云数据库CRUD自动化测试套件设计与实现

一、项目概述

基于HarmonyOS 5分布式能力的云数据库自动化测试系统，针对分布式数据同步场景下的CRUD操作进行自动化验证。该系统参考《鸿蒙跨端U同步》中的多设备数据一致性机制，构建完整的测试框架，确保分布式环境下数据操作的原子性、一致性和同步时效性。

二、核心架构设计

+---------------------+
|  测试控制中心       |
|  (Test Orchestrator)|
+----------+----------+
           |
+----------v----------+    +---------------------+
|  分布式测试代理     |<--->|  被测云数据库       |
|  (Test Agent)       |    |  (Cloud DB)         |
+----------+----------+    +---------------------+
           |
+----------v----------+
|  设备集群           |
|  (Device Cluster)   |
+---------------------+

三、分布式测试基础框架

// 分布式测试协调器
public class DistributedTestCoordinator {
    private static final String TEST_SYNC_KEY = "dist_test_sync";
    private DistributedDataManager dataManager;
    
    public DistributedTestCoordinator(Context context) {
        dataManager = DistributedDataManagerFactory.getInstance()
            .createDistributedDataManager(new ManagerConfig(context));
    }
    
    // 启动分布式测试会话
    public String startTestSession(TestConfig config) {
        String sessionId = UUID.randomUUID().toString();
        DistributedTestSession session = new DistributedTestSession(
            sessionId,
            config.getTestCases(),
            System.currentTimeMillis()
        );
        
        dataManager.putString(TEST_SYNC_KEY + "_" + sessionId, 
            new Gson().toJson(session));
        return sessionId;
    }
    
    // 注册测试结果监听
    public void registerResultListener(String sessionId, TestResultListener listener) {
        dataManager.registerDataChangeListener(TEST_SYNC_KEY + "_" + sessionId, 
            new DataChangeListener() {
                @Override
                public void onDataChanged(String deviceId, String key, String value) {
                    TestResultUpdate update = new Gson().fromJson(value, TestResultUpdate.class);
                    listener.onTestResultUpdate(update);
                }
            });
    }
    
    // 同步测试状态
    public void syncTestState(String sessionId, TestState state) {
        dataManager.putString(TEST_SYNC_KEY + "_state_" + sessionId, 
            new Gson().toJson(state));
    }
}

四、CRUD测试核心实现

1. 分布式创建测试

public class CreateOperationTest {
    private static final String TEST_DATA_KEY = "test_user_data";
    private CloudDBZone mCloudDBZone;
    private DistributedTestCoordinator coordinator;
    
    public void testDistributedCreate(String sessionId) {
        // 1. 生成测试数据
        User testUser = generateTestUser();
        
        // 2. 本地创建操作
        Completable localCreate = Completable.fromAction(() -> {
            mCloudDBZone.executeUpsert(testUser);
        });
        
        // 3. 验证跨设备同步
        Completable syncVerify = verifySyncOnAllDevices(sessionId, testUser);
        
        // 4. 组合测试流程
        localCreate.andThen(syncVerify)
            .subscribeOn(Schedulers.io())
            .observeOn(AndroidSchedulers.mainThread())
            .subscribe(
                () -> coordinator.reportTestResult(sessionId, "CREATE", true),
                e -> coordinator.reportTestResult(sessionId, "CREATE", false)
            );
    }
    
    private Completable verifySyncOnAllDevices(String sessionId, User expected) {
        return Completable.create(emitter -> {
            // 1. 注册设备响应监听
            AtomicInteger responseCount = new AtomicInteger(0);
            int expectedDevices = coordinator.getJoinedDevices(sessionId).size();
            
            coordinator.registerDataListener(TEST_DATA_KEY + "_" + sessionId, 
                new DataListener() {
                    @Override
                    public void onDataChanged(String deviceId, String data) {
                        User actual = new Gson().fromJson(data, User.class);
                        if (!expected.equals(actual)) {
                            emitter.onError(new AssertionError("Data mismatch on device " + deviceId));
                            return;
                        }
                        
                        if (responseCount.incrementAndGet() == expectedDevices) {
                            emitter.onComplete();
                        }
                    }
                });
            
            // 2. 设置超时监控
            new Handler(Looper.getMainLooper()).postDelayed(() -> {
                if (!emitter.isDisposed()) {
                    emitter.onError(new TimeoutException("Sync verification timeout"));
                }
            }, 5000);
        });
    }
}

2. 分布式读取测试

public class ReadOperationTest {
    private CloudDBZone mCloudDBZone;
    
    public void testDistributedReadConsistency(String sessionId, String userId) {
        // 1. 在所有设备上发起并发读取
        List<Completable> readTasks = coordinator.getJoinedDevices(sessionId)
            .stream()
            .map(deviceId -> Completable.fromAction(() -> {
                User user = mCloudDBZone.executeQuery(
                    CloudDBZoneQuery.where(User.class).equalTo("userId", userId));
                
                coordinator.reportReadResult(sessionId, deviceId, user);
            }))
            .collect(Collectors.toList());
        
        // 2. 验证读取结果一致性
        Completable.concat(readTasks)
            .subscribeOn(Schedulers.io())
            .observeOn(AndroidSchedulers.mainThread())
            .subscribe(
                () -> verifyConsistentReads(sessionId),
                e -> coordinator.reportTestResult(sessionId, "READ", false)
            );
    }
    
    private void verifyConsistentReads(String sessionId) {
        Map<String, User> readResults = coordinator.getReadResults(sessionId);
        User firstResult = readResults.values().iterator().next();
        
        for (Map.Entry<String, User> entry : readResults.entrySet()) {
            if (!firstResult.equals(entry.getValue())) {
                coordinator.reportTestResult(sessionId, "READ", false);
                return;
            }
        }
        
        coordinator.reportTestResult(sessionId, "READ", true);
    }
}

3. 分布式更新测试

public class UpdateOperationTest {
    private static final String LOCK_KEY = "update_lock";
    
    public void testDistributedUpdate(String sessionId, String userId) {
        // 1. 获取分布式锁
        acquireDistributedLock(sessionId)
            .flatMapCompletable(lockToken -> {
                // 2. 执行更新操作
                User original = mCloudDBZone.executeQuery(
                    CloudDBZoneQuery.where(User.class).equalTo("userId", userId));
                
                User updated = new User(original);
                updated.setNickname("Updated_" + System.currentTimeMillis());
                
                return Completable.fromAction(() -> {
                    mCloudDBZone.executeUpsert(updated);
                })
                .andThen(verifySyncOnAllDevices(sessionId, updated))
                .doFinally(() -> releaseDistributedLock(lockToken));
            })
            .subscribe(
                () -> coordinator.reportTestResult(sessionId, "UPDATE", true),
                e -> coordinator.reportTestResult(sessionId, "UPDATE", false)
            );
    }
    
    private Single<String> acquireDistributedLock(String sessionId) {
        return Single.create(emitter -> {
            String lockToken = UUID.randomUUID().toString();
            boolean acquired = coordinator.tryAcquireLock(
                LOCK_KEY + "_" + sessionId, lockToken, 5000);
            
            if (acquired) {
                emitter.onSuccess(lockToken);
            } else {
                emitter.onError(new RuntimeException("Failed to acquire distributed lock"));
            }
        });
    }
}

4. 分布式删除测试

public class DeleteOperationTest {
    public void testDistributedDelete(String sessionId, String userId) {
        // 1. 执行删除操作
        Completable.fromAction(() -> {
            mCloudDBZone.executeDelete(
                CloudDBZoneQuery.where(User.class).equalTo("userId", userId));
        })
        // 2. 验证所有设备数据消失
        .andThen(verifyDeletionOnAllDevices(sessionId, userId))
        .subscribe(
            () -> coordinator.reportTestResult(sessionId, "DELETE", true),
            e -> coordinator.reportTestResult(sessionId, "DELETE", false)
        );
    }
    
    private Completable verifyDeletionOnAllDevices(String sessionId, String userId) {
        return Completable.create(emitter -> {
            AtomicInteger responseCount = new AtomicInteger(0);
            int expectedDevices = coordinator.getJoinedDevices(sessionId).size();
            
            coordinator.registerDataListener(TEST_DATA_KEY + "_del_" + sessionId, 
                new DataListener() {
                    @Override
                    public void onDataChanged(String deviceId, String data) {
                        if (!"DELETED".equals(data)) {
                            emitter.onError(new AssertionError(
                                "Deletion not confirmed on device " + deviceId));
                            return;
                        }
                        
                        if (responseCount.incrementAndGet() == expectedDevices) {
                            emitter.onComplete();
                        }
                    }
                });
            
            // 触发各设备验证
            coordinator.broadcastVerifyCommand(sessionId, 
                "VERIFY_DELETE_" + userId);
        });
    }
}

五、测试报告生成系统

public class TestReportGenerator {
    private static final int SYNC_TIME_THRESHOLD = 1000; // 1秒同步阈值
    
    public TestReport generateReport(String sessionId) {
        TestReport report = new TestReport(sessionId);
        
        // 1. 收集所有测试结果
        Map<String, TestResult> allResults = coordinator.getAllTestResults(sessionId);
        
        // 2. 计算关键指标
        calculateMetrics(report, allResults);
        
        // 3. 识别同步异常
        detectSyncAnomalies(report, allResults);
        
        // 4. 生成建议
        generateRecommendations(report);
        
        return report;
    }
    
    private void calculateMetrics(TestReport report, Map<String, TestResult> results) {
        // 计算操作成功率
        long successCount = results.values().stream()
            .filter(TestResult::isSuccess)
            .count();
        report.setSuccessRate((double) successCount / results.size());
        
        // 计算平均同步时间
        double avgSyncTime = results.values().stream()
            .mapToLong(TestResult::getSyncTime)
            .average()
            .orElse(0);
        report.setAvgSyncTime(avgSyncTime);
    }
    
    private void detectSyncAnomalies(TestReport report, Map<String, TestResult> results) {
        // 识别超时同步
        List<String> timeoutOperations = results.entrySet().stream()
            .filter(e -> e.getValue().getSyncTime() > SYNC_TIME_THRESHOLD)
            .map(Map.Entry::getKey)
            .collect(Collectors.toList());
        report.setSlowSyncOperations(timeoutOperations);
        
        // 识别不一致数据
        List<String> inconsistentOperations = results.entrySet().stream()
            .filter(e -> e.getValue().hasDataDiscrepancy())
            .map(Map.Entry::getKey)
            .collect(Collectors.toList());
        report.setInconsistentOperations(inconsistentOperations);
    }
}

六、设备集群管理

public class DeviceClusterManager {
    private static final String CLUSTER_MEMBER_KEY = "cluster_members";
    private DistributedDataManager dataManager;
    
    public void joinCluster(String sessionId, DeviceInfo device) {
        // 获取当前成员列表
        String currentMembers = dataManager.getString(CLUSTER_MEMBER_KEY + "_" + sessionId);
        Set<DeviceInfo> members = currentMembers == null ? 
            new HashSet<>() : 
            new Gson().fromJson(currentMembers, new TypeToken<Set<DeviceInfo>>(){}.getType());
        
        // 添加新成员
        members.add(device);
        dataManager.putString(CLUSTER_MEMBER_KEY + "_" + sessionId, 
            new Gson().toJson(members));
    }
    
    public void leaveCluster(String sessionId, String deviceId) {
        // 获取当前成员列表
        String currentMembers = dataManager.getString(CLUSTER_MEMBER_KEY + "_" + sessionId);
        if (currentMembers == null) return;
        
        Set<DeviceInfo> members = new Gson().fromJson(currentMembers, 
            new TypeToken<Set<DeviceInfo>>(){}.getType());
        
        // 移除成员
        members.removeIf(d -> d.getDeviceId().equals(deviceId));
        dataManager.putString(CLUSTER_MEMBER_KEY + "_" + sessionId, 
            new Gson().toJson(members));
    }
    
    public List<DeviceInfo> getClusterMembers(String sessionId) {
        String currentMembers = dataManager.getString(CLUSTER_MEMBER_KEY + "_" + sessionId);
        if (currentMembers == null) return Collections.emptyList();
        
        return new ArrayList<>(
            new Gson().fromJson(currentMembers, 
                new TypeToken<Set<DeviceInfo>>(){}.getType())
        );
    }
}

七、测试场景示例

多设备玩家数据同步测试

public class MultiPlayerSyncTest {
    private static final String PLAYER_DATA_KEY = "player_data";
    
    public void testMultiDevicePlayerSync(String sessionId) {
        // 1. 模拟多设备玩家数据更新
        List<Completable> updateTasks = new ArrayList<>();
        for (int i = 0; i < 5; i++) {
            String deviceId = "device_" + i;
            updateTasks.add(simulatePlayerUpdate(sessionId, deviceId));
        }
        
        // 2. 并发执行并验证同步
        Completable.concat(updateTasks)
            .subscribeOn(Schedulers.io())
            .observeOn(AndroidSchedulers.mainThread())
            .subscribe(
                () -> verifyFinalPlayerStates(sessionId),
                e -> coordinator.reportTestResult(sessionId, "MULTI_PLAYER_SYNC", false)
            );
    }
    
    private Completable simulatePlayerUpdate(String sessionId, String deviceId) {
        return Completable.fromAction(() -> {
            // 模拟玩家数据变更
            Player player = getCurrentPlayer(deviceId);
            player.setScore(player.getScore() + 10);
            player.setLastUpdateTime(System.currentTimeMillis());
            
            // 执行更新
            mCloudDBZone.executeUpsert(player);
            
            // 等待同步
            Thread.sleep(200);
        });
    }
    
    private void verifyFinalPlayerStates(String sessionId) {
        // 获取所有设备上的最终玩家状态
        Map<String, Player> finalStates = coordinator.getAllPlayerStates(sessionId);
        
        // 验证数据一致性
        Player first = finalStates.values().iterator().next();
        boolean allMatch = finalStates.values().stream()
            .allMatch(p -> p.equals(first));
        
        coordinator.reportTestResult(sessionId, "MULTI_PLAYER_SYNC", allMatch);
    }
}

八、技术创新点

1. ​​分布式测试编排​​：跨设备协调测试流程
2. ​​自动同步验证​​：内置数据一致性检查机制
3. ​​真实场景模拟​​：支持多设备并发操作测试
4. ​​智能异常检测​​：自动识别同步延迟和数据不一致
5. ​​可视化报告​​：直观展示测试结果和性能指标

九、总结

本云数据库CRUD自动化测试套件基于HarmonyOS 5分布式能力，实现了以下核心价值：

1. ​​全面覆盖​​：支持创建、读取、更新、删除全操作链验证
2. ​​真实分布式​​：多设备协同测试还原真实使用场景
3. ​​自动化验证​​：内置断言机制自动判断测试结果
4. ​​性能基准​​：提供同步时效性等关键指标测量

系统充分借鉴了鸿蒙跨端U同步中的数据同步机制，特别针对游戏等实时交互场景的多设备数据一致性需求进行了专项优化。未来可结合机器学习预测潜在同步问题，并与CI/CD流程深度集成，为分布式应用开发提供强有力的质量保障。