本文首发自个人博客：https://blog.smile13.com/articles/2018/11/30/1543589289882.html
一、无输出的算子
1.foreach算子
功能：对 RDD 中的每个元素都应用 f 函数操作，无返回值。源码： /** * Applies a function f to all elements of this RDD. */ def foreach(f: T => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF)) }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[20] at parallelize at <console>:24 scala> rdd1.foreach(x => printf("%d ", x)) 1 2 3 4 5 6 7 8 9
2.foreachPartition算子
功能：该函数和foreach类似，不同的是,foreach是直接在每个partition中直接对iterator执行foreach操作,传入的function只是在foreach内部使用, 而foreachPartition是在每个partition中把iterator给传入的function,让function自己对iterator进行处理（可以避免内存溢出）。 简单来说，foreach的iterator是针对的rdd中的元素，而foreachPartition的iterator是针对的分区本身。源码： /** * Return a new RDD by applying a function to each partition of this RDD, while tracking the index * of the original partition. * * `preservesPartitioning` indicates whether the input function preserves the partitioner, which * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitionsWithIndex[U: ClassTag]( f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = withScope { val cleanedF = sc.clean(f) new MapPartitionsRDD( this, (context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter), preservesPartitioning) }示例： scala> val rdd1 = sc.parallelize(1 to 9, 2) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[23] at parallelize at <console>:24 scala> rdd1.foreachPartition(x => printf("%s ", x.size)) 4 5
二、输出到HDFS等文件系统的算子
1.saveAsTextFile算子
功能：该函数将数据输出，以文本文件的形式写入本地文件系统或者HDFS等。Spark将对每个元素调用toString方法，将数据元素转换为文本文件中的一行记录。若将文件保存到本地文件系统，那么只会保存在executor所在机器的本地目录。源码： /** * Save this RDD as a text file, using string representations of elements. */ def saveAsTextFile(path: String): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 // // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit // Ordering for it and will use the default `null`. However, it's a `Comparable[NullWritable]` // in Hadoop 2.+, so the compiler will call the implicit `Ordering.ordered` method to create an // Ordering for `NullWritable`. That's why the compiler will generate different anonymous // classes for `saveAsTextFile` in Hadoop 1.+ and Hadoop 2.+. // // Therefore, here we provide an explicit Ordering `null` to make sure the compiler generate // same bytecodes for `saveAsTextFile`. val nullWritableClassTag = implicitly[ClassTag[NullWritable]] val textClassTag = implicitly[ClassTag[Text]] val r = this.mapPartitions { iter => val text = new Text() iter.map { x => text.set(x.toString) (NullWritable.get(), text) } } RDD.rddToPairRDDFunctions(r)(nullWritableClassTag, textClassTag, null) .saveAsHadoopFile[TextOutputFormat[NullWritable, Text]](path) }示例： scala> val rdd1 = sc.parallelize(1 to 9, 2) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[26] at parallelize at <console>:24 scala> rdd1.saveAsTextFile("file:///opt/app/test/saveAsTextFileTest.txt")
2.saveAsObjectFile算子
功能：该函数用于将RDD以ObjectFile形式写入本地文件系统或者HDFS等。源码： /** * Save this RDD as a SequenceFile of serialized objects. */ def saveAsObjectFile(path: String): Unit = withScope { this.mapPartitions(iter => iter.grouped(10).map(_.toArray)) .map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x)))) .saveAsSequenceFile(path) }示例： scala> val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[40] at parallelize at <console>:24 scala> rdd1.saveAsObjectFile("file:///opt/app/test/saveAsObejctFileTest.txt")
3.saveAsHadoopFile算子
功能：该函数将RDD存储在HDFS上的文件中,可以指定outputKeyClass、outputValueClass以及压缩格式,每个分区输出一个文件。源码： /** * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. * * @note We should make sure our tasks are idempotent when speculation is enabled, i.e. do * not use output committer that writes data directly. * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad * result of using direct output committer with speculation enabled. */def saveAsHadoopFile( path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf(self.context.hadoopConfiguration), codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf hadoopConf.setOutputKeyClass(keyClass) hadoopConf.setOutputValueClass(valueClass) conf.setOutputFormat(outputFormatClass) for (c <- codec) { hadoopConf.setCompressMapOutput(true) hadoopConf.set("mapreduce.output.fileoutputformat.compress", "true") hadoopConf.setMapOutputCompressorClass(c) hadoopConf.set("mapreduce.output.fileoutputformat.compress.codec", c.getCanonicalName) hadoopConf.set("mapreduce.output.fileoutputformat.compress.type", CompressionType.BLOCK.toString) } // Use configured output committer if already set if (conf.getOutputCommitter == null) { hadoopConf.setOutputCommitter(classOf[FileOutputCommitter]) } // When speculation is on and output committer class name contains "Direct", we should warn // users that they may loss data if they are using a direct output committer. val speculationEnabled = self.conf.getBoolean("spark.speculation", false) val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "") if (speculationEnabled && outputCommitterClass.contains("Direct")) { val warningMessage = s"$outputCommitterClass may be an output committer that writes data directly to " + "the final location. Because speculation is enabled, this output committer may " + "cause data loss (see the case in SPARK-10063). If possible, please use an output " + "committer that does not have this behavior (e.g. FileOutputCommitter)." logWarning(warningMessage) } FileOutputFormat.setOutputPath(hadoopConf, SparkHadoopWriterUtils.createPathFromString(path, hadoopConf)) saveAsHadoopDataset(hadoopConf) }示例： val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) rdd1.saveAsHadoopFile("hdfs://192.168.199.201:8020/test",classOf[ClassTag[Text]],classOf[IntWritable],classOf[TextOutputFormat[Text,IntWritable]])
4.saveAsSequenceFile算子
功能：该函数用于将RDD以Hadoop SequenceFile的形式写入本地文件系统或者HDFS等。源码： /** * Output the RDD as a Hadoop SequenceFile using the Writable types we infer from the RDD's key * and value types. If the key or value are Writable, then we use their classes directly; * otherwise we map primitive types such as Int and Double to IntWritable, DoubleWritable, etc, * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ def saveAsSequenceFile( path: String, codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { def anyToWritable[U <% Writable](u: U): Writable = u // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and // valueWritableClass at the compile time. To implement that, we need to add type parameters to // SequenceFileRDDFunctions. however, SequenceFileRDDFunctions is a public class so it will be a // breaking change. val convertKey = self.keyClass != _keyWritableClass val convertValue = self.valueClass != _valueWritableClass logInfo("Saving as sequence file of type " + s"(${_keyWritableClass.getSimpleName},${_valueWritableClass.getSimpleName})" ) val format = classOf[SequenceFileOutputFormat[Writable, Writable]] val jobConf = new JobConf(self.context.hadoopConfiguration) if (!convertKey && !convertValue) { self.saveAsHadoopFile(path, _keyWritableClass, _valueWritableClass, format, jobConf, codec) } else if (!convertKey && convertValue) { self.map(x => (x._1, anyToWritable(x._2))).saveAsHadoopFile( path, _keyWritableClass, _valueWritableClass, format, jobConf, codec) } else if (convertKey && !convertValue) { self.map(x => (anyToWritable(x._1), x._2)).saveAsHadoopFile( path, _keyWritableClass, _valueWritableClass, format, jobConf, codec) } else if (convertKey && convertValue) { self.map(x => (anyToWritable(x._1), anyToWritable(x._2))).saveAsHadoopFile( path, _keyWritableClass, _valueWritableClass, format, jobConf, codec) } }示例： scala> val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[38] at parallelize at <console>:24 scala> rdd1.saveAsSequenceFile("file:///opt/app/test/saveAsSequenceFileTest1.txt")
5.saveAsHadoopDataset算子
功能：该函数使用旧的Hadoop API将RDD输出到任何Hadoop支持的存储系统，例如Hbase,为该存储系统使用Hadoop JobConf 对象。源码： /** * Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for * that storage system. The JobConf should set an OutputFormat and any output paths required * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop * MapReduce job. */ def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope { val config = new HadoopMapRedWriteConfigUtil[K, V](new SerializableJobConf(conf)) SparkHadoopWriter.write( rdd = self, config = config) }示例： val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) var jobConf = new JobConf() jobConf.setOutputFormat(classOf[TextOutputFormat[Text,IntWritable]]) jobConf.setOutputKeyClass(classOf[Text]) jobConf.setOutputValueClass(classOf[IntWritable]) jobConf.set("mapred.output.dir","/test/") rdd1.saveAsHadoopDataset(jobConf)
6.saveAsNewAPIHadoopFile算子
功能：该函数用于将RDD数据保存到HDFS上，使用新版本Hadoop API。用法基本同saveAsHadoopFile。源码： /** * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ def saveAsNewAPIHadoopFile( path: String, keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], conf: Configuration = self.context.hadoopConfiguration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = NewAPIHadoopJob.getInstance(hadoopConf) job.setOutputKeyClass(keyClass) job.setOutputValueClass(valueClass) job.setOutputFormatClass(outputFormatClass) val jobConfiguration = job.getConfiguration jobConfiguration.set("mapreduce.output.fileoutputformat.outputdir", path) saveAsNewAPIHadoopDataset(jobConfiguration) }示例： val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) rdd1.saveAsNewAPIHadoopFile("hdfs://192.168.199.201:8020/test",classOf[Text],classOf[IntWritable],classOf[output.TextOutputFormat[Text,IntWritable]])
7.saveAsNewAPIHadoopDataset算子
功能：使用新的Hadoop API将RDD输出到任何Hadoop支持的存储系统，例如Hbase,为该存储系统使用Hadoop Configuration对象。Conf设置一个OutputFormat和任何需要的输出路径(如要写入的表名)，就像为Hadoop MapReduce作业配置的那样。源码： /** * Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop * Configuration object for that storage system. The Conf should set an OutputFormat and any * output paths required (e.g. a table name to write to) in the same way as it would be * configured for a Hadoop MapReduce job. * * @note We should make sure our tasks are idempotent when speculation is enabled, i.e. do * not use output committer that writes data directly. * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad * result of using direct output committer with speculation enabled. */ def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope { val config = new HadoopMapReduceWriteConfigUtil[K, V](new SerializableConfiguration(conf)) SparkHadoopWriter.write( rdd = self, config = config) }示例： val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) var jobConf = new JobConf() jobConf.setOutputFormat(classOf[TextOutputFormat[Text,IntWritable]]) jobConf.setOutputKeyClass(classOf[Text]) jobConf.setOutputValueClass(classOf[IntWritable]) jobConf.set("mapred.output.dir","/test/") rdd1.saveAsNewAPIHadoopDataset(jobConf)
三、输出scala集合和数据类型的算子
1.first算子
功能：返回RDD中的第一个元素，不排序。源码： /** * Return the first element in this RDD. */ def first(): T = withScope { take(1) match { case Array(t) => t case _ => throw new UnsupportedOperationException("empty collection") } }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24 scala> val rdd2 = rdd1.first() rdd2: Int = 1 scala> print(rdd2) 1
2.count算子
功能：返回RDD中的元素数量。源码： /** * Return the number of elements in the RDD. */ def count(): Long = sc.runJob(this, Utils.getIteratorSize _).sum示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at <console>:24 scala> println(rdd1.count()) 9
3.reduce算子
功能：将RDD中元素两两传递给输入函数，同时产生一个新值，新值与RDD中下一个元素再被传递给输入函数，直到最后只有一个值为止。源码： /** * Reduces the elements of this RDD using the specified commutative and * associative binary operator. */ def reduce(f: (T, T) => T): T = withScope { val cleanF = sc.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { if (iter.hasNext) { Some(iter.reduceLeft(cleanF)) } else { None } } var jobResult: Option[T] = None val mergeResult = (index: Int, taskResult: Option[T]) => { if (taskResult.isDefined) { jobResult = jobResult match { case Some(value) => Some(f(value, taskResult.get)) case None => taskResult } } } sc.runJob(this, reducePartition, mergeResult) // Get the final result out of our Option, or throw an exception if the RDD was empty jobResult.getOrElse(throw new UnsupportedOperationException("empty collection")) }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at parallelize at <console>:24 scala> val rdd2 = rdd1.reduce((x,y) => x + y) rdd2: Int = 45
4.collect算子
功能：将一个RDD以一个Array数组形式返回其中的所有元素。源码： /** * Return an array that contains all of the elements in this RDD. * * @note This method should only be used if the resulting array is expected to be small, as * all the data is loaded into the driver's memory. */ def collect(): Array[T] = withScope { val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray) Array.concat(results: _*) }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at parallelize at <console>:24 scala> rdd1.collect res3: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9)
5.take算子
功能：返回一个包含数据集前n个元素的数组（从0下标到n-1下标的元素），不排序。源码： /** * Take the first num elements of the RDD. It works by first scanning one partition, and use the * results from that partition to estimate the number of additional partitions needed to satisfy * the limit. * * @note This method should only be used if the resulting array is expected to be small, as * all the data is loaded into the driver's memory. * * @note Due to complications in the internal implementation, this method will raise * an exception if called on an RDD of `Nothing` or `Null`. */ def take(num: Int): Array[T] = withScope { val scaleUpFactor = Math.max(conf.getInt("spark.rdd.limit.scaleUpFactor", 4), 2) if (num == 0) { new Array[T](0) } else { val buf = new ArrayBuffer[T] val totalParts = this.partitions.length var partsScanned = 0 while (buf.size < num && partsScanned < totalParts) { // The number of partitions to try in this iteration. It is ok for this number to be // greater than totalParts because we actually cap it at totalParts in runJob. var numPartsToTry = 1L val left = num - buf.size if (partsScanned > 0) { // If we didn't find any rows after the previous iteration, quadruple and retry. // Otherwise, interpolate the number of partitions we need to try, but overestimate // it by 50%. We also cap the estimation in the end. if (buf.isEmpty) { numPartsToTry = partsScanned * scaleUpFactor } else { // As left > 0, numPartsToTry is always >= 1 numPartsToTry = Math.ceil(1.5 * left * partsScanned / buf.size).toInt numPartsToTry = Math.min(numPartsToTry, partsScanned * scaleUpFactor) } } val p = partsScanned.until(math.min(partsScanned + numPartsToTry, totalParts).toInt) val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p) res.foreach(buf ++= _.take(num - buf.size)) partsScanned += p.size } buf.toArray } }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[4] at parallelize at <console>:24 scala> val rdd2 = rdd1.take(3) rdd2: Array[Int] = Array(1, 2, 3)
6.top算子
功能：从按降序排列的RDD中获取前N个元素，或者有可选的key函数决定顺序，返回一个数组。源码： /** * Returns the top k (largest) elements from this RDD as defined by the specified * implicit Ordering[T] and maintains the ordering. This does the opposite of * [[takeOrdered]]. For example: * {{{ * sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1) * // returns Array(12) * * sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2) * // returns Array(6, 5) * }}} * * @note This method should only be used if the resulting array is expected to be small, as * all the data is loaded into the driver's memory. * * @param num k, the number of top elements to return * @param ord the implicit ordering for T * @return an array of top elements */def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { takeOrdered(num)(ord.reverse) }示例： scala> val rdd1 = sc.parallelize(1 to 9) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[5] at parallelize at <console>:24 scala> val rdd2 = rdd1.top(3) rdd2: Array[Int] = Array(9, 8, 7)
7.takeOrdered算子
功能：返回RDD中前n个元素，并按默认顺序排序（升序）或者按自定义比较器顺序排序。源码： /** * Returns the first k (smallest) elements from this RDD as defined by the specified * implicit Ordering[T] and maintains the ordering. This does the opposite of [[top]]. * For example: * {{{ * sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1) * // returns Array(2) * * sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2) * // returns Array(2, 3) * }}} * * @note This method should only be used if the resulting array is expected to be small, as * all the data is loaded into the driver's memory. * * @param num k, the number of elements to return * @param ord the implicit ordering for T * @return an array of top elements */def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { if (num == 0) { Array.empty } else { val mapRDDs = mapPartitions { items => // Priority keeps the largest elements, so let's reverse the ordering. val queue = new BoundedPriorityQueue[T](num)(ord.reverse) queue ++= collectionUtils.takeOrdered(items, num)(ord) Iterator.single(queue) } if (mapRDDs.partitions.length == 0) { Array.empty } else { mapRDDs.reduce { (queue1, queue2) => queue1 ++= queue2 queue1 }.toArray.sorted(ord) } }}示例： scala> val rdd1 = sc.makeRDD(Seq(5,4,2,1,3,6)) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[7] at makeRDD at <console>:24 scala> val rdd2 = rdd1.takeOrdered(3) rdd2: Array[Int] = Array(1, 2, 3)
8.aggregate算子
功能：aggregate函数将每个分区里面的元素进行聚合（seqOp），然后用combine函数将每个分区的结果和初始值(zeroValue)进行combine操作。这个函数最终返回的类型不需要和RDD中元素类型一致。源码： /** * Aggregate the elements of each partition, and then the results for all the partitions, using * given combine functions and a neutral "zero value". This function can return a different result * type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U * and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are * allowed to modify and return their first argument instead of creating a new U to avoid memory * allocation. * * @param zeroValue the initial value for the accumulated result of each partition for the * `seqOp` operator, and also the initial value for the combine results from * different partitions for the `combOp` operator - this will typically be the * neutral element (e.g. `Nil` for list concatenation or `0` for summation) * @param seqOp an operator used to accumulate results within a partition * @param combOp an associative operator used to combine results from different partitions */def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) val cleanCombOp = sc.clean(combOp) val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) val mergeResult = (index: Int, taskResult: U) => jobResult = combOp(jobResult, taskResult) sc.runJob(this, aggregatePartition, mergeResult) jobResult }示例： scala> val rdd1 = sc.parallelize(1 to 9, 3) rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[11] at parallelize at <console>:24 》 scala> val rdd2 = rdd1.aggregate((0,0))( | (acc,number) => (acc._1 + number, acc._2 + 1), | (par1,par2) => (par1._1 + par2._1, par1._2 + par2._2) | ) rdd2: (Int, Int) = (45,9)
9.fold算子
功能：通过op函数聚合各分区中的元素及合并各分区的元素，op函数需要两个参数，在开始时第一个传入的参数为zeroValue,T为RDD数据集的数据类型，，其作用相当于SeqOp和comOp函数都相同的aggregate函数。源码： /** * Aggregate the elements of each partition, and then the results for all the partitions, using a * given associative function and a neutral "zero value". The function * op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object * allocation; however, it should not modify t2. * * This behaves somewhat differently from fold operations implemented for non-distributed * collections in functional languages like Scala. This fold operation may be applied to * partitions individually, and then fold those results into the final result, rather than * apply the fold to each element sequentially in some defined ordering. For functions * that are not commutative, the result may differ from that of a fold applied to a * non-distributed collection. * * @param zeroValue the initial value for the accumulated result of each partition for the `op` * operator, and also the initial value for the combine results from different * partitions for the `op` operator - this will typically be the neutral * element (e.g. `Nil` for list concatenation or `0` for summation) * @param op an operator used to both accumulate results within a partition and combine results * from different partitions */def fold(zeroValue: T)(op: (T, T) => T): T = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) val cleanOp = sc.clean(op) val foldPartition = (iter: Iterator[T]) => iter.fold(zeroValue)(cleanOp) val mergeResult = (index: Int, taskResult: T) => jobResult = op(jobResult, taskResult) sc.runJob(this, foldPartition, mergeResult) jobResult }示例： scala> val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 5), ("a", 4)), 2) rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[13] at parallelize at <console>:24 scala> val rdd2 = rdd1.fold(("e", 0))((val1, val2) => { if (val1._2 >= val2._2) val1 else val2}) rdd2: (String, Int) = (d,5) scala> println(rdd2) (d,5)
10.lookup算子
功能：该函数对（Key，Value）型的RDD操作，返回指定Key对应的元素形成的Seq。 这个函数处理优化的部分在于，如果这个RDD包含分区器，则只会对应处理K所在的分区，然后返回由（K，V）形成的Seq。 如果RDD不包含分区器，则需要对全RDD元素进行暴力扫描处理，搜索指定K对应的元素源码： /** * Return the list of values in the RDD for key `key`. This operation is done efficiently if the * RDD has a known partitioner by only searching the partition that the key maps to. */ def lookup(key: K): Seq[V] = self.withScope { self.partitioner match { case Some(p) => val index = p.getPartition(key) val process = (it: Iterator[(K, V)]) => { val buf = new ArrayBuffer[V] for (pair <- it if pair._1 == key) { buf += pair._2 } buf } : Seq[V] val res = self.context.runJob(self, process, Array(index)) res(0) case None => self.filter(_._1 == key).map(_._2).collect() } }示例： scala> val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 4), ("a", 5)), 2) rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[14] at parallelize at <console>:24 scala> val rdd2 = rdd1.lookup("a") rdd2: Seq[Int] = WrappedArray(1, 5)
11.countByKey算子
功能：用于统计RDD[K,V]中每个K的数量，返回具有每个key的计数的（k，int）pairs的Map。源码： /** * Count the number of elements for each key, collecting the results to a local Map. * * @note This method should only be used if the resulting map is expected to be small, as * the whole thing is loaded into the driver's memory. * To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ def countByKey(): Map[K, Long] = self.withScope { self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap }示例： scala> val rdd1 = sc.parallelize(Array(("a", 1), ("b", 2), ("c", 3), ("d", 4), ("a", 5)), 2) rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[17] at parallelize at <console>:24 scala> val rdd2 = rdd1.countByKey() rdd2: scala.collection.Map[String,Long] = Map(d -> 1, b -> 1, a -> 2, c -> 1)
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