使用java、scala、python三种语言实现spark rdd的flatmap算子的使用
时间: 2024-06-11 07:09:32 浏览: 13
Java实现:
```java
JavaRDD<String> lines = sc.textFile("path/to/file");
JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String s) {
return Arrays.asList(s.split(" "));
}
});
```
Scala实现:
```scala
val lines = sc.textFile("path/to/file")
val words = lines.flatMap(line => line.split(" "))
```
Python实现:
```python
lines = sc.textFile("path/to/file")
words = lines.flatMap(lambda line: line.split(" "))
```
相关问题
使用java、scala、python三种语言实现spark rdd的reduce算子的使用
Java:
```
JavaRDD<Integer> numbers = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
int sum = numbers.reduce((a, b) -> a + b);
System.out.println(sum);
```
Scala:
```
val numbers = sc.parallelize(Seq(1, 2, 3, 4, 5))
val sum = numbers.reduce(_ + _)
println(sum)
```
Python:
```
numbers = sc.parallelize([1, 2, 3, 4, 5])
sum = numbers.reduce(lambda a, b: a + b)
print(sum)
```
用java,scala,python三种语言实现spark rdd的glom算子
Java实现:
```java
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import scala.Tuple2;
import java.util.ArrayList;
import java.util.List;
public class GlomExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("GlomExample").setMaster("local");
JavaSparkContext sc = new JavaSparkContext(conf);
List<Integer> data = new ArrayList<>();
for (int i = 1; i <= 10; i++) {
data.add(i);
}
JavaRDD<Integer> rdd = sc.parallelize(data, 2);
JavaRDD<List<Integer>> glomRdd = rdd.glom();
List<List<Integer>> result = glomRdd.collect();
for (int i = 0; i < result.size(); i++) {
System.out.println("Partition " + i + ": " + result.get(i));
}
sc.stop();
}
}
```
Scala实现:
```scala
import org.apache.spark.{SparkConf, SparkContext}
object GlomExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("GlomExample").setMaster("local")
val sc = new SparkContext(conf)
val data = 1 to 10
val rdd = sc.parallelize(data, 2)
val glomRdd = rdd.glom()
val result = glomRdd.collect()
for (i <- result.indices) {
println(s"Partition $i: ${result(i).toList}")
}
sc.stop()
}
}
```
Python实现:
```python
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("GlomExample").setMaster("local")
sc = SparkContext(conf=conf)
data = range(1, 11)
rdd = sc.parallelize(data, 2)
glom_rdd = rdd.glom()
result = glom_rdd.collect()
for i in range(len(result)):
print(f"Partition {i}: {list(result[i])}")
sc.stop()
```