ClickHouse vs RisingWave in 2026: OLAP vs Streaming SQL

The Fundamental Difference: OLAP Over History vs Streaming Over Live Data

Before comparing specific features, it is worth being precise about what problem each database solves, because this choice is ultimately about use case fit rather than which database is "better."

ClickHouse is a columnar OLAP database. It is engineered for one purpose: executing analytical queries over large, primarily immutable datasets as fast as possible. When you want to know "how many orders did we receive last month, broken down by region and product category," and that query needs to scan 5 billion rows and return in under a second, ClickHouse is one of the best tools on the planet for that job. Its MergeTree storage engine with columnar compression, vectorized execution, and aggressive use of parallelism make it genuinely exceptional at this.

RisingWave is a streaming SQL database. It is engineered for a different purpose: continuously processing live data streams and maintaining the results of those computations as queryable materialized views. When you want to know "what is the current number of active sessions right now, updated every second as users log in and log out," ClickHouse struggles because that question involves continuous updates. RisingWave handles it natively because it performs incremental view maintenance over INSERT, UPDATE, and DELETE events.

The word "incremental" is critical here. When data changes in a ClickHouse materialized view, the view only processes new inserts. When data changes in a RisingWave materialized view, the view updates incrementally to reflect inserts, updates, and deletes, all with low latency.

Why 2026 Is Different

Three shifts in 2026 have made this choice more consequential.

Real-time expectations have become standard. In 2022, a dashboard refreshing every 15 minutes was acceptable for most business intelligence. By 2026, the standard has moved. Operations teams expect dashboards that reflect the last 30 seconds of data. Fraud systems need to evaluate transactions in milliseconds. Customer-facing features like recommendation engines and pricing systems run on live state.

AI agents require fresh data. AI agents built in 2025 and 2026 consume structured data as part of their context. An agent managing customer support or inventory replenishment needs the current state, not a snapshot from this morning's batch job. This has created new demand for databases that serve fresh, queryable state without requiring a pipeline rebuild every time the query changes.

CDC adoption accelerated. Change Data Capture, the practice of streaming database change events (inserts, updates, deletes) from operational databases into downstream systems, has moved from a specialist technique to a standard architectural pattern. ClickHouse's append-only Kafka engine was not designed for CDC workloads. RisingWave was built with CDC as a first-class concern.

ClickHouse Strengths

ClickHouse deserves its reputation. If your workload fits its strengths, it is hard to beat.

Columnar compression and scan performance. ClickHouse stores data in columns rather than rows. For analytical queries that read only a subset of columns across billions of rows, this dramatically reduces I/O. Combined with ZSTD or LZ4 compression, a 1 TB dataset can compress to 100-200 GB and still be queried at full speed. This is where ClickHouse achieves scan rates that other databases cannot match.

MergeTree storage engine family. ClickHouse's storage engine is purpose-built for analytics. MergeTree organizes data by primary key and stores data in parts that are merged in the background. ReplacingMergeTree handles deduplication. SummingMergeTree aggregates rows with the same key. AggregatingMergeTree pre-computes aggregations at write time. These specialized engines let you optimize storage layout for your query patterns at write time.

Materialized views for pre-aggregation. ClickHouse materialized views fire when new rows are inserted and write summarized results to a target table. This is useful for pre-aggregating high-volume event streams. If you are ingesting 10 million events per hour and only ever query hourly totals, a materialized view can reduce query time from seconds to milliseconds by maintaining those totals continuously. The key limitation: this works for append-only data. If a row is updated or deleted, the materialized view does not retroactively adjust.

SQL compatibility. ClickHouse SQL is largely standard-compatible, with extensions for analytical functions, window functions, and array operations. Teams familiar with SQL can write ClickHouse queries without learning a new language. It also has drivers and connectors for virtually every language and BI tool.

Operational simplicity for analytics workloads. A single ClickHouse node can handle analytical workloads that would require a large Spark cluster. ClickHouse Cloud simplifies operations further with automatic scaling, backups, and monitoring. For a data team that primarily runs ad-hoc analytical queries over historical data, ClickHouse can be significantly cheaper and simpler than alternatives.

Excellent for: log analytics, time-series over immutable event data, ad-hoc analytical queries over large historical datasets, BI dashboards over data warehoused data, product analytics, cost-efficiency for batch analytical workloads.

RisingWave Strengths

RisingWave approaches the problem from a completely different angle.

Incremental view maintenance over live streams. This is RisingWave's core capability. When you write CREATE MATERIALIZED VIEW, RisingWave does not recompute the view on every query. It maintains the view incrementally as new data arrives. When a row is inserted, updated, or deleted in a source table or CDC stream, RisingWave computes only the delta needed to update the view. The view is always current, and reading from it is a simple key lookup, not a full recomputation.

Native CDC support without Kafka. RisingWave includes built-in CDC connectors for PostgreSQL, MySQL, MongoDB, and SQL Server. You can point RisingWave directly at a running database and it will capture change events without requiring an intermediate Kafka cluster. This matters for teams that want to build real-time pipelines without the operational overhead of Kafka.

-- Connect directly to a PostgreSQL database via CDC
CREATE SOURCE orders_cdc WITH (
    connector = 'postgres-cdc',
    hostname = 'db.example.com',
    port = '5432',
    username = 'rw',
    password = '...',
    database.name = 'ecommerce',
    schema.name = 'public',
    table.name = 'orders'
);

PostgreSQL wire protocol. RisingWave listens on port 4566 and is compatible with PostgreSQL clients. Any application, BI tool, or library that works with PostgreSQL works with RisingWave. You can connect psql, DBeaver, Metabase, or a Python application using psycopg2 without any special drivers.

Streaming SQL with windowed aggregations. RisingWave extends standard SQL with streaming-specific constructs. TUMBLE windows let you group events into fixed-duration buckets that continuously produce results as time advances.

CREATE MATERIALIZED VIEW hourly_revenue AS
SELECT
    merchant_id,
    SUM(amount) AS total_revenue,
    window_start,
    window_end
FROM TUMBLE(transactions, event_time, INTERVAL '1' HOUR)
GROUP BY merchant_id, window_start, window_end;

Built-in vector support. RisingWave includes a vector(n) type, cosine similarity via the <=> operator, and HNSW indexing. You can call openai_embedding() directly in SQL to compute embeddings. This means RisingWave can serve as a real-time vector store for AI applications without requiring a separate vector database.

Streaming JOINs. RisingWave supports JOIN operations between live streams and between streams and dimension tables. You can join an orders stream to a customers table and a products table in a single materialized view. ClickHouse materialized views do not support complex JOINs in the view definition.

Excellent for: real-time metrics with frequent updates, CDC-driven pipelines, streaming JOINs, serving fresh state to applications and AI agents, sub-second aggregations over live data, use cases involving INSERT + UPDATE + DELETE patterns.

Feature Comparison Table

The Materialized View Difference: A Concrete Example

The clearest way to understand the architectural difference is to look at a specific use case: tracking the number of active user sessions on a platform.

A "session" is created when a user logs in and closed when they log out. At any moment, you want to know how many sessions are currently active. This requires tracking both INSERT (login) and DELETE (logout) events.

In RisingWave, this is straightforward:

-- Source: CDC stream from sessions table in your operational database
CREATE SOURCE sessions_cdc WITH (
    connector = 'postgres-cdc',
    hostname = 'db.example.com',
    port = '5432',
    username = 'rw',
    password = '...',
    database.name = 'app',
    schema.name = 'public',
    table.name = 'sessions'
);

-- Materialized view: count currently active sessions
-- Updates instantly when a session is created or closed
CREATE MATERIALIZED VIEW active_session_count AS
SELECT COUNT(*) AS active_sessions
FROM sessions_cdc
WHERE closed_at IS NULL;

-- Query the live count at any time
SELECT active_sessions FROM active_session_count;

Every time a session is opened (INSERT) or closed (UPDATE setting closed_at), RisingWave updates the materialized view incrementally. The query returns the current count in milliseconds.

In ClickHouse, the approach requires workarounds. ClickHouse is designed for immutable data, so handling updates requires using ReplacingMergeTree with a version column, or issuing explicit ALTER TABLE ... UPDATE statements (which are heavy operations). A materialized view in ClickHouse would only receive INSERT events from the Kafka engine and would not decrement when a session closes.

A common ClickHouse workaround is to insert a "close" event with a negative value and sum them, but this requires all consumers of the data to understand this convention and handle it correctly. The simpler ClickHouse approach is to recompute the count periodically with a full scan rather than maintain it incrementally, which works fine at lower frequency but cannot deliver sub-second latency for a continuously changing metric.

For how many orders we received last month, broken down by country, across 10 billion rows -- ClickHouse is faster. That is a full-scan analytical query over immutable historical data, which is exactly what ClickHouse is built for.

Which Should You Choose: A Decision Matrix

The Hybrid Architecture: Using Both Together

Many teams use both databases, assigning each the workload it is best suited for. This is not a compromise. It is a genuinely good architecture.

RisingWave handles the real-time layer: it ingests Kafka streams and CDC events, computes continuously updated materialized views, and serves live state to applications and dashboards that need fresh data.

ClickHouse handles the historical layer: it stores the immutable record of events and aggregations for analytical queries, cost reporting, compliance, and BI tools.

RisingWave has a native ClickHouse sink. You can define a sink that writes RisingWave materialized view results to a ClickHouse table continuously. This creates a pipeline where:

1. Events arrive in Kafka or from a CDC source.
2. RisingWave processes them and maintains real-time aggregations.
3. RisingWave sinks processed data to ClickHouse for historical storage.
4. Applications query RisingWave for live state (e.g., "what is the current fraud score for this user?").
5. BI tools query ClickHouse for historical analysis (e.g., "what was our fraud rate last quarter?").

-- Sink from RisingWave materialized view into ClickHouse
CREATE SINK fraud_scores_to_clickhouse
FROM fraud_scores_mv
WITH (
    connector = 'clickhouse',
    clickhouse.url = 'http://clickhouse.example.com:8123',
    clickhouse.user = 'default',
    clickhouse.password = '...',
    clickhouse.database = 'analytics',
    clickhouse.table = 'fraud_scores'
);

This hybrid approach gives you the best of both: millisecond-fresh state from RisingWave for operational use cases, and ClickHouse's exceptional scan performance for historical analytics.

ClickHouse Materialized Views vs RisingWave Materialized Views: Key Distinctions

Because both databases use the term "materialized view," it is worth being precise about how they differ.

ClickHouse materialized views are triggered by INSERT operations on the source table. They execute a SELECT over the newly inserted rows and write results to a target table. They are essentially insert-time triggers. They do not update when source rows change, and they do not support complex JOINs in the view definition (only simple transformations and aggregations over the new rows). They are excellent for pre-aggregating high-volume insert streams.

RisingWave materialized views implement incremental view maintenance. They maintain a consistent, queryable result set that reflects the current state of source data, including all inserts, updates, and deletes. They support complex SQL including JOINs across multiple sources, window functions, and subqueries. The trade-off is that complex materialized views consume more memory and CPU because RisingWave maintains state for in-flight computations.

For append-only insert workloads like log ingestion, both can work. For workloads involving CDC streams, frequent updates, or live state that needs to reflect deletions, RisingWave materialized views handle these cases correctly and ClickHouse requires workarounds.

Operational Considerations

ClickHouse operations: ClickHouse Cloud simplifies operations substantially. Self-hosted ClickHouse requires planning around storage layout, merges, and table schema. Sharding and replication require careful design for large clusters. ClickHouse is a mature, well-documented system with strong community support.

RisingWave operations: RisingWave separates storage from compute, storing streaming state on S3-compatible object storage. This makes scaling compute independently of storage straightforward. RisingWave Cloud provides a managed option. Because materialized views are defined in SQL, changing streaming logic means dropping and recreating views rather than redeploying application code.

Summary

ClickHouse and RisingWave solve different problems at different points in the data architecture.

If your primary need is fast analytical queries over historical, mostly-immutable data at large scale, ClickHouse is one of the best options available. Its columnar storage, MergeTree engine, and vectorized query execution deliver performance that is hard to match for batch analytical workloads.

If your primary need is maintaining continuously updated views over live data streams, with correct handling of inserts, updates, and deletes, RisingWave is the better choice. Its incremental view maintenance, native CDC support, and PostgreSQL-compatible SQL interface make it the leading streaming SQL database for real-time operational use cases.

For many teams in 2026, the answer is not one or the other. RisingWave handles the streaming layer and serves live state. ClickHouse stores the historical record and powers analytical queries. RisingWave's native ClickHouse sink makes this architecture straightforward to build and operate.