storm trident 的介绍与使用
一.trident 的介绍
trident 的英文意思是三叉戟，在这里我的理解是因为之前我们通过之前的学习topology spout bolt 去处理数据是没有问题的，但trident 的对spout bolt 更高层次的一个抽象，其实现功能是一样的，只不过是trident做了更多的优化和封装.如果对处理的性能要求比较高，建议要采用spout bolt 来处理，反之则可以用trident
trident 你可以这样理解，本身这个拓扑就是分散的，如果一个spout 可以有2个bolt，跟三叉戟比较像。(个人理解)
因为trident是对storm 更高一层的抽象，它与之前学的spout bolt处理数据流的方式不一样，trident 是以batch(一组tuples)为单位进行处理的。
二.trident API操作
trident采用批处理的方式来处理数据，其API的操作是对数据处理的方式改成了函数。对数据处理的操作有：filter sum aggregator等
function函数的操作都是对流中的tuple进行操作的
下面介绍 trident 常用的API
.each(Fields inputFields, Filter filter）
作用：操作batch中的每一个tuple内容，一般与Filter或者Function函数配合使用。 .peek(Consumer action）
作用：不作任务操作，传的参数是consumer，类似于System.out.println .partitionBy(Fields fields)
作用：将tuples中的数据按设置的字段重定向到下一处理逻辑，设置相同字段的tuple一定会被分配到同一个线程中处理。
三.trident 的常用函数
.FilterFunction 过滤
作用：对一组batch 中的tuple数据过滤 实现过程是：自定义类实现BaseFilter接口，重写isKeep()方法，在each()方法中使用自定义的类即可 .SumFunction 求和
作用：对流中的数据进行加减
实现过程：自定义类实现BaseFunction接口，重写execute方法，在each()方法中使用
.MapFunction (一对一函数)
作用： 对一个tuple进行自定义操作
实现过程：自定义类实现MapFunction接口，重写execute()方法，通过map()方法使用
.ProjectionFunction (投影函数)
作用：投影函数，只保留stream中指定字段的数据。
实现过程：在project()方法中定义所需字段即可
例：有一个Stream包含如下字段： ["x","y","z"]，使用投影： project(new Fields("y", "z")) 则输出的流仅包含 ["y","z"]字段 .repatition(重定向)
作用：重定向是指tuple通过下面哪种方式路由到下一层 shuffle： 通过随机分配算法来均衡tuple到各个分区 broadcast： 每个tuple都被广播到所有的分区，这种方式在drcp时非常有用，比如在每个分区上做stateQuery partitionBy：根据指定的字段列表进行划分，具体做法是用指定字段列表的hash值对分区个数做取模运算，确保相同字段列表的数据被划分到同一个分区 global： 所有的tuple都被发送到这个分区上，这个分区用来处理整个Stream的tuple数据的，但这个线程是独立起的 batchGlobal：一个batch中的tuple都被发送到同一个分区，不同的batch会去往不同的分区 partition： 通过一个自定义的分区函数来进行分区，这个自定义函数实现了 backtype.storm.grouping.CustomStreamGrouping
.Aggregation(聚合)
在storm的trident中处理数据是以批的形式进行处理的，所以在聚合时也是对批量内的数据进行的。经过aggregation的tuple，是被改变了原有的数据状态 在Aggregator接口中有3个方法需要实现 init() : 当batch接收到数据时执行。并对tuple中的数据进行初始化
aggregate(): 在接收到batch中的每一个tuple时执行，该方法一个重定向方法，它会随机启动一个单独的线程来进行聚合操作
complete() : 在一个batch的结束时执行
它是对当前partition上的各个batch执行聚合操作，它不是一个重定向操作，即统计batch上的tuple的操作
6.2 aggregator 对一批batch中的tuple数据进行聚合
6.3 ReduceAggregator
对一批batch中第n个元素的操作
对一批batch中的tuple进行聚合操作，它是一个重定向操作
持久化聚合器，在聚合之前先将数据存到一个位置，然后再对数据进行聚合操作
6.6 AggregateChina
聚合链，对一批batch 中的tuple进行多条件聚合操作
7.GroupBy
GroupBy会根据指定字段，把整个stream切分成一个个grouped stream，如果在grouped stream上做聚合操作，那么聚合就会发生在这些grouped stream上而不是整个batch。 如果groupBy后面跟的是aggregator，则是聚合操作，如果跟的是partitionAggregate，则不是聚合操作。
四.trident常用函数示例
1.FilterFunction
需求：在 一组数据中，过滤出第1个值与第2个值相加的值是偶数的
public class FilterTrident {
private static final Logger LOG = LoggerFactory.getLogger(FilterTrident.class);
@SuppressWarnings("unchecked")
public static void main(String[] args) throws InterruptedException {
FixedBatchSpout spout = new FixedBatchSpout(new Fields("a","b","c","d"), 3,
new Values(1,4,7,10),
new Values(1,1,3,11),
new Values(2,2,7,1),
new Values(2,5,7,2));
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(4);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
// peek: 不做任务操作，因为参数的consumer
// each：spout中的指定元素进行操作
topology.newStream("filter", spout).parallelismHint(1)
.localOrShuffle()
.peek(input -> LOG.info("peek1 ================{},{},{},{}",input.get(0),input.get(1),input.get(2),input.get(3)))
.parallelismHint(2)
.localOrShuffle()
.each(new Fields("a","b"),new CheckEvenSumFilter())
.parallelismHint(2)
.localOrShuffle()
.peek(input -> LOG.info("peek2 +++++++++++++++++++{},{},{},{}",
input.getIntegerByField("a"),input.getIntegerByField("b"),
input.getIntegerByField("c"),input.getIntegerByField("d"))
).parallelismHint(1);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("FilterTrident", conf, topology.build());
LOG.warn("==================================================");
LOG.warn("the LocalCluster topology {} is submitted.","FilterTrident");
LOG.warn("==================================================");
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("FilterTrident");
cluster.shutdown();
}
private static class CheckEvenSumFilter extends BaseFilter{
@Override
public boolean isKeep(TridentTuple tuple) {
Integer a = tuple.getIntegerByField("a");
Integer b = tuple.getIntegerByField("b");
return (a + b) % 2 == 0;
}
}
}
2.SumFunction
需求：对一组数据中的前2个数求各
public class SumFunctionTrident {
private static final Logger LOG = LoggerFactory.getLogger(SumFunctionTrident.class);
@SuppressWarnings("unchecked")
public static void main(String[] args) throws InterruptedException {
FixedBatchSpout spout = new FixedBatchSpout(new Fields("a","b","c","d"), 3,
new Values(1,4,7,10),
new Values(1,1,3,11),
new Values(2,2,7,1),
new Values(2,5,7,2));
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(4);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
// peek: 不做任务操作，因为参数的consumer
// each：spout中的指定元素进行操作
topology.newStream("function", spout).parallelismHint(1)
.localOrShuffle()
.peek(input -> LOG.info("peek1 ================{},{},{},{}",input.get(0),input.get(1),input.get(2),input.get(3)))
.parallelismHint(2)
.localOrShuffle()
.each(new Fields("a","b"),new SumFunction(),new Fields("sum"))
.parallelismHint(2)
.localOrShuffle()
.peek(input -> LOG.info("peek2 ================{},{},{},{},{}",
input.getIntegerByField("a"),input.getIntegerByField("b"),input.getIntegerByField("c"),input.getIntegerByField("d"),input.getIntegerByField("sum")))
.parallelismHint(1);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("SumFunctionTrident", conf, topology.build());
LOG.warn("==================================================");
LOG.warn("the LocalCluster topology {} is submitted.","SumFunctionTrident");
LOG.warn("==================================================");
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("HelloTridentTopology");
cluster.shutdown();
}
private static class SumFunction extends BaseFunction{
@Override
public void execute(TridentTuple tuple, TridentCollector collector) {
Integer a = tuple.getIntegerByField("a");
Integer b = tuple.getIntegerByField("b");
collector.emit(new Values(a+b));
}
}
}
3.MapFunction
需求：对一组batch中的tuple进行大小写转换
public class MapFunctionTrident {
private static final Logger LOG = LoggerFactory.getLogger(MapFunctionTrident.class);
@SuppressWarnings("unchecked")
public static void main(String[] args) throws InterruptedException, AlreadyAliveException, InvalidTopologyException, AuthorizationException {
boolean isRemoteMode = false;
if(args.length > 0){
isRemoteMode = true;
}
FixedBatchSpout spout = new FixedBatchSpout(new Fields("line"),3,
new Values("hello stream"),
new Values("hello kafka"),
new Values("hello hadoop"),
new Values("hello scala"),
new Values("hello java")
);
spout.setCycle(true);
TridentTopology topology = new TridentTopology();
Config conf = new Config();
conf.setNumWorkers(4);
conf.setDebug(false);
topology.newStream("hello", spout).parallelismHint(1)
.localOrShuffle()
.map(new MyMapFunction(),new Fields("upper"))
.parallelismHint(2)
.partition(Grouping.fields(ImmutableList.of("upper")))
.peek(input ->LOG.warn("================>> peek process value:{}",input.getStringByField("upper")))
.parallelismHint(3);
if(isRemoteMode){
StormSubmitter.submitTopology("HelloTridentTopology", conf, topology.build());
LOG.warn("==================================================");
LOG.warn("the remote topology {} is submitted.","HelloTridentTopology");
LOG.warn("==================================================");
}else{
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("HelloTridentTopology", conf, topology.build());
LOG.warn("==================================================");
LOG.warn("the LocalCluster topology {} is submitted.","HelloTridentTopology");
LOG.warn("==================================================");
TimeUnit.SECONDS.sleep(5);
cluster.killTopology("HelloTridentTopology");
cluster.shutdown();
}
}
private static class MyMapFunction implements MapFunction{
private static final Logger LOG = LoggerFactory.getLogger(MyMapFunction.class);
@Override
public Values execute(TridentTuple input) {
String line = input.getStringByField("line");
LOG.warn("================>> myMapFunction process execute:value :{}",line);
return new Values(line.toUpperCase());
}
}
}
4.ProjectionFunctionTrident
需求：对一组tuple的数据，取部分数据
public class ProjectionFunctionTrident {
private static final Logger LOG = LoggerFactory.getLogger(ProjectionFunctionTrident.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
FixedBatchSpout spout = new FixedBatchSpout(new Fields("x","y","z"), 3,
new Values(1,2,3),
new Values(4,5,6),
new Values(7,8,9),
new Values(10,11,12)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("ProjectionTrident", spout).parallelismHint(1)
.localOrShuffle().peek(tridentTuple ->LOG.info("================ {}",tridentTuple)).parallelismHint(2)
.shuffle()
.project(new Fields("y","z")).parallelismHint(2)
.localOrShuffle().peek(tridentTuple ->LOG.info(">>>>>>>>>>>>>>>> {}",tridentTuple)).parallelismHint(2);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("ProjectionTrident", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("ProjectionTrident");
cluster.shutdown();
}
}
5.2 Broadcast
需求：将一组batch 的tuple数据发送到所有partition上
public class BroadcastRepartitionTrident {
private static final Logger LOG = LoggerFactory.getLogger(BroadcastRepartitionTrident.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
FixedBatchSpout spout = new FixedBatchSpout(new Fields("language","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("haddop",3),
new Values("java",4),
new Values("haddop",5)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("BroadcastRepartitionTrident", spout).parallelismHint(1)
.broadcast().peek(tridentTuple ->LOG.info("================ {}",tridentTuple))
.parallelismHint(2);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("BroadcastRepartitionTrident", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("BroadcastRepartitionTrident");
cluster.shutdown();
}
}
5.3 PartitionBy
需求：将一组batch中的tuple通过设置的字段分到同一个task中执行
public class PartitionByRepartitionTrident {
private static final Logger LOG = LoggerFactory.getLogger(PartitionByRepartitionTrident.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
//FixedBatchSpout()里面参数解释：
// 1.spout 的字段名称的设置
// 2.设置数据几个为一个批次
// 3.字段值的设置
FixedBatchSpout spout = new FixedBatchSpout(new Fields("language","age"), 3,
new Values("java",23),
new Values("scala",3),
new Values("haddop",10),
new Values("java",23),
new Values("haddop",10)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("PartitionByRepartitionTrident", spout).parallelismHint(1)
.partitionBy(new Fields("language")).peek(tridentTuple ->LOG.info("++++++++++++++++ {}",tridentTuple))
.parallelismHint(3);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("PartitionByRepartitionTrident", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("PartitionByRepartitionTrident");
cluster.shutdown();
}
}
5.4 Global
需求：对一组batch中的tuple 进行全局分组统计
public class GlobalRepatitionTrident {
private static final Logger LOG = LoggerFactory.getLogger(GlobalRepatitionTrident.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
//FixedBatchSpout()里面参数解释：
// 1.spout 的字段名称的设置
// 2.设置数据几个为一个批次
// 3.字段值的设置
FixedBatchSpout spout = new FixedBatchSpout(new Fields("language","age"), 3,
new Values("java",23),
new Values("scala",3),
new Values("haddop",10),
new Values("java",23),
new Values("haddop",10)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("PartitionByRepartitionTrident", spout).parallelismHint(1)
.partitionBy(new Fields("language"))
.parallelismHint(3) //不管配多少个并行度，都没有影响
.peek(tridentTuple ->LOG.info(" ================= {}",tridentTuple))
.global()
.peek(tridentTuple ->LOG.info(" >>>>>>>>>>>>>>>>> {}",tridentTuple));
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("GlobalRepatitionTrident", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("GlobalRepatitionTrident");
cluster.shutdown();
}
}
5.5 batchGlobal
需求：不同batch的tuple分到不同的task中
public class BatchGlobalRepatitionTrident2 {
private static final Logger LOG = LoggerFactory.getLogger(BatchGlobalRepatitionTrident2.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
FixedBatchSpout spout = new FixedBatchSpout(new Fields("language","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("BatchGlobalRepatitionTrident2", spout).parallelismHint(1)
.batchGlobal().peek(tridentTuple ->LOG.info("++++++++++++++++ {}",tridentTuple))
.parallelismHint(3);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("BatchGlobalRepatitionTrident2", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("BatchGlobalRepatitionTrident2");
cluster.shutdown();
}
}
5.6 partition
需求：自定义partition
public class CustomRepartitionTrident {
private static final Logger LOG = LoggerFactory.getLogger(CustomRepartitionTrident.class);
public static void main(String [] args) throws InterruptedException{
@SuppressWarnings("unchecked")
FixedBatchSpout spout = new FixedBatchSpout(new Fields("language","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("haddop",3),
new Values("java",4),
new Values("haddop",5)
);
spout.setCycle(false);
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
TridentTopology topology = new TridentTopology();
topology.newStream("CustomRepartitionTrident", spout).parallelismHint(1)
.partition(new HighTaskIDGrouping()).peek(tridentTuple ->LOG.info("++++++++++++++++ {}",tridentTuple))
.parallelismHint(2);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("CustomRepartitionTrident", conf, topology.build());
TimeUnit.SECONDS.sleep(30);
cluster.killTopology("CustomRepartitionTrident");
cluster.shutdown();
}
}
/**
* 自定义grouping ：
* 让task编号更大的执行任务
* @author pengbo.zhao
*
*/
public class HighTaskIDGrouping implements CustomStreamGrouping{
private int taskID;
@Override
public void prepare(WorkerTopologyContext context, GlobalStreamId stream, List<Integer> targetTasks) {
//List<Integer> targetTasks: 下游所有的tasks的集合
ArrayList<Integer> tasks = new ArrayList<>(targetTasks);
Collections.sort(tasks); //从小到大排列
this.taskID = tasks.get(tasks.size() -1);
}
@Override
public List<Integer> chooseTasks(int taskId, List<Object> values) {
return Arrays.asList(taskID);
}
}
6.1 partitionAggregate
需求：对一组batch中tuple个数的统计
public class PartitionAggregateTrident {
private static final Logger LOG = LoggerFactory.getLogger(PartitionAggregateTrident.class);
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testPartitionAggregtor(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("PartitionAggregateTrident", spout).parallelismHint(2)//内部的优先级参数是1，所以我们写2是无效的
.shuffle()
.partitionAggregate(new Fields("name","age"), new Count(),new Fields("count"))
.parallelismHint(2)
// .each(new Fields("count"),new Debug());
.peek(input ->LOG.info(" >>>>>>>>>>>>>>>>> {}",input.getLongByField("count")));
this.submitTopology("PartitionAggregateTrident", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
}
6.2 aggregator
需求：对tuple中的数据进行统计
public class AggregateTrident {
private static final Logger LOG = LoggerFactory.getLogger(AggregateTrident.class);
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testPartitionAggregtor(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("AggregateTrident", spout).parallelismHint(2)
.partitionBy(new Fields("name"))
.aggregate(new Fields("name","age"), new Count(),new Fields("count"))
// .aggregate(new Fields("name","age"), new CountAsAggregator(),new Fields("count"))
.parallelismHint(2)
.each(new Fields("count"),new Debug())
.peek(input -> LOG.info("============> count:{}",input.getLongByField("count")));
this.submitTopology("AggregateTrident", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
}
6.3 reduceAggregator
需求：对一批batch 中的tuple第0个元素求和。 即一批batch中的多少条tuple，对tuple中的指定字段求和
public class ReduceAggregatorTrident {
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testReduceAggregator(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("ReduceAggregator", spout).parallelismHint(2)
.partitionBy(new Fields("name"))
.aggregate(new Fields("age","name"), new MyReduce(),new Fields("sum"))
.parallelismHint(5)
.each(new Fields("sum"),new Debug());
this.submitTopology("ReduceAggregator", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
static class MyReduce implements ReducerAggregator<Integer>{
@Override
public Integer init() {
return 0; //初始值为0
}
@Override
public Integer reduce(Integer curr, TridentTuple tuple) {
return curr + tuple.getInteger(0);
}
}
}
6.4 combinerAggregate
需求：对tuple中的字段进行求和操作
public class CombinerAggregate {
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testCombinerAggregate(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("CombinerAggregate", spout).parallelismHint(2)
.partitionBy(new Fields("name"))
.aggregate(new Fields("age"), new MyCount(),new Fields("count"))
.parallelismHint(5)
.each(new Fields("count"),new Debug());
this.submitTopology("CombinerAggregate", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
static class MyCount implements CombinerAggregator<Integer>{
@Override
public Integer init(TridentTuple tuple) {
return tuple.getInteger(0);
}
@Override
public Integer combine(Integer val1, Integer val2) {
return val1 + val2;
}
@Override
public Integer zero() {
return 0;
}
}
}
6.5 persistenceAggregator
需求：对一批batch中tuple元素进行统计
public class PersistenceAggregator {
private static final Logger LOG = LoggerFactory.getLogger(PersistenceAggregator.class);
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testPersistenceAggregator(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("testPersistenceAggregator", spout).parallelismHint(2)
.partitionBy(new Fields("name"))
.persistentAggregate(new MemoryMapState.Factory(), new Fields("name"), new Count(),new Fields("count"))
.parallelismHint(4)
.newValuesStream()
.peek(input ->LOG.info("count:{}",input.getLongByField("count")));
this.submitTopology("testPersistenceAggregator", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
}
6.6 AggregateChina
需求：对batch中的tuple进行统计、求和、统计操作
public class AggregateChina {
private static final Logger LOG = LoggerFactory.getLogger(AggregateChina.class);
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"),3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testAggregateChina(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("AggregateChina", spout).parallelismHint(2)
.partitionBy(new Fields("name"))
.chainedAgg()
.aggregate(new Fields("name"),new Count(), new Fields("count"))
.aggregate(new Fields("age"),new Sum(), new Fields("sum"))
.aggregate(new Fields("age"),new Count(), new Fields("count2"))
.chainEnd()
.peek(tuple->LOG.info("{}",tuple));
this.submitTopology("AggregateChina", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
}
7.GroupBy
需求：对一批batch中的tuple按name来分组，求对分组后的tuple中的数据进行统计
public class GroupBy {
private static final Logger LOG = LoggerFactory.getLogger(GroupBy.class);
private FixedBatchSpout spout;
@SuppressWarnings("unchecked")
@Before
public void setSpout(){
this.spout = new FixedBatchSpout(new Fields("name","age"), 3,
new Values("java",1),
new Values("scala",2),
new Values("scala",3),
new Values("haddop",4),
new Values("java",5),
new Values("haddop",6)
);
this.spout.setCycle(false);
}
@Test
public void testGroupBy(){
TridentTopology topoloty = new TridentTopology();
topoloty.newStream("GroupBy", spout).parallelismHint(1)
// .partitionBy(new Fields("name"))
.groupBy(new Fields("name"))
.aggregate(new Count(), new Fields("count"))
.peek(tuple -> LOG.info("{},{}",tuple.getFields(),tuple));
this.submitTopology("GroupBy", topoloty.build());
}
public void submitTopology(String name,StormTopology topology) {
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(name, createConf(), topology);
try {
TimeUnit.MINUTES.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
cluster.killTopology(name);
cluster.shutdown();
}
public Config createConf(){
Config conf = new Config();
conf.setNumWorkers(3);
conf.setDebug(false);
return conf;
}
}
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