运行Cucumber时如何监控垃圾回收？(How to monitor garbage collection when running Cucumber?)

 根据设计，Spark 转换是惰性的，您必须使用操作才能从中检索具体值。 
 
 例如，以下转换将始终保持惰性： 
 
JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
 
 像map返回RDD这样的函数，你只能通过执行诸如reduce动作将这些RDD转换为实际值： 
 
int totalLength = lineLengths.reduce((a, b) -> a + b);
 
 没有标志会使map返回一个具体值（例如，整数列表）。 
 
 最重要的是，你可以使用collect或任何其他Spark动作来“防止变形的懒惰”： 
 
JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
List<Integer> collectedLengths = lineLengths.collect()
 
 但请记住，在大型数据集上使用collect可能是一个非常糟糕的做法，使您的驱动程序内存不足。 

By design, Spark transformations are lazy, and you must use an action in order to retrieve a concrete value out of them.
 
For example, the following transformations will always remain lazy:
 
JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
 
Functions like map return RDDs, and you can only turn those RDDs into real values by performing actions, such as reduce:
 
int totalLength = lineLengths.reduce((a, b) -> a + b);
 
There is no flag that will make map return a concrete value (for example, a list of integers).
 
The bottom line is that you can use collect or any other Spark action to 'prevent the laziness' of a transformation:
 
JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
List<Integer> collectedLengths = lineLengths.collect()
 
Remember, though, the using collect on a large dataset will probably be a very bad practice, making your driver run out of memory.