airflow-clickhouse-plugin 1.3.0

airflow-clickhouse-plugin — Airflow plugin to execute ClickHouse commands and queries

Airflow ClickHouse Plugin

🔝 The most popular Apache Airflow plugin for ClickHouse, ranked in the top 1% of downloads on PyPI. Based on awesome mymarilyn/clickhouse-driver.

This plugin provides two families of operators: richer clickhouse_driver.Client.execute-based and standardized compatible with Python DB API 2.0.

Both operators' families are fully supported and covered with tests for different versions of Airflow and Python.

clickhouse-driver family

• ClickHouseOperator
• ClickHouseHook
• ClickHouseSensor

These operators are based on mymarilyn/clickhouse-driver's Client.execute method and arguments. They offer a full functionality of clickhouse-driver and are recommended if you are starting fresh with ClickHouse in Airflow.

Features

• SQL Templating: SQL queries and other parameters are templated.
• Multiple SQL Queries: execute run multiple SQL queries within a single ClickHouseOperator. The result of the last query is pushed to XCom (configurable by do_xcom_push).
• Logging: executed queries are logged in a visually pleasing format, making it easier to track and debug.
• Efficient Native ClickHouse Protocol: Utilizes efficient native ClickHouse TCP protocol, thanks to clickhouse-driver. Does not support HTTP protocol.
• Custom Connection Parameters: Supports additional ClickHouse connection parameters, such as various timeouts, compression, secure, through the Airflow Connection.extra property.

See reference and examples below.

Installation and dependencies

pip install -U airflow-clickhouse-plugin

Dependencies: only apache-airflow and clickhouse-driver.

Python DB API 2.0 family

• Operators:
  • ClickHouseSQLExecuteQueryOperator
  • ClickHouseSQLColumnCheckOperator
  • ClickHouseSQLTableCheckOperator
  • ClickHouseSQLCheckOperator
  • ClickHouseSQLValueCheckOperator
  • ClickHouseSQLIntervalCheckOperator
  • ClickHouseSQLThresholdCheckOperator
  • ClickHouseBranchSQLOperator
• ClickHouseDbApiHook
• ClickHouseSqlSensor

These operators combine clickhouse_driver.dbapi with apache-airflow-providers-common-sql. While they have limited functionality compared to Client.execute (not all arguments are supported), they provide a standardized interface. This is useful when porting Airflow pipelines to ClickHouse from another SQL provider backed by common.sql Airflow package, such as MySQL, Postgres, BigQuery, and others.

The feature set of this version is fully based on common.sql Airflow provider: refer to its reference and examples for details.

An example is also available below.

Installation and dependencies

Add common.sql extra when installing the plugin: pip install -U airflow-clickhouse-plugin[common.sql] — to enable DB API 2.0 operators.

Dependencies: apache-airflow-providers-common-sql (usually pre-packed with Airflow) in addition to apache-airflow and clickhouse-driver.

Python and Airflow versions support

Different versions of the plugin support different combinations of Python and Airflow versions. We primarily support Airflow 2.0+ and Python 3.8+. If you need to use the plugin with older Python-Airflow combinations, pick a suitable plugin version:

airflow-clickhouse-plugin version	Airflow version	Python version
1.3.0	>=2.0.0,<2.10.0	~=3.8
1.2.0	>=2.0.0,<2.9.0	~=3.8
1.1.0	>=2.0.0,<2.8.0	~=3.8
1.0.0	>=2.0.0,<2.7.0	~=3.8
0.11.0	~=2.0.0,>=2.2.0,<2.7.0	~=3.7
0.10.0,0.10.1	~=2.0.0,>=2.2.0,<2.6.0	~=3.7
0.9.0,0.9.1	~=2.0.0,>=2.2.0,<2.5.0	~=3.7
0.8.2	>=2.0.0,<2.4.0	~=3.7
0.8.0,0.8.1	>=2.0.0,<2.3.0	~=3.6
0.7.0	>=2.0.0,<2.2.0	~=3.6
0.6.0	~=2.0.1	~=3.6
>=0.5.4,<0.6.0	~=1.10.6	>=2.7 or >=3.5.*
>=0.5.0,<0.5.4	==1.10.6	>=2.7 or >=3.5.*

~= means compatible release, see PEP 440 for an explanation.

DB API 2.0 functionality requires apache-airflow>=2.2 and apache-airflow-providers-common-sql>=1.3: earlier versions are not supported.

Previous versions of the plugin might require pandas extra: pip install airflow-clickhouse-plugin[pandas]==0.11.0. Check out earlier versions of README.md for details.

Usage

To see examples scroll down. To run them, create an Airflow connection to ClickHouse.

ClickHouseOperator reference

To import ClickHouseOperator use from airflow_clickhouse_plugin.operators.clickhouse import ClickHouseOperator.

Supported arguments:

• sql (templated, required): query (if argument is a single str) or multiple queries (iterable of str). Supports files with .sql extension.
• clickhouse_conn_id: Airflow connection id. Connection schema is described below. Default connection id is clickhouse_default.
• Arguments of clickhouse_driver.Client.execute method:
  • parameters (templated): passed params of the execute method. (Renamed to avoid name conflict with Airflow tasks' params argument.)
    • dict for SELECT queries.
    • list/tuple/generator for INSERT queries.
    • If multiple queries are provided via sql then the parameters are passed to all of them.
  • with_column_types (not templated).
  • external_tables (templated).
  • query_id (templated).
  • settings (templated).
  • types_check (not templated).
  • columnar (not templated).
  • For the documentation of these arguments, refer to clickhouse_driver.Client.execute API reference.
• database (templated): if present, overrides schema of Airflow connection.
• Other arguments (including a required task_id) are inherited from Airflow BaseOperator.

Result of the last query is pushed to XCom (disable using do_xcom_push=False argument).

In other words, the operator simply wraps ClickHouseHook.execute method.

See example below.

ClickHouseHook reference

To import ClickHouseHook use from airflow_clickhouse_plugin.hooks.clickhouse import ClickHouseHook.

Supported kwargs of constructor (__init__ method):

• clickhouse_conn_id: Airflow connection id. Connection schema is described below. Default connection id is clickhouse_default.
• database: if present, overrides schema of Airflow connection.

Defines ClickHouseHook.execute method which simply wraps clickhouse_driver.Client.execute. It has all the same arguments, except of:

• sql (instead of execute's query): query (if argument is a single str) or multiple queries (iterable of str).

ClickHouseHook.execute returns a result of the last query.

Also, the hook defines get_conn() method which returns an underlying clickhouse_driver.Client instance.

See example below.

ClickHouseSensor reference

To import ClickHouseSensor use from airflow_clickhouse_plugin.sensors.clickhouse import ClickHouseSensor.

This class wraps ClickHouseHook.execute method into an Airflow sensor. Supports all the arguments of ClickHouseOperator and additionally:

• is_success: a callable which accepts a single argument — a return value of ClickHouseHook.execute. If a return value of is_success is truthy, the sensor succeeds. By default, the callable is bool: i.e. if the return value of ClickHouseHook.execute is truthy, the sensor succeeds. Usually, execute is a list of records returned by query: thus, by default it is falsy if no records are returned.
• is_failure: a callable which accepts a single argument — a return value of ClickHouseHook.execute. If a return value of is_failure is truthy, the sensor raises AirflowException. By default, is_failure is None and no failure check is performed.

See example below.

How to create an Airflow connection to ClickHouse

As a type of a new connection, choose SQLite or any other SQL database. There is no special ClickHouse connection type yet, so we use any SQL as the closest one.

All the connection attributes are optional: default host is localhost and other credentials have defaults defined by clickhouse-driver. If you use non-default values, set them according to the connection schema.

If you use a secure connection to ClickHouse (this requires additional configurations on ClickHouse side), set extra to {"secure":true}. All extra connection parameters are passed to clickhouse_driver.Client as-is.

ClickHouse connection schema

clickhouse_driver.Client is initialized with attributes stored in Airflow Connection attributes:

Airflow Connection attribute	Client.__init__ argument
host	host
port (int)	port
schema	database
login	user
password	password
extra	**kwargs

database argument of ClickHouseOperator, ClickHouseHook, ClickHouseSensor, and others overrides schema attribute of the Airflow connection.

Extra arguments

You may set non-standard arguments of clickhouse_driver.Client, such as timeouts, compression, secure, etc. using Airflow's Connection.extra attribute. The attribute should contain a JSON object which will be deserialized and all of its properties will be passed as-is to the Client.

For example, if Airflow connection contains extra='{"secure": true}' then the Client.__init__ will receive secure=True keyword argument in addition to other connection attributes.

Compression

You should install specific packages to support compression. For example, for lz4:

pip3 install clickhouse-cityhash lz4

Then you should include compression parameter in airflow connection uri: extra='{"compression":"lz4"}'. You can get additional information about extra options from official documentation of clickhouse-driver.

Connection URI with compression will look like clickhouse://login:password@host:port/?compression=lz4.

See official documentation to learn more about connections management in Airflow.

Default Values

If some Airflow connection attribute is not set, it is not passed to clickhouse_driver.Client. In such cases, the plugin uses a default value from the corresponding clickhouse_driver.Connection argument. For instance, user defaults to 'default'.

This means that the plugin itself does not define any default values for the ClickHouse connection. You may fully rely on default values of the clickhouse-driver version you use.

The only exception is host: if the attribute of Airflow connection is not set then 'localhost' is used.

Default connection

By default, the plugin uses Airflow connection with id 'clickhouse_default'.

Examples

ClickHouseOperator example

from airflow import DAG
from airflow_clickhouse_plugin.operators.clickhouse import ClickHouseOperator
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago

with DAG(
        dag_id='update_income_aggregate',
        start_date=days_ago(2),
) as dag:
    ClickHouseOperator(
        task_id='update_income_aggregate',
        database='default',
        sql=(
            '''
                INSERT INTO aggregate
                SELECT eventDt, sum(price * qty) AS income FROM sales
                WHERE eventDt = '{{ ds }}' GROUP BY eventDt
            ''', '''
                OPTIMIZE TABLE aggregate ON CLUSTER {{ var.value.cluster_name }}
                PARTITION toDate('{{ execution_date.format('%Y-%m-01') }}')
            ''', '''
                SELECT sum(income) FROM aggregate
                WHERE eventDt BETWEEN
                    '{{ execution_date.start_of('month').to_date_string() }}'
                    AND '{{ execution_date.end_of('month').to_date_string() }}'
            ''',
            # result of the last query is pushed to XCom
        ),
        # query_id is templated and allows to quickly identify query in ClickHouse logs
        query_id='{{ ti.dag_id }}-{{ ti.task_id }}-{{ ti.run_id }}-{{ ti.try_number }}',
        clickhouse_conn_id='clickhouse_test',
    ) >> PythonOperator(
        task_id='print_month_income',
        python_callable=lambda task_instance:
            # pulling XCom value and printing it
            print(task_instance.xcom_pull(task_ids='update_income_aggregate')),
    )

ClickHouseHook example

from airflow import DAG
from airflow_clickhouse_plugin.hooks.clickhouse import ClickHouseHook
from airflow.providers.sqlite.hooks.sqlite import SqliteHook
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago


def sqlite_to_clickhouse():
    sqlite_hook = SqliteHook()
    ch_hook = ClickHouseHook()
    records = sqlite_hook.get_records('SELECT * FROM some_sqlite_table')
    ch_hook.execute('INSERT INTO some_ch_table VALUES', records)


with DAG(
        dag_id='sqlite_to_clickhouse',
        start_date=days_ago(2),
) as dag:
    dag >> PythonOperator(
        task_id='sqlite_to_clickhouse',
        python_callable=sqlite_to_clickhouse,
    )

Important note: don't try to insert values using ch_hook.execute('INSERT INTO some_ch_table VALUES (1)') literal form. clickhouse-driver requires values for INSERT query to be provided via parameters due to specifics of the native ClickHouse protocol.

ClickHouseSensor example

from airflow import DAG
from airflow_clickhouse_plugin.sensors.clickhouse import ClickHouseSensor
from airflow_clickhouse_plugin.operators.clickhouse import ClickHouseOperator
from airflow.utils.dates import days_ago


with DAG(
        dag_id='listen_warnings',
        start_date=days_ago(2),
) as dag:
    dag >> ClickHouseSensor(
        task_id='poke_events_count',
        database='monitor',
        sql="SELECT count() FROM warnings WHERE eventDate = '{{ ds }}'",
        is_success=lambda cnt: cnt > 10000,
    ) >> ClickHouseOperator(
        task_id='create_alert',
        database='alerts',
        sql='''
            INSERT INTO events SELECT eventDate, count()
            FROM monitor.warnings WHERE eventDate = '{{ ds }}'
        ''',
    )

DB API 2.0: ClickHouseSqlSensor and ClickHouseSQLExecuteQueryOperator example

from airflow import DAG
from airflow_clickhouse_plugin.sensors.clickhouse_dbapi import ClickHouseSqlSensor
from airflow_clickhouse_plugin.operators.clickhouse_dbapi import ClickHouseSQLExecuteQueryOperator
from airflow.utils.dates import days_ago


with DAG(
        dag_id='listen_warnings',
        start_date=days_ago(2),
) as dag:
    dag >> ClickHouseSqlSensor(
        task_id='poke_events_count',
        hook_params=dict(schema='monitor'),
        sql="SELECT count() FROM warnings WHERE eventDate = '{{ ds }}'",
        success=lambda cnt: cnt > 10000,
        conn_id=None,  # required by common.sql SqlSensor; use None for default
    ) >> ClickHouseSQLExecuteQueryOperator(
        task_id='create_alert',
        database='alerts',
        sql='''
            INSERT INTO events SELECT eventDate, count()
            FROM monitor.warnings WHERE eventDate = '{{ ds }}'
        ''',
    )

How to run tests

Unit tests: python3 -m unittest discover -t tests -s unit

Integration tests require access to a ClickHouse server. Here is how to set up a local test environment using Docker:

• Run ClickHouse server in a local Docker container: docker run -p 9000:9000 --ulimit nofile=262144:262144 -it clickhouse/clickhouse-server
• Run tests with Airflow connection details set via environment variable: PYTHONPATH=src AIRFLOW_CONN_CLICKHOUSE_DEFAULT=clickhouse://localhost python3 -m unittest discover -t tests -s integration
• Stop the container after running the tests to deallocate its resources.

Run all (unit&integration) tests with ClickHouse connection defined: PYTHONPATH=src AIRFLOW_CONN_CLICKHOUSE_DEFAULT=clickhouse://localhost python3 -m unittest discover -s tests

GitHub Actions

GitHub Action is configured for this project.

Run all tests inside Docker

Start a ClickHouse server inside Docker: docker exec -it $(docker run --rm -d clickhouse/clickhouse-server) bash

The above command will open bash inside the container.

Install dependencies into container and run tests (execute inside container):

apt-get update
apt-get install -y python3 python3-pip git make
git clone https://github.com/whisklabs/airflow-clickhouse-plugin.git
cd airflow-clickhouse-plugin
python3 -m pip install -r requirements.txt
PYTHONPATH=src AIRFLOW_CONN_CLICKHOUSE_DEFAULT=clickhouse://localhost python3 -m unittest discover -s tests

Stop the container.

Contributors

• Created by Anton Bryzgalov, @bryzgaloff, originally at Whisk, Samsung
• Inspired by Viktor Taranenko, @viktortnk (Whisk, Samsung)

Community contributors:

• Danila Ganchar, @d-ganchar
• Mikhail, @glader
• Alexander Chashnikov, @ne1r0n
• Simone Brundu, @saimon46
• @gkarg
• Stanislav Morozov, @r3b-fish
• Sergey Bychkov, @SergeyBychkov
• @was-av
• Maxim Tarasov, @MaximTar
• @dvnrvn
• Giovanni Corsetti, @CorsettiS
• Dmytro Zhyzniev, @1ng4lipt
• Anton Bezdenezhnykh, @GaMeRaM
• Andrey, @bobelev
• Misha Epikhin, @epikhinm