序
本文主要展示一下reactive streams的一些transform操作
mergeWith
@Test
public void testMerge(){
Flux flux1 = Flux.interval(Duration.ofSeconds(1))
.take(3)
.map(e -> "[flux1]:"+e);
Flux mergeFlux = Flux.interval(Duration.ofSeconds(1))
.delayElements(Duration.ofSeconds(1))
.take(3)
.map(e -> "[flux2]:"+e)
.mergeWith(flux1);
mergeFlux.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
mergeFlux.blockLast();
} 输出实例
21:18:07.583 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:18:08.618 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:0
21:18:09.619 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:1
21:18:09.645 [parallel-6] INFO com.example.demo.TransformTest - subscribe:[flux2]:0
21:18:10.619 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:2
21:18:10.649 [parallel-8] INFO com.example.demo.TransformTest - subscribe:[flux2]:1
21:18:11.654 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux2]:2可以发现，他们是交叉合并的。
concatWith
@Test
public void testConcat(){
Flux flux1 = Flux.interval(Duration.ofSeconds(1))
.take(3)
.map(e -> "[flux1]:"+e);
Flux concatFlux = Flux.interval(Duration.ofSeconds(1))
.delayElements(Duration.ofSeconds(1))
.take(3)
.map(e -> "[flux2]:"+e)
.concatWith(flux1);
concatFlux.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
concatFlux.blockLast();
} 输出
21:19:00.779 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:19:02.832 [parallel-4] INFO com.example.demo.TransformTest - subscribe:[flux2]:0
21:19:03.836 [parallel-6] INFO com.example.demo.TransformTest - subscribe:[flux2]:1
21:19:04.840 [parallel-8] INFO com.example.demo.TransformTest - subscribe:[flux2]:2
21:19:05.845 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:0
21:19:06.845 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:1
21:19:07.844 [parallel-2] INFO com.example.demo.TransformTest - subscribe:[flux1]:2可以发现concatWith只是连接两个flux的数据，并不是按emit的顺序交叉来
zipWith
@Test
public void testZip(){
List firstList = Lists.newArrayList("a","b","c","d","e","a","b");
List secondList = Lists.newArrayList("1","2","3","4","5");
Flux> zipFlux = Flux.fromIterable(firstList)
.zipWith(Flux.fromIterable(secondList));
zipFlux.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
} 输出如下
21:20:59.506 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:20:59.516 [main] INFO com.example.demo.TransformTest - subscribe:[a,1]
21:20:59.517 [main] INFO com.example.demo.TransformTest - subscribe:[b,2]
21:20:59.517 [main] INFO com.example.demo.TransformTest - subscribe:[c,3]
21:20:59.517 [main] INFO com.example.demo.TransformTest - subscribe:[d,4]
21:20:59.517 [main] INFO com.example.demo.TransformTest - subscribe:[e,5]可以发现flux1相比flux2多余的数据没有被zip
flatMap
@Test
public void testFlatMap(){
List secondList = Lists.newArrayList("1","2","3","4","5");
Flux flatMapFlux = Flux.fromIterable(secondList)
.flatMap((str) ->{
return Mono.just(str).repeat(2).map(String::toUpperCase).delayElements(Duration.ofMillis(1));
});
flatMapFlux.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
flatMapFlux.blockLast();
Flux mapFlux = Flux.fromIterable(secondList)
.repeat(2)
.map(String::toUpperCase);
mapFlux.subscribe(e -> {
LOGGER.info("map subscribe:{}",e);
});
mapFlux.blockLast();
} 输出
21:33:46.904 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:33:46.958 [parallel-1] INFO com.example.demo.TransformTest - subscribe:1
21:33:46.959 [parallel-1] INFO com.example.demo.TransformTest - subscribe:2
21:33:46.959 [parallel-1] INFO com.example.demo.TransformTest - subscribe:3
21:33:46.960 [parallel-1] INFO com.example.demo.TransformTest - subscribe:4
21:33:46.960 [parallel-1] INFO com.example.demo.TransformTest - subscribe:5
21:33:46.960 [parallel-1] INFO com.example.demo.TransformTest - subscribe:2
21:33:46.960 [parallel-7] INFO com.example.demo.TransformTest - subscribe:3
21:33:46.960 [parallel-8] INFO com.example.demo.TransformTest - subscribe:4
21:33:46.960 [parallel-1] INFO com.example.demo.TransformTest - subscribe:5
21:33:46.961 [parallel-6] INFO com.example.demo.TransformTest - subscribe:1
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:1
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:2
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:3
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:4
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:5
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:1
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:2
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:3
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:4
21:33:46.963 [main] INFO com.example.demo.TransformTest - map subscribe:5flatMap是异步的
reduce
@Test
public void testReduce(){
List secondList = Lists.newArrayList("1","2","3","4","5");
Mono reduceMono = Flux.fromIterable(secondList)
.flatMap(e -> Mono.just(e).map(item -> Integer.valueOf(item)))
.reduce((total, e) -> total + e);
reduceMono.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
} 输出
21:36:29.978 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:36:30.014 [main] INFO com.example.demo.TransformTest - subscribe:15groupBy
@Test
public void testGroup(){
List firstList = Lists.newArrayList("a","b","c","d","e","a","b");
Flux> groupFlux = Flux.fromIterable(firstList)
.map(String::toUpperCase)
.groupBy(key -> key);
groupFlux.subscribe(e -> {
LOGGER.info("subscribe:{}",e.collectList().subscribe(item -> {
LOGGER.info("item:{}",item);
}));
});
} 输出
21:37:00.912 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:37:00.949 [main] INFO com.example.demo.TransformTest - subscribe:reactor.core.publisher.LambdaMonoSubscriber@5faeada1
21:37:00.951 [main] INFO com.example.demo.TransformTest - subscribe:reactor.core.publisher.LambdaMonoSubscriber@1563da5
21:37:00.951 [main] INFO com.example.demo.TransformTest - subscribe:reactor.core.publisher.LambdaMonoSubscriber@2bbf4b8b
21:37:00.951 [main] INFO com.example.demo.TransformTest - subscribe:reactor.core.publisher.LambdaMonoSubscriber@30a3107a
21:37:00.951 [main] INFO com.example.demo.TransformTest - subscribe:reactor.core.publisher.LambdaMonoSubscriber@33c7e1bb
21:37:00.951 [main] INFO com.example.demo.TransformTest - item:[A, A]
21:37:00.952 [main] INFO com.example.demo.TransformTest - item:[B, B]
21:37:00.952 [main] INFO com.example.demo.TransformTest - item:[C]
21:37:00.952 [main] INFO com.example.demo.TransformTest - item:[D]
21:37:00.952 [main] INFO com.example.demo.TransformTest - item:[E]first
@Test
public void testFirst(){
List firstList = Lists.newArrayList("a","b","c","d","e","a","b");
List secondList = Lists.newArrayList("1","2","3","4","5");
Flux firstFlux = Flux.fromIterable(firstList)
.delayElements(Duration.ofMillis(200));
Flux secondFlux = Flux.fromIterable(secondList)
.take(2);
Flux result = Flux.first(firstFlux, secondFlux);
result.subscribe(e -> {
LOGGER.info("subscribe:{}",e);
});
} toIterable
@Test
public void testToIterable(){
List firstList = Lists.newArrayList("a","b","c","d","e","a","b");
Iterable itr = Flux.fromIterable(firstList)
.map(String::toUpperCase)
.toIterable();
itr.forEach(e -> LOGGER.info(e));
} 输出
21:39:35.031 [main] DEBUG reactor.util.Loggers$LoggerFactory - Using Slf4j logging framework
21:39:35.045 [main] INFO com.example.demo.TransformTest - A
21:39:35.045 [main] INFO com.example.demo.TransformTest - B
21:39:35.045 [main] INFO com.example.demo.TransformTest - C
21:39:35.045 [main] INFO com.example.demo.TransformTest - D
21:39:35.045 [main] INFO com.example.demo.TransformTest - E
21:39:35.045 [main] INFO com.example.demo.TransformTest - A
21:39:35.045 [main] INFO com.example.demo.TransformTest - B小结
reactive streams的操作相当于在jdk的streams的基础上实现了reactive化，可以参照着了解。
doc
- Reactor – How to Combine Publishers (Flux/Mono)