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explain() Method

The explain() method shows the execution plan for a DataStore query, helping you understand what operations will be performed and what SQL will be generated.

Basic Usage​

from chdb import datastore as pd

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

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

# View execution plan
query.explain()

Syntax​

explain(verbose=False) -> None

Parameters:

ParameterTypeDefaultDescription
verboseboolFalseShow additional metadata

Output Format​

Standard Output​

================================================================================
Execution Plan (in execution order)
================================================================================

 [1] 📊 Data Source: file('sales.csv', 'csv')

Operations:
────────────────────────────────────────────────────────────────────────────────
    ️  Segment 1 [chDB] (from source): Operations 2-5
    ️  Note: SQL operations after Pandas ops use Python() table function

 [2] 🚀 [chDB] WHERE: "amount" > 1000
 [3] 🚀 [chDB] GROUP BY: region
 [4] 🚀 [chDB] AGGREGATE: sum(amount), avg(amount)
 [5] 🚀 [chDB] ORDER BY: sum DESC

────────────────────────────────────────────────────────────────────────────────
Final State: 📊 Pending (lazy, not yet executed)
             └─> Will execute when print(), .to_df(), .execute() is called

────────────────────────────────────────────────────────────────────────────────
Generated SQL Query:
────────────────────────────────────────────────────────────────────────────────

SELECT region, SUM(amount) AS sum, AVG(amount) AS mean
FROM file('sales.csv', 'csv')
WHERE "amount" > 1000
GROUP BY region
ORDER BY sum DESC

================================================================================

Icons Legend​

IconMeaning
📊Data source
🚀chDB (SQL) operation
🐼pandas operation

Verbose Output​

query.explain(verbose=True)

Verbose mode shows additional details for each operation, including the full SQL query with internal row-order tracking mechanisms.

Three Execution Phases​

The explain output shows operations in three phases:

Phase 1: SQL Query Building (Lazy)​

Operations that compile to SQL:

  1. Source: file('sales.csv', 'CSVWithNames')
  2. Filter: amount > 1000      
  3. GroupBy: region
  4. Aggregate: sum(amount)

Phase 2: Execution Point​

When a trigger occurs:

  5. Execute SQL -> DataFrame
     Trigger: to_df() called

Phase 3: DataFrame Operations​

Operations after execution:

  6. [pandas] pivot_table(...)
  7. [pandas] apply(custom_func)

Understanding the Plan​

Source Information​

Source: file('sales.csv', 'CSVWithNames')
  • file() - ClickHouse file() table function
  • 'CSVWithNames' - File format with header

Other source types:

Source: s3('bucket/data.parquet', ...)
Source: mysql('host', 'db', 'table', ...)
Source: __dataframe__  (pandas DataFrame input)

Filter Operations​

Filter: amount > 1000 AND status = 'active'

Shows the WHERE clause that will be applied.

GroupBy and Aggregate​

GroupBy: region, category
Aggregate: sum(amount), avg(amount), count(id)

Shows GROUP BY columns and aggregation functions.

Sort Operations​

Sort: sum DESC, region ASC

Shows ORDER BY clause.

Limit Operations​

Limit: 10
Offset: 100

Shows LIMIT and OFFSET.

Engine Information​

When using verbose mode, you can see which engine will be used:

Filter: amount > 1000
  - Engine: chdb
  - Pushdown: Yes

Apply: custom_function
  - Engine: pandas
  - Pushdown: No

Pushdown​

  • Yes: Operation will be executed at the data source (SQL)
  • No: Operation requires pandas execution

Examples​

Simple Query​

ds = pd.read_csv("data.csv")
ds.filter(ds['age'] > 25).explain()

================================================================================
Execution Plan (in execution order)
================================================================================

 [1] 📊 Data Source: file('data.csv', 'csv')

Operations:
────────────────────────────────────────────────────────────────────────────────
    ️  Segment 1 [chDB] (from source): Operations 2-2

 [2] 🚀 [chDB] WHERE: "age" > 25

────────────────────────────────────────────────────────────────────────────────
Generated SQL Query:
────────────────────────────────────────────────────────────────────────────────

SELECT * FROM file('data.csv', 'csv') WHERE "age" > 25

================================================================================

Complex Aggregation​

query = (ds
    .filter(ds['date'] >= '2024-01-01')
    .filter(ds['amount'] > 100)
    .select('region', 'category', 'amount')
    .groupby('region', 'category')
    .agg({
        'amount': ['sum', 'mean', 'count']
    })
    .sort('sum', ascending=False)
    .limit(20)
)
query.explain()

================================================================================
Execution Plan (in execution order)
================================================================================

 [1] 📊 Data Source: file('sales.csv', 'csv')

Operations:
────────────────────────────────────────────────────────────────────────────────
    ️  Segment 1 [chDB] (from source): Operations 2-8

 [2] 🚀 [chDB] WHERE: "date" >= '2024-01-01'
 [3] 🚀 [chDB] WHERE: "amount" > 100
 [4] 🚀 [chDB] SELECT: region, category, amount
 [5] 🚀 [chDB] GROUP BY: region, category
 [6] 🚀 [chDB] AGGREGATE: sum(amount), avg(amount), count(amount)
 [7] 🚀 [chDB] ORDER BY: sum DESC
 [8] 🚀 [chDB] LIMIT: 20

────────────────────────────────────────────────────────────────────────────────
Generated SQL Query:
────────────────────────────────────────────────────────────────────────────────

SELECT region, category, 
       SUM(amount) AS sum, 
       AVG(amount) AS mean, 
       COUNT(amount) AS count
FROM file('sales.csv', 'csv')
WHERE "date" >= '2024-01-01' AND "amount" > 100
GROUP BY region, category
ORDER BY sum DESC
LIMIT 20

================================================================================

Mixed SQL and pandas​

When operations cannot be fully pushed to SQL, the plan shows multiple segments:

query = (ds
    .filter(ds['age'] > 25)           # SQL
    .groupby('city')                   # SQL
    .agg({'salary': 'mean'})           # SQL
    .apply(lambda x: x * 1.1)          # pandas (triggers segment split)
    .filter(ds['mean'] > 50000)        # SQL (new segment)
)
query.explain()

================================================================================
Execution Plan (in execution order)
================================================================================

 [1] 📊 Data Source: file('data.csv', 'csv')

Operations:
────────────────────────────────────────────────────────────────────────────────
    ️  Segment 1 [chDB] (from source): Operations 2-4
    ️  Segment 2 [Pandas] (on DataFrame): Operation 5
    ️  Segment 3 [chDB] (on DataFrame): Operation 6
    ️  Note: SQL operations after Pandas ops use Python() table function

 [2] 🚀 [chDB] WHERE: "age" > 25
 [3] 🚀 [chDB] GROUP BY: city
 [4] 🚀 [chDB] AGGREGATE: avg(salary)
 [5] 🐼 [Pandas] APPLY: lambda
 [6] 🚀 [chDB] WHERE: "mean" > 50000

================================================================================

Debugging with explain()​

Check Filter Logic​

# Verify your filter is correct
query = ds.filter((ds['age'] > 25) & (ds['city'] == 'NYC'))
query.explain()
# Output shows: Filter: age > 25 AND city = 'NYC'

Verify Column Selection​

# Check column pruning
query = ds.select('name', 'age').filter(ds['age'] > 25)
query.explain()
# Output shows: SELECT name, age FROM ... WHERE age > 25

Understand Aggregation​

# Check aggregation functions
query = ds.groupby('dept').agg({'salary': ['sum', 'mean', 'std']})
query.explain()
# Output shows: SELECT dept, SUM(salary), AVG(salary), stddevPop(salary)

Best Practices​

1. Check Before Executing Large Queries​

# Always explain first for large data
query = ds.complex_pipeline()
query.explain()  # Check plan

# If plan looks correct
result = query.to_df()  # Execute

2. Use Verbose for Debugging​

# When something seems wrong
query.explain(verbose=True)
# Shows engine selection and pushdown info

3. Compare with to_sql()​

# explain() shows the plan
query.explain()

# to_sql() shows just the SQL
print(query.to_sql())

# Both useful for different purposes

4. Check Pushdown Status​

# Verbose mode shows if operations are pushed down
query.explain(verbose=True)

# If Pushdown: No, operation runs in pandas
# Consider restructuring query for better performance