RisingWave vs Spark Structured Streaming: Real-Time vs Micro-Batch
RisingWave is a true streaming database with sub-100ms latency. Spark Structured Streaming uses micro-batching with seconds-to-minutes latency. Choose RisingWave for real-time requirements and SQL simplicity. Choose Spark if you're already in the Spark ecosystem and seconds-level latency is acceptable.
Architecture Differences
| Aspect | RisingWave | Spark Structured Streaming |
| Processing model | True streaming (event-at-a-time) | Micro-batching |
| Latency | Sub-100ms | Seconds to minutes |
| Language | SQL (PostgreSQL) | Python/Scala + Spark SQL |
| State | S3 (disaggregated) | HDFS/S3 (checkpoint) |
| Deployment | Standalone / K8s | Spark cluster required |
| Serving | Built-in (PG protocol) | External DB required |
| Batch + streaming | Streaming-first | Unified (same API) |
| Ecosystem | Growing | Massive (Databricks, EMR) |
When Latency Matters
If your SLA is "results within seconds," Spark works. If it's "results within 100ms," RisingWave is the only option among these two.
Frequently Asked Questions
Should I use Spark or RisingWave for real-time analytics?
If you need sub-second latency, use RisingWave. If seconds-level latency is acceptable and you're already using Spark for batch processing, Spark Structured Streaming is the path of least resistance.
Can RisingWave replace Spark?
For real-time streaming workloads, yes. For batch processing, ML training, and large-scale data science, Spark remains the better tool. Many architectures use both: RisingWave for real-time, Spark for batch analytics on the same Iceberg lakehouse.
