apache-spark之Spark 中的任务是什么 Spark Worker如何执行jar文件 阅读了有关http://spark.apache.org/docs/0.8.0/cluster-overview.html的一些文档后,我有一些问题想要澄清。 以Spark 为例: JavaSparkContext spark = new JavaSparkContext( new SparkConf().setJars("...").setSparkHome...); JavaRDD<Stri...
4.RDD里面封装的其实是逻辑,它的责任是告诉程序在运行时,要以什么样的逻辑去处理这一类数据。 5.RDD中有一个叫做preferred location的列表,里面存储着分区的优先位置,而优先位置的概念是指,在spark分配任务给executor的时候,会优先分配给存有这个任务的数据的那个节点上的executor,这样executor在执行任务的时候,就不用...
每个Parquet 分区写入 1 个文件非常容易(请参阅Spark dataframe write method writing many small files): data.repartition($"key").write.partitionBy("key").parquet("/location") 如果您想设置任意数量的文件(或具有相同大小的文件),您需要使用另一个可以使用的属性进一步重新分区您的数据(我无法告诉您这可能...
5. // 示例:从Kafka读取数据JavaInputDStream<ConsumerRecord<String,String>>stream=KafkaUtils.createDirectStream(jssc,LocationStrategies.PreferConsistent(),ConsumerStrategies.<String,String>Subscribe(topicsSet,kafkaParams)); 1. 2. 3. 4. 5. 6. 7. 2. 窗口操作基础 窗口操作是Spark Streaming的核心功能之...
val spark = SparkSessionHelper.createSparkSessionV2(config, “/spark.conf.json”) import spark.implicits._ // Always re-submit bootstrap jobs via Cauce. config.CheckPointLocation = config.CheckPointLocation + spark.sparkContext.applicationId
.option("checkpointLocation","/to/HDFS-compatible/dir") .option("kafka.bootstrap.servers","host1:port1,host2:port2") .start() kafka的schema为key: binary;value: binary;topic: string;partition: int;offset: long;timestamp: long 读数据有三个选择: ...
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对已经cache过的TaskLocation进行清理。 clearCacheLocs() logInfo("Got job "+job.jobId+" ("+callSite+") with "+partitions.length + " output partitions (allowLocal="+allowLocal+")") logInfo("Final stage: "+finalStage+" ("+finalStage.name+")") ...
Properties>producerConfigsBroadcast=jsc.sparkContext().broadcast(producerConfigs);finalBroadcast<String>topicBroadcast=jsc.sparkContext().broadcast("event_out");// 创建Direct方式的StreamJavaInputDStream<ConsumerRecord<String,String>>messages=KafkaUtils.createDirectStream(jsc,LocationStrategies.PreferConsistent(...
* location preferences (hostnames of Spark nodes) for each object. * Create a new partition for each collection item. */ def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope { assertNotStopped() val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1...