DataStore Debugging

DataStore provides comprehensive debugging tools to understand and optimize your data pipelines.

Debugging Tools Overview

Tool	Purpose	When to Use
explain()	View execution plan	Understand what SQL will run
Profiler	Measure performance	Find slow operations
Logging	View execution details	Debug unexpected behavior

Quick Decision Matrix

Need	Tool	Command
See execution plan	explain()	ds.explain()
Measure performance	Profiler	config.enable_profiling()
Debug SQL queries	Logging	config.enable_debug()
All of the above	Combined	See below

Quick Setup

Enable All Debugging

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

# Enable all debugging
config.enable_debug()        # Verbose logging
config.enable_profiling()    # Performance tracking

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

# View execution plan
result.explain()

# Get profiler report
from chdb.datastore.config import get_profiler
profiler = get_profiler()
profiler.report()

---

explain() Method

View the execution plan before running a query.

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

query = (ds
    .filter(ds['amount'] > 1000)
    .groupby('region')
    .agg({'amount': ['sum', 'mean']})
)

# View plan
query.explain()

Output:

Pipeline:
  Source: file('data.csv', 'CSVWithNames')
  Filter: amount > 1000
  GroupBy: region
  Aggregate: sum(amount), avg(amount)

Generated SQL:
SELECT region, SUM(amount) AS sum, AVG(amount) AS mean
FROM file('data.csv', 'CSVWithNames')
WHERE amount > 1000
GROUP BY region

See explain() Documentation for details.

---

Profiling

Measure execution time for each operation.

from chdb.datastore.config import config, get_profiler

# Enable profiling
config.enable_profiling()

# Run 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()
profiler.report(min_duration_ms=0.1)

Output:

Performance Report
==================
Step                          Duration    Calls
----                          --------    -----
read_csv                      1.234s      1
filter                        0.002s      1
groupby                       0.001s      1
agg                           0.089s      1
sort                          0.045s      1
head                          0.001s      1
to_df (SQL execution)         0.567s      1
----                          --------    -----
Total                         1.939s      7

See Profiling Guide for details.

---

Logging

View detailed execution logs.

from chdb.datastore.config import config

# Enable debug logging
config.enable_debug()

# Run operations - logs will show:
# - SQL queries generated
# - Execution engine used
# - Cache hits/misses
# - Timing information

Log output example:

DEBUG - DataStore: Creating from file 'data.csv'
DEBUG - Query: SELECT region, SUM(amount) FROM ... WHERE amount > 1000 GROUP BY region
DEBUG - Engine: Using chdb for aggregation
DEBUG - Execution time: 0.089s
DEBUG - Cache: Storing result (key: abc123)

See Logging Configuration for details.

---

Common Debugging Scenarios

1. Query Not Returning Expected Results

# Step 1: View the execution plan
query = ds.filter(ds['age'] > 25).groupby('city').sum()
query.explain(verbose=True)

# Step 2: Enable logging to see SQL
config.enable_debug()

# Step 3: Run and check logs
result = query.to_df()

2. Query Running Slowly

# Step 1: Enable profiling
config.enable_profiling()

# Step 2: Run your query
result = process_data()

# Step 3: Check profiler report
profiler = get_profiler()
profiler.report()

# Step 4: Identify slow operations and optimize

3. Understanding Engine Selection

# Enable verbose logging
config.enable_debug()

# Run operations
result = ds.filter(ds['x'] > 10).apply(custom_func)

# Logs will show which engine was used for each operation:
# DEBUG - filter: Using chdb engine
# DEBUG - apply: Using pandas engine (custom function)

4. Debugging Cache Issues

# Enable debug to see cache operations
config.enable_debug()

# First run
result1 = ds.filter(ds['x'] > 10).to_df()
# LOG: Cache miss, executing query

# Second run (should use cache)
result2 = ds.filter(ds['x'] > 10).to_df()
# LOG: Cache hit, returning cached result

# If not caching when expected, check:
# - Are operations identical?
# - Is cache enabled? config.cache_enabled

---

Best Practices

1. Debug in Development, Not Production

# Development
config.enable_debug()
config.enable_profiling()

# Production
config.set_log_level(logging.WARNING)
config.set_profiling_enabled(False)

2. Use explain() Before Running Large Queries

# Build query
query = ds.filter(...).groupby(...).agg(...)

# Check plan first
query.explain()

# If plan looks good, execute
result = query.to_df()

3. Profile Before Optimizing

# Don't guess what's slow - measure it
config.enable_profiling()
result = your_pipeline()
get_profiler().report()

4. Check SQL When Results Are Wrong

# View generated SQL
print(query.to_sql())

# Compare with expected SQL
# Run SQL directly in ClickHouse to verify

---

Debugging Tools Summary

Tool	Command	Output
Explain plan	ds.explain()	Execution steps + SQL
Verbose explain	ds.explain(verbose=True)	+ Metadata
View SQL	ds.to_sql()	SQL query string
Enable debug	config.enable_debug()	Detailed logs
Enable profiling	config.enable_profiling()	Timing data
Profiler report	get_profiler().report()	Performance summary
Clear profiler	get_profiler().reset()	Clear timing data

---

Next Steps

• explain() Method - Detailed execution plan documentation
• Profiling Guide - Performance measurement
• Logging Configuration - Log level and format setup