Spark Streaming: Discretized Streams (D-Streams) Explained | Spark Streaming & Fault Tolerance
Автор: MindFDev
Загружено: 2026-01-02
Просмотров: 1
In this video, we explain Discretized Streams (D-Streams)—the core processing model behind Spark Streaming—and how it enables fault-tolerant, high-throughput real-time data processing.
Instead of using traditional continuous operators, D-Streams treat streaming data as a sequence of short, deterministic batch jobs built on Resilient Distributed Datasets (RDDs). This design allows fast parallel recovery using lineage, ensuring exactly-once semantics even in the presence of hardware failures or slow nodes.
We also discuss how modern systems like Structured Streaming extend these ideas using checkpointing to reliably recover queries and state.
What you’ll learn:
What are Discretized Streams (D-Streams)?
Why traditional real-time systems struggle at scale
Role of RDDs and lineage in fault tolerance
Exactly-once processing guarantees
Unified streaming, batch, and interactive analytics
Checkpointing and recovery in Structured Streaming
📌 Ideal for Big Data, Apache Spark, and Distributed Systems learners.
#Spark Streaming, #D-Streams, #Discretized Streams, #RDD, #Structured Streaming, #Fault Tolerance, #Real-Time Data Processing, #Big Data, #Apache Spark
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