Executing statements in Clickhouse

Warning

Methods below read all the rows returned from DB to Spark driver memory, and then convert them to DataFrame.

Do NOT use them to read large amounts of data. Use DBReader or Clickhouse.sql instead.

How to

There are 2 ways to execute some statement in Clickhouse

Use Clickhouse.fetch

Use this method to execute some SELECT query which returns small number or rows, like reading Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame.

Method accepts JDBCOptions.

Connection opened using this method should be then closed with connection.close() or with connection:.

Warning

Please take into account Clickhouse <-> Spark type mapping.

Syntax support

This method supports any query syntax supported by Clickhouse, like:

• ✅︎ SELECT ... FROM ...

• ✅︎ WITH alias AS (...) SELECT ...

• ✅︎ SELECT func(arg1, arg2) - call function

• ✅︎ SHOW ...

• ❌ SET ...; SELECT ...; - multiple statements not supported

Examples

from onetl.connection import Clickhouse

clickhouse = Clickhouse(...)

df = clickhouse.fetch(
    "SELECT value FROM some.reference_table WHERE key = 'some_constant'",
    options=Clickhouse.JDBCOptions(query_timeout=10),
)
clickhouse.close()
value = df.collect()[0][0]  # get value from first row and first column

Use Clickhouse.execute

Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it.

Method accepts JDBCOptions.

Connection opened using this method should be then closed with connection.close() or with connection:.

Syntax support

This method supports any query syntax supported by Clickhouse, like:

• ✅︎ CREATE TABLE ..., CREATE VIEW ..., DROP TABLE ..., and so on

• ✅︎ ALTER ...

• ✅︎ INSERT INTO ... SELECT ..., UPDATE ..., DELETE ..., and so on

• ✅︎ DROP TABLE ..., DROP VIEW ..., and so on

• ✅︎ other statements not mentioned here

• ❌ SET ...; SELECT ...; - multiple statements not supported

Examples

from onetl.connection import Clickhouse

clickhouse = Clickhouse(...)

with clickhouse:
    # automatically close connection after exiting this context manager
    clickhouse.execute("DROP TABLE schema.table")
    clickhouse.execute(
        """
        CREATE TABLE schema.table AS (
            id UInt8,
            key String,
            value Float32
        )
        ENGINE = MergeTree()
        ORDER BY id
        """,
        options=Clickhouse.JDBCOptions(query_timeout=10),
    )

Notes

These methods read all the rows returned from DB to Spark driver memory, and then convert them to DataFrame.

So it should NOT be used to read large amounts of data. Use DBReader or Clickhouse.sql instead.

Options

pydantic model onetl.connection.db_connection.jdbc_mixin.options.JDBCOptions

Generic options, related to specific JDBC driver.

Note

You can pass any value supported by underlying JDBC driver class, even if it is not mentioned in this documentation.

field query_timeout: int | None = None (alias 'queryTimeout')

The number of seconds the driver will wait for a statement to execute. Zero means there is no limit.

This option depends on driver implementation, some drivers can check the timeout of each query instead of an entire JDBC batch.

field fetchsize: int | None = None

How many rows to fetch per round trip.

Tuning this option can influence performance of reading.

Warning

Default value depends on driver. For example, Oracle has default fetchsize=10.