RisingWave vs ksqlDB: SQL Streaming Without Kafka Lock-In

If your team processes streaming data with SQL, you have likely evaluated ksqlDB. It was one of the first tools to bring a SQL interface to Apache Kafka, making stream processing accessible to engineers who did not want to write Java or Scala. But as streaming workloads grow more complex and data sources diversify beyond Kafka, ksqlDB's tight coupling to the Kafka ecosystem starts to create friction.

RisingWave takes a different approach. It is a streaming database that speaks PostgreSQL-compatible SQL, operates independently of any specific message broker, and stores its own state. The result is a system that can replace both your stream processor and parts of your serving layer, without requiring Kafka as a prerequisite.

This article provides a fair, technical comparison of RisingWave and ksqlDB across six dimensions: architecture, SQL dialect, state management, multi-source support, operational complexity, and cost. The goal is to help you decide which tool fits your streaming workload.

Architecture: Kafka-Dependent vs Standalone Streaming Database

The architectural difference between these two systems is fundamental and affects almost every operational decision you make.

ksqlDB: A SQL Layer on Kafka Streams

ksqlDB is, at its core, a SQL abstraction over Kafka Streams. When you submit a query, ksqlDB parses the SQL, constructs a Kafka Streams topology, and runs it as a continuous consumer-producer pipeline within the Kafka ecosystem.

This means ksqlDB requires a running Apache Kafka cluster as a hard dependency. Every input must be a Kafka topic. Every intermediate result is written back to Kafka topics. State changelogs are stored in Kafka. The system cannot function without Kafka, and it cannot read from any other data source natively.

The scaling model is also coupled to Kafka. ksqlDB's parallelism is determined by the number of partitions on the input Kafka topics. To scale ksqlDB, you often need to repartition your Kafka topics first, which is a non-trivial operational task.

RisingWave: An Independent Streaming Database

RisingWave is a distributed streaming database built from the ground up in Rust. It uses a decoupled compute and storage architecture where compute nodes handle query processing and storage is offloaded to S3-compatible object storage.

This architecture means RisingWave has no hard dependency on any message broker. It can ingest data from Kafka, but also from Apache Pulsar, Redpanda, Amazon Kinesis, PostgreSQL CDC, MySQL CDC, MongoDB CDC, and 50+ other connectors. Compute and storage scale independently: you add compute nodes to handle more processing, or let object storage grow elastically to handle more state.

RisingWave also functions as a database. You can query materialized view results directly using any PostgreSQL client or driver, without needing to sink results to a separate serving database. This eliminates an entire tier from many streaming architectures.

SQL Dialect: Custom Syntax vs PostgreSQL Compatibility

The SQL experience differs significantly between the two systems, and this has practical implications for developer productivity and tooling compatibility.

ksqlDB's Custom SQL Dialect

ksqlDB uses its own SQL-like language that borrows from standard SQL but introduces Kafka-specific concepts. You work with STREAM and TABLE as first-class types, and the syntax for creating them is specific to ksqlDB:

-- ksqlDB syntax
CREATE STREAM pageviews (
  user_id VARCHAR KEY,
  url VARCHAR,
  view_time BIGINT
) WITH (
  KAFKA_TOPIC = 'pageviews',
  VALUE_FORMAT = 'JSON'
);

CREATE TABLE pageview_counts AS
  SELECT url, COUNT(*) AS view_count
  FROM pageviews
  WINDOW TUMBLING (SIZE 1 HOUR)
  GROUP BY url
  EMIT CHANGES;

This dialect has several constraints. It only supports equi-joins between streams and tables. Subqueries are not supported. CTEs (WITH clauses) are unavailable. The windowing syntax is ksqlDB-specific rather than following SQL standard syntax. If you know PostgreSQL or any ANSI-SQL database, you still need to learn ksqlDB's particular conventions.

ksqlDB also cannot connect to standard database tooling. Since it does not speak the PostgreSQL wire protocol, you cannot use tools like psql, DBeaver, or any JDBC/ODBC driver that expects a PostgreSQL-compatible interface.

RisingWave's PostgreSQL-Compatible SQL

RisingWave implements PostgreSQL-compatible SQL, which means most standard SQL syntax works out of the box. The same query concept in RisingWave looks like this:

-- RisingWave syntax
CREATE SOURCE pageviews (
  user_id VARCHAR,
  url VARCHAR,
  view_time TIMESTAMPTZ
) WITH (
  connector = 'kafka',
  topic = 'pageviews',
  properties.bootstrap.server = 'localhost:9092'
) FORMAT PLAIN ENCODE JSON;

CREATE MATERIALIZED VIEW pageview_counts AS
  SELECT url, COUNT(*) AS view_count,
         window_start, window_end
  FROM TUMBLE(pageviews, view_time, INTERVAL '1 HOUR')
  GROUP BY url, window_start, window_end;

RisingWave supports complex joins (including non-equi joins and multi-way joins), subqueries, CTEs, window functions following SQL standard syntax, and user-defined functions in Python, Java, and JavaScript. Because it speaks the PostgreSQL wire protocol, you can connect with psql, any PostgreSQL driver (JDBC, Python's psycopg2, Node's pg), or visualization tools like Grafana and Metabase directly.

The practical benefit: your team's existing SQL knowledge transfers directly. There is no new dialect to learn, and your existing tooling ecosystem works without modification.

State Management: Kafka Changelogs vs Native Tiered Storage

How a streaming system manages state determines its reliability, performance, and operational overhead.

ksqlDB's Kafka-Backed State

ksqlDB stores state using a dual mechanism. It materializes state locally in RocksDB for fast key-value lookups, while simultaneously writing state changelogs back to Kafka topics. This design allows ksqlDB to recover state by replaying changelogs from Kafka if a node fails.

The problem with this approach is resource amplification. For every byte of state your application maintains, ksqlDB stores it in RocksDB locally and writes a changelog entry to Kafka. This means your Kafka cluster handles both the original data stream and all state changelog traffic, which can be several times the volume of the original data. In practice, teams report that ksqlDB's state management can consume several times more resources than the actual state size.

State TTL (time-to-live) management in ksqlDB is also limited. Configuring retention policies for state stores requires careful tuning, and getting it wrong can lead to unbounded state growth or premature data expiration.

RisingWave's Native State Store

RisingWave manages state internally using a tiered storage architecture. Hot state lives in memory and local SSD for fast access, while cold state is persisted to S3-compatible object storage. The system handles checkpointing, compaction, and recovery natively without depending on an external system like Kafka.

This design means state management does not put additional pressure on your message broker. State scales independently with object storage (which is practically unlimited and inexpensive), and recovery happens through RisingWave's built-in checkpoint mechanism rather than replaying Kafka topics.

RisingWave also supports exactly-once semantics for state consistency, with barrier-based checkpointing that ensures materialized view results are always consistent, even during failures and recovery.

Multi-Source Support: Kafka-Only vs Source-Agnostic

Modern data architectures rarely rely on a single streaming platform. This is where the tools diverge most sharply.

ksqlDB: Kafka Topics Only

ksqlDB can only read from and write to Kafka topics. If your data lives in PostgreSQL, MySQL, MongoDB, Amazon Kinesis, or any other system, you must first land it in Kafka before ksqlDB can process it. This typically means running Kafka Connect with the appropriate source connectors, adding another system to deploy, monitor, and maintain.

For organizations that have fully standardized on Confluent's platform, this may be acceptable. But for teams running heterogeneous data infrastructure, it creates a hard requirement to route everything through Kafka, even when the data source and destination have nothing to do with event streaming.

RisingWave: 50+ Native Connectors

RisingWave connects natively to over 50 data sources and sinks. On the ingestion side, supported sources include:

• Message brokers: Apache Kafka, Apache Pulsar, Redpanda, Amazon Kinesis, NATS, MQTT
• Databases (CDC): PostgreSQL, MySQL, MongoDB, TiDB
• Storage: Amazon S3, Google Cloud Storage
• Other: Webhooks, NATS JetStream

On the output side, RisingWave can sink data to Apache Iceberg, Delta Lake, Snowflake, ClickHouse, Elasticsearch, StarRocks, Apache Doris, Redis, PostgreSQL, BigQuery, and more.

This means you can build streaming pipelines that join a Kafka topic with a PostgreSQL CDC stream, aggregate the results, and sink to Apache Iceberg, all in a single RisingWave deployment without any intermediary systems.

Operational Complexity: Dual-System vs Single-System

The operational burden of running a streaming SQL system extends far beyond the initial deployment.

Running ksqlDB in Production

Operating ksqlDB means operating two systems: ksqlDB itself and the Kafka cluster it depends on. Each has its own:

• Deployment: ksqlDB servers must be provisioned alongside your Kafka brokers, ZooKeeper nodes (or KRaft controllers), and optionally Kafka Connect workers and Schema Registry
• Monitoring: You need dashboards for Kafka broker health, consumer lag, partition distribution, and ksqlDB query performance separately
• Scaling: Adding processing capacity to ksqlDB often requires repartitioning Kafka topics, a process that involves creating new topics, migrating data, and updating all consumers
• Upgrades: ksqlDB version upgrades must be coordinated with Kafka version compatibility, and rolling upgrades can be complex when stateful queries are running

Every ksqlDB push query adds a continuous consumer to a Kafka topic, and every pull query can burst-read an entire topic. This additional load is hard to predict and can impact other use cases sharing the same Kafka cluster.

Running RisingWave in Production

RisingWave operates as a single system with no external dependencies beyond object storage (S3 or compatible). A production deployment includes:

• Compute nodes: Handle stream processing and query serving
• Meta node: Manages cluster metadata and coordination
• Compactor nodes: Handle background storage compaction
• Object storage: S3, MinIO, or compatible storage for persistent state

Scaling is straightforward: add compute nodes for more processing capacity. There is no need to repartition upstream data sources. Monitoring is unified in a single system rather than spread across Kafka and ksqlDB.

For teams that want to avoid self-hosting entirely, RisingWave Cloud provides a fully managed service that handles deployment, scaling, and upgrades automatically.

Cost Comparison: Infrastructure and Licensing

Cost differences between these systems emerge from both infrastructure requirements and licensing models.

ksqlDB Infrastructure Costs

Running ksqlDB requires paying for:

1. Kafka cluster: Brokers, storage, and network transfer for both data and state changelogs
2. ksqlDB servers: Compute instances sized for your query workload
3. Kafka Connect (often needed): Additional workers if you need to ingest non-Kafka data
4. Schema Registry (recommended): Another service for Avro/Protobuf schema management

The state changelog amplification means your Kafka storage and network costs scale multiplicatively with your state size, not linearly.

On the licensing side, ksqlDB is source-available under the Confluent Community License, which prohibits using it to build competing SaaS products. Advanced features like multi-zone availability and enterprise support require a Confluent Platform or Confluent Cloud subscription.

RisingWave Infrastructure Costs

RisingWave's infrastructure footprint is simpler:

1. Compute nodes: Sized for your processing workload
2. Object storage: S3 or compatible (typically the cheapest storage option in cloud environments)

There is no separate message broker cost for state management, no changelog amplification, and no additional services required for basic operation.

RisingWave is licensed under the Apache 2.0 license, which places no restrictions on use, modification, or distribution. There is no separate enterprise license required for core features.

Feature Comparison Table

When to Choose ksqlDB

ksqlDB remains a solid choice in specific scenarios:

• All-Confluent stack: If your organization has standardized on Confluent Platform and all data already flows through Kafka, ksqlDB integrates seamlessly without introducing a new system
• Simple Kafka transformations: For straightforward filtering, mapping, and aggregation of Kafka topics, ksqlDB's tight Kafka integration provides a low-friction experience
• Existing ksqlDB investment: If your team has already built and tuned ksqlDB queries in production, migration cost may not justify switching for workloads that are working well

When to Choose RisingWave

RisingWave is the better fit when:

• Multi-source ingestion: Your streaming pipelines need to join or aggregate data from Kafka, databases (via CDC), and other sources
• Complex SQL requirements: You need subqueries, CTEs, non-equi joins, or advanced windowing that ksqlDB does not support
• PostgreSQL tooling compatibility: Your team relies on PostgreSQL clients, JDBC drivers, or BI tools that expect a PostgreSQL-compatible interface
• Independent scaling: You want to scale stream processing without repartitioning Kafka or expanding your Kafka cluster
• Reducing infrastructure: You want to eliminate the overhead of managing Kafka solely for state management
• Open-source licensing: You need Apache 2.0 licensing without restrictions on deployment or use

What is the difference between RisingWave and ksqlDB?

RisingWave is a standalone streaming database that uses PostgreSQL-compatible SQL and stores its own state in object storage. ksqlDB is a SQL interface built on top of Kafka Streams that requires a running Kafka cluster for all operations. The core difference is that RisingWave operates independently of any message broker, while ksqlDB is architecturally inseparable from Kafka.

Can RisingWave replace ksqlDB for Kafka stream processing?

Yes. RisingWave supports Apache Kafka as a source and sink connector, so it can process Kafka topics with the same SQL-based approach that ksqlDB provides. The difference is that RisingWave also supports 50+ other data sources, uses PostgreSQL-compatible SQL (which supports more complex queries than ksqlDB's dialect), and manages its own state without writing changelogs back to Kafka.

Do I need Kafka to use RisingWave?

No. RisingWave is a standalone streaming database that does not require Kafka. It can ingest data from Kafka, but also from Pulsar, Kinesis, PostgreSQL CDC, MySQL CDC, MongoDB CDC, Amazon S3, and many other sources. If your use case does not involve Kafka at all, RisingWave still functions as a complete stream processing and serving solution.

How does state management differ between RisingWave and ksqlDB?

ksqlDB stores state in local RocksDB instances and writes state changelogs to Kafka topics for durability, which amplifies your Kafka storage and network usage. RisingWave manages state natively using tiered storage: hot data in memory, warm data on local SSD, and persistent state in S3-compatible object storage. This means RisingWave's state management does not create additional load on your message broker.

Conclusion

RisingWave and ksqlDB both bring SQL to stream processing, but they make fundamentally different architectural choices. ksqlDB integrates deeply with Kafka, which is a strength if Kafka is the center of your data platform and a constraint if it is not. RisingWave operates as an independent database that happens to support Kafka as one of many data sources.

Key takeaways:

• Architecture: RisingWave is a standalone database; ksqlDB is a Kafka Streams wrapper
• SQL: RisingWave speaks PostgreSQL; ksqlDB uses a custom dialect with limited join and query support
• State: RisingWave manages state internally with tiered storage; ksqlDB relies on Kafka for state changelogs
• Sources: RisingWave connects to 50+ sources natively; ksqlDB reads only from Kafka topics
• Operations: RisingWave is a single system to manage; ksqlDB requires managing both ksqlDB and Kafka

For teams evaluating their streaming SQL options, the decision comes down to how central Kafka is to your architecture. If you want SQL streaming without Kafka lock-in, RisingWave provides that flexibility.

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