RisingWave vs ClickHouse: Streaming Database vs OLAP Database

No — they solve different problems. ClickHouse is an OLAP database optimized for fast analytical queries over stored data. RisingWave is a streaming database that processes data incrementally as it arrives. Use ClickHouse when you need interactive ad-hoc analytics; use RisingWave when you need continuously fresh results, real-time alerting, or streaming ETL.

Yes. RisingWave has a native ClickHouse sink connector. A common pattern is to use RisingWave for real-time stream processing and materialized views, then sink aggregated results into ClickHouse for interactive analytical queries and historical analysis.

Choose RisingWave when your priority is data freshness — real-time monitoring, alerting, fraud detection, streaming ETL, or continuously updated dashboards. RisingWave processes data incrementally as events arrive, delivering sub-second result freshness without scheduling batch queries.

Choose ClickHouse when you need fast ad-hoc analytical queries over large historical datasets — interactive reporting, data exploration, and columnar aggregations. ClickHouse's column-oriented engine is optimized for full-table scans and can query billions of rows in milliseconds.

ClickHouse offers two types of materialized views, both with significant limitations. Insert-triggered MVs fire on each insert and can only do simple aggregations: no joins across tables, no handling of updates or deletes, and no consistency guarantees across multiple MVs. Refreshable MVs re-execute the full query on a fixed schedule, which means freshness depends on how often you can afford to re-scan. In contrast, RisingWave materialized views are incrementally maintained with exactly-once semantics. They support multi-way joins, temporal windowing, out-of-order processing, and always reflect the latest state with consistent snapshot reads.

Yes. RisingWave uses row-based storage, so point lookups on materialized views return in single-digit milliseconds. This makes it suitable for serving real-time dashboards, APIs, and application features directly, similar to how teams use Redis or DynamoDB as a serving layer. ClickHouse's column-oriented engine excels at scanning billions of rows for analytics, but point queries are not its primary design target.

RisingWave uses PostgreSQL-compatible SQL, while ClickHouse uses its own SQL dialect. If your team already uses PostgreSQL tools (psql, JDBC, psycopg2, Grafana), RisingWave integrates seamlessly. ClickHouse requires its own client libraries and has different SQL syntax for many operations.

RisingWave stores state in S3-compatible object storage, which is significantly cheaper than ClickHouse's local SSD storage for large datasets. However, for pure OLAP query workloads, ClickHouse's columnar compression delivers excellent cost-per-query. The best cost structure depends on your workload mix.

Yes. RisingWave supports vector(n) type and HNSW indexing for similarity search, plus built-in openai_embedding() for generating embeddings. ClickHouse also supports vector search. Both can serve as vector stores for AI/RAG applications.