SparkRDD常用算子
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sparkRDD算子
分区/只读/依赖/缓存/checkPoint
Transformation
map(func)
返回一个新的RDD,该RDD由每一个输入元素经过func函数转换后组成
scala> var source = sc.parallelize(1 to 10)
source: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[8] at parallelize at <console>:24
scala> source.collect()
res7: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
scala> val mapadd = source.map(_ * 2)
mapadd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[9] at map at <console>:26
scala> mapadd.collect()
res8: Array[Int] = Array(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
filter(func)
返回一个新的RDD,该RDD由经过func函数计算后返回值为true的输入元素组成
scala> var sourceFilter = sc.parallelize(Array("xiaoming","xiaojiang","xiaohe","dazhi"))
sourceFilter: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[10] at parallelize at <console>:24
scala> val filter = sourceFilter.filter(_.contains("xiao"))
filter: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[11] at filter at <console>:26
scala> sourceFilter.collect()
res9: Array[String] = Array(xiaoming, xiaojiang, xiaohe, dazhi)
scala> filter.collect()
res10: Array[String] = Array(xiaoming, xiaojiang, xiaohe)
flatmap(func)
类似于map,但是每一个输入元素可以被映射为0或多个输出元素(所以func应该返回一个序列,而不是单一元素)
scala> val sourceFlat = sc.parallelize(1 to 5)
sourceFlat: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[12] at parallelize at <console>:24
scala> sourceFlat.collect()
res11: Array[Int] = Array(1, 2, 3, 4, 5)
scala> val flatMap = sourceFlat.flatMap(1 to _)
flatMap: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[13] at flatMap at <console>:26
scala> flatMap.collect()
res12: Array[Int] = Array(1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5)
mapPartition(func)
类似于map,但独立地在RDD的每一个分片上运行,因此在类型为T的RDD上运行时,func的函数类型必须是Iterator[T] => Iterator[U]。假设有N个元素,有M个分区,那么map的函数的将被调用N次,而mapPartitions被调用M次,一个函数一次处理所有分区
mapPartitionWithIndex(func)
类似于mapPartitions,但func带有一个整数参数表示分片的索引值,因此在类型为T的RDD上运行时,func的函数类型必须是(Int, Interator[T]) => Iterator[U]
sample(withReplacement,fraction,seed)
以指定的随机种子随机抽样出数量为fraction的数据,withReplacement表示是抽出的数据是否放回,true为有放回的抽样,false为无放回的抽样,seed用于指定随机数生成器种子。例子从RDD中随机且有放回的抽出50%的数据,随机种子值为3(即可能以1 2 3的其中一个起始值)
tabkesample
和Sample的区别是:takeSample返回的是最终的结果集合。
union(otherDataset)
对源RDD和参数RDD求并集后返回一个新的RDD
scala> val rdd1 = sc.parallelize(1 to 5)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[23] at parallelize at <console>:24
scala> val rdd2 = sc.parallelize(5 to 10)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[24] at parallelize at <console>:24
scala> val rdd3 = rdd1.union(rdd2)
rdd3: org.apache.spark.rdd.RDD[Int] = UnionRDD[25] at union at <console>:28
scala> rdd3.collect()
res18: Array[Int] = Array(1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 10)
intersection(otherDataset)
对源RDD和参数RDD求交集后返回一个新的RDD
scala> val rdd1 = sc.parallelize(1 to 7)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[26] at parallelize at <console>:24
scala> val rdd2 = sc.parallelize(5 to 10)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[27] at parallelize at <console>:24
scala> val rdd3 = rdd1.intersection(rdd2)
rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[33] at intersection at <console>:28
scala> rdd3.collect()
[Stage 15:=============================>(2 + 2)
res19: Array[Int] = Array(5, 6, 7)
distinct(numTasks)
对源RDD进行去重后返回一个新的RDD. 默认情况下,只有8个并行任务来操作,但是可以传入一个可选的numTasks参数改变它。
scala> val distinctRdd = sc.parallelize(List(1,2,1,5,2,9,6,1))
distinctRdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[34] at parallelize at <console>:24
scala> val unionRDD = distinctRdd.distinct()
unionRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[37] at distinct at <console>:26
scala> unionRDD.collect()
[Stage 16:> (0 + 4) [Stage 16:=============================>(2 + 2)
res20: Array[Int] = Array(1, 9, 5, 6, 2)
scala> val unionRDD = distinctRdd.distinct(2)
unionRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[40] at distinct at <console>:26
scala> unionRDD.collect()
res21: Array[Int] = Array(6, 2, 1, 9, 5)
partitionBy
对RDD进行分区操作,如果原有的partionRDD和现有的partionRDD是一致的话就不进行分区, 否则会生成ShuffleRDD.
reduceByKey(fun,[numTasks])
在一个(K,V)的RDD上调用,返回一个(K,V)的RDD,使用指定的reduce函数,将相同key的值聚合到一起,reduce任务的个数可以通过第二个可选的参数来设置
groupByKey
groupByKey也是对每个key进行操作,但只生成一个sequence。
combinerByKey[C]
对相同K,把V合并成一个集合.
aggregateBykey()
在kv对的RDD中,,按key将value进行分组合并,合并时,将每个value和初始值作为seq函数的参数,进行计算,返回的结果作为一个新的kv对,然后再将结果按照key进行合并,最后将每个分组的value传递给combine函数进行计算(先将前两个value进行计算,将返回结果和下一个value传给combine函数,以此类推),将key与计算结果作为一个新的kv对输出。
seqOp函数用于在每一个分区中用初始值逐步迭代value,combOp函数用于合并每个分区中的结果****
foldByKey()
aggregateByKey的简化操作,seqop和combop相同
sortByKey([ascending],[numTasks])
在一个(K,V)的RDD上调用,K必须实现Ordered接口,返回一个按照key进行排序的(K,V)的RDD
scala> val rdd = sc.parallelize(Array((3,"aa"),(6,"cc"),(2,"bb"),(1,"dd")))
rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[14] at parallelize at <console>:24
scala> rdd.sortByKey(true).collect()
res9: Array[(Int, String)] = Array((1,dd), (2,bb), (3,aa), (6,cc))
scala> rdd.sortByKey(false).collect()
res10: Array[(Int, String)] = Array((6,cc), (3,aa), (2,bb), (1,dd))
sortBy(func,[ascending],[numTasks])
与sortByKey类似,但是更灵活,可以用func先对数据进行处理,按照处理后的数据比较结果排序。
scala> val rdd = sc.parallelize(List(1,2,3,4))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[21] at parallelize at <console>:24
scala> rdd.sortBy(x => x).collect()
res11: Array[Int] = Array(1, 2, 3, 4)
scala> rdd.sortBy(x => x%3).collect()
res12: Array[Int] = Array(3, 4, 1, 2)
join(otherDataset,[numTasks])
在类型为(K,V)和(K,W)的RDD上调用,返回一个相同key对应的所有元素对在一起的(K,(V,W))的RDD
scala> val rdd = sc.parallelize(Array((1,"a"),(2,"b"),(3,"c")))
rdd: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[32] at parallelize at <console>:24
scala> val rdd1 = sc.parallelize(Array((1,4),(2,5),(3,6)))
rdd1: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[33] at parallelize at <console>:24
scala> rdd.join(rdd1).collect()
res13: Array[(Int, (String, Int))] = Array((1,(a,4)), (2,(b,5)), (3,(c,6)))
cogroup(otherDataset,[numTasks])
在类型为(K,V)和(K,W)的RDD上调用,返回一个(K,(Iterable
cartsian(otherDataset)
笛卡尔积
pipe(command,[envVars])
对于每个分区,都执行一个perl或者shell脚本,返回输出的RDD
Shell脚本
#!/bin/sh
echo "AA"
while read LINE; do
echo ">>>"$LINE
done
注意:shell脚本需要集群中的所有节点都能访问到。
scala> val rdd = sc.parallelize(List("hi","Hello","how","are","you"),1)
rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[50] at parallelize at <console>:24
scala> rdd.pipe("/home/bigdata/pipe.sh").collect()
res18: Array[String] = Array(AA, >>>hi, >>>Hello, >>>how, >>>are, >>>you)
scala> val rdd = sc.parallelize(List("hi","Hello","how","are","you"),2)
rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[52] at parallelize at <console>:24
scala> rdd.pipe("/home/bigdata/pipe.sh").collect()
res19: Array[String] = Array(AA, >>>hi, >>>Hello, AA, >>>how, >>>are, >>>you)
pipe.sh:
#!/bin/sh
echo "AA"
while read LINE; do
echo ">>>"$LINE
done
coalesce(numPartitions)
缩减分区数,用于大数据集过滤后,提高小数据集的执行效率。
scala> val rdd = sc.parallelize(1 to 16,4)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[54] at parallelize at <console>:24
scala> rdd.partitions.size
res20: Int = 4
scala> val coalesceRDD = rdd.coalesce(3)
coalesceRDD: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[55] at coalesce at <console>:26
scala> coalesceRDD.partitions.size
res21: Int = 3
repartition(numPartitions)
根据分区数重新通过网络对数据随机洗牌
repartitionAndSortWithPartition(partitioner)
repartitionAndSortWithinPartitions函数是repartition函数的变种,与repartition函数不同的是,repartitionAndSortWithinPartitions在给定的partitioner内部进行排序,性能比repartition要高。
golm
将每一个分区形成一个数组,形成新的RDD类型RDD[Array[T]]
scala> val rdd = sc.parallelize(1 to 16,4)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[65] at parallelize at <console>:24
scala> rdd.glom().collect()
res25: Array[Array[Int]] = Array(Array(1, 2, 3, 4), Array(5, 6, 7, 8), Array(9, 10, 11, 12), Array(13, 14, 15, 16))
mapValues
针对(K,V)形式的类型只对V进行操作
scala> val rdd3 = sc.parallelize(Array((1,"a"),(1,"d"),(2,"b"),(3,"c")))
rdd3: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[67] at parallelize at <console>:24
scala> rdd3.mapValues(_+"|||").collect()
res26: Array[(Int, String)] = Array((1,a|||), (1,d|||), (2,b|||), (3,c|||))
subtract
计算差的一中函数去除两个RDD中相同的元素,不同的RDD保存下来
scala> val rdd = sc.parallelize(3 to 8)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[70] at parallelize at <console>:24
scala> val rdd1 = sc.parallelize(1 to 5)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[71] at parallelize at <console>:24
scala> rdd.subtract(rdd1).collect()
res27: Array[Int] = Array(8, 6, 7)
Action
reduce(func)
通过func函数聚集RDD中的所有元素,这个功能必须是可交换且可并联的
collect
在驱动程序中,以数组的形式返回数据集的所有元素
count()
返回RDD的元素个数
first
返回RDD的第一个元素(类似于take(1))
take(n)
返回一个由数据集的前n个元素组成的数组
tabkeSample(withReplacement,num, [seed])
返回一个数组,该数组由从数据集中随机采样的num个元素组成,可以选择是否用随机数替换不足的部分,seed用于指定随机数生成器种子
takeOrdered(n)
返回前几个的排序
aggregate(zeroValue: U)(seqOp: (U, T) ? U, combOp: (U, U) ? U)
aggregate函数将每个分区里面的元素通过seqOp和初始值进行聚合,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。
fold(num)(func)
折叠操作,aggregate的简化操作,seqop和combop一样。
saveAsTextFile(path)
将数据集的元素以textfile的形式保存到HDFS文件系统或者其他支持的文件系统,对于每个元素,Spark将会调用toString方法,将它装换为文件中的文本
saveAsSequenceFile(path)
将数据集中的元素以Hadoop sequencefile的格式保存到指定的目录下,可以使HDFS或者其他Hadoop支持的文件系统。
saveAsObjectFile(path)
用于将RDD中的元素序列化成对象,存储到文件中。
countByKey()
针对(K,V)类型的RDD,返回一个(K,Int)的map,表示每一个key对应的元素个数。
foreach(func)
在数据集的每一个元素上,运行函数func进行更新。
数值RDD统计操作
方法 | 含义 |
---|---|
count() | RDD中元素个数 |
mean() | 元素的平均值 |
sum() | 总和 |
max() | 最大值 |
min() | 最小值 |
variance() | 元素的平方差 |
sampleVariance() | 采样计算出平方差 |
stdev() | 标准差 |
sampleStdev() | 采样计算出标准差 |
读数据与保存数据的主要方式
文本文件输入输出
scala> sc.textFile("./README.md")
res6: org.apache.spark.rdd.RDD[String] = ./README.md MapPartitionsRDD[7] at textFile at <console>:25
scala> val readme = sc.textFile("./README.md")
readme: org.apache.spark.rdd.RDD[String] = ./README.md MapPartitionsRDD[9] at textFile at <console>:24
scala> readme.collect()
res7: Array[String] = Array(# Apache Spark, "", Spark is a fast and general cluster...
scala> readme.saveAsTextFile("hdfs://node01:8020/test")
JSON文件输入输出
scala> import org.json4s._
import org.json4s._
scala> import org.json4s.jackson.JsonMethods._
import org.json4s.jackson.JsonMethods._
scala> import org.json4s.jackson.Serialization
import org.json4s.jackson.Serialization
scala> var result = sc.textFile("examples/src/main/resources/people.json")
result: org.apache.spark.rdd.RDD[String] = examples/src/main/resources/people.json MapPartitionsRDD[7] at textFile at <console>:47
scala> implicit val formats = Serialization.formats(ShortTypeHints(List()))
formats: org.json4s.Formatsval dateFormat: org.json4s.DateFormat; val typeHints: org.json4s.TypeHints = org.json4s.Serialization$$anon$1@61f2c1da
scala> result.collect()
res3: Array[String] = Array("name":"Michael", "name":"Andy", "age":30, "name":"Justin", "age":19)
CSV文件输入输出
SequenceFile文件输入输出
scala> val data=sc.parallelize(List((2,"aa"),(3,"bb"),(4,"cc"),(5,"dd"),(6,"ee")))
data: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[16] at parallelize at <console>:24
scala> data.saveAsSequenceFile("hdfs://node01:8020/sequdata")
scala> val sdata = sc.sequenceFile[Int,String]("hdfs://node01:8020/sequdata/*")
sdata: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD[19] at sequenceFile at <console>:24
scala> sdata.collect()
res14: Array[(Int, String)] = Array((2,aa), (3,bb), (4,cc), (5,dd), (6,ee))
对象文件输入输出
scala> val data=sc.parallelize(List((2,"aa"),(3,"bb"),(4,"cc"),(5,"dd"),(6,"ee")))
data: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[20] at parallelize at <console>:24
scala> data.saveAsObjectFile("hdfs://node01:8020/objfile")
scala> import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDD
scala> val objrdd =sc.objectFile[(Int,String)]("hdfs://node01:8020/objfile/p*")
objrdd: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD[28] at objectFile at <console>:25
scala> objrdd.collect()
res20: Array[(Int, String)] = Array((2,aa), (3,bb), (4,cc), (5,dd), (6,ee))
Hadoop输入输出格式
scala> import org.apache.hadoop.io._
import org.apache.hadoop.io._
scala> val data = sc.parallelize(Array((30,"hadoop"), (71,"hive"), (11,"cat")))
data: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[47] at parallelize at <console>:35
scala> data.saveAsNewAPIHadoopFile("hdfs://node01:8020/output4/",classOf[LongWritable] ,classOf[Text] ,classOf[org.apache.hadoop.mapreduce.lib.output.TextOutputFormat[LongWritable, Text]])
文件系统的输入输出
Spark 支持读写很多种文件系统, 像本地文件系统、Amazon S3、HDFS等。
数据库的输入输出
mysql读取
def main (args: Array[String] )
val sparkConf = new SparkConf ().setMaster ("local[2]").setAppName ("JdbcApp")
val sc = new SparkContext (sparkConf)
val rdd = new org.apache.spark.rdd.JdbcRDD (
sc,
() =>
Class.forName ("com.mysql.jdbc.Driver").newInstance()
java.sql.DriverManager.getConnection ("jdbc:mysql://localhost:3306/rdd", "root", "hive")
,
"select * from rddtable where id >= ? and id <= ?;",
1,
10,
1,
r => (r.getInt(1), r.getString(2)))
println (rdd.count () )
rdd.foreach (println (_) )
sc.stop ()
MySQL写入
def main(args: Array[String])
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("HBaseApp")
val sc = new SparkContext(sparkConf)
val data = sc.parallelize(List("Female", "Male","Female"))
data.foreachPartition(insertData)
def insertData(iterator: Iterator[String]): Unit =
Class.forName ("com.mysql.jdbc.Driver").newInstance()
val conn = java.sql.DriverManager.getConnection("jdbc:mysql://localhost:3306/rdd", "root", "admin")
iterator.foreach(data =>
val ps = conn.prepareStatement("insert into rddtable(name) values (?)")
ps.setString(1, data)
ps.executeUpdate()
)
Hbase数据库读取
def main(args: Array[String])
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("HBaseApp")
val sc = new SparkContext(sparkConf)
val conf = HBaseConfiguration.create()
//HBase中的表名
conf.set(TableInputFormat.INPUT_TABLE, "fruit")
val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
classOf[org.apache.hadoop.hbase.client.Result])
val count = hBaseRDD.count()
println("hBaseRDD RDD Count:"+ count)
hBaseRDD.cache()
hBaseRDD.foreach
case (_, result) =>
val key = Bytes.toString(result.getRow)
val name = Bytes.toString(result.getValue("info".getBytes, "name".getBytes))
val color = Bytes.toString(result.getValue("info".getBytes, "color".getBytes))
println("Row key:" + key + " Name:" + name + " Color:" + color)
sc.stop()
Hbase数据库写入
def main(args: Array[String])
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("HBaseApp")
val sc = new SparkContext(sparkConf)
val conf = HBaseConfiguration.create()
val jobConf = new JobConf(conf)
jobConf.setOutputFormat(classOf[TableOutputFormat])
jobConf.set(TableOutputFormat.OUTPUT_TABLE, "fruit_spark")
val fruitTable = TableName.valueOf("fruit_spark")
val tableDescr = new HTableDescriptor(fruitTable)
tableDescr.addFamily(new HColumnDescriptor("info".getBytes))
val admin = new HBaseAdmin(conf)
if (admin.tableExists(fruitTable))
admin.disableTable(fruitTable)
admin.deleteTable(fruitTable)
admin.createTable(tableDescr)
def convert(triple: (Int, String, Int)) =
val put = new Put(Bytes.toBytes(triple._1))
put.addImmutable(Bytes.toBytes("info"), Bytes.toBytes("name"), Bytes.toBytes(triple._2))
put.addImmutable(Bytes.toBytes("info"), Bytes.toBytes("price"), Bytes.toBytes(triple._3))
(new ImmutableBytesWritable, put)
val initialRDD = sc.parallelize(List((1,"apple",11), (2,"banana",12), (3,"pear",13)))
val localData = initialRDD.map(convert)
localData.saveAsHadoopDataset(jobConf)
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