Spark学习之Spark调优与调试（7）

当创建一个SparkContext时就会创建一个SparkConf实例。

优先级最高的是在用户代码中显示调用set()方法设置选项；
其次是通过spark-submit传递的参数；
再次是写在配置文件里的值；
最后是系统的默认值。

需求：使用Spark shell完成简单的日志分析应用。

scala> val input =sc.textFile("/home/spark01/Documents/input.text")
input: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[3] at textFile at <console>:27

scala> val tokenized = input.map(line=>line.split(" ")).filter(words=>words.size>0)
tokenized: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[5] at filter at <console>:29

scala> val counts = tokenized.map(words=>(words(0),1)).reduceByKey{(a,b)=>a+b}
counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[7] at reduceByKey at <console>:31

scala> // see RDD

scala> input.toDebugString
res0: String = 
(1) MapPartitionsRDD[3] at textFile at <console>:27 []
 |  /home/spark01/Documents/input.text HadoopRDD[2] at textFile at <console>:27 []

scala> counts.toDebugString
res1: String = 
(1) ShuffledRDD[7] at reduceByKey at <console>:31 []
 +-(1) MapPartitionsRDD[6] at map at <console>:31 []
    |  MapPartitionsRDD[5] at filter at <console>:29 []
    |  MapPartitionsRDD[4] at map at <console>:29 []
    |  MapPartitionsRDD[3] at textFile at <console>:27 []
    |  /home/spark01/Documents/input.text HadoopRDD[2] at textFile at <console>:27 []

scala> counts.collect()
res2: Array[(String, Int)] = Array((ERROR,1), (##input.text##,1), (INFO,4), ("",2), (WARN,2))

scala> counts.cache()
res3: counts.type = ShuffledRDD[7] at reduceByKey at <console>:31

scala> counts.collect()
res5: Array[(String, Int)] = Array((ERROR,1), (##input.text##,1), (INFO,4), ("",2), (WARN,2))

scala>

默认情况地址是http://localhost:4040
通过浏览器可以查看已经运行过的作业（job）的详细情况
如图下图：

代码层面：并行度、序列化格式、内存管理
运行环境：硬件供给。