Why Choose RisingWave Over Apache Spark Structured Streaming

A simpler and more efficient approach to streaming.

Choose Friction-Less Path to Stream Processing

Say goodbye to:

Steep Learning Curves

Manual State Management

Endless Query Optimizations

RisingWave

With PostgreSQL compatibility, RisingWave lets you build real-time applications using standard SQL. Developers can get started quickly — no need to learn a new execution model or specialized APIs.

RisingWave automates state management and recovery — exactly-once semantics and checkpointing are handled behind the scenes, so you can focus on business logic, not infrastructure.

RisingWave is built to handle real-time SQL natively — joins, aggregations, and windowing functions work out of the box with minimal tuning, delivering consistently low-latency results.

Spark Structured Streaming

Spark’s micro-batch model, trigger-based execution, and custom APIs require deeper expertise. Developers must understand Spark’s internals and streaming semantics before they can build reliable pipelines.

Managing stateful operations in Spark often involves setting up checkpoints, configuring watermarking, and tuning memory. Without careful setup, long recovery times or data inconsistencies can occur.

Due to its batch-oriented architecture, optimizing Spark Streaming queries often requires configuring micro-batch triggers, avoiding shuffles, and managing job parallelism — all of which add operational overhead.

What Makes RisingWave the Clear Choice Over Spark Structured Streaming?

RisingWave

RisingWave is designed from the ground up as a cloud-native system with decoupled compute and storage.

S3-native architecture: RisingWave persists data and state directly to S3 or compatible object storage, reducing storage cost and improving durability
Real-time fault recovery: Supports fast recovery even on complex joins and time windows — no long replays or shuffle rebuilds
Separation of compute and storage: Each layer scales independently, enabling better resource efficiency and avoiding over-provisioning

Spark Structured Streaming

Micro-batch execution introduces inherent latency and complicates real-time guarantees

State is checkpointed periodically, requiring manual tuning, and recovery can be slow for large workloads

Coupled execution and shuffle-heavy workloads drive up resource cost, especially when processing stateful joins or windowed aggregations

Micro-batch execution introduces inherent latency and complicates real-time guarantees

State is checkpointed periodically, requiring manual tuning, and recovery can be slow for large workloads

Coupled execution and shuffle-heavy workloads drive up resource cost, especially when processing stateful joins or windowed aggregations

Stream Processing Made Easy

More Reasons to Move to RisingWave

While Spark Structured Streaming is powerful for batch-aligned workloads, RisingWave’s cloud-native architecture, built-in storage, and real-time SQL engine offer a simpler, faster, and more cost-efficient experience for streaming use cases.

	RisingWave	Spark Structured Streaming
License	Apache License 2.0	Apache License 2.0
System category	Streaming database	Micro-batch stream processing engine
Architecture	Cloud-native, decoupled compute-storage	Batch-first architecture with micro-batch execution
Programming API	SQL + UDF (Python, Java, more)	DataFrame API (Scala, Java, Python), limited SQL
Client libraries	Java, Python, Node.js, more	Spark client bindings only
State management	Native state persisted in S3 or compatible object storage	In-memory state with checkpointing to HDFS/S3
Query serving	Supports concurrent ad-hoc SQL query serving	Not designed for interactive queries; job-based execution
Correctness	Exactly-once semantics, out-of-order support, snapshot read	Exactly-once semantics, but no built-in snapshot isolation
Integrations and tooling	PostgreSQL ecosystem, cloud-native tools, Apache Iceberg™	Hadoop ecosystem, Spark ecosystem
Learning curve	Shallow (PostgreSQL-style SQL)	Moderate to steep (requires Spark + streaming concepts)
Failure recovery	Instant via S3-backed storage	May require reprocessing; checkpoint restore time varies
Dynamic scaling	Transparent and online	Requires job restarts or auto-scaling scripts
Performance cost	Low — decoupled storage reduces pressure on compute	High — shuffle-intensive, micro-batch overhead
Typical use cases	Streaming ETL, online serving, real-time metrics	Streaming ETL, incremental batch, log pipelines

RisingWave

License	Apache License 2.0
System category	Streaming database
Architecture	Cloud-native, decoupled compute-storage
Programming API	SQL + UDF (Python, Java, more)
Client libraries	Java, Python, Node.js, more
State management	Native state persisted in S3 or compatible object storage
Query serving	Supports concurrent ad-hoc SQL query serving
Correctness	Exactly-once semantics, out-of-order support, snapshot read
Integrations and tooling	PostgreSQL ecosystem, cloud-native tools, Apache Iceberg™
Learning curve	Shallow (PostgreSQL-style SQL)
Failure recovery	Instant via S3-backed storage
Dynamic scaling	Transparent and online
Performance cost	Low — decoupled storage reduces pressure on compute
Typical use cases	Streaming ETL, online serving, real-time metrics