Spark Structured Streaming框架(5)之進程管理

　　Structured Streaming提供一些API來管理Streaming對象。用戶能夠經過這些API來手動管理已經啓動的Streaming，保證在系統中的Streaming有序執行。

1. StreamingQuery

 

　　在調用DataStreamWriter方法的start啓動Streaming後，會返回一個StreamingQuery對象。因此用戶就能夠經過這個對象來管理Streaming。

以下所示：

val query = df.writeStream.format("console").start() // get the query object   query.id // get the unique identifier of the running query that persists across restarts from checkpoint data   query.runId // get the unique id of this run of the query, which will be generated at every start/restart   query.name // get the name of the auto-generated or user-specified name   query.explain() // print detailed explanations of the query   query.stop() // stop the query   query.awaitTermination() // block until query is terminated, with stop() or with error   query.exception // the exception if the query has been terminated with error   query.recentProgress // an array of the most recent progress updates for this query   query.lastProgress // the most recent progress update of this streaming query

 

2. StreamingQueryManager

 

　　Structured Streaming提供了另一個管理Streaming的接口是：StreamingQueryManager。用戶能夠經過SparkSession對象的streams方法得到。

以下所示：

val spark: SparkSession = ... val streamManager = spark.streams() streamManager.active // get the list of currently active streaming queries   streamManager.get(id) // get a query object by its unique id   streamManager.awaitAnyTermination() // block until any one of them terminates

 

3. 參考文獻

 

[1]. Structured Streaming Programming Guide.

[2]. Kafka Integration Guide.