背景：数据膨胀的隐忧

在实际开发中，我们经常遇到需要记录任务执行历史的场景，比如：

• 任务日志：记录每个任务的执行情况、耗时、结果
• 操作日志：记录用户的操作行为、时间、IP
• 系统日志：记录系统的运行状态、错误信息、性能指标
• 业务日志：记录业务流程、状态变更、数据变更
• 审计日志：记录关键操作、操作人、操作时间

然而，随着系统运行时间的增长，这些历史数据会不断积累，带来严重的问题：

数据库性能下降

问题：历史数据量过大，导致数据库查询性能下降

-- 查询最近的任务记录，但需要扫描大量历史数据
SELECT * FROM task_execution_log 
WHERE create_time > '2024-01-01' 
ORDER BY create_time DESC 
LIMIT 100;

影响：

• 查询响应时间变长
• 索引效率降低
• 数据库负载增加

存储成本增加

问题：历史数据占用大量存储空间，成本持续增长

// 每天产生数百万条日志记录
for (int i = 0; i < 1000000; i++) {
    TaskExecutionLog log = new TaskExecutionLog();
    log.setCreateTime(new Date());
    log.setTaskId("task-" + i);
    log.setStatus("COMPLETED");
    taskExecutionLogRepository.save(log);
}

影响：

• 磁盘空间不足
• 备份时间变长
• 存储成本增加

系统性能下降

问题：大量历史数据影响系统整体性能

// 统计任务执行情况，需要扫描大量数据
List<TaskExecutionLog> logs = taskExecutionLogRepository.findAll();
long completedCount = logs.stream()
        .filter(log -> "COMPLETED".equals(log.getStatus()))
        .count();

影响：

• 内存占用增加
• GC 频率提高
• 系统响应变慢

数据维护困难

问题：历史数据量大，数据维护变得困难

-- 删除过期数据，但执行时间很长
DELETE FROM task_execution_log 
WHERE create_time < DATE_SUB(NOW(), INTERVAL 90 DAY);

影响：

• 删除操作耗时
• 锁表时间长
• 影响业务运行

核心概念：历史归档与数据清理

1. 历史归档

定义：将历史数据从主数据库迁移到归档存储，保留查询能力

特点：

• 减少主数据库数据量
• 保留历史数据查询能力
• 降低存储成本
• 提高查询性能

实现方式：

• 分库分表归档
• 对象存储归档
• 文件存储归档
• 数据仓库归档

2. 数据清理

定义：删除不需要的历史数据，释放存储空间

特点：

• 释放存储空间
• 减少数据维护成本
• 提高系统性能
• 降低备份成本

实现方式：

• 定时清理
• 基于策略清理
• 基于规则清理
• 手动清理

3. 归档策略

定义：根据数据特点制定不同的归档策略

特点：

• 基于时间的归档
• 基于数据量的归档
• 基于业务规则的归档
• 基于数据重要性的归档

实现方式：

• 时间窗口归档
• 数据量阈值归档
• 业务规则归档
• 混合策略归档

4. 清理策略

定义：根据数据特点制定不同的清理策略

特点：

• 基于时间的清理
• 基于数据量的清理
• 基于业务规则的清理
• 基于数据重要性的清理

实现方式：

• 时间窗口清理
• 数据量阈值清理
• 业务规则清理
• 混合策略清理

实现方案：历史归档与数据清理

方案一：基于时间窗口的归档

优点：

• 实现简单
• 策略清晰
• 易于理解

缺点：

• 不够灵活
• 可能归档重要数据
• 无法适应业务变化

代码实现：

@Service
public class TimeWindowArchiveService {

    @Autowired
    private TaskExecutionLogRepository repository;

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Scheduled(cron = "0 0 2 * * ?")
    public void archiveOldData() {
        LocalDateTime archiveTime = LocalDateTime.now().minusDays(90);
        
        List<TaskExecutionLog> logsToArchive = repository.findByCreateTimeBefore(archiveTime);
        
        for (TaskExecutionLog log : logsToArchive) {
            TaskExecutionLogArchive archive = convertToArchive(log);
            archiveRepository.save(archive);
        }
        
        repository.deleteAll(logsToArchive);
        
        log.info("Archived {} logs older than {}", logsToArchive.size(), archiveTime);
    }

    private TaskExecutionLogArchive convertToArchive(TaskExecutionLog log) {
        TaskExecutionLogArchive archive = new TaskExecutionLogArchive();
        BeanUtils.copyProperties(log, archive);
        archive.setArchiveTime(LocalDateTime.now());
        return archive;
    }

}

方案二：基于数据量阈值的归档

优点：

• 更灵活
• 可以控制归档频率
• 适应数据增长

缺点：

• 需要监控数据量
• 配置复杂
• 可能频繁归档

代码实现：

@Service
public class DataVolumeArchiveService {

    @Autowired
    private TaskExecutionLogRepository repository;

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Value("${archive.data-volume-threshold:1000000}")
    private long dataVolumeThreshold;

    @Scheduled(fixedRate = 3600000)
    public void archiveByDataVolume() {
        long currentCount = repository.count();
        
        if (currentCount > dataVolumeThreshold) {
            long archiveCount = currentCount - dataVolumeThreshold;
            
            Pageable pageable = PageRequest.of(0, (int) archiveCount, 
                    Sort.by(Sort.Direction.ASC, "createTime"));
            
            List<TaskExecutionLog> logsToArchive = repository.findAll(pageable).getContent();
            
            for (TaskExecutionLog log : logsToArchive) {
                TaskExecutionLogArchive archive = convertToArchive(log);
                archiveRepository.save(archive);
            }
            
            repository.deleteAll(logsToArchive);
            
            log.info("Archived {} logs due to data volume threshold", logsToArchive.size());
        }
    }

    private TaskExecutionLogArchive convertToArchive(TaskExecutionLog log) {
        TaskExecutionLogArchive archive = new TaskExecutionLogArchive();
        BeanUtils.copyProperties(log, archive);
        archive.setArchiveTime(LocalDateTime.now());
        return archive;
    }

}

方案三：基于业务规则的归档

优点：

• 符合业务需求
• 可以保留重要数据
• 灵活性高

缺点：

• 实现复杂
• 需要业务知识
• 维护成本高

代码实现：

@Service
public class BusinessRuleArchiveService {

    @Autowired
    private TaskExecutionLogRepository repository;

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Scheduled(cron = "0 0 2 * * ?")
    public void archiveByBusinessRule() {
        LocalDateTime archiveTime = LocalDateTime.now().minusDays(90);
        
        List<TaskExecutionLog> logsToArchive = repository.findByCreateTimeBefore(archiveTime);
        
        List<TaskExecutionLog> filteredLogs = logsToArchive.stream()
                .filter(log -> shouldArchive(log))
                .collect(Collectors.toList());
        
        for (TaskExecutionLog log : filteredLogs) {
            TaskExecutionLogArchive archive = convertToArchive(log);
            archiveRepository.save(archive);
        }
        
        repository.deleteAll(filteredLogs);
        
        log.info("Archived {} logs based on business rules", filteredLogs.size());
    }

    private boolean shouldArchive(TaskExecutionLog log) {
        if ("CRITICAL".equals(log.getPriority())) {
            return false;
        }
        
        if ("FAILED".equals(log.getStatus()) && log.getCreateTime().isAfter(
                LocalDateTime.now().minusDays(30))) {
            return false;
        }
        
        return true;
    }

    private TaskExecutionLogArchive convertToArchive(TaskExecutionLog log) {
        TaskExecutionLogArchive archive = new TaskExecutionLogArchive();
        BeanUtils.copyProperties(log, archive);
        archive.setArchiveTime(LocalDateTime.now());
        return archive;
    }

}

方案四：混合策略归档

优点：

• 综合多种策略
• 更加灵活
• 适应性强

缺点：

• 实现复杂
• 配置复杂
• 维护成本高

代码实现：

@Service
public class HybridArchiveService {

    @Autowired
    private TaskExecutionLogRepository repository;

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Value("${archive.time-window-days:90}")
    private int timeWindowDays;

    @Value("${archive.data-volume-threshold:1000000}")
    private long dataVolumeThreshold;

    @Scheduled(cron = "0 0 2 * * ?")
    public void archiveByHybridStrategy() {
        LocalDateTime timeWindow = LocalDateTime.now().minusDays(timeWindowDays);
        long currentCount = repository.count();
        
        List<TaskExecutionLog> logsToArchive = new ArrayList<>();
        
        if (currentCount > dataVolumeThreshold) {
            long archiveCount = currentCount - dataVolumeThreshold;
            
            Pageable pageable = PageRequest.of(0, (int) archiveCount, 
                    Sort.by(Sort.Direction.ASC, "createTime"));
            
            List<TaskExecutionLog> volumeBasedLogs = repository.findAll(pageable).getContent();
            logsToArchive.addAll(volumeBasedLogs);
        } else {
            List<TaskExecutionLog> timeBasedLogs = repository.findByCreateTimeBefore(timeWindow);
            logsToArchive.addAll(timeBasedLogs);
        }
        
        List<TaskExecutionLog> filteredLogs = logsToArchive.stream()
                .filter(log -> shouldArchive(log))
                .distinct()
                .collect(Collectors.toList());
        
        for (TaskExecutionLog log : filteredLogs) {
            TaskExecutionLogArchive archive = convertToArchive(log);
            archiveRepository.save(archive);
        }
        
        repository.deleteAll(filteredLogs);
        
        log.info("Archived {} logs using hybrid strategy", filteredLogs.size());
    }

    private boolean shouldArchive(TaskExecutionLog log) {
        if ("CRITICAL".equals(log.getPriority())) {
            return false;
        }
        
        if ("FAILED".equals(log.getStatus()) && log.getCreateTime().isAfter(
                LocalDateTime.now().minusDays(30))) {
            return false;
        }
        
        return true;
    }

    private TaskExecutionLogArchive convertToArchive(TaskExecutionLog log) {
        TaskExecutionLogArchive archive = new TaskExecutionLogArchive();
        BeanUtils.copyProperties(log, archive);
        archive.setArchiveTime(LocalDateTime.now());
        return archive;
    }

}

完整实现：历史归档与数据清理

1. 归档配置

@Data
@ConfigurationProperties(prefix = "archive")
@Component
public class ArchiveConfig {

    private boolean enabled = true;

    private int timeWindowDays = 90;

    private long dataVolumeThreshold = 1000000;

    private int batchSize = 1000;

    private String cronExpression = "0 0 2 * * ?";

    private boolean enableBusinessRuleFilter = true;

    private List<String> excludedPriorities = Arrays.asList("CRITICAL");

    private int failedTaskRetentionDays = 30;

}

2. 归档任务实体

@Data
@Entity
@Table(name = "task_execution_log")
public class TaskExecutionLog {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;

    private String taskId;

    private String taskName;

    private String status;

    private String priority;

    private LocalDateTime createTime;

    private LocalDateTime startTime;

    private LocalDateTime endTime;

    private Long executionTime;

    private String errorMessage;

    private String userId;

    private String businessType;

    @Column(columnDefinition = "TEXT")
    private String metadata;

}

3. 归档数据实体

@Data
@Entity
@Table(name = "task_execution_log_archive")
public class TaskExecutionLogArchive {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;

    private Long originalId;

    private String taskId;

    private String taskName;

    private String status;

    private String priority;

    private LocalDateTime createTime;

    private LocalDateTime startTime;

    private LocalDateTime endTime;

    private Long executionTime;

    private String errorMessage;

    private String userId;

    private String businessType;

    @Column(columnDefinition = "TEXT")
    private String metadata;

    private LocalDateTime archiveTime;

    private String archiveReason;

}

4. 归档服务

@Service
@Slf4j
public class TaskArchiveService {

    @Autowired
    private TaskExecutionLogRepository repository;

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Autowired
    private ArchiveConfig archiveConfig;

    @Autowired
    private ArchiveStatisticsService statisticsService;

    @Scheduled(cron = "${archive.cron-expression:0 0 2 * * ?}")
    public void archiveTaskLogs() {
        if (!archiveConfig.isEnabled()) {
            log.info("Archive is disabled");
            return;
        }

        log.info("Starting task log archive process");

        try {
            ArchiveResult result = performArchive();
            
            statisticsService.recordArchiveResult(result);
            
            log.info("Archive completed: {}", result);
        } catch (Exception e) {
            log.error("Archive failed", e);
            statisticsService.recordArchiveFailure(e);
        }
    }

    private ArchiveResult performArchive() {
        ArchiveResult result = new ArchiveResult();
        result.setStartTime(LocalDateTime.now());

        List<TaskExecutionLog> logsToArchive = findLogsToArchive();
        
        if (logsToArchive.isEmpty()) {
            log.info("No logs to archive");
            result.setArchivedCount(0);
            result.setEndTime(LocalDateTime.now());
            return result;
        }

        result.setTotalCount(logsToArchive.size());

        int batchSize = archiveConfig.getBatchSize();
        int totalBatches = (int) Math.ceil((double) logsToArchive.size() / batchSize);

        for (int i = 0; i < totalBatches; i++) {
            int fromIndex = i * batchSize;
            int toIndex = Math.min(fromIndex + batchSize, logsToArchive.size());
            
            List<TaskExecutionLog> batch = logsToArchive.subList(fromIndex, toIndex);
            archiveBatch(batch, result);
        }

        deleteArchivedLogs(logsToArchive);

        result.setEndTime(LocalDateTime.now());
        return result;
    }

    private List<TaskExecutionLog> findLogsToArchive() {
        LocalDateTime timeWindow = LocalDateTime.now().minusDays(archiveConfig.getTimeWindowDays());
        long currentCount = repository.count();

        List<TaskExecutionLog> logsToArchive = new ArrayList<>();

        if (currentCount > archiveConfig.getDataVolumeThreshold()) {
            long archiveCount = currentCount - archiveConfig.getDataVolumeThreshold();
            
            Pageable pageable = PageRequest.of(0, (int) archiveCount, 
                    Sort.by(Sort.Direction.ASC, "createTime"));
            
            List<TaskExecutionLog> volumeBasedLogs = repository.findAll(pageable).getContent();
            logsToArchive.addAll(volumeBasedLogs);
            
            log.info("Found {} logs to archive based on data volume threshold", 
                    volumeBasedLogs.size());
        } else {
            List<TaskExecutionLog> timeBasedLogs = repository.findByCreateTimeBefore(timeWindow);
            logsToArchive.addAll(timeBasedLogs);
            
            log.info("Found {} logs to archive based on time window", 
                    timeBasedLogs.size());
        }

        if (archiveConfig.isEnableBusinessRuleFilter()) {
            logsToArchive = logsToArchive.stream()
                    .filter(this::shouldArchive)
                    .collect(Collectors.toList());
            
            log.info("After business rule filtering: {} logs to archive", 
                    logsToArchive.size());
        }

        return logsToArchive;
    }

    private boolean shouldArchive(TaskExecutionLog log) {
        if (archiveConfig.getExcludedPriorities().contains(log.getPriority())) {
            return false;
        }

        if ("FAILED".equals(log.getStatus())) {
            LocalDateTime failureTime = log.getCreateTime();
            LocalDateTime retentionTime = LocalDateTime.now().minusDays(
                    archiveConfig.getFailedTaskRetentionDays());
            
            if (failureTime.isAfter(retentionTime)) {
                return false;
            }
        }

        return true;
    }

    private void archiveBatch(List<TaskExecutionLog> batch, ArchiveResult result) {
        List<TaskExecutionLogArchive> archives = batch.stream()
                .map(this::convertToArchive)
                .collect(Collectors.toList());

        archiveRepository.saveAll(archives);
        
        result.setArchivedCount(result.getArchivedCount() + archives.size());
        
        log.debug("Archived batch of {} logs", archives.size());
    }

    private void deleteArchivedLogs(List<TaskExecutionLog> logs) {
        repository.deleteAll(logs);
        log.info("Deleted {} archived logs from main table", logs.size());
    }

    private TaskExecutionLogArchive convertToArchive(TaskExecutionLog log) {
        TaskExecutionLogArchive archive = new TaskExecutionLogArchive();
        archive.setOriginalId(log.getId());
        BeanUtils.copyProperties(log, archive);
        archive.setArchiveTime(LocalDateTime.now());
        archive.setArchiveReason(determineArchiveReason(log));
        return archive;
    }

    private String determineArchiveReason(TaskExecutionLog log) {
        LocalDateTime timeWindow = LocalDateTime.now().minusDays(archiveConfig.getTimeWindowDays());
        
        if (log.getCreateTime().isBefore(timeWindow)) {
            return "TIME_WINDOW";
        }
        
        return "DATA_VOLUME";
    }

}

5. 数据清理服务

@Service
@Slf4j
public class DataCleanupService {

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Autowired
    private CleanupConfig cleanupConfig;

    @Autowired
    private CleanupStatisticsService statisticsService;

    @Scheduled(cron = "${cleanup.cron-expression:0 0 3 * * ?}")
    public void cleanupArchivedData() {
        if (!cleanupConfig.isEnabled()) {
            log.info("Cleanup is disabled");
            return;
        }

        log.info("Starting archived data cleanup process");

        try {
            CleanupResult result = performCleanup();
            
            statisticsService.recordCleanupResult(result);
            
            log.info("Cleanup completed: {}", result);
        } catch (Exception e) {
            log.error("Cleanup failed", e);
            statisticsService.recordCleanupFailure(e);
        }
    }

    private CleanupResult performCleanup() {
        CleanupResult result = new CleanupResult();
        result.setStartTime(LocalDateTime.now());

        List<TaskExecutionLogArchive> archivesToDelete = findArchivesToDelete();
        
        if (archivesToDelete.isEmpty()) {
            log.info("No archives to delete");
            result.setDeletedCount(0);
            result.setEndTime(LocalDateTime.now());
            return result;
        }

        result.setTotalCount(archivesToDelete.size());

        int batchSize = cleanupConfig.getBatchSize();
        int totalBatches = (int) Math.ceil((double) archivesToDelete.size() / batchSize);

        for (int i = 0; i < totalBatches; i++) {
            int fromIndex = i * batchSize;
            int toIndex = Math.min(fromIndex + batchSize, archivesToDelete.size());
            
            List<TaskExecutionLogArchive> batch = archivesToDelete.subList(fromIndex, toIndex);
            deleteBatch(batch, result);
        }

        result.setEndTime(LocalDateTime.now());
        return result;
    }

    private List<TaskExecutionLogArchive> findArchivesToDelete() {
        LocalDateTime retentionTime = LocalDateTime.now().minusDays(
                cleanupConfig.getRetentionDays());
        
        List<TaskExecutionLogArchive> archivesToDelete = 
                archiveRepository.findByArchiveTimeBefore(retentionTime);
        
        log.info("Found {} archives to delete older than {}", 
                archivesToDelete.size(), retentionTime);
        
        return archivesToDelete;
    }

    private void deleteBatch(List<TaskExecutionLogArchive> batch, CleanupResult result) {
        archiveRepository.deleteAll(batch);
        
        result.setDeletedCount(result.getDeletedCount() + batch.size());
        
        log.debug("Deleted batch of {} archives", batch.size());
    }

}

6. 归档统计服务

@Service
@Slf4j
public class ArchiveStatisticsService {

    @Autowired
    private ArchiveStatisticsRepository statisticsRepository;

    public void recordArchiveResult(ArchiveResult result) {
        ArchiveStatistics statistics = new ArchiveStatistics();
        statistics.setType("ARCHIVE");
        statistics.setStartTime(result.getStartTime());
        statistics.setEndTime(result.getEndTime());
        statistics.setTotalCount(result.getTotalCount());
        statistics.setArchivedCount(result.getArchivedCount());
        statistics.setDuration(Duration.between(result.getStartTime(), result.getEndTime()).toMillis());
        statistics.setStatus("SUCCESS");
        statistics.setCreateTime(LocalDateTime.now());
        
        statisticsRepository.save(statistics);
        
        log.info("Archive statistics recorded: {}", statistics);
    }

    public void recordArchiveFailure(Exception e) {
        ArchiveStatistics statistics = new ArchiveStatistics();
        statistics.setType("ARCHIVE");
        statistics.setStartTime(LocalDateTime.now());
        statistics.setEndTime(LocalDateTime.now());
        statistics.setTotalCount(0);
        statistics.setArchivedCount(0);
        statistics.setDuration(0);
        statistics.setStatus("FAILED");
        statistics.setErrorMessage(e.getMessage());
        statistics.setCreateTime(LocalDateTime.now());
        
        statisticsRepository.save(statistics);
        
        log.error("Archive failure recorded: {}", statistics);
    }

    public List<ArchiveStatistics> getRecentArchiveStatistics(int days) {
        LocalDateTime startTime = LocalDateTime.now().minusDays(days);
        return statisticsRepository.findByTypeAndCreateTimeAfter("ARCHIVE", startTime);
    }

}

7. 清理统计服务

@Service
@Slf4j
public class CleanupStatisticsService {

    @Autowired
    private ArchiveStatisticsRepository statisticsRepository;

    public void recordCleanupResult(CleanupResult result) {
        ArchiveStatistics statistics = new ArchiveStatistics();
        statistics.setType("CLEANUP");
        statistics.setStartTime(result.getStartTime());
        statistics.setEndTime(result.getEndTime());
        statistics.setTotalCount(result.getTotalCount());
        statistics.setArchivedCount(result.getDeletedCount());
        statistics.setDuration(Duration.between(result.getStartTime(), result.getEndTime()).toMillis());
        statistics.setStatus("SUCCESS");
        statistics.setCreateTime(LocalDateTime.now());
        
        statisticsRepository.save(statistics);
        
        log.info("Cleanup statistics recorded: {}", statistics);
    }

    public void recordCleanupFailure(Exception e) {
        ArchiveStatistics statistics = new ArchiveStatistics();
        statistics.setType("CLEANUP");
        statistics.setStartTime(LocalDateTime.now());
        statistics.setEndTime(LocalDateTime.now());
        statistics.setTotalCount(0);
        statistics.setArchivedCount(0);
        statistics.setDuration(0);
        statistics.setStatus("FAILED");
        statistics.setErrorMessage(e.getMessage());
        statistics.setCreateTime(LocalDateTime.now());
        
        statisticsRepository.save(statistics);
        
        log.error("Cleanup failure recorded: {}", statistics);
    }

    public List<ArchiveStatistics> getRecentCleanupStatistics(int days) {
        LocalDateTime startTime = LocalDateTime.now().minusDays(days);
        return statisticsRepository.findByTypeAndCreateTimeAfter("CLEANUP", startTime);
    }

}

8. 归档管理控制器

@RestController
@RequestMapping("/api/archive")
@Slf4j
public class ArchiveManagementController {

    @Autowired
    private TaskArchiveService archiveService;

    @Autowired
    private DataCleanupService cleanupService;

    @Autowired
    private ArchiveStatisticsService statisticsService;

    @PostMapping("/trigger-archive")
    public Result<String> triggerArchive() {
        try {
            archiveService.archiveTaskLogs();
            return Result.success("Archive triggered");
        } catch (Exception e) {
            log.error("Failed to trigger archive", e);
            return Result.error(500, e.getMessage());
        }
    }

    @PostMapping("/trigger-cleanup")
    public Result<String> triggerCleanup() {
        try {
            cleanupService.cleanupArchivedData();
            return Result.success("Cleanup triggered");
        } catch (Exception e) {
            log.error("Failed to trigger cleanup", e);
            return Result.error(500, e.getMessage());
        }
    }

    @GetMapping("/statistics")
    public Result<List<ArchiveStatistics>> getStatistics(
            @RequestParam(defaultValue = "30") int days) {
        try {
            List<ArchiveStatistics> archiveStats = 
                    statisticsService.getRecentArchiveStatistics(days);
            List<ArchiveStatistics> cleanupStats = 
                    statisticsService.getRecentCleanupStatistics(days);
            
            List<ArchiveStatistics> allStats = new ArrayList<>();
            allStats.addAll(archiveStats);
            allStats.addAll(cleanupStats);
            
            allStats.sort(Comparator.comparing(ArchiveStatistics::getCreateTime).reversed());
            
            return Result.success(allStats);
        } catch (Exception e) {
            log.error("Failed to get statistics", e);
            return Result.error(500, e.getMessage());
        }
    }

}

核心流程：历史归档与数据清理

1. 归档流程

sequenceDiagram
    participant Scheduler
    participant ArchiveService
    participant Repository
    participant ArchiveRepository

    Scheduler->>ArchiveService: 触发归档
    ArchiveService->>Repository: 查询待归档数据
    Repository-->>ArchiveService: 数据列表
    ArchiveService->>ArchiveRepository: 批量保存归档数据
    ArchiveRepository-->>ArchiveService: 保存成功
    ArchiveService->>Repository: 删除已归档数据
    Repository-->>ArchiveService: 删除成功
    ArchiveService->>ArchiveService: 记录归档统计

2. 清理流程

sequenceDiagram
    participant Scheduler
    participant CleanupService
    participant ArchiveRepository

    Scheduler->>CleanupService: 触发清理
    CleanupService->>ArchiveRepository: 查询过期归档数据
    ArchiveRepository-->>CleanupService: 数据列表
    CleanupService->>ArchiveRepository: 批量删除数据
    ArchiveRepository-->>CleanupService: 删除成功
    CleanupService->>CleanupService: 记录清理统计

3. 混合策略流程

sequenceDiagram
    participant ArchiveService
    participant Repository
    participant Filter

    ArchiveService->>Repository: 查询数据量
    Repository-->>ArchiveService: 当前数据量
    alt 数据量超过阈值
        ArchiveService->>Repository: 按数据量查询
        Repository-->>ArchiveService: 数据列表
    else 数据量未超过阈值
        ArchiveService->>Repository: 按时间窗口查询
        Repository-->>ArchiveService: 数据列表
    end
    ArchiveService->>Filter: 业务规则过滤
    Filter-->>ArchiveService: 过滤后数据
    ArchiveService->>ArchiveService: 执行归档

技术要点：历史归档与数据清理

1. 归档策略

时间窗口归档

public List<TaskExecutionLog> archiveByTimeWindow(int days) {
    LocalDateTime archiveTime = LocalDateTime.now().minusDays(days);
    return repository.findByCreateTimeBefore(archiveTime);
}

数据量阈值归档

public List<TaskExecutionLog> archiveByDataVolume(long threshold) {
    long currentCount = repository.count();
    
    if (currentCount > threshold) {
        long archiveCount = currentCount - threshold;
        
        Pageable pageable = PageRequest.of(0, (int) archiveCount, 
                Sort.by(Sort.Direction.ASC, "createTime"));
        
        return repository.findAll(pageable).getContent();
    }
    
    return Collections.emptyList();
}

业务规则归档

public List<TaskExecutionLog> archiveByBusinessRule() {
    List<TaskExecutionLog> allLogs = repository.findAll();
    
    return allLogs.stream()
            .filter(this::shouldArchive)
            .collect(Collectors.toList());
}

private boolean shouldArchive(TaskExecutionLog log) {
    if ("CRITICAL".equals(log.getPriority())) {
        return false;
    }
    
    if ("FAILED".equals(log.getStatus()) && 
        log.getCreateTime().isAfter(LocalDateTime.now().minusDays(30))) {
        return false;
    }
    
    return true;
}

2. 批量处理

分批归档

public void archiveInBatches(List<TaskExecutionLog> logs, int batchSize) {
    int totalBatches = (int) Math.ceil((double) logs.size() / batchSize);
    
    for (int i = 0; i < totalBatches; i++) {
        int fromIndex = i * batchSize;
        int toIndex = Math.min(fromIndex + batchSize, logs.size());
        
        List<TaskExecutionLog> batch = logs.subList(fromIndex, toIndex);
        archiveBatch(batch);
    }
}

private void archiveBatch(List<TaskExecutionLog> batch) {
    List<TaskExecutionLogArchive> archives = batch.stream()
            .map(this::convertToArchive)
            .collect(Collectors.toList());
    
    archiveRepository.saveAll(archives);
}

分批删除

public void deleteInBatches(List<TaskExecutionLog> logs, int batchSize) {
    int totalBatches = (int) Math.ceil((double) logs.size() / batchSize);
    
    for (int i = 0; i < totalBatches; i++) {
        int fromIndex = i * batchSize;
        int toIndex = Math.min(fromIndex + batchSize, logs.size());
        
        List<TaskExecutionLog> batch = logs.subList(fromIndex, toIndex);
        repository.deleteAll(batch);
    }
}

3. 错误处理

事务处理

@Transactional
public void archiveWithTransaction(List<TaskExecutionLog> logs) {
    try {
        List<TaskExecutionLogArchive> archives = logs.stream()
                .map(this::convertToArchive)
                .collect(Collectors.toList());
        
        archiveRepository.saveAll(archives);
        repository.deleteAll(logs);
    } catch (Exception e) {
        log.error("Archive failed", e);
        throw new ArchiveException("Archive failed", e);
    }
}

重试机制

@Retryable(value = {Exception.class}, maxAttempts = 3, backoff = @Backoff(delay = 1000))
public void archiveWithRetry(List<TaskExecutionLog> logs) {
    List<TaskExecutionLogArchive> archives = logs.stream()
            .map(this::convertToArchive)
            .collect(Collectors.toList());
    
    archiveRepository.saveAll(archives);
    repository.deleteAll(logs);
}

4. 性能优化

索引优化

CREATE INDEX idx_create_time ON task_execution_log(create_time);
CREATE INDEX idx_status ON task_execution_log(status);
CREATE INDEX idx_priority ON task_execution_log(priority);

分页查询

public List<TaskExecutionLog> findWithPagination(int page, int size) {
    Pageable pageable = PageRequest.of(page, size, 
            Sort.by(Sort.Direction.ASC, "createTime"));
    
    Page<TaskExecutionLog> page = repository.findAll(pageable);
    return page.getContent();
}

批量插入

public void batchInsert(List<TaskExecutionLogArchive> archives) {
    int batchSize = 1000;
    int totalBatches = (int) Math.ceil((double) archives.size() / batchSize);
    
    for (int i = 0; i < totalBatches; i++) {
        int fromIndex = i * batchSize;
        int toIndex = Math.min(fromIndex + batchSize, archives.size());
        
        List<TaskExecutionLogArchive> batch = archives.subList(fromIndex, toIndex);
        archiveRepository.saveAll(batch);
    }
}

最佳实践：历史归档与数据清理

1. 合理设置归档策略

原则：

• 根据业务需求设置归档策略
• 保留重要的历史数据
• 定期评估归档策略
• 适应业务变化

示例：

@ConfigurationProperties(prefix = "archive")
public class ArchiveConfig {
    private boolean enabled = true;
    private int timeWindowDays = 90;
    private long dataVolumeThreshold = 1000000;
    private int batchSize = 1000;
    private String cronExpression = "0 0 2 * * ?";
    private boolean enableBusinessRuleFilter = true;
    private List<String> excludedPriorities = Arrays.asList("CRITICAL");
    private int failedTaskRetentionDays = 30;
}

2. 监控归档过程

原则：

• 实时监控归档过程
• 记录归档统计信息
• 及时发现和解决问题
• 分析归档效果

示例：

@Service
@Slf4j
public class ArchiveMonitorService {

    @Autowired
    private ArchiveStatisticsService statisticsService;

    @Scheduled(fixedRate = 60000)
    public void monitorArchiveProcess() {
        List<ArchiveStatistics> recentStats = 
                statisticsService.getRecentArchiveStatistics(7);
        
        long successCount = recentStats.stream()
                .filter(stat -> "SUCCESS".equals(stat.getStatus()))
                .count();
        
        long failureCount = recentStats.stream()
                .filter(stat -> "FAILED".equals(stat.getStatus()))
                .count();
        
        if (failureCount > 0) {
            sendAlert("Archive failures detected: " + failureCount);
        }
        
        long avgDuration = recentStats.stream()
                .filter(stat -> "SUCCESS".equals(stat.getStatus()))
                .mapToLong(ArchiveStatistics::getDuration)
                .average()
                .orElse(0);
        
        if (avgDuration > 300000) {
            sendAlert("Archive duration too long: " + avgDuration + "ms");
        }
    }

    private void sendAlert(String message) {
        log.error("Archive alert: {}", message);
    }

}

3. 定期评估归档效果

原则：

• 定期评估归档效果
• 分析归档统计数据
• 优化归档策略
• 提高归档效率

示例：

@Service
@Slf4j
public class ArchiveEvaluationService {

    @Autowired
    private ArchiveStatisticsService statisticsService;

    @Scheduled(cron = "0 0 4 * * SUN")
    public void evaluateArchiveEffectiveness() {
        List<ArchiveStatistics> weeklyStats = 
                statisticsService.getRecentArchiveStatistics(7);
        
        long totalArchived = weeklyStats.stream()
                .mapToLong(ArchiveStatistics::getArchivedCount)
                .sum();
        
        long avgDuration = (long) weeklyStats.stream()
                .filter(stat -> "SUCCESS".equals(stat.getStatus()))
                .mapToLong(ArchiveStatistics::getDuration)
                .average()
                .orElse(0);
        
        log.info("Archive evaluation - Total archived: {}, Avg duration: {}ms", 
                totalArchived, avgDuration);
        
        if (avgDuration > 300000) {
            recommendOptimization();
        }
    }

    private void recommendOptimization() {
        log.warn("Archive duration is too long, consider optimization:");
        log.warn("1. Increase batch size");
        log.warn("2. Optimize database indexes");
        log.warn("3. Consider parallel processing");
    }

}

4. 备份归档数据

原则：

• 定期备份归档数据
• 使用可靠的备份方案
• 验证备份完整性
• 测试恢复流程

示例：

@Service
@Slf4j
public class ArchiveBackupService {

    @Autowired
    private TaskExecutionLogArchiveRepository archiveRepository;

    @Scheduled(cron = "0 0 5 * * SUN")
    public void backupArchiveData() {
        LocalDateTime backupTime = LocalDateTime.now();
        
        try {
            List<TaskExecutionLogArchive> allArchives = 
                    archiveRepository.findAll();
            
            String backupFileName = "archive-backup-" + 
                    backupTime.format(DateTimeFormatter.ofPattern("yyyyMMdd-HHmmss")) + ".json";
            
            String backupFilePath = "/backup/" + backupFileName;
            
            String jsonData = JSON.toJSONString(allArchives);
            
            Files.write(Paths.get(backupFilePath), 
                    jsonData.getBytes(StandardCharsets.UTF_8));
            
            log.info("Archive backup completed: {}", backupFilePath);
        } catch (Exception e) {
            log.error("Archive backup failed", e);
        }
    }

}

常见问题：历史归档与数据清理

1. 归档失败

问题：归档过程中出现错误，导致数据不一致

原因：

• 事务处理不当
• 批量操作失败
• 数据库连接问题
• 磁盘空间不足

解决方案：

@Transactional
public void archiveWithTransaction(List<TaskExecutionLog> logs) {
    try {
        List<TaskExecutionLogArchive> archives = logs.stream()
                .map(this::convertToArchive)
                .collect(Collectors.toList());
        
        archiveRepository.saveAll(archives);
        repository.deleteAll(logs);
        
        log.info("Archive completed successfully: {} logs", logs.size());
    } catch (Exception e) {
        log.error("Archive failed, rolling back transaction", e);
        throw new ArchiveException("Archive failed", e);
    }
}

2. 清理误删

问题：清理过程中误删重要数据

原因：

• 清理策略不当
• 时间窗口设置错误
• 业务规则错误
• 缺少备份

解决方案：

public List<TaskExecutionLogArchive> findArchivesToDelete() {
    LocalDateTime retentionTime = LocalDateTime.now().minusDays(
            cleanupConfig.getRetentionDays());
    
    List<TaskExecutionLogArchive> archivesToDelete = 
            archiveRepository.findByArchiveTimeBefore(retentionTime);
    
    List<TaskExecutionLogArchive> filteredArchives = archivesToDelete.stream()
            .filter(archive -> shouldDelete(archive))
            .collect(Collectors.toList());
    
    log.info("Found {} archives to delete after filtering", 
            filteredArchives.size());
    
    return filteredArchives;
}

private boolean shouldDelete(TaskExecutionLogArchive archive) {
    if ("CRITICAL".equals(archive.getPriority())) {
        return false;
    }
    
    if ("FAILED".equals(archive.getStatus())) {
        LocalDateTime failureTime = archive.getCreateTime();
        LocalDateTime retentionTime = LocalDateTime.now().minusDays(90);
        
        if (failureTime.isAfter(retentionTime)) {
            return false;
        }
    }
    
    return true;
}

3. 性能问题

问题：归档和清理过程影响系统性能

原因：

• 批量操作过大
• 索引不当
• 并发操作过多
• 资源竞争

解决方案：

public void archiveWithPerformanceOptimization() {
    int batchSize = 1000;
    int delayBetweenBatches = 100;
    
    List<TaskExecutionLog> logsToArchive = findLogsToArchive();
    
    int totalBatches = (int) Math.ceil((double) logsToArchive.size() / batchSize);
    
    for (int i = 0; i < totalBatches; i++) {
        int fromIndex = i * batchSize;
        int toIndex = Math.min(fromIndex + batchSize, logsToArchive.size());
        
        List<TaskExecutionLog> batch = logsToArchive.subList(fromIndex, toIndex);
        
        archiveBatch(batch);
        
        try {
            Thread.sleep(delayBetweenBatches);
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            break;
        }
    }
}

4. 数据不一致

问题：归档和清理后数据不一致

原因：

• 并发操作
• 事务隔离级别不当
• 缓存不一致
• 数据同步问题

解决方案：

@Transactional(isolation = Isolation.SERIALIZABLE)
public void archiveWithSerializableIsolation(List<TaskExecutionLog> logs) {
    List<TaskExecutionLogArchive> archives = logs.stream()
            .map(this::convertToArchive)
            .collect(Collectors.toList());
    
    archiveRepository.saveAll(archives);
    repository.deleteAll(logs);
    
    clearCache();
}

性能测试：历史归档与数据清理

测试环境

• 服务器：4核8G，100Mbps带宽
• 数据库：MySQL 8.0
• 测试场景：1000万条记录

测试结果

测试结论

1. 查询性能提升：归档后查询性能提升 10 倍
2. 存储成本降低：清理后存储成本降低 80%
3. 系统负载降低：归档和清理后系统负载降低 50%
4. 备份时间缩短：清理后备份时间缩短 96%

互动话题

1. 你在实际项目中如何实现历史数据归档？有哪些经验分享？
2. 对于数据清理，你认为哪种策略最有效？
3. 你遇到过哪些归档和清理相关的问题？如何解决的？
4. 在微服务架构中，如何实现跨服务的数据归档和清理？

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