Event-time Aggregation and Watermarking in Apache Spark’s Structured Streaming

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1. This is the fourth post in a multi-part series about how you can perform complex streaming analytics using Apache Spark.
   
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3. Continuous applications often require near real-time decisions on real-time aggregated statistics—such as health of and readings from IoT devices or detecting anomalous behavior. In this blog, we will explore how easily streaming aggregations can be expressed in Structured Streaming, and how naturally late, and out-of-order data is handled.

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Event-time Aggregation and Watermarking in Apache Spark’s Structured Streaming.pdf (673.32 KB)

2017-5-28 02:32:56 上传

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关键词：Apache Spark Aggregation structured streaming Structure

1. Streaming Aggregations
   
2. Structured Streaming allows users to express the same streaming query as a batch query, and the Spark SQL engine incrementalizes the query and executes on streaming data. For example, suppose you have a streaming DataFrame having events with signal strength from IoT devices, and you want to calculate the running average signal strength for each device, then you would write the following Python code:
   
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4. # DataFrame w/ schema [eventTime: timestamp, deviceId: string, signal: bigint]
   
5. eventsDF = ...
   
6. 
   
7. avgSignalDF = eventsDF.groupBy("deviceId").avg("signal")
   
8. This code is no different if eventsDF was a DataFrame on static data. However, in this case, the average will be continuously updated as new events arrive. You choose different output modes for writing the updated averages to external systems like file systems and databases. Furthermore, you can also implement custom aggregations using Spark’s user-defined aggregation function (UDAFs).

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