本文整理汇总了Java中storm.kafka.trident.TridentKafkaUpdater类的典型用法代码示例。如果您正苦于以下问题：Java TridentKafkaUpdater类的具体用法？Java TridentKafkaUpdater怎么用？Java TridentKafkaUpdater使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
TridentKafkaUpdater类属于storm.kafka.trident包，在下文中一共展示了TridentKafkaUpdater类的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: buildTopology
import storm.kafka.trident.TridentKafkaUpdater; //导入依赖的package包/类
@Override
public StormTopology buildTopology(Config topologyConf) throws Exception {
IBatchSpout wordSpout = new FileBasedBatchSpout("words.txt", new Fields("word"), 10);
TridentTopology topology = new TridentTopology();
Stream wordsStream = topology.newStream("someWords", wordSpout);
TridentKafkaStateFactory stateFactory = TridentConnectorUtil.getTridentKafkaStateFactory(TOPIC_NAME, kafkaBrokerlist, "word", "word", topologyConf);
wordsStream.partitionPersist(stateFactory, new Fields("word"), new TridentKafkaUpdater(), new Fields()).parallelismHint(1);
JmsStateFactory jmsStateFactory = TridentConnectorUtil.getJmsStateFactory(jmsConnectionString, JMS_QUEUE_NAME);
wordsStream.partitionPersist(jmsStateFactory, new Fields("word"), new JmsUpdater(), new Fields()).parallelismHint(1);
Stream kafkaStream = topology.newStream("kafkaTridentSpout", TridentConnectorUtil.getTridentKafkaEmitter(zkConnString, TOPIC_NAME, topologyConf)).parallelismHint(1);
Stream jmsStream = topology.newStream("jmsTridentSpout", TridentConnectorUtil.getTridentJmsSpouts(jmsConnectionString, JMS_QUEUE_NAME, topologyConf, "words")).parallelismHint(1);
kafkaStream = kafkaStream.global().each(new Fields("str"), new TridentWordCount(), new Fields("word","count")).parallelismHint(1);
jmsStream = jmsStream.global().each(new Fields("words"), new TridentWordCount(), new Fields("word","count")).parallelismHint(1);
HBaseStateFactory hBaseStateFactory = TridentConnectorUtil.getTridentHbaseFactory(hbaseUrl, TABLE_NAME, "word", COLUMN_FAMILY, Lists.newArrayList("word"),
Lists.newArrayList("count"), topologyConf);
TridentState tridentState = jmsStream.global().partitionPersist(hBaseStateFactory, new Fields("word", "count"), new HBaseUpdater(), new Fields()).parallelismHint(1);
HdfsStateFactory tridentHdfsFactory = TridentConnectorUtil.getTridentHdfsFactory(hdfsUrl, HDFS_SRC_DIR, HDFS_ROTATION_DIR, "word", "count");
kafkaStream.global().partitionPersist(tridentHdfsFactory, new Fields("word", "count"), new HdfsUpdater(), new Fields()).parallelismHint(1);
CassandraStateFactory cassandraStateFactory = TridentConnectorUtil.getCassandraStateFactory(cassandraConnString, KEY_SPACE_NAME, "word", COLUMN_FAMILY, topologyConf);
Map<String, Class> fieldToTypeMap = Maps.newHashMap();
fieldToTypeMap.put("word", String.class);
fieldToTypeMap.put("count", Long.class);
SimpleCassandraTridentTupleMapper mapper = new SimpleCassandraTridentTupleMapper(KEY_SPACE_NAME, COLUMN_FAMILY, "word",fieldToTypeMap);
kafkaStream.global().partitionPersist(cassandraStateFactory, new Fields("word", "count"),
new CassandraUpdater(mapper), new Fields()).parallelismHint(1);
return topology.build();
}
示例2: main
import storm.kafka.trident.TridentKafkaUpdater; //导入依赖的package包/类
public static void main(String... args) throws AlreadyAliveException, InvalidTopologyException, AuthorizationException {
// starting to build topology
TridentTopology topology = new TridentTopology();
// Kafka as an opaque trident spout
OpaqueTridentKafkaSpout spout = new OpaqueTridentKafkaSpoutBuilder(Conf.zookeeper, Conf.inputTopic).build();
Stream stream = topology.newStream(kafkaSpout, spout);
// mapping transaction messages to pairs: (person,amount)
Stream atomicTransactions = stream.each(strF, Functions.mapToPersonAmount, personAmountF);
// bolt to println data
atomicTransactions.each(personAmountF, Functions.printlnFunction, emptyF);
// aggregating transactions and mapping to Kafka messages
Stream transactionsGroupped = atomicTransactions.groupBy(personF)
.persistentAggregate(new MemoryMapState.Factory(), amountF, new Sum(), sumF).newValuesStream()
.each(personSumF, Functions.mapToKafkaMessage, keyMessageF);
// Kafka as a bolt -- producing to outputTopic
TridentKafkaStateFactory stateFactory = new TridentKafkaStateFactory() //
.withKafkaTopicSelector(new DefaultTopicSelector(Conf.outputTopic)) //
.withTridentTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper<String, String>(key, message));
transactionsGroupped.partitionPersist(stateFactory, keyMessageF, new TridentKafkaUpdater(), emptyF);
// submitting topology to local cluster
new LocalCluster().submitTopology(kafkaAccountsTopology, topologyConfig(), topology.build());
// waiting a while, then running Kafka producer
Sleep.seconds(5);
KafkaProduceExample.start(20);
}