Python

To interact with RisingWave from Python applications, we recommend using the risingwave-py SDK. This SDK provides a simple way to perform ad-hoc queries, subscribe to changes, and define event handlers for tables and materialized views, making it easier to integrate real-time data into applications. For details, see Python SDK.

As RisingWave is wire-compatible with PostgreSQL, you can also use third-party PostgreSQL drivers like psycopg2 and sqlalchemy to interact with RisingWave from your Python applications.

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Use psycopg2 to connect to RisingWave

In this section, we use the psycopg2 driver to connect to RisingWave.

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Run RisingWave

To learn about how to run RisingWave, see Run RisingWave.

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Install the psgcopg2 driver

For information about how to install psycopg and the difference between psycopg and psycopg-binary, see the official psycopg documentation.

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Connect to RisingWave

To connect to RisingWave via psycopg2:

import psycopg2

conn = psycopg2.connect(host="127.0.0.1", port=4566, user="root", dbname="dev")

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Create a source

The code below creates a source walk with the datagen connector. The datagen connector is used to generate mock data. The walk source consists of two columns, distance and duration, which respectively represent the distance and the duration of a walk. The source is a simplified version of the data that is tracked by smart watches.

import psycopg2

conn = psycopg2.connect(host="localhost", port=4566, user="root", dbname="dev") # Connect to RisingWave.
conn.autocommit = True # Set queries to be automatically committed.

with conn.cursor() as cur:
    cur.execute("""
CREATE TABLE walk(distance INT, duration INT)
WITH (
    connector = 'datagen',
    fields.distance.kind = 'sequence',
    fields.distance.start = '1',
    fields.distance.end  = '60',
    fields.duration.kind = 'sequence',
    fields.duration.start = '1',
    fields.duration.end = '30',
    datagen.rows.per.second='15',
    datagen.split.num = '1'
) FORMAT PLAIN ENCODE JSON""") # Execute the query.

conn.close() # Close the connection.

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Create a materialized view

The code in this section creates a materialized view counter to capture the latest total distance and duration.

import psycopg2

conn = psycopg2.connect(host="localhost", port=4566, user="root", dbname="dev")
conn.autocommit = True

with conn.cursor() as cur:
    cur.execute("""CREATE MATERIALIZED VIEW counter
    AS SELECT
    SUM(distance) as total_distance,
    SUM(duration) as total_duration
    FROM walk;""")

conn.close()

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Query a materialized view

The code in this section queries the materialized view counter to get real-time data.

import psycopg2

conn = psycopg2.connect(host="localhost", port=4566, user="root", dbname="dev")
conn.autocommit = True

with conn.cursor() as cur:
    cur.execute("SELECT * FROM counter;")
    print(cur.fetchall())
conn.close()

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Use sqlalchemy to connect to RisingWave

In this section, we use the SQLAlchemy driver to connect to RisingWave.

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Run RisingWave

To learn about how to run RisingWave, see Run RisingWave.

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Install necessary Python packages

Ensure you have Python3 installed.

For more information about sqlalchemy, see the SQLAlchemy. Refer to the documentation version that corresponds to the version of SQLAlchemy that you run.

For information about how to install psycopg-binary, see the official psycopg documentation.

pip3 install SQLAlchemy sqlalchemy-risingwave psycopg2-binary

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Connect to RisingWave

To connect to RisingWave via sqlalchemy:

DB_URI = 'risingwave+psycopg2://root@risingwave-standalone:4566/dev'

engine = create_engine(DB_URI)

Note that RisingWave does not provide direct compatibility with sqlaclehmy-postgres so risingwave+psycopg2 is used as the URI scheme. The rest of the URL follows the same format as the PostgreSQL driver.

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Create a source

The code below creates a table users using the engine created in the previous section.

with engine.connect() as conn:
    conn.execute("""CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY,
    name VARCHAR,
    age INTEGER)""")

You can create materialized views and query from materialized views using the same format shown above.