Real-Time Mode in Apache Spark Structured Streaming

Yogesh_Verma_ — Wed, 01 Oct 2025 11:37:10 GMT

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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Re: Real-Time Mode in Apache Spark Structured Streaming

Advika — Mon, 06 Oct 2025 12:05:34 GMT

And now in Public Preview! Thank you for writing this up, @Yogesh_Verma_.