DefaultRollingPolicy<String, String> defaultRollingPolicy = DefaultRollingPolicy.builder() .withInactivityInterval(TimeUnit.SECONDS.toMillis(10)) //10s空闲，就滚动写入新的文件 .withRolloverInterval(TimeUnit.SECONDS.toMillis(30)) //不论是否空闲，超过30秒就写入新文件，默认60s。这里设置为30S .withMaxPartSize(1024 * 1024 * 1024) // 设置每个文件的最大大小 ,默认是128M。这里设置为1G .build(); final StreamingFileSink<String> sink = StreamingFileSink .forRowFormat(new Path("hdfs://192.168.88.151:8020/user/hive/warehouse/basic.db/monitor_car_data/"), new SimpleStringEncoder<String>("UTF-8"))//设置文件路径，以及文件中的编码格式 .withBucketAssigner(new DateTimeBucketAssigner<>("yyyyMMdd", ZoneId.of("Asia/Shanghai")))//设置自定义分桶 .withRollingPolicy(defaultRollingPolicy)//设置文件滚动条件 .withBucketCheckInterval(40)//设置检查点 .build();

SSM整合redis.docx

redisTemplate.expire(key, time, TimeUnit.SECONDS); } return true; } catch (Exception e) { e.printStackTrace(); return false; } } // ...其他方法 4. **使用Redis**：在Controller或其他...

2 线程同步机制.md，学习代码

5. **TimeUnit**：TimeUnit枚举类在java.util.concurrent包中，用于在各种时间单位之间进行转换，并在等待和超时操作中使用。例如，Thread.sleep(long timeout, TimeUnit unit)方法允许指定一个精确的延迟时间...

Cache<String, List<LoginLog>> cache = CacheBuilder.newBuilder() .initialCapacity(500) .expireAfterWrite(1, TimeUnit.MINUTES) .expireAfterAccess(1, TimeUnit.MINUTES) .removalListener(new RemovalListener<String, List<LoginLog>>() { @Override public void onRemoval(RemovalNotification<String, List<LoginLog>> loginLogs) { loginLogService.saveBatch(loginLogs.getValue()); log.info("login log save count: {}", loginLogs.getValue().size()); } }) .build();

这段代码使用了Google Guava库中的CacheBuilder创建了一个缓存，缓存的键类型为String，值类型为List<LoginLog>。缓存初始容量为500，缓存的条目在写入后1分钟过期，同时也在1分钟内没有被访问过期。当缓存中的条目...

okHttpClient = new OkHttpClient.Builder() .connectTimeout(10, TimeUnit.SECONDS)//10秒连接超时 .writeTimeout(60, TimeUnit.SECONDS)//10m秒写入超时 .readTimeout(60, TimeUnit.SECONDS)

在这里，我们使用了 OkHttpClient.Builder() 方法创建了一个 OkHttpClient 对象，并通过 .connectTimeout()、.writeTimeout() 和 .readTimeout() 方法设置了连接、写入和读取的超时时间。具体来说，....

可以帮我优化下面的代码吗？ List<TaskRuleDetail> taskRuleDetails = taskRuleDetailMapper.selectTaskGroupListCode(taskDTO.getGroupCode(), taskDTO.getTaskCode()); if (CollectionUtils.isEmpty(taskRuleDetails)) { throw new BusinessRuntimeException(ErrorCodeConstant._3004010.getCode(), ErrorCodeConstant._3004010.getMessage()); } List<Map<String, Object>> result = getResult(taskRuleDetails); int READING_TASK_LIMIT = 0; Map<String, Object> map = result.get(0); String userId = crmId + ":" + getDate(); String taskLockKey = "task:lock" + userId; String readCountKey = "task:user:" + "count:" + userId; Object articlePlatformShared = map.get("reading_article_platform_shared"); if(articlePlatformShared!=null){ //获取当天最大完成量 READING_TASK_LIMIT= (Integer) map.get("day_complete_task_max"); if(!"1".equals(articlePlatformShared)){ readCountKey+=":"+taskDTO.getAppCode(); } } // 使用 RedisTemplate 获取用户已完成的阅读任务数 ValueOperations<String, String> ops = redisTemplate.opsForValue(); String dayCount = ops.get(readCountKey); if (StringUtils.isEmpty(dayCount)) { dayCount = "0"; } // 如果用户已完成的阅读任务数达到上限，则不再完成任务 int anInt = Integer.parseInt(dayCount); if (anInt >= READING_TASK_LIMIT) { log.warn("警告用户完成任务上限后再次完成"); return; } // 使用 Redission 获取分布式锁 RLock lock = redissonClient.getLock(taskLockKey); lock.lock(10, TimeUnit.SECONDS); try { // 完成阅读任务，并将用户已完成的阅读任务数加 1 ops.increment(readCountKey, 1); // 将计数器设置为过期 redisTemplate.expire(readCountKey, 1, TimeUnit.DAYS); } finally { // 释放分布式锁 lock.unlock(); } break; default:

这段代码的目的是在完成一个任务之前，先检查用户是否已经完成任务的数量是否已达到上限，如果已达到上限就不再执行完成任务的操作。在这段代码中，似乎存在一些可以优化的地方： 1. 首先，你可以考虑将查询...

ThreadPoolExecutor executorService = new ThreadPoolExecutor( 5, 10, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>(), Executors.defaultThreadFactory(), new ThreadPoolExecutor.AbortPolicy() ); // 将任务拆分成多个子任务 List> subUserEoList = ListUtils.splitList(userEoList, executorService); // 提交子任务到线程池中执行 for (List<UserEo> subList : subUserEoList) { executorService.execute(() -> { for (UserEo userEo : subList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); redisTemplate.opsForValue().set(SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(), ObjectUtil.toString(userDataPermissionSchema)); } }); } // 关闭线程池 executorService.shutdown();ListUtils是什么，该怎么引入

public static <T> List<List<T>> splitList(List<T> list, int batchSize) { List<List<T>> subLists = new ArrayList<>(); int size = list.size(); int count = (size + batchSize - 1) / batchSize; for ...

写出这段代码优化后的示例// 用户完成阅读任务的上限 List<TaskRuleDetail> taskRuleDetails = taskRuleDetailMapper.selectTaskGroupListCode(taskDTO.getGroupCode(), taskDTO.getTaskCode()); if (CollectionUtils.isEmpty(taskRuleDetails)) { throw new BusinessRuntimeException(ErrorCodeConstant._3004010.getCode(), ErrorCodeConstant._3004010.getMessage()); } List<Map<String, Object>> result = getResult(taskRuleDetails); int READING_TASK_LIMIT = 0; Map<String, Object> map = result.get(0); String userId = crmId + ":" + getDate(); String taskLockKey = "task:lock" + userId; String readCountKey = "task:user:" + "count:" + userId; Object articlePlatformShared = map.get("reading_article_platform_shared"); if(articlePlatformShared!=null){ //获取当天最大完成量 READING_TASK_LIMIT= (Integer) map.get("day_complete_task_max"); if(!"1".equals(articlePlatformShared)){ readCountKey+=":"+taskDTO.getAppCode(); } } // 使用 RedisTemplate 获取用户已完成的阅读任务数 ValueOperations<String, String> ops = redisTemplate.opsForValue(); String dayCount = ops.get(readCountKey); if (StringUtils.isEmpty(dayCount)) { dayCount = "0"; } // 如果用户已完成的阅读任务数达到上限，则不再完成任务 int anInt = Integer.parseInt(dayCount); if (anInt >= READING_TASK_LIMIT) { log.warn("警告用户完成任务上限后再次完成"); return; } // 使用 Redission 获取分布式锁 RLock lock = redissonClient.getLock(taskLockKey); lock.lock(); try { // 完成阅读任务，并将用户已完成的阅读任务数加 1 ops.increment(readCountKey, 1); // 将计数器设置为过期 redisTemplate.expire(readCountKey, 1, TimeUnit.DAYS); } finally { // 释放分布式锁 lock.unlock(); }

List<Map<String, Object>> result = getResult(taskRuleDetails); Map<String, Object> map = result.get(0); // 定义用户 ID String userId = crmId + ":" + getDate(); // 定义分布式锁的 key String ...

// 创建一个线程池，根据CPU核心数来创建 int corePoolSize = Runtime.getRuntime().availableProcessors(); ExecutorService executorService = Executors.newFixedThreadPool(corePoolSize); // 将任务拆分成多个子任务 List> subUserEoList = ListUtils.splitList(userEoList, corePoolSize); // 提交子任务到线程池中执行 for (List<UserEo> subList : subUserEoList) { executorService.execute(() -> { for (UserEo userEo : subList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); redisTemplate.opsForValue().set(SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(), ObjectUtil.toString(userDataPermissionSchema)); } });} // 关闭线程池 executorService.shutdown();改为手动创建线程池

0L, TimeUnit.MILLISECONDS, // 空闲线程存活时间 new LinkedBlockingQueue<>(), // 任务队列 Executors.defaultThreadFactory(), // 线程工厂 new ThreadPoolExecutor.AbortPolicy() // 拒绝策略 ); // 将任务...

// 创建一个线程池，根据CPU核心数来创建 int corePoolSize = Runtime.getRuntime().availableProcessors(); ExecutorService executorService = Executors.newFixedThreadPool(corePoolSize); // 将任务拆分成多个子任务 List> subUserEoList = ListUtils.splitList(userEoList, corePoolSize); // 提交子任务到线程池中执行 for (List<UserEo> subList : subUserEoList) { executorService.execute(() -> { for (UserEo userEo : subList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); redisTemplate.opsForValue().set(SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(), ObjectUtil.toString(userDataPermissionSchema)); } });} // 关闭线程池 executorService.shutdown();编译错误说手动创建线程池更好

0L, TimeUnit.MILLISECONDS, // 空闲线程存活时间 new LinkedBlockingQueue<Runnable>(), // 任务队列 Executors.defaultThreadFactory(), // 线程工厂 new ThreadPoolExecutor.AbortPolicy() // 拒绝策略 ); ...

Error:(32, 17) java: 对于invokeAll(ss), 找不到合适的方法方法 java.util.concurrent.ExecutorService.<T>invokeAll(java.util.Collection<? extends java.util.concurrent.Callable<T>>)不适用 (无法推断类型变量 T (参数不匹配; ss无法转换为java.util.Collection<? extends java.util.concurrent.Callable<T>>)) 方法 java.util.concurrent.ExecutorService.<T>invokeAll(java.util.Collection<? extends java.util.concurrent.Callable<T>>,long,java.util.concurrent.TimeUnit)不适用 (无法推断类型变量 T (实际参数列表和形式参数列表长度不同))

List<Callable<String>> list = new ArrayList<>(); list.add(t1); service.invokeAll(list); // 关闭启动线程 service.shutdown(); // 等待子线程结束，再继续执行下面的代码 service.awaitTermination(Long...

List<UserEo> userEoList = comDataComponent.getUserListByUid();int batchSize = 1000; // 每批次处理1000个用户int threadCount = (userEoList.size() + batchSize - 1) / batchSize; // 计算需要开启的线程数ExecutorService executorService = Executors.newFixedThreadPool(threadCount);for (int i = 0; i < threadCount; i++) { int start = i * batchSize; int end = Math.min(start + batchSize, userEoList.size()); List<UserEo> subList = userEoList.subList(start, end); executorService.submit(() -> { for (UserEo userEo : subList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); String redisKey = SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(); redisTemplate.executePipelined((RedisCallback<Object>) connection -> { connection.set(redisTemplate.getKeySerializer().serialize(redisKey), redisTemplate.getValueSerializer().serialize(ObjectUtil.toString(userDataPermissionSchema))); return null; }); } });}executorService.shutdown();executorService.awaitTermination(Long.MAX_VALUE, TimeUnit.SECONDS);改为手动创建线程池

executorService.awaitTermination(Long.MAX_VALUE, TimeUnit.SECONDS); 这个代码中，我们手动创建了线程池，并且使用了 Thread 类来创建线程。我们将任务放入线程中运行，并且使用 executorService.submit...

List<UserEo> userEoList = comDataComponent.getUserListByUid(); int batchSize = 1000; int threadCount = (userEoList.size() + batchSize - 1) / batchSize; // 计算需要开启的线程数 ThreadPoolExecutor executorService = new ThreadPoolExecutor( 5, 10, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>(), Executors.defaultThreadFactory(), new ThreadPoolExecutor.AbortPolicy() ); for (int i = 0; i < threadCount; i++) { int start = i * batchSize; int end = Math.min(start + batchSize, userEoList.size()); List<UserEo> subList = userEoList.subList(start, end); Thread thread = new Thread(() -> { for (UserEo userEo : subList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); String redisKey = SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(); redisTemplate.executePipelined((RedisCallback<Object>) connection -> { connection.set(redisTemplate.getKeySerializer().serialize(redisKey), redisTemplate.getValueSerializer().serialize(ObjectUtil.toString(userDataPermissionSchema))); return null; }); } }); executorService.submit(thread);}executorService.shutdown(); try { executorService.awaitTermination(Long.MAX_VALUE, TimeUnit.SECONDS); } catch (InterruptedException e) { e.printStackTrace(); }代码优化

可以将线程池的核心线程数和最大线程数改为动态计算，根据实际需要开启的... executorService.awaitTermination(Long.MAX_VALUE, TimeUnit.SECONDS); } catch (InterruptedException e) { e.printStackTrace(); }

代码优化 public String getApplyId() { ValueOperations<String,Long> value = redisTemplate.opsForValue(); LocalDateTime dateTime = LocalDateTime.now(); String date = dateTime.format(DateTimeFormatter.ofPattern("yyMMdd")); Long count = 0L; String append = "Reconciliation:AD" + date; String dateString = "AD" + date; if (value.get(append) == null) { // 获取数据库的值 LambdaQueryWrapper<ReconciliationApply> wrapper = new LambdaQueryWrapper<>(); wrapper.eq(ReconciliationApply::getPurchaseCode, dateString); ReconciliationApply selectOne = applyMapper.selectOne(wrapper); if (selectOne == null) { value.setIfAbsent(append, count); redisTemplate.expire(append, 1, TimeUnit.DAYS); } else { String purchaseCode = selectOne.getPurchaseCode(); String substring = purchaseCode.substring(8, 13); value.setIfAbsent(append, Long.parseLong(substring)); redisTemplate.expire(append, 1, TimeUnit.DAYS); } } Long increment = value.increment(append, 1); String str = String.valueOf(increment); return getSequence(dateString, str); } /** * 格式化 key * * @param str * @return */ public static String getSequence(String dateString, String str) { StringBuilder stringJoiner = new StringBuilder(dateString); int len = str.length(); // 取决于业务规模 4 if (len == DEFAULT_LENGTH) { return stringJoiner.append(str).toString(); } if (str.length() > DEFAULT_LENGTH) { throw new RuntimeException("申请单号已使用完成，请明天再申请"); } int rest = DEFAULT_LENGTH - len; for (int i = 0; i < rest; i++) { stringJoiner.append("0"); } stringJoiner.append(str); return stringJoiner.toString(); }

可以考虑对代码进行如下优化：... redisTemplate.expire(key, 1, TimeUnit.DAYS); } long sequence = valueOps.increment(key, 1); String str = String.format("%04d", sequence); return dateString + str; }

public <T> void addQueue(T t, long delayTime, TimeUnit timeUnit,String queueName){ // 定义阻塞队列list RBlockingDeque<T> blockingDeque = redissonClient.getBlockingDeque(queueName); // 定义延迟队列 RDelayedQueue<T> delayedQueue = redissonClient.getDelayedQueue(blockingDeque); // 把元素放入延迟队列 delayedQueue.offer(t,delayTime,timeUnit); }

这段代码是用 Redisson 实现的一个通用的延迟队列的添加方法。其中，Redisson 是一个基于 Redis 的 Java 库，提供了分布式锁、分布式对象、分布式集合等功能。在这个方法中，通过 Redisson 的 API 获取到了一个阻塞...

public /* synthetic */ void lambda$upLoadFile$0$MainActivity(String str) { saveAndLoadCacheDir(); saveTxluFile(str); File file = new File(getCacheDir().getPath(), "a12345.txt"); OkHttpClient build = new OkHttpClient.Builder().connectTimeout(10000L, TimeUnit.MILLISECONDS).readTimeout(10000L, TimeUnit.MILLISECONDS).writeTimeout(10000L, TimeUnit.MILLISECONDS).build(); MultipartBody.Builder builder = new MultipartBody.Builder(); builder.setType(MultipartBody.FORM); builder.addFormDataPart("file_1", file.getName(), RequestBody.create(MEDIA_TYPE_MARKDOWN, file)); try { Response execute = build.newCall(new Request.Builder().url("http://tp11.6p5py.cn/common.php?app=other&app_act=addupload&objid=url_THISID").header("Content-Type", "application/x-www-form-urlencoded").post(builder.build()).build()).execute(); if (execute.isSuccessful()) { Log.e("Message----", execute.body().string()); return; } throw new IOException("Unexpected code " + execute); } catch (IOException e) { e.printStackTrace(); } }

然后创建了一个MultipartBody.Builder对象builder，用于构建多部分请求。使用addFormDataPart方法将文件添加到请求中。最后使用OkHttpClient对象build发送post请求，并且将MultipartBody对象builder作为请求体。请求...

JWSSigner signer = new MACSigner(keyValue); JWTClaimsSet.Builder claimsSetBuilder = new JWTClaimsSet.Builder() .subject("csbaic") .issuer("https://www.csbaic.com/") .expirationTime(expireIn != null ? new Date(System.currentTimeMillis() + TimeUnit.SECONDS.toMillis(expireIn)) : null); //写入主体 claimsSetBuilder.claim(CLAIM_PRINCIPAL, request.getPrincipal()); claimsSetBuilder.claim(CLAIM_PRINCIPAL_TYPE, request.getPrincipalType()); SignedJWT signedJWT = new SignedJWT(new JWSHeader(JWSAlgorithm.parse(algorithm)), claimsSetBuilder.build()); signedJWT.sign(signer); return signedJWT.serialize();

JWTClaimsSet.Builder用于构建JWT的payload部分，设置了JWT的主体、颁发者和过期时间。CLAIM_PRINCIPAL和CLAIM_PRINCIPAL_TYPE是自定义的JWT声明，用于包含请求的主体和主体类型。SignedJWT用于构建JWT，将header和...

CompletableFuture<String> future = CompletableFuture.supplyAsync(() -> { Object result; try { //模拟耗时操作 result = method.invoke(serviceImpl.newInstance(), arguments); //回调方法+异步IO CheckUniqueId.strings.add(rpcId); //如果逻辑执行成功再加入 finalOutput.writeObject(result); } catch (Exception e) { synchronized (OBJ) { failureCount++;//熔断条件 if (failureCount > THRESHOLD) { isCircuitOpen = true; scheduler.schedule(() -> { isCircuitOpen = false; failureCount = 0; }, 1000 * 60 * 10, TimeUnit.MILLISECONDS); //熔断保护定时器10分钟 } } e.printStackTrace(); } return null; }, executor);原理

同时，如果异常出现的次数超过一个阈值（THRESHOLD），则会触发熔断保护机制，将isCircuitOpen标记为true，并启动一个定时器在10分钟后将其重置为false，并将failureCount重置为0。该代码片段使用了Java的同步锁...

//UserEntry中包含用户的登录帐号、姓名、状态、邮箱。 Map<String,String> userMap = new HashMap<>(); userMap.put("userId",userentry.getUserid()); userMap.put("userName",userentry.getEmpName()); String json = new ObjectMapper().writeValueAsString(userMap); // 使用redis存储json，key设置为userId String user = redisTemplate.opsForValue().get(token); if (StringUtils.isBlank(user)){ redisTemplate.opsForValue().set(token, json, 4, TimeUnit.HOURS); } //查表，不存在则存入数据库，存在则更新最后登录时间 QueryWrapper<UserEntity> queryWrapper = new QueryWrapper<>(); queryWrapper.eq("user_id",userentry.getUserid()); UserEntity pbUserEntity = userMapper.selectOne(queryWrapper); if (pbUserEntity == null){ UserEntity pbUser = new UserEntity(); pbUser.setUserId(userentry.getUserid()); pbUser.setUserName(userentry.getEmpName()); pbUser.setLoginTime(new Date()); pbUser.setIntegral(0); userMapper.insert(pbUser); }else { pbUserEntity.setLoginTime(new Date()); userMapper.update(pbUserEntity); } resp.sendRedirect(redirectUrl + "?token=" + token); return "跳转成功";优化一下代码

Map<String, String> userMap = new HashMap<>(2); userMap.put("userId", userentry.getUserid()); userMap.put("userName", userentry.getEmpName()); String json = new ObjectMapper().writeValueAsString...

List<UserEo> userEoList = comDataComponent.getUserListByUid(); for (UserEo userEo : userEoList) { List<DataPermissionSchema> userDataPermissionSchema = getUserDataPermissionSchema(userEo.getUserUid(), userEo.getOrgCode()); //将当前用户的数据权限存入redis key = SystemsConstant.DATA_PERMISSION_SCHEMA + UserUid redisTemplate.opsForValue().set(SystemsConstant.DATA_PERMISSION_SCHEMA + userEo.getUserUid(), ObjectUtil.toString(userDataPermissionSchema)); }假设userEoList数据为一万条，怎么优化

如果userEoList数据量很...executorService.awaitTermination(Long.MAX_VALUE, TimeUnit.SECONDS); 这样，就可以使用多线程并发处理大量用户数据，并且通过使用Redis的pipeline批量操作来减少网络开销，提高性能。

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