[英]How to convert DataFrame to Dataset in Apache Spark in Java?
[英]Parse CSV as DataFrame/DataSet with Apache Spark and Java
我是新来的火花,我想使用group-by&reduce从CSV中找到以下内容(使用一行):
Department, Designation, costToCompany, State
Sales, Trainee, 12000, UP
Sales, Lead, 32000, AP
Sales, Lead, 32000, LA
Sales, Lead, 32000, TN
Sales, Lead, 32000, AP
Sales, Lead, 32000, TN
Sales, Lead, 32000, LA
Sales, Lead, 32000, LA
Marketing, Associate, 18000, TN
Marketing, Associate, 18000, TN
HR, Manager, 58000, TN
我想通过Department,Designation,State简化包含sum(costToCompany)和TotalEmployeeCount的附加列的CSV
应得到如下结果:
Dept, Desg, state, empCount, totalCost
Sales,Lead,AP,2,64000
Sales,Lead,LA,3,96000
Sales,Lead,TN,2,64000
有没有办法使用转换和动作来实现这一点。 或者我们应该进行RDD操作?
创建一个类(模式)来封装你的结构(它不是方法B所必需的,但如果你使用Java,它会使你的代码更容易阅读)
public class Record implements Serializable { String department; String designation; long costToCompany; String state; // constructor , getters and setters }
加载CVS(JSON)文件
JavaSparkContext sc; JavaRDD<String> data = sc.textFile("path/input.csv"); //JavaSQLContext sqlContext = new JavaSQLContext(sc); // For previous versions SQLContext sqlContext = new SQLContext(sc); // In Spark 1.3 the Java API and Scala API have been unified JavaRDD<Record> rdd_records = sc.textFile(data).map( new Function<String, Record>() { public Record call(String line) throws Exception { // Here you can use JSON // Gson gson = new Gson(); // gson.fromJson(line, Record.class); String[] fields = line.split(","); Record sd = new Record(fields[0], fields[1], fields[2].trim(), fields[3]); return sd; } });
此时您有两种方法:
注册表(使用您定义的Schema类)
JavaSchemaRDD table = sqlContext.applySchema(rdd_records, Record.class); table.registerAsTable("record_table"); table.printSchema();
使用所需的Query-group-by查询表
JavaSchemaRDD res = sqlContext.sql(" select department,designation,state,sum(costToCompany),count(*) from record_table group by department,designation,state ");
在这里,您还可以使用SQL方法执行您想要的任何其他查询
使用复合键映射: Department
, Designation
, State
JavaPairRDD<String, Tuple2<Long, Integer>> records_JPRDD = rdd_records.mapToPair(new PairFunction<Record, String, Tuple2<Long, Integer>>(){ public Tuple2<String, Tuple2<Long, Integer>> call(Record record){ Tuple2<String, Tuple2<Long, Integer>> t2 = new Tuple2<String, Tuple2<Long,Integer>>( record.Department + record.Designation + record.State, new Tuple2<Long, Integer>(record.costToCompany,1) ); return t2; }
});
reduceByKey使用复合键, costToCompany
列,并按键累计记录数
JavaPairRDD<String, Tuple2<Long, Integer>> final_rdd_records = records_JPRDD.reduceByKey(new Function2<Tuple2<Long, Integer>, Tuple2<Long, Integer>, Tuple2<Long, Integer>>() { public Tuple2<Long, Integer> call(Tuple2<Long, Integer> v1, Tuple2<Long, Integer> v2) throws Exception { return new Tuple2<Long, Integer>(v1._1 + v2._1, v1._2+ v2._2); } });
可以使用Spark内置CSV阅读器解析CSV文件 。 它将在成功读取文件时返回DataFrame / DataSet。 在DataFrame / DataSet之上,您可以轻松应用类似SQL的操作。
spark
import org.apache.spark.sql.SparkSession;
SparkSession spark = SparkSession
.builder()
.appName("Java Spark SQL Example")
.getOrCreate();
StructType
为行创建架构 import org.apache.spark.sql.types.StructType;
StructType schema = new StructType()
.add("department", "string")
.add("designation", "string")
.add("ctc", "long")
.add("state", "string");
Dataset<Row> df = spark.read()
.option("mode", "DROPMALFORMED")
.schema(schema)
.csv("hdfs://path/input.csv");
1. SQL方式
在spark sql metastore中注册表以执行SQL操作
df.createOrReplaceTempView("employee");
在已注册的数据帧上运行SQL查询
Dataset<Row> sqlResult = spark.sql( "SELECT department, designation, state, SUM(ctc), COUNT(department)" + " FROM employee GROUP BY department, designation, state"); sqlResult.show(); //for testing
2.对象链接或编程或类似Java的方式
为sql函数执行必要的导入
import static org.apache.spark.sql.functions.count; import static org.apache.spark.sql.functions.sum;
在
groupBy
/ dataset上使用groupBy
和agg
对数据执行count
和sum
Dataset<Row> dfResult = df.groupBy("department", "designation", "state") .agg(sum("ctc"), count("department")); // After Spark 1.6 columns mentioned in group by will be added to result by default dfResult.show();//for testing
"org.apache.spark" % "spark-core_2.11" % "2.0.0"
"org.apache.spark" % "spark-sql_2.11" % "2.0.0"
以下可能不完全正确,但它应该让您了解如何处理数据。 它不漂亮,应该用case类等替换,但作为如何使用spark api的一个简单例子,我希望它足够了:)
val rawlines = sc.textfile("hdfs://.../*.csv")
case class Employee(dep: String, des: String, cost: Double, state: String)
val employees = rawlines
.map(_.split(",") /*or use a proper CSV parser*/
.map( Employee(row(0), row(1), row(2), row(3) )
# the 1 is the amount of employees (which is obviously 1 per line)
val keyVals = employees.map( em => (em.dep, em.des, em.state), (1 , em.cost))
val results = keyVals.reduceByKey{ a,b =>
(a._1 + b._1, b._1, b._2) # (a.count + b.count , a.cost + b.cost )
}
#debug output
results.take(100).foreach(println)
results
.map( keyval => someThingToFormatAsCsvStringOrWhatever )
.saveAsTextFile("hdfs://.../results")
或者您可以使用SparkSQL:
val sqlContext = new SQLContext(sparkContext)
# case classes can easily be registered as tables
employees.registerAsTable("employees")
val results = sqlContext.sql("""select dep, des, state, sum(cost), count(*)
from employees
group by dep,des,state"""
对于JSON,如果您的文本文件每行包含一个JSON对象,则可以使用sqlContext.jsonFile(path)
让Spark SQL将其作为SchemaRDD
(将自动推断该模式)。 然后,您可以将其注册为表并使用SQL进行查询。 您还可以手动将文本文件加载为每个记录包含一个JSON对象的RDD[String]
,并使用sqlContext.jsonRDD(rdd)
将其作为SchemaRDD
。 当您需要预处理数据时, jsonRDD
非常有用。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.