【SparkAPI】countApprox、countApproxDistinct、countApproxDistinctByK

/** * Approximate version of count() that returns a potentially incomplete result * within a timeout, even if not all tasks have finished. * * The confidence is the probability that the error bounds of the result will * contain the true value. That is, if countApprox were called repeatedly * with confidence 0.9, we would expect 90% of the results to contain the * true count. The confidence must be in the range [0,1] or an exception will * be thrown. * * @param timeout maximum time to wait for the job, in milliseconds * @param confidence the desired statistical confidence in the result * @return a potentially incomplete result, with error bounds */

count（）的相似版本，返回可能不完整的结果，即使不是所有任务都已完成也要在规定时间内完成。

置信度是结果的误差范围将包含真值。也就是说，如果反复调用count近似值置信度为0.9时，我们预计90%的结果将包含真实计数。置信度必须在[0,1]范围内，否则异常将被扔掉。

@timeout 参数超时等待作业的最长时间（毫秒）

@confidence 参数置信度结果中所需的统计置信度

@返回一个可能不完整的结果，带有错误界限

// java public static PartialResult countApprox(long timeout, double confidence) public static PartialResult countApprox(long timeout) // scala def countApprox(timeout: Long): PartialResult[BoundedDouble] def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

/** * Return approximate number of distinct elements in the RDD. * * The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: * Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available * here. * * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */

返回RDD中不同元素的近似数目。

所使用的算法基于streamlib实现的“HyperLogLog in Practice:

Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm"

@参数相对精度。较小的值创建需要更多空间的计数器。

必须大于0.000017。

// java public static long countApproxDistinct(double relativeSD) // scala def countApproxDistinct(relativeSD: Double): Long

public class CountApproxDistinct { public static void main(String[] args) { System.setProperty("hadoop.home.dir", "E:\\hadoop-2.7.1"); SparkConf sparkConf = new SparkConf().setMaster("local").setAppName("Spark_DEMO"); JavaSparkContext sc = new JavaSparkContext(sparkConf); // 示例1 演示过程 JavaPairRDD javaPairRDD1 = sc.parallelizePairs(Lists.newArrayList( new Tuple2("cat", "11"), new Tuple2("dog", "22"), new Tuple2("cat", "33"), new Tuple2("pig", "44"), new Tuple2("duck", "55"), new Tuple2("cat", "66")), 3); System.out.println(javaPairRDD1.countApproxDistinct(0.1)); System.out.println(javaPairRDD1.countApproxDistinct(0.001)); } }

5 19/03/20 15:56:03 INFO DAGScheduler: Job 0 finished: countApproxDistinct at CountApproxDistinct.java:23, took 0.773368 s 19/03/20 15:56:03 INFO SparkContext: Starting job: countApproxDistinct at CountApproxDistinct.java:24 19/03/20 15:56:03 INFO DAGScheduler: Got job 1 (countApproxDistinct at CountApproxDistinct.java:24) with 3 output partitions 19/03/20 15:56:03 INFO DAGScheduler: ResultStage 1 (countApproxDistinct at CountApproxDistinct.java:24) finished in 0.469 s 19/03/20 15:56:03 INFO DAGScheduler: Job 1 finished: countApproxDistinct at CountApproxDistinct.java:24, took 0.521162 s 6 19/03/20 15:56:03 INFO SparkContext: Invoking stop() from shutdown hook

/** * Return approximate number of distinct values for each key in this RDD. */

返回此RDD中每个键的近似不同值数

适用于键值对类型（tuple）的RDD。它countApproxDistinct 相似。但是返回的类型不同，这个计算的是RDD中每个key值的出现次数，返回的value值即次数。

参数relativeSD用于控制计算的精准度。 越小表示准确度越高。

// java public JavaPairRDD countApproxDistinctByKey(double relativeSD, Partitioner partitioner) public JavaPairRDD countApproxDistinctByKey(double relativeSD, int numPartitions) public JavaPairRDD countApproxDistinctByKey(double relativeSD) // scala def countApproxDistinctByKey(relativeSD: Double): JavaPairRDD[K, Long] def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaPairRDD[(K, Long)] def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): JavaPairRDD[(K, Long)]

public class CountApproxDistinctByKey { public static void main(String[] args) { System.setProperty("hadoop.home.dir", "E:\\hadoop-2.7.1"); SparkConf sparkConf = new SparkConf().setMaster("local").setAppName("Spark_DEMO"); JavaSparkContext sc = new JavaSparkContext(sparkConf); JavaPairRDD javaPairRDD1 = sc.parallelizePairs(Lists.newArrayList( new Tuple2("cat", "11"), new Tuple2("dog", "22"), new Tuple2("cat", "33"), new Tuple2("pig", "44"), new Tuple2("duck", "55"), new Tuple2("cat", "66")), 3); JavaPairRDD javaPairRDD = javaPairRDD1.countApproxDistinctByKey(0.01); javaPairRDD.foreach(new VoidFunction>() { public void call(Tuple2 stringLongTuple2) throws Exception { System.out.println(stringLongTuple2); } }); } }

19/03/20 16:09:48 INFO Executor: Running task 2.0 in stage 3.0 (TID 11) (duck,1) (cat,3) 19/03/20 16:09:48 INFO ShuffleBlockFetcherIterator: Getting 2 non-empty blocks out of 3 blocks 19/03/20 16:09:48 INFO ShuffleBlockFetcherIterator: Started 0 remote fetches in 0 ms 19/03/20 16:09:48 INFO Executor: Finished task 2.0 in stage 3.0 (TID 11). 1009 bytes result sent to driver 19/03/20 16:09:48 INFO TaskSetManager: Finished task 2.0 in stage 3.0 (TID 11) in 15 ms on localhost (executor driver) (3/3) 19/03/20 16:09:48 INFO TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool 19/03/20 16:09:48 INFO DAGScheduler: ResultStage 3 (foreach at CountApproxDistinct.java:28) finished in 0.062 s 19/03/20 16:09:48 INFO DAGScheduler: Job 2 finished: foreach at CountApproxDistinct.java:28, took 0.317332 s (dog,1) (pig,1)

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