Spark Structured Streaming
A stream processing module of Apache Spark that provides scalable stream computing capabilities based on the DataFrame/Dataset API, supporting event-time windows and fault-tolerance semantics.
Timeline (last 90 days)
Spark Structured Streaming's micro-batch model provides exactly-once semantics.
Structured Streaming is the stream computing engine built into Apache Spark.
Streaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
Combining event triggering with incremental computation is the typical form of modern real-time data pipelines (such as those built on Kafka, Spark Structured Streaming or Delta Live Tables).
All Facts (5)
Spark Structured Streaming's micro-batch model provides exactly-once semantics.
50%UnverifiedStructured Streaming is the stream computing engine built into Apache Spark.
50%UnverifiedStreaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
50%UnverifiedCombining event triggering with incremental computation is the typical form of modern real-time data pipelines (such as those built on Kafka, Spark Structured Streaming or Delta Live Tables).
50%UnverifiedSpark Streaming将流拆成微批处理引入固定批次延迟，而Flink采用真正的事件驱动流式模型，每条记录独立触发计算，端到端延迟可低至毫秒级
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