RisingWave — Cloud-Native Streaming Database in Rust

What it is

RisingWave is a cloud-native streaming database written in Rust. It ingests data from Kafka, Pulsar, Kinesis, and other sources, processes it with standard SQL including joins, aggregations, and window functions, and serves results through incrementally maintained materialized views. It replaces the common pattern of pairing a stream processor (Flink, Spark Streaming) with a serving database.

RisingWave is designed for data engineers and backend developers who need real-time analytics, event-driven applications, or continuous data pipelines. If you are running Flink plus PostgreSQL for stream processing, RisingWave consolidates both into a single system with a PostgreSQL-compatible interface.

How it saves time or tokens

RisingWave eliminates the operational complexity of maintaining separate stream processing and serving layers. You write SQL to define your streaming logic instead of Java/Scala Flink jobs. Materialized views update incrementally as new data arrives, so queries return fresh results without manual refresh. The PostgreSQL wire protocol means existing tools (psql, Grafana, Metabase) connect directly. Less infrastructure means fewer things to configure, monitor, and debug.

How to use

1. Start RisingWave: docker run -it --pull=always -p 4566:4566 risingwavelabs/risingwave:latest playground.
2. Connect with any PostgreSQL client: psql -h localhost -p 4566 -d dev.
3. Create sources, materialized views, and sinks using standard SQL.

Example

-- Connect a Kafka source
CREATE SOURCE orders (
    order_id INT,
    user_id INT,
    amount DECIMAL,
    created_at TIMESTAMP
) WITH (
    connector = 'kafka',
    topic = 'orders',
    properties.bootstrap.server = 'kafka:9092'
) FORMAT PLAIN ENCODE JSON;

-- Create an incrementally maintained materialized view
CREATE MATERIALIZED VIEW hourly_revenue AS
SELECT
    date_trunc('hour', created_at) AS hour,
    SUM(amount) AS total_revenue,
    COUNT(*) AS order_count
FROM orders
GROUP BY date_trunc('hour', created_at);

-- Query real-time results
SELECT * FROM hourly_revenue ORDER BY hour DESC LIMIT 5;

Related on TokRepo

• Database AI tools -- database management and querying tools
• AI tools for automation -- data pipeline automation

Common pitfalls

• Treating RisingWave as a general-purpose OLTP database. It excels at streaming queries and materialized views but is not designed for high-frequency point updates or transactional workloads.
• Not planning for state storage. Materialized views consume memory and storage proportional to the result set size. Monitor state size for high-cardinality aggregations.
• Expecting full PostgreSQL compatibility. While RisingWave supports the PostgreSQL wire protocol and many SQL features, some PostgreSQL-specific functions and extensions are not available.