Real-Time Mode in Spark Streaming
Apache Spark™ Structured Streaming has been the backbone of mission-critical pipelines for years — from ETL to near real-time analytics and machine learning.
Now, Databricks has introduced something game-changing: Real-Time Mode – a new trigger type that processes events as soon as they arrive, with latencies in the tens of milliseconds.
This opens the door for ultra-low-latency use cases like fraud detection, live personalization, and real-time ML feature serving, all without rewriting your existing code.
Existing Trigger Modes in Structured Streaming
Before Real-Time Mode, Spark offered three main trigger types:
Processing Time Trigger
Trigger Once
Available Now
These worked well for micro-batch processing but still introduced some delay.
What is Real-Time Mode?
Real-Time Mode introduces continuous, low-latency processing in Spark Structured Streaming.
p99 latency as low as single-digit milliseconds
Works with the same Structured Streaming APIs you already use
Just a single configuration change needed – no re-platforming
Real Time mode Internal Benchmark
Real-World Use Cases
Some exciting examples where Real-Time Mode shines:
Fraud Detection (Banking): Flag suspicious transactions from Kafka streams in under 200 milliseconds
Personalized Retail Experiences: Update recommendations or product offers in real time.
Travel & Search Apps: Instantly update search history/session state across devices.
Food Delivery Apps: Update ML features like driver location in milliseconds, improving ETA accuracy.
Payments Authorization (Network International): Achieved 15 milliseconds latency for mission-critical payment flows
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