_config.use_pandas('upper', 'lower')

See Function Config for details.

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Performance Comparison

Benchmark results on 10M rows:

Operation	pandas (ms)	chdb (ms)	Speedup
GroupBy count	347	17	19.93x
Combined ops	1,535	234	6.56x
Complex pipeline	2,047	380	5.39x
Filter+Sort+Head	1,537	350	4.40x
GroupBy agg	406	141	2.88x
Single filter	276	526	0.52x

Key insights:

• chDB excels at aggregations and complex pipelines
• pandas is slightly faster for simple single operations
• Use auto mode to get the best of both

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Best Practices

1. Start with Auto Mode

config.use_auto()  # Let DataStore decide

2. Profile Before Forcing

config.enable_profiling()
# Run your workload
# Check profiler report to see where time is spent

3. Force Engine for Specific Workloads

# For heavy aggregation workloads
config.use_chdb()

# For pandas compatibility testing
config.use_pandas()

4. Use explain() to Understand Execution

ds = pd.read_csv("data.csv")
query = ds.filter(ds['age'] > 25).groupby('city').agg({'salary': 'sum'})

# See what SQL will be generated
query.explain()

---

Troubleshooting

Issue: Operation slower than expected

# Check current engine
print(config.execution_engine)

# Enable debug to see what's happening
config.enable_debug()

# Try forcing specific engine
config.use_chdb()  # or config.use_pandas()

Issue: Unsupported operation in chdb mode

# Some pandas operations aren't supported in SQL
# Solution: use auto mode
config.use_auto()

# Or explicitly convert to pandas first
df = ds.to_df()
result = df.some_pandas_specific_operation()

Issue: Memory issues with large data

# Use chdb engine to avoid loading all data into memory
config.use_chdb()

# Filter early to reduce data size
result = ds.filter(ds['date'] >= '2024-01-01').to_df()

# For maximum throughput on large datasets, use performance mode
# which enables parallel Parquet reading and single-SQL aggregation
config.use_performance_mode()

Performance Mode

If you are running heavy aggregation workloads and don't need exact pandas output compatibility (row order, MultiIndex, dtype corrections), consider using Performance Mode. It automatically sets the engine to chdb and removes all pandas compatibility overhead.