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Spark Streaming Real-Time

⬢ 層級 2技術
高
薪資影響
10 個月
學習時長
困難
難度
12
職業
一覽

Spark Structured Streaming is Spark's API for processing continuous data streams with low latency. Includes handling late-arriving data, window aggregations, stateful processing, and integration with Kafka/Kinesis. Used by data engineers building real-time pipelines. Takes 10-12 weeks to develop advanced competence. Sits between Spark SQL and stream processing systems.

什麼是 Spark Streaming Real-Time

Spark Structured Streaming is Apache Spark's API for processing continuous streams of data in real-time. It treats data streams as unbounded tables, allowing you to write SQL or DataFrame queries that run continuously. Structured Streaming handles complexities like late-arriving data, stateful processing, and fault tolerance. Applications include real-time analytics dashboards, anomaly detection, data pipelines, and event-driven systems. Spark Structured Streaming is the foundation for real-time data platforms.

🔧 工具與生態
Spark Structured StreamingKafkaKinesisDelta LakePySparkApache FlinkScalaDatabricks

📋 開始之前

Spark Sql Data

💰 各地區薪資

地區初級中級高級
USA$110,000$180,000$280,000
UK£85,000£145,000£230,000
EU€90,000€150,000€240,000
CANADACA$105,000CA$175,000CA$270,000

⚖ 對比

Spark Sql Data

❓ 常見問題

What's the difference between Spark Structured Streaming and regular Spark batch?
Streaming processes continuous data with low latency. Batch processes static data. Structured Streaming uses SQL; you write a query that runs continuously.
How does Spark Structured Streaming handle late-arriving data?
Watermarks define when you expect data. Data arriving after the watermark is out-of-order. Spark can handle it but you must decide: include it or drop it.
Can I use window aggregations in streaming?
Yes. Tumbling windows (fixed size, no overlap), sliding windows (overlap), session windows (defined by inactivity). Windows are essential for streaming analytics.
How low-latency can Spark Structured Streaming achieve?
With micro-batch processing, typically 500ms-1s latency. With continuous mode, sub-second. Not as low as single-event systems but good for most use cases.
What's the cost of running a streaming application?
Cost scales with compute allocated. Always-on clusters cost money. Right-size your cluster; auto-scaling helps. Spot instances can reduce cost.

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