storm的基本概念别人总结的，
https://blog.csdn.net/pickinfo/article/details/50488226
编程模型最关键最难就是实现局部聚合的业务逻辑
聚合类实现Aggregator接口重写方法aggregate，聚合使用存储中间聚合过程状态的类，本地hashmap的去重逻辑
还有加入redis后进行的一些去重操作，数据的持久（判断三天内的带播控量）
public class SaleSum implements Aggregator<SaleSumState> {
private Logger logger = org.slf4j.LoggerFactory.getLogger(SaleSum.class);
/**
*
*/
private static final long serialVersionUID = -6879728480425771684L;
private int partitionIndex ;
@Override
public SaleSumState init(Object batchId, TridentCollector collector) {
return new SaleSumState();
}
@Override
public void aggregate(SaleSumState val, TridentTuple tuple, TridentCollector collector) {
double oldSum=val.saleSum;
double price=tuple.getDoubleByField("price");
double newSum=oldSum+price;
val.saleSum=newSum;
}
@Override
public void complete(SaleSumState val, TridentCollector collector) {
collector.emit(new Values(val.saleSum));
}
@Override
public void prepare(Map conf, TridentOperationContext context) {
}
@Override
public void cleanup() {
}
}
public class TridentDemo {
public static final String SPOUT_ID = "kafak_spout";
public static void main(String[] args) {
1、创建一个strom此程序的topology 为TridentTopology
TridentTopology topology = new TridentTopology();
2、连接kafka的三要素：zk地址:port topic
//1.从kafak读取数据，
//只会被成功处理 一次 ，有且只有此一次 提供容错机制 处理失败会在后续的批次进行提交
BrokerHosts zkHost = new ZkHosts("hadoop01:2181,hadoop02:2181,hadoop03:2181");
TridentKafkaConfig kafkaConfig = new TridentKafkaConfig(zkHost, "test");//两种构造器
定义从哪消费相当于spark中earliest与largest
kafkaConfig.startOffsetTime = kafka.api.OffsetRequest.LatestTime();
kafkaConfig.scheme = new SchemeAsMultiScheme(new StringScheme());
//透明事务kafka的spout
OpaqueTridentKafkaSpout kafkaSpout = new OpaqueTridentKafkaSpout(kafkaConfig);
//严格模式的事务级别
TransactionalTridentKafkaSpout kafkaSpout1 = new TransactionalTridentKafkaSpout(kafkaConfig);
//普通的kafak级别 {"str","msg"}
//严格的kafak级别 {"str","msg"，上一批次的值}
Stream stream = topology.newStream(SPOUT_ID, kafkaSpout);
// stream.each(new Fields("str"),new PrintTestFilter2());
3.进行日志数据的解析，自定义解析类实现了Funtion接口，重写execute方法进行字段解析，在发送出来collector.emit(new Values(timestamp,yyyyMMddStr,yyyyMMddHHStr,yyyyMMddHHmmStr,consumer,productName,price，country,province,city));
进去的字段名定义为"str",出来的解析字段分别定义了字段名 ，后续做打印测试
Stream hasPraseSteam = stream.each(new Fields("str"), new ParseFunction(), new Fields("timeStamp", "yyyyMMddStr", "yyyyMMddHHStr", "yyyyMMddHHmmStr", "consumer", "productNmae", "price", "country", "provence", "city"));
// .each(new Fields("str", "timeStamp", "yyyyMMddStr", "yyyyMMddHHStr", "yyyyMMddHHmmStr", "consumer", "productNmae", "price", "country", "provence", "city"), new PrintTestFilter2());
4.进行一个同时进行次数与求和统计的例子，storm是一个服务器节点多个work（jvm），一个work中的task执行自己spout，bolt任务
trident中最重要的地方就是自定义聚合的实现（SaleSum类）,常常是实现业务逻辑的地方，规定如何进行数据的聚合， 进行的是各个分区的局部聚合
//1. 对每天电商的销售额
//去掉用不到的自地段 保留需要用到的字段
//分区统计的流
Stream partitionStatStream = hasPraseSteam.project(new Fields("yyyyMMddStr", "price"))
.shuffle()
.groupBy(new Fields("yyyyMMddStr"))
.chainedAgg()
.partitionAggregate(new Fields("price"), new SaleSum(), new Fields("saleTotalpartByDay")) //进行同一批次各个分区的局部销售额统计
.partitionAggregate(new Fields("price"), new Count(), new Fields("oderNumOfpartDay"))//同一批次中各个分区的订单数
.chainEnd()
.toStream()
.parallelismHint(2);
5. //全局统计 每天的总销售额进行 进行分组全局聚合一般的 顺序=============先进行分区统计，在进行全局统计（相当于hadoop的combine与spark中reducebykey）
TridentState saleGlobalState = partitionStatStream.groupBy(new Fields("yyyyMMddStr"))
.persistentAggregate(new MemoryMapState.Factory(), new Fields("saleTotalpartByDay"), new Sum(), new Fields("saleGlobalAmtDay"));
//测试
saleGlobalState.newValuesStream().each(new Fields("yyyyMMddStr", "saleGlobalAmtDay"), new PrintTestFilter2());
//全局统计 每天的订单总数
TridentState oderGlobalState = partitionStatStream.groupBy(new Fields("yyyyMMddStr"))
.persistentAggregate(new MemoryMapState.Factory(), new Fields("oderNumOfpartDay"), new Sum(), new Fields("oderGlobalAmtDay"));
oderGlobalState.newValuesStream().each(new Fields("yyyyMMddStr", "oderGlobalAmtDay"), new PrintTestFilter2());
//2.给与地域时段 维度 统计
// "timeStamp","yyyyMMddStr","yyyyMMddHHStr","yyyyMMddHHmmStr","consumer","productNmae","price","country","provence","city"
TridentState state = hasPraseSteam.project(new Fields("yyyyMMddHHStr", "price", "country", "provence", "city"))
.each(new Fields("yyyyMMddHHStr", "country", "provence", "city"), new ContactKey(), new Fields("addrAndHour"))
// .project()
.groupBy(new Fields("addrAndHour"))
.persistentAggregate(new MemoryMapState.Factory(), new Fields("price"), new Sum(), new Fields("saleAmtOfAddrAndHour"));
//测试
state.newValuesStream().each(new Fields("addrAndHour"), new PrintTestFilter2());
//3.使用hbase存入 结果状态
/**rowkey
* value
* 非实物 ：就简单存储一个value
* 严格的事实控制： 存储: batchId和统计值
* 透明事务控制 ： batchId和统计值和上个批次的统计值
*/
HBaseMapState.Options<OpaqueValue> opts=new HBaseMapState.Options<OpaqueValue>();
opts.tableName="test";
opts.columnFamily="info";
//1.1以后设置列名使用下面类
TridentHBaseMapMapper mapMapper= new SimpleTridentHBaseMapMapper("saleAmtOfAddrAndHour");
opts.mapMapper = mapMapper;
StateFactory Hbasefactory=HBaseMapState.opaque(opts);
6.进行hbase存储，storm在给apache后，（1.0版本？后）已经实现与hbase的集成接口，事物类型要与topoloy一致
persistentAggregate为最终的持久化函数，存储可以为内存/hbase,返回值为tridentState
// HBaseMapState.Options<Object> opts=new HBaseMapState.Options<Object>();
// opts.tableName="test";
// opts.columnFamily="info";
// //1.1以后设置列名使用下面类，存入hbase的列名
// TridentHBaseMapMapper mapMapper= new SimpleTridentHBaseMapMapper("saleAmtOfAddrAndHour");
// opts.mapMapper = mapMapper;
// StateFactory Hbasefactory1=HBaseMapState.nonTransactional(opts);
TridentState HbaseState = hasPraseSteam.project(new Fields("yyyyMMddHHStr", "price", "country", "provence", "city"))
.each(new Fields("yyyyMMddHHStr", "country", "provence", "city"), new ContactKey(), new Fields("addrAndHour"))
// .project()
.groupBy(new Fields("addrAndHour"))
.persistentAggregate(Hbasefactory, new Fields("price"), new Sum(), new Fields("saleAmtOfAddrAndHour"));
//进行drpc查询
LocalDRPC localDRPC = new LocalDRPC();
topology.newDRPCStream("saleAmtOfDay", localDRPC)
.each(new Fields("args"), new SplitFunction1(), new Fields("requestDate"))
.stateQuery(saleGlobalState, new Fields("requestDate"), new MapGet(),
new Fields("saleGlobalAmtOfDay1"))
.project(new Fields("requestDate", "saleGlobalAmtOfDay1"))
.each(new Fields("saleGlobalAmtOfDay1"), new FilterNull())
// .each(new Fields("requestDate", "saleGlobalAmtOfDay1"), new PrintTestFilter2())
;
topology.newDRPCStream("numOrderOfDay", localDRPC)
.each(new Fields("args"), new SplitFunction1(), new Fields("requestDate"))
.stateQuery(oderGlobalState, new Fields("requestDate"), new MapGet(),
new Fields("numOrderGlobalOfDay1"))
.project(new Fields("requestDate", "numOrderGlobalOfDay1"))
.each(new Fields("numOrderGlobalOfDay1"), new FilterNull())
;
topology.newDRPCStream("saleTotalAmtOfAddrAndHour", localDRPC)
.each(new Fields("args"), new SplitFunction1(), new Fields("requestAddrAndHour"))
.stateQuery(HbaseState, new Fields("requestAddrAndHour"),
new MapGet(), new Fields("saleTotalAmtOfAddrAndHour"))
.project(new Fields("requestAddrAndHour", "saleTotalAmtOfAddrAndHour"))
.each(new Fields("saleTotalAmtOfAddrAndHour"), new FilterNull())
;
7.提交本地还是集群运行，drpc可以进行对持久化后的state进行数据查询
Config conf = new Config();
if (args == null || args.length <= 0) {
// 本地测试
LocalCluster localCluster = new LocalCluster();
// topology名称唯一
localCluster.submitTopology("odeR", conf, topology.build());
while (true) {
try {
Thread.sleep(10000);
} catch (InterruptedException e) {
e.printStackTrace();
}
String saleAmtResult =
localDRPC.execute("saleAmtOfDay", "20160828 20160827");
System.err.println("saleAmtResult=" + saleAmtResult);
String numberOrderResult =
localDRPC.execute("numOrderOfDay", "20160828 20160827");
System.err.println("numberOrderResult=" + numberOrderResult);
String saleTotalAmtOfAddrAndHourRessult =
localDRPC.execute("saleTotalAmtOfAddrAndHour", "苏州_江苏_中国_2016082815");
System.err.println(saleTotalAmtOfAddrAndHourRessult);
}
} else {
try {
StormSubmitter.submitTopology(args[0], conf, topology.build());
} catch (AlreadyAliveException e) {
e.printStackTrace();
} catch (InvalidTopologyException e) {
e.printStackTrace();
} catch (AuthorizationException e) {
e.printStackTrace();
}
}
}
}