将数据帧中的org.apache.spark.mllib.linalg.Vector保存到Cassandra可以通过以下步骤实现:
<dependencies>
<!-- Spark dependencies -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>2.4.8</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>2.4.8</version>
</dependency>
<!-- Cassandra dependencies -->
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector_2.12</artifactId>
<version>2.5.1</version>
</dependency>
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector-java_2.12</artifactId>
<version>2.5.1</version>
</dependency>
</dependencies>
import org.apache.spark.sql.SparkSession;
SparkSession spark = SparkSession.builder()
.appName("Save Vector to Cassandra")
.master("local")
.config("spark.cassandra.connection.host", "your_cassandra_host")
.config("spark.cassandra.connection.port", "9042")
.getOrCreate();
请将"your_cassandra_host"替换为你的Cassandra主机地址。
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.functions;
// 加载数据帧
Dataset<Row> dataframe = spark.read().format("your_data_format").load("path_to_data");
// 将数据帧转换为RDD
Dataset<Vector> vectorRDD = dataframe.select("your_vector_column")
.filter(functions.col("your_vector_column").isNotNull())
.as(Encoders.kryo(Vector.class));
请将"your_data_format"替换为你的数据格式(如"csv"、"parquet"等),"path_to_data"替换为你的数据路径,"your_vector_column"替换为包含向量的列名。
import com.datastax.spark.connector.japi.CassandraJavaUtil;
CassandraJavaUtil.javaFunctions(vectorRDD.rdd())
.writerBuilder("your_keyspace", "your_table", CassandraJavaUtil.mapToRow(Vector.class))
.saveToCassandra();
请将"your_keyspace"替换为你的Cassandra键空间,"your_table"替换为你的表名。
以上步骤将数据帧中的org.apache.spark.mllib.linalg.Vector保存到Cassandra中。在这个过程中,你需要替换相关的参数和名称以适应你的实际情况。
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