方案一(使用ForeachWriter Sink方式):
val query = wordCounts.writeStream.trigger(ProcessingTime(5.seconds)) .outputMode("complete") .foreach(new ForeachWriter[Row] { var fileWriter: FileWriter = _ override def process(value: Row): Unit = { fileWriter.append(value.toSeq.mkString(",")) } override def close(errorOrNull: Throwable): Unit = { fileWriter.close() } override def open(partitionId: Long, version: Long): Boolean = { FileUtils.forceMkdir(new File(s"/tmp/example/${partitionId}")) fileWriter = new FileWriter(new File(s"/tmp/example/${partitionId}/temp")) true } }).start()
方案二(ds.writeStream().partitionBy("field")):
import org.apache.spark.sql.streaming.ProcessingTime val query = streamingSelectDF .writeStream .format("parquet") .option("path", "/mnt/sample/test-data") .option("checkpointLocation", "/mnt/sample/check") .partitionBy("zip", "day") .trigger(ProcessingTime("25 seconds")) .start()
java代码:
// Write new data to Parquet files // can be "orc", "json", "csv", etc. String hdfsFileFormat = SparkHelper.getInstance().getLTEBaseSaveHdfsFileFormat(); String queryName = "save" + this.getTopicEncodeName(topicName) + "DataToHdfs"; String saveHdfsPath = SparkHelper.getInstance().getLTEBaseSaveHdfsPath(); // The file path which partitioned by scan_start_time (format:yyyyMMddHH0000) dsParsed.writeStream() .format(hdfsFileFormat) .option("path", saveHdfsPath + topicName + "/") .option("checkpointLocation", this.checkPointPath + queryName + "/") .outputMode("append") .partitionBy("scan_start_time") .trigger(Trigger.ProcessingTime(5, TimeUnit.MINUTES)) .start();
更多方式,请参考《在Spark结构化流readStream、writeStream 输入输出,及过程ETL》
基础才是编程人员应该深入研究的问题,比如:
1)List/Set/Map内部组成原理|区别
2)mysql索引存储结构&如何调优/b-tree特点、计算复杂度及影响复杂度的因素。。。
3)JVM运行组成与原理及调优
4)Java类加载器运行原理
5)Java中GC过程原理|使用的回收算法原理
6)Redis中hash一致性实现及与hash其他区别
7)Java多线程、线程池开发、管理Lock与Synchroined区别
8)Spring IOC/AOP 原理;加载过程的。。。
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