I am working on a project that requires processing live sensor data with minimal latency. Can Apache Spark Programming effectively handle true real-time streaming, or is it strictly a micro-batch processing engine? I’ve heard mixed reviews about "Structured Streaming" and would like to know if it's a viable competitor to tools like Apache Flink for sub-second latency requirements.
3 answers
Spark handles streaming through a high-level API called Structured Streaming, which is built on the Spark SQL engine. Originally, Spark used a "DStream" model which was strictly micro-batching, meaning it collected data over a few seconds before processing. However, newer versions have introduced "Continuous Processing" mode which aims for sub-millisecond end-to-end latency. While Flink is often cited as the king of "true" streaming because it processes every event individually by default, Spark is often preferred because you can use the exact same code for both your batch historical analysis and your live streaming data, simplifying your architecture immensely.
If the logic is the same for batch and stream, how do you handle stateful operations like windowing when the data might arrive out of order?
For most business use cases, the 100ms latency of Spark micro-batches is more than fast enough. True "real-time" is rarely needed unless you're doing high-frequency trading.
Exactly. Most dashboards only refresh every minute anyway. Spark provides a great balance between processing power and ease of implementation for 95% of use cases.
Structured Streaming handles this using "watermarking." You can define how long the engine should wait for late data before discarding it. It manages the state automatically in the background, which is one of the most powerful features for developers who don't want to manage complex state logic manually.