rdd

I am able to print data in two RDD with the below code. usersRDD.foreach(println) empRDD.foreach(println) I need to compare data in two RDDs. How can I iterate and compare field data in one RDD with field data in another RDD. Eg: iterate the records and check if name and age in userRDD has a matching record in empRDD , if no put in separate RDD. I tried with userRDD.substract(empRDD) but it was comparing all the fields. Sean Owen You need to key the data in each RDD so that there is something to join records on. Have a look at groupBy for example. Then you join the resulting RDDs. For each key

资源调优 (1). 在部署 spark 集群中指定资源分配的默认参数 在 spark 安装包的 conf 下的 spark-env.sh SPARK_WORKER_CORES SPARK_WORKER_MEMORY SPARK_WORKER_INSTANCES 每台机器启动 worker 数 #### (2). 在提交 Application 的时候给当前的 Application 分配更多的资源 提交命令选项: (在提交 Application 的时候使用选项) --executor-cores --executor-memory --total-executor-cores 配置信息: (Application 的代码设置或在 Spark-default.conf 中设置) spark.executor.cores spark.executor.memory spark.max.cores 动态分配资源 启用 External shuffle Service 服务: spark.shuffle.service.enabled true Shuffle Service 服务端口, 必须和 yarn-site中的一致: spark.shuffle.service.port 7337 开启动态资源分配: spark.dynamicAllocation.enabled true 每个

1. RDD是什么？ 官方定义： 不可变（immutable）：RDD集合类似于Scala中不可变的集合，例如List，当对集合中的元素进行转换操作时，产生新的集合RDD 分区的（Partitioned）：每个RDD集由有多个分区组成，分区就是很多部分。 并行操作（Parallel）：对RDD集合操作时，可以同时对多有的分区并行操作 容灾分配（failure recovery）：RDD分区中书数据具有恢复功能，每个RDD记录从哪里来，如何依赖。 查看源码发现特点： * Internally, each RDD is characterized by five main properties: * - A list of partitions 第一点：一个RDD有一系列分区Partition组成 protected def getPartitions: Array[Partition] * - A function for computing each split 第二点：RDD中每个分区数据可以被处理分析（计算） def compute(split: Partition, context: TaskContext): Iterator[T] * - A list of dependencies on other RDDs 第三点：每个RDD依赖一些列RDD protected

If the file is loaded from HDFS by default spark creates one partition per block. But how does spark decide partitions when a file is loaded from S3 bucket? See the code of org.apache.hadoop.mapred.FileInputFormat.getSplits() . Block size depends on S3 file system implementation (see FileStatus.getBlockSize() ). E.g. S3AFileStatus just set it equals to 0 (and then FileInputFormat.computeSplitSize() comes into play). Also, you don't get splits if your InputFormat is not splittable :) Spark will treat S3 as if it were a block-based filesystem, so partitioning rules for HDFS and S3 inputs are the

Spark RDD's are constructed in immutable, fault tolerant and resilient manner. Does RDDs satisfy immutability in all scenarios? Or is there any case, be it in Streaming or Core, where RDD might fail to satisfy immutability? It depends on what you mean when you talk about RDD . Strictly speaking RDD is just a description of lineage which exists only on the driver and it doesn't provide any methods which can be used to mutate its lineage. When data is processed we can no longer talk about about RDDs but tasks nevertheless data is exposed using immutable data structures ( scala.collection

I have a problem with Spark Scala which I want count the average from the Rdd data,I create a new RDD like this, [(2,110),(2,130),(2,120),(3,200),(3,206),(3,206),(4,150),(4,160),(4,170)] I want to count them like this, [(2,(110+130+120)/3),(3,(200+206+206)/3),(4,(150+160+170)/3)] then,get the result like this, [(2,120),(3,204),(4,160)] How can I do this with scala from RDD? I use spark version 1.6 you can use aggregateByKey. val rdd = sc.parallelize(Seq((2,110),(2,130),(2,120),(3,200),(3,206),(3,206),(4,150),(4,160),(4,170))) val agg_rdd = rdd.aggregateByKey((0,0))((acc, value) => (acc._1 +

I am fetching data from HDFS and storing it in a Spark RDD. Spark creates the number of partitions based on the number of HDFS blocks. This leads to a large number of empty partitions which also get processed during piping. To remove this overhead, I want to filter out all the empty partitions from the RDD. I am aware of coalesce and repartition, but there is no guarantee that all the empty partitions will be removed. Is there any other way to go about this? There isn't an easy way to simply delete the empty partitions from a RDD. coalesce doesn't guarantee that the empty partitions will be