Spark与Cassandra输入/输出

Question

想象一下以下场景:Spark应用程序(Java实现)正在使用Cassandra数据库加载,转换为RDD并处理数据.该应用程序还从数据库中蒸出新数据,这些数据也由自定义接收器处理.流处理的输出存储在数据库中.该实现使用Spring Data Cassandra与数据库集成.

CassandraConfig:

@Configuration
@ComponentScan(basePackages = {"org.foo"})
@PropertySource(value = { "classpath:cassandra.properties" })
public class CassandraConfig {

    @Autowired
    private Environment env;

    @Bean
    public CassandraClusterFactoryBean cluster() {
        CassandraClusterFactoryBean cluster = new CassandraClusterFactoryBean();
        cluster.setContactPoints(env.getProperty("cassandra.contactpoints"));
        cluster.setPort(Integer.parseInt(env.getProperty("cassandra.port")));

        return cluster;
    }

    @Bean
    public CassandraMappingContext mappingContext() {
        return new BasicCassandraMappingContext();
    }

    @Bean
    public CassandraConverter converter() {
        return new MappingCassandraConverter(mappingContext());
    }

    @Bean
    public CassandraSessionFactoryBean session() throws Exception {
        CassandraSessionFactoryBean session = new CassandraSessionFactoryBean();
        session.setCluster(cluster().getObject());
        session.setKeyspaceName(env.getProperty("cassandra.keyspace"));
        session.setConverter(converter());
        session.setSchemaAction(SchemaAction.NONE);

        return session;
    }

    @Bean
    public CassandraOperations cassandraTemplate() throws Exception {
        return new CassandraTemplate(session().getObject());
    }

}

DataProcessor.main方法:

// Initialize spring application context
ApplicationContext applicationContext = new AnnotationConfigApplicationContext(CassandraConfig.class);
ApplicationContextHolder.setApplicationContext(applicationContext);
CassandraOperations cassandraOperations = applicationContext.getBean(CassandraOperations.class);
// Initialize spark context
SparkConf conf = new SparkConf().setAppName("test-spark").setMaster("local[2]");
JavaSparkContext sc = new JavaSparkContext(conf);

// Load data pages
List<Event> pagingResults = cassandraOperations.select("select * from event where event_type = 'event_type1' order by creation_time desc limit " + DATA_PAGE_SIZE, Event.class);
// Parallelize the first page
JavaRDD<Event> rddBuffer = sc.parallelize(pagingResults);

while(pagingResults != null && !pagingResults.isEmpty()) {
    Event lastEvent = pagingResults.get(pagingResults.size() - 1);
    pagingResults = cassandraOperations.select("select * from event where event_type = 'event_type1' and creation_time < " + lastEvent.getPk().getCreationTime() + " order by creation_time desc limit " + DATA_PAGE_SIZE, Event.class);
    // Parallelize page and add to the existing
    rddBuffer = rddBuffer.union(sc.parallelize(pagingResults));
}

// data processing
...

预计初始加载会有大量数据.因此,数据在rddBuffer中进行分页,加载和分发.

还有以下选项:

1. Spark-Cassandra示例(https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala),尽管文档数量最少对于这个例子.
2. Calliope项目(http://tuplejump.github.io/calliope/)

我想知道Spark与Cassandra集成的最佳实践是什么.在我的实施中,最好的选择是什么？

Apache Spark 1.0.0,Apache Cassandra 2.0.8

Answer 1

与卡桑德拉和火花的最简便的方法是使用官方的开源卡桑德拉驱动程序由DataStax开发星火:https://github.com/datastax/spark-cassandra-connector

这个驱动程序是在Cassandra Java Driver之上构建的,它提供了Cassandra和Spark之间的直接桥梁.与Calliope不同,它不使用Hadoop接口.此外,它还提供以下独特功能:

• 支持所有Cassandra数据类型,包括集合,开箱即用
• 将Cassandra行轻量映射到自定义类或元组,而无需在Scala中使用任何implicits或其他高级功能
• 将任何RDD保存到Cassandra
• 完全支持Cassandra虚拟节点
• 能够在服务器端过滤/选择,例如利用Cassandra集群列或二级索引