Spark with Cassandra input/output

问题

Picture the following senario: A Spark application (Java implementation) is using Cassandra database to load, convert to RDD and process the data. Also the application is steaming new data from the database which are also processed by a custom receiver. The output of the streaming process is stored in the database. The implementation is using Spring Data Cassandra from the integration with the database.

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 method:

// 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
...

It is expected to have a big amount of data for the initial loading. For this reason the data are paginated, loaded and distributed in rddBuffer.

There are also the following options available:

The Spark-Cassandra example (https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala), although there is minimum amount of documentation for this example.
The Calliope project (http://tuplejump.github.io/calliope/)

I would like to know what is the best practice for the integration of Spark with Cassandra. What would be the best option to follow in my implementation?

Apache Spark 1.0.0, Apache Cassandra 2.0.8

回答1:

The easiest way to work with Cassandra and Spark is to use the official open source Cassandra driver for Spark developed by DataStax: https://github.com/datastax/spark-cassandra-connector

This driver has been built on top of Cassandra Java Driver and provides a direct bridge between Cassandra and Spark. Unlike Calliope, it does not use the Hadoop interface. Additionally it offers the following unique features:

support for all Cassandra data types, including collections, out of the box
lightweight mapping of Cassandra rows to custom classes or tuples without the need to use any implicits or other advanced features in Scala
saving any RDDs to Cassandra
full support for Cassandra Virtual Nodes
ability to filter / select on the server side, e.g. leveraging Cassandra clustering columns or secondary indexes

回答2:

The approach in the code above is a classical centralized algorithm that would work only if executed in one node. Both Cassandra and Spark are distributed systems and therefore it's necessary to model the process in such a way that it can be distributed among a number of nodes.

There are few approaches possible: If you know the keys of the rows to fetch, you could do something simple like this: (using the DataStax Java Driver)

val data = sparkContext.parallelize(keys).map{key => 
   val cluster = val cluster = Cluster.builder.addContactPoint(host).build()
   val session  = cluster.connect(keyspace)
   val statement = session.prepare("...cql...);")
   val boundStatement = new BoundStatement(sttmt)
   session.execute(session.execute(boundStatement.bind(...data...)
}

This will effectively distribute the fetching of keys across the Spark Cluster. Note how the connection to C* is done within the closure as this ensures that the connection is established when the task is executed on each separate, distributed worker.

Given that your example uses a wildcard (i.e. the keys are not known), using the Hadoop interface of Cassandra is a good option. The Spark-Cassandra example linked in the question illustrates the use of this Hadoop interface on Cassandra.

Calliope is a library that encapsulates the complexity of using the Hadoop interface, by providing a simple API to access that functionality. It's available only in Scala, as it uses specific Scala features (like implicits and macros in the upcoming release) With Calliope, you basically declare how to convert your RDD[type] into a row key and row value, and Calliope takes care of configuring the hadoop interfaces to to the job. We have found that Calliope (and the underlying hadoop interfaces) are 2-4x faster than using a driver to interact with Cassandra.

Conclusion: I'd walk away from the Spring-Data configuration to access Cassandra, as this will limit you to a single node. Consider a simple parallelized access if possible or explore using Calliope in Scala.

来源：https://stackoverflow.com/questions/24451124/spark-with-cassandra-input-output

标签

java

cassandra

apache-spark

spring-data-cassandra