本文开始前，问大家一个问题，你觉得一份业务代码，尤其是互联网业务代码，都有哪些特点？
我能想到的有这几点：
- 互联网业务迭代快，工期紧，导致代码结构混乱，几乎没有代码注释和文档。
- 互联网人员变动频繁，很容易接手别人的老项目，新人根本没时间吃透代码结构，紧迫的工期又只能让屎山越堆越大。
- 多人一起开发，每个人的编码习惯不同，工具类代码各用个的，业务命名也经常冲突，影响效率。
每当我们新启动一个代码仓库，都是信心满满，结构整洁。但是时间越往后，代码就变得腐败不堪，技术债务越来越庞大。
这种情况有解决方案吗？也是有的：
- 组内设计完善的应用架构，让代码的腐烂来得慢一些。（当然很难做到完全不腐烂）
- 设计尽量简单，让不同层级的开发都能快速看懂并上手开发，而不是在一堆复杂的没人看懂的代码上堆更多的屎山。
而COLA，我们今天的主角，就是为了提供一个可落地的业务代码结构规范，让你的代码腐烂的尽可能慢一些，让团队的开发效率尽可能快一些。
https://github.com/alibaba/COLA
https://blog.csdn.net/significantfrank/article/details/110934799
SPRING 框架下 如果要做去重，在数据量大的时候会爆ERROR，可改用如下 写法：
private boolean needReorderCheck(String requestId) {
boolean result = false;
// try(MongoCursor<String> mongoCursor =
// mongoTemplate.getCollection(mongoTemplate.getCollectionName(AccountNumProductLineIndex.class))
// .distinct(KEY, Filters.eq(REQUEST_ID, requestId), String.class)
// .iterator()
// )
try(MongoCursor<Document> mongoCursor =
mongoTemplate.getCollection(mongoTemplate.getCollectionName(AccountNumProductLineIndex.class))
.aggregate(
Arrays.asList(
Aggregates.project(
Projections.fields(
Projections.excludeId(),
Projections.include(KEY),
Projections.include(REQUEST_ID)
)
),
Aggregates.match(Filters.eq(REQUEST_ID, requestId)),
Aggregates.group("$" + KEY)
)
)
.allowDiskUse(true)
.iterator();
)
{
String key = null;
boolean breakMe = false;
LOGGER.info("needReorderCheck.key --> start");
while(mongoCursor.hasNext()) {
if(breakMe) {
mongoCursor.close();
break;
}
Document keyDocument = mongoCursor.next();
key = keyDocument.getString("_id");
// key = mongoCursor.next().getString(KEY);
// LOGGER.info("needReorderCheck.keyDocument --> {}, key --> {}", keyDocument, key);
try(MongoCursor<Document> indexMongoCursor =
mongoTemplate.getCollection(AccountNumProductLineIndex.COLLECTION_NAME)
.find(Filters.and(Filters.eq(REQUEST_ID, requestId), Filters.eq(KEY, key)))
.iterator()
)
{
int preIndex = -1, currentIndex = -1;
Document preIndexDocument = null, currentIndexDocument;
while(indexMongoCursor.hasNext()) {
currentIndexDocument = indexMongoCursor.next();
// System.out.println(currentIndexDocument.toJson());
if(preIndexDocument != null) {
currentIndex = currentIndexDocument.getInteger(INDEX);
preIndex = preIndexDocument.getInteger(INDEX);
if(currentIndex - preIndex > 1) {
indexMongoCursor.close();
breakMe = true;
result = true;
break;
}
}
preIndexDocument = currentIndexDocument;
}
}
}
}
return result;
}
@JsonFormat(shape=JsonFormat.Shape.STRING, pattern="yyyy-MM-dd'T'HH:mm:ss.SSSZ", timezone="America/Phoenix")
private Date date;
https://www.amitph.com/spring-webclient-large-file-download/https://github.com/amitrp/spring-examples/blob/main/spring-webflux-webclient/src/main/java/com/amitph/spring/webclients/service/FileDownloaderWebClientService.javaimport lombok.RequiredArgsConstructor;
import org.springframework.core.io.buffer.DataBuffer;
import org.springframework.core.io.buffer.DataBufferUtils;
import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;
import reactor.core.publisher.Flux;
import reactor.core.publisher.Mono;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.StandardOpenOption;
import java.util.Objects;
@Service
@RequiredArgsConstructor
public class FileDownloaderWebClientService {
private final WebClient webClient;
/**
* Reads the complete file in-memory. Thus, only useful for very large file
*/
public void downloadUsingByteArray(Path destination) throws IOException {
Mono<byte[]> monoContents = webClient
.get()
.uri("/largefiles/1")
.retrieve()
.bodyToMono(byte[].class);
Files.write(destination, Objects.requireNonNull(monoContents.share().block()),
StandardOpenOption.CREATE);
}
/**
* Reading file using Mono will try to fit the entire file into the DataBuffer.
* Results in exception when the file is larger than the DataBuffer capacity.
*/
public void downloadUsingMono(Path destination) {
Mono<DataBuffer> dataBuffer = webClient
.get()
.uri("/largefiles/1")
.retrieve()
.bodyToMono(DataBuffer.class);
DataBufferUtils.write(dataBuffer, destination,
StandardOpenOption.CREATE)
.share().block();
}
/**
* Having using Flux we can download files of any size safely.
* Optionally, we can configure DataBuffer capacity for better memory utilization.
*/
public void downloadUsingFlux(Path destination) {
Flux<DataBuffer> dataBuffer = webClient
.get()
.uri("/largefiles/1")
.retrieve()
.bodyToFlux(DataBuffer.class);
DataBufferUtils.write(dataBuffer, destination,
StandardOpenOption.CREATE)
.share().block();
}
}