chDB Python API reference

Complete Python API reference for chDB

Python API Reference

Core Query Functions

chdb.query

Execute SQL query using chDB engine.

This is the main query function that executes SQL statements using the embedded ClickHouse engine. Supports various output formats and can work with in-memory or file-based databases.

Syntax

chdb.query(sql, output_format='CSV', path='', udf_path='')

Parameters

Parameter	Type	Default	Description
sql	str	required	SQL query string to execute
output_format	str	"CSV"	Output format for results. Supported formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "Arrow" - Apache Arrow format • "Parquet" - Parquet format • "DataFrame" - Pandas DataFrame • "ArrowTable" - PyArrow Table • "Debug" - Enable verbose logging
path	str	""	Database file path. Defaults to in-memory database. Can be a file path or ":memory:" for in-memory database
udf_path	str	""	Path to User-Defined Functions directory

Returns

Returns the query result in the specified format:

Return Type	Condition
str	For text formats like CSV, JSON
pd.DataFrame	When output_format is "DataFrame" or "dataframe"
pa.Table	When output_format is "ArrowTable" or "arrowtable"
chdb result object	For other formats

Raises

Exception	Condition
ChdbError	If the SQL query execution fails
ImportError	If required dependencies are missing for DataFrame/Arrow formats

Examples

>>> # Basic CSV query
>>> result = chdb.query("SELECT 1, 'hello'")
>>> print(result)
"1,hello"

>>> # Query with DataFrame output
>>> df = chdb.query("SELECT 1 as id, 'hello' as msg", "DataFrame")
>>> print(df)
   id    msg
0   1  hello

>>> # Query with file-based database
>>> result = chdb.query("CREATE TABLE test (id INT) ENGINE = Memory", path="mydb.chdb")

>>> # Query with UDF
>>> result = chdb.query("SELECT my_udf('test')", udf_path="/path/to/udfs")

---

chdb.sql

Execute SQL query using chDB engine.

This is the main query function that executes SQL statements using the embedded ClickHouse engine. Supports various output formats and can work with in-memory or file-based databases.

Syntax

chdb.sql(sql, output_format='CSV', path='', udf_path='')

Parameters

Parameter	Type	Default	Description
sql	str	required	SQL query string to execute
output_format	str	"CSV"	Output format for results. Supported formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "Arrow" - Apache Arrow format • "Parquet" - Parquet format • "DataFrame" - Pandas DataFrame • "ArrowTable" - PyArrow Table • "Debug" - Enable verbose logging
path	str	""	Database file path. Defaults to in-memory database. Can be a file path or ":memory:" for in-memory database
udf_path	str	""	Path to User-Defined Functions directory

Returns

Returns the query result in the specified format:

Return Type	Condition
str	For text formats like CSV, JSON
pd.DataFrame	When output_format is "DataFrame" or "dataframe"
pa.Table	When output_format is "ArrowTable" or "arrowtable"
chdb result object	For other formats

Raises

Exception	Condition
ChdbError	If the SQL query execution fails
ImportError	If required dependencies are missing for DataFrame/Arrow formats

Examples

>>> # Basic CSV query
>>> result = chdb.query("SELECT 1, 'hello'")
>>> print(result)
"1,hello"

>>> # Query with DataFrame output
>>> df = chdb.query("SELECT 1 as id, 'hello' as msg", "DataFrame")
>>> print(df)
   id    msg
0   1  hello

>>> # Query with file-based database
>>> result = chdb.query("CREATE TABLE test (id INT) ENGINE = Memory", path="mydb.chdb")

>>> # Query with UDF
>>> result = chdb.query("SELECT my_udf('test')", udf_path="/path/to/udfs")

---

chdb.to_arrowTable

Convert query result to PyArrow Table.

Converts a chDB query result to a PyArrow Table for efficient columnar data processing. Returns an empty table if the result is empty.

Syntax

chdb.to_arrowTable(res)

Parameters

Parameter	Description
res	chDB query result object containing binary Arrow data

Returns

Return type	Description
pa.Table	PyArrow Table containing the query results

Raises

Error type	Description
ImportError	If pyarrow or pandas aren’t installed

Example

>>> result = chdb.query("SELECT 1 as id, 'hello' as msg", "Arrow")
>>> table = chdb.to_arrowTable(result)
>>> print(table.to_pandas())
   id    msg
0   1  hello

---

chdb.to_df

Convert query result to pandas DataFrame.

Converts a chDB query result to a pandas DataFrame by first converting to PyArrow Table and then to pandas using multi-threading for better performance.

Syntax

chdb.to_df(r)

Parameters

Parameter	Description
r	chDB query result object containing binary Arrow data

Returns

Return Type	Description
pd.DataFrame	pandas DataFrame containing the query results

Raises

Exception	Condition
ImportError	If pyarrow or pandas aren’t installed

Example

>>> result = chdb.query("SELECT 1 as id, 'hello' as msg", "Arrow")
>>> df = chdb.to_df(result)
>>> print(df)
   id    msg
0   1  hello

Connection and Session Management

The following Session Functions are available:

chdb.connect

Create a connection to chDB background server.

This function establishes a Connection to the chDB (ClickHouse) database engine. Only one open connection is allowed per process. Multiple calls with the same connection string will return the same connection object.

chdb.connect(connection_string: str = ':memory:') → Connection

Parameters:

Parameter	Type	Default	Description
connection_string	str	":memory:"	Database connection string. See formats below.

Basic formats

Format	Description
":memory:"	In-memory database (default)
"test.db"	Relative path database file
"file:test.db"	Same as relative path
"/path/to/test.db"	Absolute path database file
"file:/path/to/test.db"	Same as absolute path

With query parameters

Format	Description
"file:test.db?param1=value1&param2=value2"	Relative path with params
"file::memory:?verbose&log-level=test"	In-memory with params
"///path/to/test.db?param1=value1&param2=value2"	Absolute path with params

Query parameter handling

Query parameters are passed to ClickHouse engine as startup arguments. Special parameter handling:

Special Parameter	Becomes	Description
mode=ro	--readonly=1	Read-only mode
verbose	(flag)	Enables verbose logging
log-level=test	(setting)	Sets logging level

For a complete parameter list, see clickhouse local --help --verbose

Returns

Return Type	Description
Connection	Database connection object that supports: • Creating cursors with Connection.cursor() • Direct queries with Connection.query() • Streaming queries with Connection.send_query() • Context manager protocol for automatic cleanup

Raises

Exception	Condition
RuntimeError	If connection to database fails

Only one connection per process is supported. Creating a new connection will close any existing connection.

Examples

>>> # In-memory database
>>> conn = connect()
>>> conn = connect(":memory:")
>>>
>>> # File-based database
>>> conn = connect("my_data.db")
>>> conn = connect("/path/to/data.db")
>>>
>>> # With parameters
>>> conn = connect("data.db?mode=ro")  # Read-only mode
>>> conn = connect(":memory:?verbose&log-level=debug")  # Debug logging
>>>
>>> # Using context manager for automatic cleanup
>>> with connect("data.db") as conn:
...     result = conn.query("SELECT 1")
...     print(result)
>>> # Connection automatically closed

See also

• Connection - Database connection class
• Cursor - Database cursor for DB-API 2.0 operations

Exception Handling

class chdb.ChdbError

Bases: Exception

Base exception class for chDB-related errors.

This exception is raised when chDB query execution fails or encounters an error. It inherits from the standard Python Exception class and provides error information from the underlying ClickHouse engine.

---

class chdb.session.Session

Bases: object

Session will keep the state of query. If path is None, it will create a temporary directory and use it as the database path and the temporary directory will be removed when the session is closed. You can also pass in a path to create a database at that path where will keep your data.

You can also use a connection string to pass in the path and other parameters.

class chdb.session.Session(path=None)

Examples

Connection String	Description
":memory:"	In-memory database
"test.db"	Relative path
"file:test.db"	Same as above
"/path/to/test.db"	Absolute path
"file:/path/to/test.db"	Same as above
"file:test.db?param1=value1&param2=value2"	Relative path with query params
"file::memory:?verbose&log-level=test"	In-memory database with query params
"///path/to/test.db?param1=value1&param2=value2"	Absolute path with query params

Connection string args handling

Connection strings containing query params like “file:test.db?param1=value1&param2=value2” “param1=value1” will be passed to ClickHouse engine as start up args.

For more details, see clickhouse local –help –verbose

Some special args handling:

• “mode=ro” would be “–readonly=1” for clickhouse (read-only mode)

Important

• There can be only one session at a time. If you want to create a new session, you need to close the existing one.
• Creating a new session will close the existing one.

---

cleanup

Cleanup session resources with exception handling.

This method attempts to close the session while suppressing any exceptions that might occur during the cleanup process. It’s particularly useful in error handling scenarios or when you need to ensure cleanup happens regardless of the session state.

Syntax

cleanup()

This method will never raise an exception, making it safe to call in finally blocks or destructors.

Examples

>>> session = Session("test.db")
>>> try:
...     session.query("INVALID SQL")
... finally:
...     session.cleanup()  # Safe cleanup regardless of errors

See also

• close() - For explicit session closing with error propagation

---

close

Close the session and cleanup resources.

This method closes the underlying connection and resets the global session state. After calling this method, the session becomes invalid and can’t be used for further queries.

Syntax

close()

This method is automatically called when the session is used as a context manager or when the session object is destroyed.

Important

Any attempt to use the session after calling close() will result in an error.

Examples

>>> session = Session("test.db")
>>> session.query("SELECT 1")
>>> session.close()  # Explicitly close the session

---

query

Execute a SQL query and return the results.

This method executes a SQL query against the session’s database and returns the results in the specified format. The method supports various output formats and maintains session state between queries.

Syntax

query(sql, fmt='CSV', udf_path='')

Parameters

Parameter	Type	Default	Description
sql	str	required	SQL query string to execute
fmt	str	"CSV"	Output format for results. Available formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "TabSeparated" - Tab-separated values • "Pretty" - Pretty-printed table format • "JSONCompact" - Compact JSON format • "Arrow" - Apache Arrow format • "Parquet" - Parquet format
udf_path	str	""	Path to user-defined functions. If not specified, uses the UDF path from session initialization

Returns

Returns query results in the specified format. The exact return type depends on the format parameter:

• String formats (CSV, JSON, etc.) return str
• Binary formats (Arrow, Parquet) return bytes

Raises

Exception	Condition
RuntimeError	If the session is closed or invalid
ValueError	If the SQL query is malformed

The “Debug” format isn’t supported and will be automatically converted to “CSV” with a warning. For debugging, use connection string parameters instead.

Warning

This method executes the query synchronously and loads all results into memory. For large result sets, consider using send_query() for streaming results.

Examples

>>> session = Session("test.db")
>>>
>>> # Basic query with default CSV format
>>> result = session.query("SELECT 1 as number")
>>> print(result)
number
1

>>> # Query with JSON format
>>> result = session.query("SELECT 1 as number", fmt="JSON")
>>> print(result)
{"number": "1"}

>>> # Complex query with table creation
>>> session.query("CREATE TABLE test (id INT, name String) ENGINE = Memory")
>>> session.query("INSERT INTO test VALUES (1, 'Alice'), (2, 'Bob')")
>>> result = session.query("SELECT * FROM test ORDER BY id")
>>> print(result)
id,name
1,Alice
2,Bob

See also

• send_query() - For streaming query execution
• sql - Alias for this method

---

send_query

Execute a SQL query and return a streaming result iterator.

This method executes a SQL query against the session’s database and returns a streaming result object that allows you to iterate over the results without loading everything into memory at once. This is particularly useful for large result sets.

Syntax

send_query(sql, fmt='CSV') → StreamingResult

Parameters

Parameter	Type	Default	Description
sql	str	required	SQL query string to execute
fmt	str	"CSV"	Output format for results. Available formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "TabSeparated" - Tab-separated values • "JSONCompact" - Compact JSON format • "Arrow" - Apache Arrow format • "Parquet" - Parquet format

Returns

Return Type	Description
StreamingResult	A streaming result iterator that yields query results incrementally. The iterator can be used in for loops or converted to other data structures

Raises

Exception	Condition
RuntimeError	If the session is closed or invalid
ValueError	If the SQL query is malformed

The “Debug” format isn’t supported and will be automatically converted to “CSV” with a warning. For debugging, use connection string parameters instead.

The returned StreamingResult object should be consumed promptly or stored appropriately, as it maintains a connection to the database.

Examples

>>> session = Session("test.db")
>>> session.query("CREATE TABLE big_table (id INT, data String) ENGINE = MergeTree() order by id")
>>>
>>> # Insert large dataset
>>> for i in range(1000):
...     session.query(f"INSERT INTO big_table VALUES ({i}, 'data_{i}')")
>>>
>>> # Stream results to avoid memory issues
>>> streaming_result = session.send_query("SELECT * FROM big_table ORDER BY id")
>>> for chunk in streaming_result:
...     print(f"Processing chunk: {len(chunk)} bytes")
...     # Process chunk without loading entire result set

>>> # Using with context manager
>>> with session.send_query("SELECT COUNT(*) FROM big_table") as stream:
...     for result in stream:
...         print(f"Count result: {result}")

See also

• query() - For non-streaming query execution
• chdb.state.sqlitelike.StreamingResult - Streaming result iterator

---

sql

Execute a SQL query and return the results.

This method executes a SQL query against the session’s database and returns the results in the specified format. The method supports various output formats and maintains session state between queries.

Syntax

sql(sql, fmt='CSV', udf_path='')

Parameters

Parameter	Type	Default	Description
sql	str	required	SQL query string to execute
fmt	str	"CSV"	Output format for results. Available formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "TabSeparated" - Tab-separated values • "Pretty" - Pretty-printed table format • "JSONCompact" - Compact JSON format • "Arrow" - Apache Arrow format • "Parquet" - Parquet format
udf_path	str	""	Path to user-defined functions. If not specified, uses the UDF path from session initialization

Returns

Returns query results in the specified format. The exact return type depends on the format parameter:

• String formats (CSV, JSON, etc.) return str
• Binary formats (Arrow, Parquet) return bytes

Raises:

Exception	Condition
RuntimeError	If the session is closed or invalid
ValueError	If the SQL query is malformed

The “Debug” format isn’t supported and will be automatically converted to “CSV” with a warning. For debugging, use connection string parameters instead.

Warning

This method executes the query synchronously and loads all results into memory. For large result sets, consider using send_query() for streaming results.

Examples

>>> session = Session("test.db")
>>>
>>> # Basic query with default CSV format
>>> result = session.query("SELECT 1 as number")
>>> print(result)
number
1

>>> # Query with JSON format
>>> result = session.query("SELECT 1 as number", fmt="JSON")
>>> print(result)
{"number": "1"}

>>> # Complex query with table creation
>>> session.query("CREATE TABLE test (id INT, name String) ENGINE = MergeTree() order by id")
>>> session.query("INSERT INTO test VALUES (1, 'Alice'), (2, 'Bob')")
>>> result = session.query("SELECT * FROM test ORDER BY id")
>>> print(result)
id,name
1,Alice
2,Bob

See also

• send_query() - For streaming query execution
• sql - Alias for this method

State Management

chdb.state.connect

Create a Connection to the chDB background server.

This function establishes a connection to the chDB (ClickHouse) database engine. Only one open connection is allowed per process. Multiple calls with the same connection string will return the same connection object.

Syntax

chdb.state.connect(connection_string: str = ':memory:') → Connection

Parameters

Parameter	Type	Default	Description
connection_string(str, optional)	str	":memory:"	Database connection string. See formats below.

Basic formats

Supported connection string formats:

Format	Description
":memory:"	In-memory database (default)
"test.db"	Relative path database file
"file:test.db"	Same as relative path
"/path/to/test.db"	Absolute path database file
"file:/path/to/test.db"	Same as absolute path

With query parameters

Format	Description
"file:test.db?param1=value1&param2=value2"	Relative path with params
"file::memory:?verbose&log-level=test"	In-memory with params
"///path/to/test.db?param1=value1&param2=value2"	Absolute path with params

Query parameter handling

Query parameters are passed to ClickHouse engine as startup arguments. Special parameter handling:

Special Parameter	Becomes	Description
mode=ro	--readonly=1	Read-only mode
verbose	(flag)	Enables verbose logging
log-level=test	(setting)	Sets logging level

For a complete parameter list, see clickhouse local --help --verbose

Returns

Return Type	Description
Connection	Database connection object that supports: • Creating cursors with Connection.cursor() • Direct queries with Connection.query() • Streaming queries with Connection.send_query() • Context manager protocol for automatic cleanup

Raises

Exception	Condition
RuntimeError	If connection to database fails

Warning

Only one connection per process is supported. Creating a new connection will close any existing connection.

Examples

>>> # In-memory database
>>> conn = connect()
>>> conn = connect(":memory:")
>>>
>>> # File-based database
>>> conn = connect("my_data.db")
>>> conn = connect("/path/to/data.db")
>>>
>>> # With parameters
>>> conn = connect("data.db?mode=ro")  # Read-only mode
>>> conn = connect(":memory:?verbose&log-level=debug")  # Debug logging
>>>
>>> # Using context manager for automatic cleanup
>>> with connect("data.db") as conn:
...     result = conn.query("SELECT 1")
...     print(result)
>>> # Connection automatically closed

See also

• Connection - Database connection class
• Cursor - Database cursor for DB-API 2.0 operations

class chdb.state.sqlitelike.Connection

Bases: object

Syntax

class chdb.state.sqlitelike.Connection(connection_string: str)

---

close

Close the connection and cleanup resources.

This method closes the database connection and cleans up any associated resources including active cursors. After calling this method, the connection becomes invalid and can’t be used for further operations.

Syntax

close() → None

This method is idempotent - calling it multiple times is safe.

Warning

Any ongoing streaming queries will be cancelled when the connection is closed. Ensure all important data is processed before closing.

Examples

>>> conn = connect("test.db")
>>> # Use connection for queries
>>> conn.query("CREATE TABLE test (id INT) ENGINE = Memory")
>>> # Close when done
>>> conn.close()

>>> # Using with context manager (automatic cleanup)
>>> with connect("test.db") as conn:
...     conn.query("SELECT 1")
...     # Connection automatically closed

---

cursor

Create a Cursor object for executing queries.

This method creates a database cursor that provides the standard DB-API 2.0 interface for executing queries and fetching results. The cursor allows for fine-grained control over query execution and result retrieval.

Syntax

cursor() → Cursor

Returns

Return Type	Description
Cursor	A cursor object for database operations

Creating a new cursor will replace any existing cursor associated with this connection. Only one cursor per connection is supported.

Examples

>>> conn = connect(":memory:")
>>> cursor = conn.cursor()
>>> cursor.execute("CREATE TABLE test (id INT, name String) ENGINE = Memory")
>>> cursor.execute("INSERT INTO test VALUES (1, 'Alice')")
>>> cursor.execute("SELECT * FROM test")
>>> rows = cursor.fetchall()
>>> print(rows)
((1, 'Alice'),)

See also

• Cursor - Database cursor implementation

---

query

Execute a SQL query and return the complete results.

This method executes a SQL query synchronously and returns the complete result set. It supports various output formats and automatically applies format-specific post-processing.

Syntax

query(query: str, format: str = 'CSV') → Any

Parameters:

Parameter	Type	Default	Description
query	str	required	SQL query string to execute
format	str	"CSV"	Output format for results. Supported formats: • "CSV" - Comma-separated values (string) • "JSON" - JSON format (string) • "Arrow" - Apache Arrow format (bytes) • "Dataframe" - Pandas DataFrame (requires pandas) • "Arrowtable" - PyArrow Table (requires pyarrow)

Returns

Return Type	Description
str	For string formats (CSV, JSON)
bytes	For Arrow format
pandas.DataFrame	For dataframe format
pyarrow.Table	For arrowtable format

Raises

Exception	Condition
RuntimeError	If query execution fails
ImportError	If required packages for format aren’t installed

Warning

This method loads the entire result set into memory. For large results, consider using send_query() for streaming.

Examples

>>> conn = connect(":memory:")
>>>
>>> # Basic CSV query
>>> result = conn.query("SELECT 1 as num, 'hello' as text")
>>> print(result)
num,text
1,hello

>>> # DataFrame format
>>> df = conn.query("SELECT number FROM numbers(5)", "dataframe")
>>> print(df)
   number
0       0
1       1
2       2
3       3
4       4

See also

• send_query() - For streaming query execution

---

send_query

Execute a SQL query and return a streaming result iterator.

This method executes a SQL query and returns a StreamingResult object that allows you to iterate over the results without loading everything into memory at once. This is ideal for processing large result sets.

Syntax

send_query(query: str, format: str = 'CSV') → StreamingResult

Parameters

Parameter	Type	Default	Description
query	str	required	SQL query string to execute
format	str	"CSV"	Output format for results. Supported formats: • "CSV" - Comma-separated values • "JSON" - JSON format • "Arrow" - Apache Arrow format (enables record_batch() method) • "dataframe" - Pandas DataFrame chunks • "arrowtable" - PyArrow Table chunks

Returns

Return Type	Description
StreamingResult	A streaming iterator for query results that supports: • Iterator protocol (for loops) • Context manager protocol (with statements) • Manual fetching with fetch() method • PyArrow RecordBatch streaming (Arrow format only)

Raises

Exception	Condition
RuntimeError	If query execution fails
ImportError	If required packages for format aren’t installed

Only the “Arrow” format supports the record_batch() method on the returned StreamingResult.

Examples

>>> conn = connect(":memory:")
>>>
>>> # Basic streaming
>>> stream = conn.send_query("SELECT number FROM numbers(1000)")
>>> for chunk in stream:
...     print(f"Processing chunk: {len(chunk)} bytes")

>>> # Using context manager for cleanup
>>> with conn.send_query("SELECT * FROM large_table") as stream:
...     chunk = stream.fetch()
...     while chunk:
...         process_data(chunk)
...         chunk = stream.fetch()

>>> # Arrow format with RecordBatch streaming
>>> stream = conn.send_query("SELECT * FROM data", "Arrow")
>>> reader = stream.record_batch(rows_per_batch=10000)
>>> for batch in reader:
...     print(f"Batch shape: {batch.num_rows} x {batch.num_columns}")

See also

• query() - For non-streaming query execution
• StreamingResult - Streaming result iterator

---

class chdb.state.sqlitelike.StreamingResult

Bases: object

Streaming result iterator for processing large query results.

This class provides an iterator interface for streaming query results without loading the entire result set into memory. It supports various output formats and provides methods for manual result fetching and PyArrow RecordBatch streaming.

class chdb.state.sqlitelike.StreamingResult

---

fetch

Fetch the next chunk of streaming results.

This method retrieves the next available chunk of data from the streaming query result. The format of the returned data depends on the format specified when the streaming query was initiated.

Syntax

fetch() → Any

Returns

Return Type	Description
str	For text formats (CSV, JSON)
bytes	For binary formats (Arrow, Parquet)
None	When the result stream is exhausted

Examples

>>> stream = conn.send_query("SELECT * FROM large_table")
>>> chunk = stream.fetch()
>>> while chunk is not None:
...     process_data(chunk)
...     chunk = stream.fetch()

---

cancel

Cancel the streaming query and cleanup resources.

This method cancels any ongoing streaming query and releases associated resources. It should be called when you want to stop processing results before the stream is exhausted.

Syntax

cancel() → None

Examples

>>> stream = conn.send_query("SELECT * FROM very_large_table")
>>> for i, chunk in enumerate(stream):
...     if i >= 10:  # Only process first 10 chunks
...         stream.cancel()
...         break
...     process_data(chunk)

---

close

Close the streaming result and cleanup resources.

Alias for cancel(). Closes the streaming result iterator and releases any associated resources.

Syntax

close() → None

---

record_batch

Create a PyArrow RecordBatchReader for efficient batch processing.

This method creates a PyArrow RecordBatchReader that allows efficient iteration over the query results in Arrow format. This is the most efficient way to process large result sets when using PyArrow.

Syntax

record_batch(rows_per_batch: int = 1000000) → pa.RecordBatchReader

Parameters

Parameter	Type	Default	Description
rows_per_batch	int	1000000	Number of rows per batch

Returns

Return Type	Description
pa.RecordBatchReader	PyArrow RecordBatchReader for iterating over batches

This method is only available when the streaming query was initiated with format="Arrow". Using it with other formats will raise an error.

Examples

>>> stream = conn.send_query("SELECT * FROM data", format="Arrow")
>>> reader = stream.record_batch(rows_per_batch=10000)
>>> for batch in reader:
...     print(f"Processing batch: {batch.num_rows} rows")
...     df = batch.to_pandas()
...     process_dataframe(df)

---

Iterator Protocol

StreamingResult supports the Python iterator protocol, allowing it to be used directly in for loops:

>>> stream = conn.send_query("SELECT number FROM numbers(1000000)")
>>> for chunk in stream:
...     print(f"Chunk size: {len(chunk)} bytes")

---

Context Manager Protocol

StreamingResult supports the context manager protocol for automatic resource cleanup:

>>> with conn.send_query("SELECT * FROM data") as stream:
...     for chunk in stream:
...         process(chunk)
>>> # Stream automatically closed

---

class chdb.state.sqlitelike.Cursor

Bases: object

class chdb.state.sqlitelike.Cursor(connection)

---

close

Close the cursor and cleanup resources.

This method closes the cursor and cleans up any associated resources. After calling this method, the cursor becomes invalid and can’t be used for further operations.

Syntax

close() → None

This method is idempotent - calling it multiple times is safe. The cursor is also automatically closed when the connection is closed.

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT 1")
>>> result = cursor.fetchone()
>>> cursor.close()  # Cleanup cursor resources

---

column_names

Return a list of column names from the last executed query.

This method returns the column names from the most recently executed SELECT query. The names are returned in the same order as they appear in the result set.

Syntax

column_names() → list

Returns

Return Type	Description
list	List of column name strings, or empty list if no query has been executed or the query returned no columns

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT id, name, email FROM users LIMIT 1")
>>> print(cursor.column_names())
['id', 'name', 'email']

See also

• column_types() - Get column type information
• description - DB-API 2.0 column description

---

column_types

Return a list of column types from the last executed query.

This method returns the ClickHouse column type names from the most recently executed SELECT query. The types are returned in the same order as they appear in the result set.

Syntax

column_types() → list

Returns

Return Type	Description
list	List of ClickHouse type name strings, or empty list if no query has been executed or the query returned no columns

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT toInt32(1), toString('hello')")
>>> print(cursor.column_types())
['Int32', 'String']

See also

• column_names() - Get column name information
• description - DB-API 2.0 column description

---

commit

Commit any pending transaction.

This method commits any pending database transaction. In ClickHouse, most operations are auto-committed, but this method is provided for DB-API 2.0 compatibility.

ClickHouse typically auto-commits operations, so explicit commits are usually not necessary. This method is provided for compatibility with standard DB-API 2.0 workflow.

Syntax

commit() → None

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("INSERT INTO test VALUES (1, 'data')")
>>> cursor.commit()

---

property description : list

Return column description as per DB-API 2.0 specification.

This property returns a list of 7-item tuples describing each column in the result set of the last executed SELECT query. Each tuple contains: (name, type_code, display_size, internal_size, precision, scale, null_ok)

Currently, only name and type_code are provided, with other fields set to None.

Returns

Return Type	Description
list	List of 7-tuples describing each column, or empty list if no SELECT query has been executed

This follows the DB-API 2.0 specification for cursor.description. Only the first two elements (name and type_code) contain meaningful data in this implementation.

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT id, name FROM users LIMIT 1")
>>> for desc in cursor.description:
...     print(f"Column: {desc[0]}, Type: {desc[1]}")
Column: id, Type: Int32
Column: name, Type: String

See also

• column_names() - Get just column names
• column_types() - Get just column types

---

execute

Execute a SQL query and prepare results for fetching.

This method executes a SQL query and prepares the results for retrieval using the fetch methods. It handles the parsing of result data and automatic type conversion for ClickHouse data types.

Syntax

execute(query: str) → None

Parameters:

Parameter	Type	Description
query	str	SQL query string to execute

Raises

Exception	Condition
Exception	If query execution fails or result parsing fails

This method follows DB-API 2.0 specifications for cursor.execute(). After execution, use fetchone(), fetchmany(), or fetchall() to retrieve results.

The method automatically converts ClickHouse data types to appropriate Python types:

• Int/UInt types → int
• Float types → float
• String/FixedString → str
• DateTime → datetime.datetime
• Date → datetime.date
• Bool → bool

Examples

>>> cursor = conn.cursor()
>>>
>>> # Execute DDL
>>> cursor.execute("CREATE TABLE test (id INT, name String) ENGINE = Memory")
>>>
>>> # Execute DML
>>> cursor.execute("INSERT INTO test VALUES (1, 'Alice')")
>>>
>>> # Execute SELECT and fetch results
>>> cursor.execute("SELECT * FROM test")
>>> rows = cursor.fetchall()
>>> print(rows)
((1, 'Alice'),)

See also

• fetchone() - Fetch single row
• fetchmany() - Fetch multiple rows
• fetchall() - Fetch all remaining rows

---

fetchall

Fetch all remaining rows from the query result.

This method retrieves all remaining rows from the current query result set starting from the current cursor position. It returns a tuple of row tuples with appropriate Python type conversion applied.

Syntax

fetchall() → tuple

Returns:

Return Type	Description
tuple	Tuple containing all remaining row tuples from the result set. Returns empty tuple if no rows are available

Warning

This method loads all remaining rows into memory at once. For large result sets, consider using fetchmany() to process results in batches.

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT id, name FROM users")
>>> all_users = cursor.fetchall()
>>> for user_id, user_name in all_users:
...     print(f"User {user_id}: {user_name}")

See also

• fetchone() - Fetch single row
• fetchmany() - Fetch multiple rows in batches

---

fetchmany

Fetch multiple rows from the query result.

This method retrieves up to ‘size’ rows from the current query result set. It returns a tuple of row tuples, with each row containing column values with appropriate Python type conversion.

Syntax

fetchmany(size: int = 1) → tuple

Parameters

Parameter	Type	Default	Description
size	int	1	Maximum number of rows to fetch

Returns

Return Type	Description
tuple	Tuple containing up to ‘size’ row tuples. May contain fewer rows if the result set is exhausted

This method follows DB-API 2.0 specifications. It will return fewer than ‘size’ rows if the result set is exhausted.

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT * FROM large_table")
>>>
>>> # Process results in batches
>>> while True:
...     batch = cursor.fetchmany(100)  # Fetch 100 rows at a time
...     if not batch:
...         break
...     process_batch(batch)

See also

• fetchone() - Fetch single row
• fetchall() - Fetch all remaining rows

---

fetchone

Fetch the next row from the query result.

This method retrieves the next available row from the current query result set. It returns a tuple containing the column values with appropriate Python type conversion applied.

Syntax

fetchone() → tuple | None

Returns:

Return Type	Description
Optional[tuple]	Next row as a tuple of column values, or None if no more rows are available

This method follows DB-API 2.0 specifications. Column values are automatically converted to appropriate Python types based on ClickHouse column types.

Examples

>>> cursor = conn.cursor()
>>> cursor.execute("SELECT id, name FROM users")
>>> row = cursor.fetchone()
>>> while row is not None:
...     user_id, user_name = row
...     print(f"User {user_id}: {user_name}")
...     row = cursor.fetchone()

See also

• fetchmany() - Fetch multiple rows
• fetchall() - Fetch all remaining rows

---

chdb.state.sqlitelike

Convert query result to PyArrow Table.

This function converts chdb query results to a PyArrow Table format, which provides efficient columnar data access and interoperability with other data processing libraries.

Syntax

chdb.state.sqlitelike.to_arrowTable(res)

Parameters:

Parameter	Type	Description
res	-	Query result object from chdb containing Arrow format data

Returns

Return Type	Description
pyarrow.Table	PyArrow Table containing the query results

Raises

Exception	Condition
ImportError	If pyarrow or pandas packages aren’t installed

This function requires both pyarrow and pandas to be installed. Install them with: pip install pyarrow pandas

Warning

Empty results return an empty PyArrow Table with no schema.

Examples

>>> import chdb
>>> result = chdb.query("SELECT 1 as num, 'hello' as text", "Arrow")
>>> table = to_arrowTable(result)
>>> print(table.schema)
num: int64
text: string
>>> print(table.to_pandas())
   num   text
0    1  hello

---

chdb.state.sqlitelike.to_df

Convert query result to Pandas DataFrame.

This function converts chdb query results to a Pandas DataFrame format by first converting to PyArrow Table and then to DataFrame. This provides convenient data analysis capabilities with Pandas API.

Syntax

chdb.state.sqlitelike.to_df(r)

Parameters:

Parameter	Type	Description
r	-	Query result object from chdb containing Arrow format data

Returns:

Return Type	Description
pandas.DataFrame	DataFrame containing the query results with appropriate column names and data types

Raises

Exception	Condition
ImportError	If pyarrow or pandas packages aren’t installed

This function uses multi-threading for the Arrow to Pandas conversion to improve performance on large datasets.

See also

• to_arrowTable() - For PyArrow Table format conversion

Examples

>>> import chdb
>>> result = chdb.query("SELECT 1 as num, 'hello' as text", "Arrow")
>>> df = to_df(result)
>>> print(df)
   num   text
0    1  hello
>>> print(df.dtypes)
num      int64
text    object
dtype: object

DataFrame Integration

class chdb.dataframe.Table

Bases:

class chdb.dataframe.Table(*args: Any, **kwargs: Any)

Database API (DBAPI) 2.0 Interface

chDB provides a Python DB-API 2.0 compatible interface for database connectivity, allowing you to use chDB with tools and frameworks that expect standard database interfaces.

The chDB DB-API 2.0 interface includes:

• Connections: Database connection management with connection strings
• Cursors: Query execution and result retrieval
• Type System: DB-API 2.0 compliant type constants and converters
• Error Handling: Standard database exception hierarchy
• Thread Safety: Level 1 thread safety (threads may share modules but not connections)

---

Core Functions

The Database API (DBAPI) 2.0 Interface implements the following core functions:

chdb.dbapi.connect

Initialize a new database connection.

Syntax

chdb.dbapi.connect(*args, **kwargs)

Parameters

Parameter	Type	Default	Description
path	str	None	Database file path. None for in-memory database

Raises

Exception	Condition
err.Error	If connection can’t be established

---

chdb.dbapi.get_client_info()

Get client version information.

Returns the chDB client version as a string for MySQLdb compatibility.

Syntax

chdb.dbapi.get_client_info()

Returns

Return Type	Description
str	Version string in format ‘major.minor.patch’

---

Type constructors

chdb.dbapi.Binary(x)

Return x as a binary type.

This function converts the input to bytes type for use with binary database fields, following the DB-API 2.0 specification.

Syntax

chdb.dbapi.Binary(x)

Parameters

Parameter	Type	Description
x	-	Input data to convert to binary

Returns

Return Type	Description
bytes	The input converted to bytes

---

Connection Class

class chdb.dbapi.connections.Connection(path=None)

Bases: object

DB-API 2.0 compliant connection to chDB database.

This class provides a standard DB-API interface for connecting to and interacting with chDB databases. It supports both in-memory and file-based databases.

The connection manages the underlying chDB engine and provides methods for executing queries, managing transactions (no-op for ClickHouse), and creating cursors.

class chdb.dbapi.connections.Connection(path=None)

Parameters

Parameter	Type	Default	Description
path	str	None	Database file path. If None, uses in-memory database. Can be a file path like ‘database.db’ or None for ‘:memory:’

Variables

Variable	Type	Description
encoding	str	Character encoding for queries, defaults to ‘utf8’
open	bool	True if connection is open, False if closed

Examples

>>> # In-memory database
>>> conn = Connection()
>>> cursor = conn.cursor()
>>> cursor.execute("SELECT 1")
>>> result = cursor.fetchall()
>>> conn.close()

>>> # File-based database
>>> conn = Connection('mydata.db')
>>> with conn.cursor() as cur:
...     cur.execute("CREATE TABLE users (id INT, name STRING) ENGINE = MergeTree() order by id")
...     cur.execute("INSERT INTO users VALUES (1, 'Alice')")
>>> conn.close()

>>> # Context manager usage
>>> with Connection() as cur:
...     cur.execute("SELECT version()")
...     version = cur.fetchone()

ClickHouse doesn’t support traditional transactions, so commit() and rollback() operations are no-ops but provided for DB-API compliance.

---

close

Close the database connection.

Closes the underlying chDB connection and marks this connection as closed. Subsequent operations on this connection will raise an Error.

Syntax

close()

Raises

Exception	Condition
err.Error	If connection is already closed

---

commit

Commit the current transaction.

Syntax

commit()

This is a no-op for chDB/ClickHouse as it doesn’t support traditional transactions. Provided for DB-API 2.0 compliance.

---

cursor

Create a new cursor for executing queries.

Syntax

cursor(cursor=None)

Parameters

Parameter	Type	Description
cursor	-	Ignored, provided for compatibility

Returns

Return Type	Description
Cursor	New cursor object for this connection

Raises

Exception	Condition
err.Error	If connection is closed

Example

>>> conn = Connection()
>>> cur = conn.cursor()
>>> cur.execute("SELECT 1")
>>> result = cur.fetchone()

---

escape

Escape a value for safe inclusion in SQL queries.

Syntax

escape(obj, mapping=None)

Parameters

Parameter	Type	Description
obj	-	Value to escape (string, bytes, number, etc.)
mapping	-	Optional character mapping for escaping

Returns

Return Type	Description
-	Escaped version of the input suitable for SQL queries

Example

>>> conn = Connection()
>>> safe_value = conn.escape("O'Reilly")
>>> query = f"SELECT * FROM users WHERE name = {safe_value}"

---

escape_string

Escape a string value for SQL queries.

Syntax

escape_string(s)

Parameters

Parameter	Type	Description
s	str	String to escape

Returns

Return Type	Description
str	Escaped string safe for SQL inclusion

---

property open

Check if the connection is open.

Returns

Return Type	Description
bool	True if connection is open, False if closed

---

query

Execute a SQL query directly and return raw results.

This method bypasses the cursor interface and executes queries directly. For standard DB-API usage, prefer using cursor() method.

Syntax

query(sql, fmt='CSV')

Parameters:

Parameter	Type	Default	Description
sql	str or bytes	required	SQL query to execute
fmt	str	"CSV"	Output format. Supported formats include “CSV”, “JSON”, “Arrow”, “Parquet”, etc.

Returns

Return Type	Description
-	Query result in the specified format

Raises

Exception	Condition
err.InterfaceError	If connection is closed or query fails

Example

>>> conn = Connection()
>>> result = conn.query("SELECT 1, 'hello'", "CSV")
>>> print(result)
"1,hello\n"

---

property resp

Get the last query response.

Returns

Return Type	Description
-	The raw response from the last query() call

This property is updated each time query() is called directly. It doesn’t reflect queries executed through cursors.

---

rollback

Roll back the current transaction.

Syntax

rollback()

This is a no-op for chDB/ClickHouse as it doesn’t support traditional transactions. Provided for DB-API 2.0 compliance.

---

Cursor Class

class chdb.dbapi.cursors.Cursor

Bases: object

DB-API 2.0 cursor for executing queries and fetching results.

The cursor provides methods for executing SQL statements, managing query results, and navigating through result sets. It supports parameter binding, bulk operations, and follows DB-API 2.0 specifications.

Don’t create Cursor instances directly. Use Connection.cursor() instead.

class chdb.dbapi.cursors.Cursor(connection)

Variable	Type	Description
description	tuple	Column metadata for the last query result
rowcount	int	Number of rows affected by the last query (-1 if unknown)
arraysize	int	Default number of rows to fetch at once (default: 1)
lastrowid	-	ID of the last inserted row (if applicable)
max_stmt_length	int	Maximum statement size for executemany() (default: 1024000)

Examples

>>> conn = Connection()
>>> cur = conn.cursor()
>>> cur.execute("SELECT 1 as id, 'test' as name")
>>> result = cur.fetchone()
>>> print(result)  # (1, 'test')
>>> cur.close()

See DB-API 2.0 Cursor Objects for complete specification details.

---

callproc

Execute a stored procedure (placeholder implementation).

Syntax

callproc(procname, args=())

Parameters

Parameter	Type	Description
procname	str	Name of stored procedure to execute
args	sequence	Parameters to pass to the procedure

Returns

Return Type	Description
sequence	The original args parameter (unmodified)

chDB/ClickHouse doesn’t support stored procedures in the traditional sense. This method is provided for DB-API 2.0 compliance but doesn’t perform any actual operation. Use execute() for all SQL operations.

Compatibility

This is a placeholder implementation. Traditional stored procedure features like OUT/INOUT parameters, multiple result sets, and server variables aren’t supported by the underlying ClickHouse engine.

---

close

Close the cursor and free associated resources.

After closing, the cursor becomes unusable and any operation will raise an exception. Closing a cursor exhausts all remaining data and releases the underlying cursor.

Syntax

close()

---

execute

Execute a SQL query with optional parameter binding.

This method executes a single SQL statement with optional parameter substitution. It supports multiple parameter placeholder styles for flexibility.

Syntax

execute(query, args=None)

Parameters

Parameter	Type	Default	Description
query	str	required	SQL query to execute
args	tuple/list/dict	None	Parameters to bind to placeholders

Returns

Return Type	Description
int	Number of affected rows (-1 if unknown)

Parameter Styles

Style	Example
Question mark style	"SELECT * FROM users WHERE id = ?"
Named style	"SELECT * FROM users WHERE name = %(name)s"
Format style	"SELECT * FROM users WHERE age = %s" (legacy)

Examples

>>> # Question mark parameters
>>> cur.execute("SELECT * FROM users WHERE id = ? AND age > ?", (123, 18))
>>>
>>> # Named parameters
>>> cur.execute("SELECT * FROM users WHERE name = %(name)s", {'name': 'Alice'})
>>>
>>> # No parameters
>>> cur.execute("SELECT COUNT(*) FROM users")

Raises

Exception	Condition
ProgrammingError	If cursor is closed or query is malformed
InterfaceError	If database error occurs during execution

---

executemany(query, args)

Execute a query multiple times with different parameter sets.

This method efficiently executes the same SQL query multiple times with different parameter values. It’s particularly useful for bulk INSERT operations.

Syntax

executemany(query, args)

Parameters

Parameter	Type	Description
query	str	SQL query to execute multiple times
args	sequence	Sequence of parameter tuples/dicts/lists for each execution

Returns

Return Type	Description
int	Total number of affected rows across all executions

Examples

>>> # Bulk insert with question mark parameters
>>> users_data = [(1, 'Alice'), (2, 'Bob'), (3, 'Charlie')]
>>> cur.executemany("INSERT INTO users VALUES (?, ?)", users_data)
>>>
>>> # Bulk insert with named parameters
>>> users_data = [
...     {'id': 1, 'name': 'Alice'},
...     {'id': 2, 'name': 'Bob'}
... ]
>>> cur.executemany(
...     "INSERT INTO users VALUES (%(id)s, %(name)s)",
...     users_data
... )

This method improves performance for multiple-row INSERT and UPDATE operations by optimizing the query execution process.

---

fetchall()

Fetch all remaining rows from the query result.

Syntax

fetchall()

Returns

Return Type	Description
list	List of tuples representing all remaining rows

Raises

Exception	Condition
ProgrammingError	If execute() hasn’t been called first

Warning

This method can consume large amounts of memory for big result sets. Consider using fetchmany() for large datasets.

Example

>>> cursor.execute("SELECT id, name FROM users")
>>> all_rows = cursor.fetchall()
>>> print(len(all_rows))  # Number of total rows

---

fetchmany

Fetch multiple rows from the query result.

Syntax

fetchmany(size=1)

Parameters

Parameter	Type	Default	Description
size	int	1	Number of rows to fetch. If not specified, uses cursor.arraysize

Returns

Return Type	Description
list	List of tuples representing the fetched rows

Raises

Exception	Condition
ProgrammingError	If execute() hasn’t been called first

Example

>>> cursor.execute("SELECT id, name FROM users")
>>> rows = cursor.fetchmany(3)
>>> print(rows)  # [(1, 'Alice'), (2, 'Bob'), (3, 'Charlie')]

---

fetchone

Fetch the next row from the query result.

Syntax

fetchone()

Returns

Return Type	Description
tuple or None	Next row as a tuple, or None if no more rows available

Raises

Exception	Condition
ProgrammingError	If execute() hasn’t been called first

Example

>>> cursor.execute("SELECT id, name FROM users LIMIT 3")
>>> row = cursor.fetchone()
>>> print(row)  # (1, 'Alice')
>>> row = cursor.fetchone()
>>> print(row)  # (2, 'Bob')

---

max_stmt_length = 1024000

Max statement size which executemany() generates.

Default value is 1024000.

---

mogrify

Return the exact query string that would be sent to the database.

This method shows the final SQL query after parameter substitution, which is useful for debugging and logging purposes.

Syntax

mogrify(query, args=None)

Parameters

Parameter	Type	Default	Description
query	str	required	SQL query with parameter placeholders
args	tuple/list/dict	None	Parameters to substitute

Returns

Return Type	Description
str	The final SQL query string with parameters substituted

Example

>>> cur.mogrify("SELECT * FROM users WHERE id = ?", (123,))
"SELECT * FROM users WHERE id = 123"

This method follows the extension to DB-API 2.0 used by Psycopg.

---

nextset

Move to the next result set (not supported).

Syntax

nextset()

Returns

Return Type	Description
None	Always returns None as multiple result sets aren’t supported

chDB/ClickHouse doesn’t support multiple result sets from a single query. This method is provided for DB-API 2.0 compliance but always returns None.

---

setinputsizes

Set input sizes for parameters (no-op implementation).

Syntax

setinputsizes(*args)

Parameters

Parameter	Type	Description
*args	-	Parameter size specifications (ignored)

This method does nothing but is required by DB-API 2.0 specification. chDB automatically handles parameter sizing internally.

---

setoutputsizes

Set output column sizes (no-op implementation).

Syntax

setoutputsizes(*args)

Parameters

Parameter	Type	Description
*args	-	Column size specifications (ignored)

This method does nothing but is required by DB-API 2.0 specification. chDB automatically handles output sizing internally.

---

Error Classes

Exception classes for chdb database operations.

This module provides a complete hierarchy of exception classes for handling database-related errors in chdb, following the Python Database API Specification v2.0.

The exception hierarchy is structured as follows:

StandardError
├── Warning
└── Error
    ├── InterfaceError
    └── DatabaseError
        ├── DataError
        ├── OperationalError
        ├── IntegrityError
        ├── InternalError
        ├── ProgrammingError
        └── NotSupportedError

Each exception class represents a specific category of database errors:

Exception	Description
Warning	Non-fatal warnings during database operations
InterfaceError	Problems with the database interface itself
DatabaseError	Base class for all database-related errors
DataError	Problems with data processing (invalid values, type errors)
OperationalError	Database operational issues (connectivity, resources)
IntegrityError	Constraint violations (foreign keys, uniqueness)
InternalError	Database internal errors and corruption
ProgrammingError	SQL syntax errors and API misuse
NotSupportedError	Unsupported features or operations

These exception classes are compliant with Python DB API 2.0 specification and provide consistent error handling across different database operations.

See also

• Python Database API Specification v2.0
• chdb.dbapi.connections - Database connection management
• chdb.dbapi.cursors - Database cursor operations

Examples

>>> try:
...     cursor.execute("SELECT * FROM nonexistent_table")
... except ProgrammingError as e:
...     print(f"SQL Error: {e}")
...
SQL Error: Table 'nonexistent_table' doesn't exist

>>> try:
...     cursor.execute("INSERT INTO users (id) VALUES (1), (1)")
... except IntegrityError as e:
...     print(f"Constraint violation: {e}")
...
Constraint violation: Duplicate entry '1' for key 'PRIMARY'

---

exception chdb.dbapi.err.DataError

Bases: DatabaseError

Exception raised for errors that are due to problems with the processed data.

This exception is raised when database operations fail due to issues with the data being processed, such as:

• Division by zero operations
• Numeric values out of range
• Invalid date/time values
• String truncation errors
• Type conversion failures
• Invalid data format for column type

Raises

Exception	Condition
DataError	When data validation or processing fails

Examples

>>> # Division by zero in SQL
>>> cursor.execute("SELECT 1/0")
DataError: Division by zero

>>> # Invalid date format
>>> cursor.execute("INSERT INTO table VALUES ('invalid-date')")
DataError: Invalid date format

---

exception chdb.dbapi.err.DatabaseError

Bases: Error

Exception raised for errors that are related to the database.

This is the base class for all database-related errors. It encompasses all errors that occur during database operations and are related to the database itself rather than the interface.

Common scenarios include:

• SQL execution errors
• Database connectivity issues
• Transaction-related problems
• Database-specific constraints violations

This serves as the parent class for more specific database error types such as DataError, OperationalError, etc.

---

exception chdb.dbapi.err.Error

Bases: StandardError

Exception that is the base class of all other error exceptions (not Warning).

This is the base class for all error exceptions in chdb, excluding warnings. It serves as the parent class for all database error conditions that prevent successful completion of operations.

This exception hierarchy follows the Python DB API 2.0 specification.

See also

• Warning - For non-fatal warnings that don’t prevent operation completion

exception chdb.dbapi.err.IntegrityError

Bases: DatabaseError

Exception raised when the relational integrity of the database is affected.

This exception is raised when database operations violate integrity constraints, including:

• Foreign key constraint violations
• Primary key or unique constraint violations (duplicate keys)
• Check constraint violations
• NOT NULL constraint violations
• Referential integrity violations

Raises

Exception	Condition
IntegrityError	When database integrity constraints are violated

Examples

>>> # Duplicate primary key
>>> cursor.execute("INSERT INTO users (id, name) VALUES (1, 'John')")
>>> cursor.execute("INSERT INTO users (id, name) VALUES (1, 'Jane')")
IntegrityError: Duplicate entry '1' for key 'PRIMARY'

>>> # Foreign key violation
>>> cursor.execute("INSERT INTO orders (user_id) VALUES (999)")
IntegrityError: Cannot add or update a child row: foreign key constraint fails

---

exception chdb.dbapi.err.InterfaceError

Bases: Error

Exception raised for errors that are related to the database interface rather than the database itself.

This exception is raised when there are problems with the database interface implementation, such as:

• Invalid connection parameters
• API misuse (calling methods on closed connections)
• Interface-level protocol errors
• Module import or initialization failures

Raises

Exception	Condition
InterfaceError	When database interface encounters errors unrelated to database operations

These errors are typically programming errors or configuration issues that can be resolved by fixing the client code or configuration.

---

exception chdb.dbapi.err.InternalError

Bases: DatabaseError

Exception raised when the database encounters an internal error.

This exception is raised when the database system encounters internal errors that aren’t caused by the application, such as:

• Invalid cursor state (cursor isn’t valid anymore)
• Transaction state inconsistencies (transaction is out of sync)
• Database corruption issues
• Internal data structure corruption
• System-level database errors

Raises

Exception	Condition
InternalError	When database encounters internal inconsistencies

Warning

Internal errors may indicate serious database problems that require database administrator attention. These errors are typically not recoverable through application-level retry logic.

These errors are generally outside the control of the application and may require database restart or repair operations.

---

exception chdb.dbapi.err.NotSupportedError

Bases: DatabaseError

Exception raised when a method or database API isn’t supported.

This exception is raised when the application attempts to use database features or API methods that aren’t supported by the current database configuration or version, such as:

• Requesting rollback() on connections without transaction support
• Using advanced SQL features not supported by the database version
• Calling methods not implemented by the current driver
• Attempting to use disabled database features

Raises

Exception	Condition
NotSupportedError	When unsupported database features are accessed

Examples

>>> # Transaction rollback on non-transactional connection
>>> connection.rollback()
NotSupportedError: Transactions are not supported

>>> # Using unsupported SQL syntax
>>> cursor.execute("SELECT * FROM table WITH (NOLOCK)")
NotSupportedError: WITH clause not supported in this database version

Check database documentation and driver capabilities to avoid these errors. Consider graceful fallbacks where possible.

---

exception chdb.dbapi.err.OperationalError

Bases: DatabaseError

Exception raised for errors that are related to the database’s operation.

This exception is raised for errors that occur during database operation and aren’t necessarily under the control of the programmer, including:

• Unexpected disconnection from database
• Database server not found or unreachable
• Transaction processing failures
• Memory allocation errors during processing
• Disk space or resource exhaustion
• Database server internal errors
• Authentication or authorization failures

Raises

Exception	Condition
OperationalError	When database operations fail due to operational issues

These errors are typically transient and may be resolved by retrying the operation or addressing system-level issues.

Warning

Some operational errors may indicate serious system problems that require administrative intervention.

---

exception chdb.dbapi.err.ProgrammingError

Bases: DatabaseError

Exception raised for programming errors in database operations.

This exception is raised when there are programming errors in the application’s database usage, including:

• Table or column not found
• Table or index already exists when creating
• SQL syntax errors in statements
• Wrong number of parameters specified in prepared statements
• Invalid SQL operations (e.g., DROP on non-existent objects)
• Incorrect usage of database API methods

Raises

Exception	Condition
ProgrammingError	When SQL statements or API usage contains errors

Examples

>>> # Table not found
>>> cursor.execute("SELECT * FROM nonexistent_table")
ProgrammingError: Table 'nonexistent_table' doesn't exist

>>> # SQL syntax error
>>> cursor.execute("SELCT * FROM users")
ProgrammingError: You have an error in your SQL syntax

>>> # Wrong parameter count
>>> cursor.execute("INSERT INTO users (name, age) VALUES (%s)", ('John',))
ProgrammingError: Column count doesn't match value count

---

exception chdb.dbapi.err.StandardError

Bases: Exception

Exception related to operation with chdb.

This is the base class for all chdb-related exceptions. It inherits from Python’s built-in Exception class and serves as the root of the exception hierarchy for database operations.

This exception class follows the Python DB API 2.0 specification for database exception handling.

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exception chdb.dbapi.err.Warning

Bases: StandardError

Exception raised for important warnings like data truncations while inserting, etc.

This exception is raised when the database operation completes but with important warnings that should be brought to the attention of the application. Common scenarios include:

• Data truncation during insertion
• Precision loss in numeric conversions
• Character set conversion warnings

This follows the Python DB API 2.0 specification for warning exceptions.

---

Module Constants

chdb.dbapi.apilevel = '2.0'

str(object=’’) -> str
str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object._\_str_\_() (if defined) or repr(object).

• encoding defaults to ‘utf-8’.
• errors defaults to ‘strict’.

---

chdb.dbapi.threadsafety = 1

int([x]) -> integer
int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x._int_(). For floating-point numbers, this truncates towards zero.

If x isn’t a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by ‘+’ or ‘-’ and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal.

>>> int(‘0b100’, base=0)
4

---

chdb.dbapi.paramstyle = 'format'

str(object=’’) -> str
str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object._str_() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

---

Type Constants

chdb.dbapi.STRING = frozenset({247, 253, 254})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.BINARY = frozenset({249, 250, 251, 252})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.NUMBER = frozenset({0, 1, 3, 4, 5, 8, 9, 13})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.DATE = frozenset({10, 14})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.TIME = frozenset({11})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.TIMESTAMP = frozenset({7, 12})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

chdb.dbapi.DATETIME = frozenset({7, 12})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

---

chdb.dbapi.ROWID = frozenset({})

Extended frozenset for DB-API 2.0 type comparison.

This class extends frozenset to support DB-API 2.0 type comparison semantics. It allows for flexible type checking where individual items can be compared against the set using both equality and inequality operators.

This is used for type constants like STRING, BINARY, NUMBER, etc. to enable comparisons like “field_type == STRING” where field_type is a single type value.

Examples

>>> string_types = DBAPISet([FIELD_TYPE.STRING, FIELD_TYPE.VAR_STRING])
>>> FIELD_TYPE.STRING == string_types  # Returns True
>>> FIELD_TYPE.INT != string_types     # Returns True
>>> FIELD_TYPE.BLOB in string_types    # Returns False

Usage Examples

Basic Query Example:

import chdb.dbapi as dbapi

print("chdb driver version: {0}".format(dbapi.get_client_info()))

# Create connection and cursor
conn = dbapi.connect()
cur = conn.cursor()

# Execute query
cur.execute('SELECT version()')
print("description:", cur.description)
print("data:", cur.fetchone())

# Clean up
cur.close()
conn.close()

Working with Data:

import chdb.dbapi as dbapi

conn = dbapi.connect()
cur = conn.cursor()

# Create table
cur.execute("""
    CREATE TABLE employees (
        id UInt32,
        name String,
        department String,
        salary Decimal(10,2)
    ) ENGINE = Memory
""")

# Insert data
cur.execute("""
    INSERT INTO employees VALUES
    (1, 'Alice', 'Engineering', 75000.00),
    (2, 'Bob', 'Marketing', 65000.00),
    (3, 'Charlie', 'Engineering', 80000.00)
""")

# Query data
cur.execute("SELECT * FROM employees WHERE department = 'Engineering'")

# Fetch results
print("Column names:", [desc[0] for desc in cur.description])
for row in cur.fetchall():
    print(row)

conn.close()

Connection Management:

import chdb.dbapi as dbapi

# In-memory database (default)
conn1 = dbapi.connect()

# Persistent database file
conn2 = dbapi.connect("./my_database.chdb")

# Connection with parameters
conn3 = dbapi.connect("./my_database.chdb?log-level=debug&verbose")

# Read-only connection
conn4 = dbapi.connect("./my_database.chdb?mode=ro")

# Automatic connection cleanup
with dbapi.connect("test.chdb") as conn:
    cur = conn.cursor()
    cur.execute("SELECT count() FROM numbers(1000)")
    result = cur.fetchone()
    print(f"Count: {result[0]}")
    cur.close()

Best Practices

1. Connection Management: Always close connections and cursors when done
2. Context Managers: Use with statements for automatic cleanup
3. Batch Processing: Use fetchmany() for large result sets
4. Error Handling: Wrap database operations in try-except blocks
5. Parameter Binding: Use parameterized queries when possible
6. Memory Management: Avoid fetchall() for very large datasets

• chDB’s DB-API 2.0 interface is compatible with most Python database tools
• The interface provides Level 1 thread safety (threads may share modules but not connections)
• Connection strings support the same parameters as chDB sessions
• All standard DB-API 2.0 exceptions are supported

Warning

• Always close cursors and connections to avoid resource leaks
• Large result sets should be processed in batches
• Parameter binding syntax follows format style: %s

User-Defined Functions (UDF)

User-defined functions module for chDB.

This module provides functionality for creating and managing user-defined functions (UDFs) in chDB. It allows you to extend chDB’s capabilities by writing custom Python functions that can be called from SQL queries.

chdb.udf.chdb_udf

Decorator for chDB Python UDF(User Defined Function).

Syntax

chdb.udf.chdb_udf(return_type='String')

Parameters

Parameter	Type	Default	Description
return_type	str	"String"	Return type of the function. Should be one of the ClickHouse data types

Notes

1. The function should be stateless. Only UDFs are supported, not UDAFs.
2. Default return type is String. The return type should be one of the ClickHouse data types.
3. The function should take in arguments of type String. All arguments are strings.
4. The function will be called for each line of input.
5. The function should be pure python function. Import all modules used IN THE FUNCTION.
6. Python interpreter used is the same as the one used to run the script.

Example

@chdb_udf()
def sum_udf(lhs, rhs):
    return int(lhs) + int(rhs)

@chdb_udf()
def func_use_json(arg):
    import json
    # ... use json module

---

chdb.udf.generate_udf

Generate UDF configuration and executable script files.

This function creates the necessary files for a User Defined Function (UDF) in chDB:

1. A Python executable script that processes input data
2. An XML configuration file that registers the UDF with ClickHouse

Syntax

chdb.udf.generate_udf(func_name, args, return_type, udf_body)

Parameters

Parameter	Type	Description
func_name	str	Name of the UDF function
args	list	List of argument names for the function
return_type	str	ClickHouse return type for the function
udf_body	str	Python source code body of the UDF function

This function is typically called by the @chdb_udf decorator and shouldn’t be called directly by users.

---

Utilities

Utility functions and helpers for chDB.

This module contains various utility functions for working with chDB, including data type inference, data conversion helpers, and debugging utilities.

---

chdb.utils.convert_to_columnar

Converts a list of dictionaries into a columnar format.

This function takes a list of dictionaries and converts it into a dictionary where each key corresponds to a column and each value is a list of column values. Missing values in the dictionaries are represented as None.

Syntax

chdb.utils.convert_to_columnar(items: List[Dict[str, Any]]) → Dict[str, List[Any]]

Parameters

Parameter	Type	Description
items	List[Dict[str, Any]]	A list of dictionaries to convert

Returns

Return Type	Description
Dict[str, List[Any]]	A dictionary with keys as column names and values as lists of column values

Example

>>> items = [
...     {"name": "Alice", "age": 30, "city": "New York"},
...     {"name": "Bob", "age": 25},
...     {"name": "Charlie", "city": "San Francisco"}
... ]
>>> convert_to_columnar(items)
{
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [30, 25, None],
    'city': ['New York', None, 'San Francisco']
}

---

chdb.utils.flatten_dict

Flattens a nested dictionary.

This function takes a nested dictionary and flattens it, concatenating nested keys with a separator. Lists of dictionaries are serialized to JSON strings.

Syntax

chdb.utils.flatten_dict(d: Dict[str, Any], parent_key: str = '', sep: str = '_') → Dict[str, Any]

Parameters

Parameter	Type	Default	Description
d	Dict[str, Any]	required	The dictionary to flatten
parent_key	str	""	The base key to prepend to each key
sep	str	"_"	The separator to use between concatenated keys

Returns

Return Type	Description
Dict[str, Any]	A flattened dictionary

Example

>>> nested_dict = {
...     "a": 1,
...     "b": {
...         "c": 2,
...         "d": {
...             "e": 3
...         }
...     },
...     "f": [4, 5, {"g": 6}],
...     "h": [{"i": 7}, {"j": 8}]
... }
>>> flatten_dict(nested_dict)
{
    'a': 1,
    'b_c': 2,
    'b_d_e': 3,
    'f_0': 4,
    'f_1': 5,
    'f_2_g': 6,
    'h': '[{"i": 7}, {"j": 8}]'
}

---

chdb.utils.infer_data_type

Infers the most suitable data type for a list of values.

This function examines a list of values and determines the most appropriate data type that can represent all the values in the list. It considers integer, unsigned integer, decimal, and float types, and defaults to “string” if the values can’t be represented by any numeric type or if all values are None.

Syntax

chdb.utils.infer_data_type(values: List[Any]) → str

Parameters

Parameter	Type	Description
values	List[Any]	A list of values to analyze. The values can be of any type

Returns

Return Type	Description
str	A string representing the inferred data type. Possible return values are: ”int8”, “int16”, “int32”, “int64”, “int128”, “int256”, “uint8”, “uint16”,“uint32”, “uint64”, “uint128”, “uint256”, “decimal128”, “decimal256”, “float32”, “float64”, or “string”.

• If all values in the list are None, the function returns “string”.
• If any value in the list is a string, the function immediately returns “string”.
• The function assumes that numeric values can be represented as integers, decimals, or floats based on their range and precision.

---

chdb.utils.infer_data_types

Infers data types for each column in a columnar data structure.

This function analyzes the values in each column and infers the most suitable data type for each column, based on a sample of the data.

Syntax

chdb.utils.infer_data_types`(column_data: Dict[str, List[Any]], n_rows: int = 10000) → List[tuple]

Parameters

Parameter	Type	Default	Description
column_data	Dict[str, List[Any]]	required	A dictionary where keys are column names and values are lists of column values
n_rows	int	10000	The number of rows to sample for type inference

Returns

Return Type	Description
List[tuple]	A list of tuples, each containing a column name and its inferred data type

Abstract Base Classes

class chdb.rwabc.PyReader(data: Any)`

Bases: ABC

class chdb.rwabc.PyReader(data: Any)

---

abstractmethod read

Read a specified number of rows from the given columns and return a list of objects, where each object is a sequence of values for a column.

abstractmethod (col_names: List[str], count: int) → List[Any]

Parameters

Parameter	Type	Description
col_names	List[str]	List of column names to read
count	int	Maximum number of rows to read

Returns

Return Type	Description
List[Any]	List of sequences, one for each column

class chdb.rwabc.PyWriter

Bases: ABC

class chdb.rwabc.PyWriter(col_names: List[str], types: List[type], data: Any)

---

abstractmethod finalize

Assemble and return the final data from blocks. Must be implemented by subclasses.

abstractmethod finalize() → bytes

Returns

Return Type	Description
bytes	The final serialized data

---

abstractmethod write

Save columns of data to blocks. Must be implemented by subclasses.

abstractmethod write(col_names: List[str], columns: List[List[Any]]) → None

Parameters

Parameter	Type	Description
col_names	List[str]	List of column names that are being written
columns	List[List[Any]]	List of columns data, each column is represented by a list

Exception Handling

class chdb.ChdbError

Bases: Exception

Base exception class for chDB-related errors.

This exception is raised when chDB query execution fails or encounters an error. It inherits from the standard Python Exception class and provides error information from the underlying ClickHouse engine.

The exception message typically contains detailed error information from ClickHouse, including syntax errors, type mismatches, missing tables/columns, and other query execution issues.

Variables

Variable	Type	Description
args	-	Tuple containing the error message and any additional arguments

Examples

>>> try:
...     result = chdb.query("SELECT * FROM non_existent_table")
... except chdb.ChdbError as e:
...     print(f"Query failed: {e}")
Query failed: Table 'non_existent_table' doesn't exist

>>> try:
...     result = chdb.query("SELECT invalid_syntax FROM")
... except chdb.ChdbError as e:
...     print(f"Syntax error: {e}")
Syntax error: Syntax error near 'FROM'

This exception is automatically raised by chdb.query() and related functions when the underlying ClickHouse engine reports an error. You should catch this exception when handling potentially failing queries to provide appropriate error handling in your application.

Version Information

chdb.chdb_version = ('3', '6', '0')

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable’s items.

If the argument is a tuple, the return value is the same object.

---

chdb.engine_version = '25.5.2.1'

str(object=’’) -> str
str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object._str_() (if defined) or repr(object).

• encoding defaults to ‘utf-8’.
• errors defaults to ‘strict’.

---

chdb.__version__ = '3.6.0'

str(object=’’) -> str
str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object._str_() (if defined) or repr(object).

• encoding defaults to ‘utf-8’.
• errors defaults to ‘strict’.