尝试Spark和Apache Hive的一些方法和功能。
1. Spark和countByValue函数
让我们遵循以下RDD值：
var rddVal = sc.parallelize（Array（1,2,2,3,4,4,5,5,5,6））; |
我们的任务是创建新的RDD，其中key将是rddVal中的唯一项值，value将是rddVal项出现的次数。
countByValue是很好的工具:
%spark var rddVal = sc.parallelize(Array(1,2,2,3,4,4,4,5,5,5,6)); val countedRDD = sc.parallelize(rddVal.countByValue().toSeq); countedRDD.collect(); |
zeppelin output:
rddVal: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at parallelize at <console>:29 countedRDD: org.apache.spark.rdd.RDD[(Int, Long)] = ParallelCollectionRDD[7] at parallelize at <console>:31 res2: Array[(Int, Long)] = Array((5,3), (1,1), (6,1), (2,2), (3,1), (4,3)) |
2. Spark和countByKey函数
有时我们有（密钥，值）RDD，我们想要计算所有密钥的出现次数。countByKey动作函数是一个很好的工具！
%spark
var rddKeyValues = sc.parallelize(Array(("A", 99), ("A",88), ("B",22), ("C",33)));
val countedKeys = sc.parallelize(rddKeyValues.countByKey().toSeq);
countedKeys.collect();
|
zeppelin output:
rddKeyValues: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[10] at parallelize at <console>:29 countedKeys: org.apache.spark.rdd.RDD[(String, Long)] = ParallelCollectionRDD[13] at parallelize at <console>:31 res4: Array[(String, Long)] = Array((B,1), (A,2), (C,1)) |
3. Spark和keyBy函数
如果您有值的RDD，你想在应用一个函数到每一个元素的，函数结果应该是新RDD的item key，keyBy功能是你的朋友。
%spark
def multiply(num: Int):Int={
return num*num;
}
val inputRDD = sc.parallelize(Array(1,2,3,4,5,6));
val resRDD = inputRDD.keyBy(multiply);
resRDD.collect();
|
zeppelin output:
multiply: (num: Int)Int inputRDD: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[14] at parallelize at <console>:29 resRDD: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[15] at keyBy at <console>:33 res5: Array[(Int, Int)] = Array((1,1), (4,2), (9,3), (16,4), (25,5), (36,6)) |
4. Apache Hive和切换到另一个数据库
%spark
import org.apache.spark.sql.hive.HiveContext;
val hc = new HiveContext(sc);
hc.sql("USE xademo");
hc.sql("SHOW TABLES").show();
|
zeppelin output:
import org.apache.spark.sql.hive.HiveContext hc: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@235d1d35 res11: org.apache.spark.sql.DataFrame = [result: string] +-------------------+-----------+ | tableName|isTemporary| +-------------------+-----------+ |call_detail_records| false| | customer_details| false| | genres| false| | justone| false| | mytable| false| | mytablexademo| false| | recharge_details| false| | workersxa| false| +-------------------+-----------+ |
5.数据帧分区
有时您被要求以某种特定格式将数据帧保存到HDFS中，并且您被迫使用动态分区。让我们来说明如何做到这一点：
输入文件workers.txt：
[root@sandbox ~]# cat workers.txt 1,Jerry,man,USA 2,Cathy,female,GBR 3,Teresa,female,GBR 4,Rut,female,USA 5,Roasie,female,AUS 6,Garry,man,GBR 7,Adam,man,GER 8,John,man,GBR 9,Jerremy,man,AUS 10,Angela,female,AUS 11,Ivanka,female,USA 12,Melania,female,USA |
Spark code:
%spark
// Dynamic partitioning when saving from Dataframe to HDFS
case class worker(id: Int, name: String, sex: String, country: String);
val fileRDD = sc.textFile("/tests/workers.txt");
val workerDF = fileRDD.map(line=>new worker(line.split(",")(0).toInt,
line.split(",")(1),
line.split(",")(2),
line.split(",")(3))).toDF();
// save dataframe also into Hive for further use
workerDF.saveAsTable("tableWorkers");
workerDF.write
.mode("overwrite")
.partitionBy("country")
.json("/tests/partition/result");
|
zeppelin output:
defined class worker fileRDD: org.apache.spark.rdd.RDD[String] = /tests/workers.txt MapPartitionsRDD[289] at textFile at <console>:114 workerDF: org.apache.spark.sql.DataFrame = [id: int, name: string, sex: string, country: string] warning: there were 1 deprecation warning(s); re-run with -deprecation for details |
HDFS 更有趣结果：
[root@sandbox ~]# hdfs dfs -ls /tests/partition/result Found 5 items -rw-r--r-- 1 zeppelin hdfs 0 2018-08-13 23:35 /tests/partition/result/_SUCCESS drwxr-xr-x - zeppelin hdfs 0 2018-08-13 23:35 /tests/partition/result/country=AUS drwxr-xr-x - zeppelin hdfs 0 2018-08-13 23:35 /tests/partition/result/country=GBR drwxr-xr-x - zeppelin hdfs 0 2018-08-13 23:35 /tests/partition/result/country=GER drwxr-xr-x - zeppelin hdfs 0 2018-08-13 23:35 /tests/partition/result/country=USA <p>[root@sandbox ~]# |
Spark为每个分区包含分组数据的每个分区创建了文件夹（例如）：
[root@sandbox ~]# hdfs dfs -cat /tests/partition/result/country=USA/part-r-00000-9adc651a-1260-466d-ba37-720a0395d450
{"id":1,"name":"Jerry","sex":"man"}
{"id":4,"name":"Rut","sex":"female"}
|
6.将分区的HDFS数据读回数据帧
%spark
val backDFJson = sqlContext.read.json("/tests/partition/result");
backDFJson.show();
|
zeppelin output:
backDFJson: org.apache.spark.sql.DataFrame = [id: bigint, name: string, sex: string, country: string] +---+-------+------+-------+ | id| name| sex|country| +---+-------+------+-------+ | 1| Jerry| man| USA| | 4| Rut|female| USA| | 11| Ivanka|female| USA| | 12|Melania|female| USA| | 5| Roasie|female| AUS| | 9|Jerremy| man| AUS| | 10| Angela|female| AUS| | 7| Adam| man| GER| | 2| Cathy|female| GBR| | 3| Teresa|female| GBR| | 6| Garry| man| GBR| | 8| John| man| GBR| +---+-------+------+-------+ |
7. Apache Hive和ORC表的动态分区
Apache Hive支持两种分区：
- 静态分区
- 动态分区
有关更多信息，我可以推荐以下博客。基本区别在于，当您将数据保存到静态分区表时，您必须使用区分分区的值来命名分区列。我是动态分区的情况，如果不存在则创建分区。不需要任何值，只需分区列。
测试任务：让我们创建按country动态分区的工作人员ORC表。并从之前创建的表“tableWorkers”中将数据保存到其中。
%spark
// dynamic partitioning on Hive Table...
import org.apache.spark.sql.hive.HiveContext;
var hc = new HiveContext(sc);
hc.sql(" DROP TABLE IF EXISTS WorkersPartitioned ");
hc.sql(" CREATE TABLE WorkersPartitioned(id INT, name String, sex String) "+
" PARTITIONED BY (country STRING) "+
" STORED AS ORC "
);
hc.sql(" SET set hive.exec.dynamic.partition=true ");
hc.sql(" SET hive.exec.dynamic.partition.mode=nonstric ");
hc.sql(" INSERT OVERWRITE TABLE WorkersPartitioned PARTITION(country) SELECT id, name, sex, country FROM tableWorkers ");
hc.sql(" SELECT * FROM WorkersPartitioned ").show();
|
注意代码“PARTITION（country）”，我们不需要输入确切的country，即动态分区。
Zeppelin output:
res165: org.apache.spark.sql.DataFrame = [key: string, value: string] res166: org.apache.spark.sql.DataFrame = [] +---+-------+------+-------+ | id| name| sex|country| +---+-------+------+-------+ | 5| Roasie|female| AUS| | 9|Jerremy| man| AUS| | 10| Angela|female| AUS| | 2| Cathy|female| GBR| | 3| Teresa|female| GBR| | 6| Garry| man| GBR| | 8| John| man| GBR| | 7| Adam| man| GER| | 1| Jerry| man| USA| | 4| Rut|female| USA| | 11| Ivanka|female| USA| | 12|Melania|female| USA| +---+-------+------+-------+ |
8. Apache Hive在表格中描述分区
import org.apache.spark.sql.hive.HiveContext;
val hc = new HiveContext(sc);
hc.sql("show partitions WorkersPartitioned").show();
|
Zeppelin output:
import org.apache.spark.sql.hive.HiveContext hc: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@2a117cf4 +-----------+ | result| +-----------+ |country=AUS| |country=GBR| |country=GER| |country=USA| +-----------+ |
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