java如何设置独立的Spark配置以在本地运行MLlib Spark示例?
我想在我的PC上本地运行Spark MLlib examples(我认为它是独立的)。我想运行JavaWord2VecExample。java。这个文件配置是为在一些只有一个主服务器的workers上运行Spark的会话设置的,但我只想在我的PC(本地)上运行这个类。原始类源代码如下:
package org.apache.spark.examples.ml;
// $example on$
import java.util.Arrays;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.ml.feature.Word2Vec;
import org.apache.spark.ml.feature.Word2VecModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// $example off$
public class JavaWord2VecExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaWord2VecExample")
.getOrCreate();
// $example on$
// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
);
StructType schema = new StructType(new StructField[]{
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> documentDF = spark.createDataFrame(data, schema);
// Learn a mapping from words to Vectors.
Word2Vec word2Vec = new Word2Vec()
.setInputCol("text")
.setOutputCol("result")
.setVectorSize(3)
.setMinCount(0);
Word2VecModel model = word2Vec.fit(documentDF);
Dataset<Row> result = model.transform(documentDF);
for (Row row : result.collectAsList()) {
List<String> text = row.getList(0);
Vector vector = (Vector) row.get(1);
System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
}
// $example off$
List<String> text = row.getList(0);
Vector vector = (Vector) row.get(1);
System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
spark.stop();
}
}
我知道,如果我想在本地PC上运行示例,我应该将SparkConf替换为SparkSession。所以,我尝试过,目前的源代码是:
package org.apache.spark.examples.ml;
// $example on$
import java.util.Arrays;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.ml.feature.Word2Vec;
import org.apache.spark.ml.feature.Word2VecModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// $example off$
public class JavaWord2VecExample {
public static void main(String[] args) {
SparkConf spark = new SparkConf()
.setAppName("JavaWord2VecExample")
.set("spark.storage.memoryFraction", "1")
.setMaster("spark://master:7077");
// $example on$
// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
);
StructType schema = new StructType(new StructField[]{
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> documentDF = spark.createDataFrame(data, schema);
// Learn a mapping from words to Vectors.
Word2Vec word2Vec = new Word2Vec()
.setInputCol("text")
.setOutputCol("result")
.setVectorSize(3)
.setMinCount(0);
Word2VecModel model = word2Vec.fit(documentDF);
Dataset<Row> result = model.transform(documentDF);
for (Row row : result.collectAsList()) {
List<String> text = row.getList(0);
Vector vector = (Vector) row.get(1);
System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
}
// $example off$
List<String> text = row.getList(0);
Vector vector = (Vector) row.get(1);
System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
spark.stop();
}
}
因此,出现了一些错误:
Error: java:cannot find symbol
对于方法createDataFrame()和stop()
我是java和Spark的新手。请帮助我修复这些错误。 谢谢你的回答
共 (0) 个答案