DataStore Profiling

The DataStore profiler helps you measure execution time and identify performance bottlenecks.

Quick Start

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

# Enable profiling
config.enable_profiling()

# Run your operations
ds = pd.read_csv("large_data.csv")
result = (ds
    .filter(ds['amount'] > 100)
    .groupby('category')
    .agg({'amount': 'sum'})
    .sort('sum', ascending=False)
    .head(10)
    .to_df()
)

# View report
profiler = get_profiler()
print(profiler.report())

Enabling Profiling

from chdb.datastore.config import config

# Enable profiling
config.enable_profiling()

# Disable profiling
config.disable_profiling()

# Check if profiling is enabled
print(config.profiling_enabled)  # True or False

Profiler API

Getting the Profiler

from chdb.datastore.config import get_profiler

profiler = get_profiler()

report()

Display a performance report.

profiler.report(min_duration_ms=0.1)

Parameters:

Parameter	Type	Default	Description
`min_duration_ms`	float	`0.1`	Only show steps >= this duration

Example output:

======================================================================
EXECUTION PROFILE
======================================================================
   45.79ms (100.0%) Total Execution
     23.25ms ( 50.8%) Query Planning [ops_count=2]
     22.29ms ( 48.7%) SQL Segment 1 [ops=2]
       20.48ms ( 91.9%) SQL Execution
        1.74ms (  7.8%) Result to DataFrame
----------------------------------------------------------------------
      TOTAL:    45.79ms
======================================================================

The report shows:

Duration in milliseconds for each step
Percentage of parent/total time
Hierarchical nesting of operations
Metadata for each step (e.g., ops_count, ops)

step()

Manually time a code block.

with profiler.step("custom_operation"):
    # Your code here
    expensive_operation()

clear()

Clear all profiling data.

profiler.clear()

summary()

Get a dictionary of step names to durations (ms).

summary = profiler.summary()
for name, duration in summary.items():
    print(f"{name}: {duration:.2f}ms")

Example output:

Total Execution: 45.79ms
Total Execution.Cache Check: 0.00ms
Total Execution.Query Planning: 23.25ms
Total Execution.SQL Segment 1: 22.29ms
Total Execution.SQL Segment 1.SQL Execution: 20.48ms
Total Execution.SQL Segment 1.Result to DataFrame: 1.74ms

Understanding the Report

Step Names

Step Name	Description
`Total Execution`	Overall execution time
`Query Planning`	Time spent planning the query
`SQL Segment N`	Execution of SQL segment N
`SQL Execution`	Actual SQL query execution
`Result to DataFrame`	Converting results to pandas
`Cache Check`	Checking query cache
`Cache Write`	Writing results to cache

Duration

Planning steps (Query Planning): Usually fast
Execution steps (SQL Execution): Where actual work happens
Transfer steps (Result to DataFrame): Converting data to pandas

Identifying Bottlenecks

======================================================================
EXECUTION PROFILE
======================================================================
  200.50ms (100.0%) Total Execution
    10.25ms (  5.1%) Query Planning [ops_count=4]
   190.00ms ( 94.8%) SQL Segment 1 [ops=4]
     185.00ms ( 97.4%) SQL Execution    <- Main bottleneck
       5.00ms (  2.6%) Result to DataFrame
----------------------------------------------------------------------
      TOTAL:   200.50ms
======================================================================

Profiling Patterns

Profile a Single Query

config.enable_profiling()
profiler = get_profiler()
profiler.clear()  # Clear previous data

# Run query
result = ds.filter(...).groupby(...).agg(...).to_df()

# View this query's profile
print(profiler.report())

Profile Multiple Queries

config.enable_profiling()
profiler = get_profiler()
profiler.clear()

# Query 1
with profiler.step("Query 1"):
    result1 = query1.to_df()

# Query 2
with profiler.step("Query 2"):
    result2 = query2.to_df()

print(profiler.report())

Compare Approaches

profiler = get_profiler()

# Approach 1: Filter then groupby
profiler.clear()
with profiler.step("filter_then_groupby"):
    result1 = ds.filter(ds['x'] > 10).groupby('y').sum().to_df()
summary1 = profiler.summary()
time1 = summary1.get('filter_then_groupby', 0)

# Approach 2: Groupby then filter
profiler.clear()
with profiler.step("groupby_then_filter"):
    result2 = ds.groupby('y').sum().filter(ds['x'] > 10).to_df()
summary2 = profiler.summary()
time2 = summary2.get('groupby_then_filter', 0)

print(f"Approach 1: {time1:.2f}ms")
print(f"Approach 2: {time2:.2f}ms")
print(f"Winner: {'Approach 1' if time1 < time2 else 'Approach 2'}")

Optimization Tips

1. Check SQL Execution Time

If SQL execution is the bottleneck:

Add more filters to reduce data
Use Parquet instead of CSV
Check for proper indexes (for database sources)

2. Check I/O Time

If read_csv or read_parquet is the bottleneck:

Use Parquet (columnar, compressed)
Read only needed columns
Filter at source if possible

3. Check Data Transfer

If to_df is slow:

Result set may be too large
Add more filters or limit
Use head() for previewing

4. Compare Engines

from chdb.datastore.config import config

# Profile with chdb
config.use_chdb()
profiler.clear()
result_chdb = query.to_df()
time_chdb = profiler.total_duration_ms

# Profile with pandas
config.use_pandas()
profiler.clear()
result_pandas = query.to_df()
time_pandas = profiler.total_duration_ms

print(f"chdb: {time_chdb:.2f}ms")
print(f"pandas: {time_pandas:.2f}ms")

Best Practices

1. Profile Before Optimizing

# Don't guess - measure!
config.enable_profiling()
result = your_query.to_df()
print(get_profiler().report())

2. Clear Between Tests

profiler.clear()  # Clear previous data
# Run test
print(profiler.report())

3. Use min_duration_ms for Focus

# Only show operations >= 100ms
profiler.report(min_duration_ms=100)

4. Profile Representative Data

# Profile with real-world data sizes
# Small test data may not show real bottlenecks

5. Disable in Production

# Development
config.enable_profiling()

# Production
config.set_profiling_enabled(False)  # Avoid overhead

Example: Full Profiling Session

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

# Setup
config.enable_profiling()
config.enable_debug()  # Also see what's happening
profiler = get_profiler()

# Load data
profiler.clear()
print("=== Loading Data ===")
ds = pd.read_csv("sales_2024.csv")  # 10M rows
print(profiler.report())

# Query 1: Simple filter
profiler.clear()
print("\n=== Query 1: Simple Filter ===")
result1 = ds.filter(ds['amount'] > 1000).to_df()
print(profiler.report())

# Query 2: Complex aggregation
profiler.clear()
print("\n=== Query 2: Complex Aggregation ===")
result2 = (ds
    .filter(ds['amount'] > 100)
    .groupby('region', 'category')
    .agg({
        'amount': ['sum', 'mean', 'count'],
        'quantity': 'sum'
    })
    .sort('sum', ascending=False)
    .head(20)
    .to_df()
)
print(profiler.report())

# Summary
print("\n=== Summary ===")
print(f"Query 1: {len(result1)} rows")
print(f"Query 2: {len(result2)} rows")

Quick Start​

Enabling Profiling​

Profiler API​

Getting the Profiler​

report()​

step()​

clear()​

summary()​

Understanding the Report​

Step Names​

Duration​

Identifying Bottlenecks​

Profiling Patterns​

Profile a Single Query​

Profile Multiple Queries​

Compare Approaches​

Optimization Tips​

1. Check SQL Execution Time​

2. Check I/O Time​

3. Check Data Transfer​

4. Compare Engines​

Best Practices​

1. Profile Before Optimizing​

2. Clear Between Tests​

3. Use min_duration_ms for Focus​

4. Profile Representative Data​

5. Disable in Production​

Example: Full Profiling Session​