StructuredStreaming

StructuredStreaming是Apache Spark提供的一种流计算（streaming）方式，它可以将流式数据作为一组连续的小批处理来处理，实现数据流和批处理的协同工作，从而支持实时流处理和交互式查询。具体来说，StructuredStreaming基于Spark SQL，提供了一种基于DataFrame的API，用户可以以与静态数据相同的方式操作流式数据，从而简化了流数据处理的复杂度。

相关问题

structuredstreaming

b'structuredstreaming'是一种在Apache Spark中处理实时数据的编程模型，它通过将流数据分解成微批次数据来实现高可靠性和高吞吐量的数据处理。同时，它还支持复杂的数据流转换操作和与外部系统的集成。

spark structured streaming

Spark Structured Streaming is a high-level API built on top of Apache Spark that provides support for continuous stream processing. It allows developers to write streaming applications using the same DataFrame and SQL APIs that are used for batch processing.

Structured Streaming is based on the concept of a continuous processing engine that can process data in real-time as it arrives, rather than traditional batch processing that runs on a fixed schedule. It provides several key features, including:

1. High-level APIs for easy development and deployment of streaming applications
2. Support for both batch and streaming data processing
3. Built-in support for fault-tolerance and recovery
4. Integration with Apache Kafka, HDFS, and other data sources
5. Support for windowed aggregations and joins

Spark Structured Streaming also provides a powerful engine for managing and optimizing streaming workloads. It automatically manages the parallelism and resource allocation for processing data streams, optimizing performance and scalability.