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Database transactions fail. They fail due to constraint violations, deadlocks, connection timeouts, serialization conflicts, disk full conditions, and dozens of other reasons. The question isn't whether your transactions will encounter failures—it's how your application responds when they do.
Robust failure handling is what separates production-ready code from demo code. A system that crashes or corrupts data on the first database error won't survive contact with the real world. Meanwhile, systems designed with failure in mind gracefully handle transient errors, preserve data integrity during permanent failures, and provide clear feedback to users and operators.
This page teaches you to classify transaction failures (transient vs. permanent), implement retry strategies for recoverable errors, handle deadlocks gracefully, manage constraint violations properly, and build defensive code that maintains data integrity even when things go wrong. You'll learn the patterns that experienced database engineers use in production systems.
Not all transaction failures are equal. The appropriate response depends on the failure type. Understanding this classification is the first step to proper error handling.
| Category | Examples | Recoverable? | Appropriate Response |
|---|---|---|---|
| Transient | Deadlock, serialization failure, connection timeout | Yes (retry) | Automatic retry with backoff |
| Constraint Violation | Unique constraint, foreign key, check constraint | No (fix input) | Report to user/caller, don't retry |
| Resource Exhaustion | Connection pool empty, disk full, memory limit | Maybe | Back-pressure, retry with circuit breaker |
| Configuration | Invalid credentials, wrong port, missing table | No | Fail fast, require deployment fix |
| Permanent | Database server down, network partition | Not immediately | Fallback/queue, alert operators |
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/** * Classify database exceptions to determine appropriate handling strategy */public class TransactionErrorClassifier { public ErrorCategory classify(Exception e) { if (e instanceof SQLException sqlEx) { return classifySqlException(sqlEx); } if (e instanceof DataAccessException dae) { return classifySpringException(dae); } // Unknown errors default to permanent (fail fast) return ErrorCategory.PERMANENT; } private ErrorCategory classifySqlException(SQLException e) { String sqlState = e.getSQLState(); if (sqlState == null) { return ErrorCategory.PERMANENT; } // Class 40: Transaction Rollback (deadlock, serialization failure) if (sqlState.startsWith("40")) { return ErrorCategory.TRANSIENT; } // Class 08: Connection Exception if (sqlState.startsWith("08")) { return ErrorCategory.TRANSIENT; } // Class 23: Integrity Constraint Violation if (sqlState.startsWith("23")) { return ErrorCategory.CONSTRAINT_VIOLATION; } // Class 53: Insufficient Resources if (sqlState.startsWith("53")) { return ErrorCategory.RESOURCE_EXHAUSTION; } // Class 57: Operator Intervention (admin shutdown, crash recovery) if (sqlState.startsWith("57")) { return ErrorCategory.PERMANENT; } // Default to permanent for unknown states return ErrorCategory.PERMANENT; } private ErrorCategory classifySpringException(DataAccessException e) { // Spring's DataAccessException hierarchy if (e instanceof DeadlockLoserDataAccessException) { return ErrorCategory.TRANSIENT; } if (e instanceof CannotAcquireLockException) { return ErrorCategory.TRANSIENT; } if (e instanceof OptimisticLockingFailureException) { return ErrorCategory.TRANSIENT; } if (e instanceof DataIntegrityViolationException) { return ErrorCategory.CONSTRAINT_VIOLATION; } if (e instanceof CannotGetJdbcConnectionException) { return ErrorCategory.TRANSIENT; // Connection pool issues } return ErrorCategory.PERMANENT; }} public enum ErrorCategory { TRANSIENT, // Retry will likely succeed CONSTRAINT_VIOLATION, // Data is invalid, don't retry RESOURCE_EXHAUSTION, // Back off, then maybe retry PERMANENT // Fail fast, alert}Transient failures—deadlocks, serialization conflicts, momentary connection issues—are often resolved simply by retrying. However, naive retry implementations can make problems worse. Effective retry requires careful strategy.
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/** * Comprehensive transaction retry implementation */public class TransactionRetryTemplate { private static final int MAX_RETRIES = 3; private static final long BASE_DELAY_MS = 100; private static final double JITTER_FACTOR = 0.3; private final TransactionErrorClassifier classifier = new TransactionErrorClassifier(); private final Random random = new Random(); /** * Execute operation with automatic retry for transient failures */ public <T> T executeWithRetry(Supplier<T> operation, String operationName) { int attempt = 0; Exception lastException = null; while (attempt < MAX_RETRIES) { try { return operation.get(); } catch (Exception e) { lastException = e; ErrorCategory category = classifier.classify(e); if (category != ErrorCategory.TRANSIENT) { // Non-retriable error - fail immediately throw wrapException(e, category, attempt); } attempt++; if (attempt >= MAX_RETRIES) { logger.warn("Operation '{}' failed after {} attempts", operationName, attempt); throw new RetryExhaustedException(operationName, lastException); } long delayMs = calculateBackoff(attempt); logger.info("Transient failure in '{}', attempt {}/{}, retrying in {}ms", operationName, attempt, MAX_RETRIES, delayMs); sleep(delayMs); } } throw new RetryExhaustedException(operationName, lastException); } /** * Exponential backoff with jitter */ private long calculateBackoff(int attempt) { // Base delay doubles each attempt: 100, 200, 400, 800... long exponentialDelay = BASE_DELAY_MS * (1L << (attempt - 1)); // Add random jitter: ± 30% double jitter = 1 + (random.nextDouble() - 0.5) * 2 * JITTER_FACTOR; return (long) (exponentialDelay * jitter); } private void sleep(long ms) { try { Thread.sleep(ms); } catch (InterruptedException e) { Thread.currentThread().interrupt(); throw new RuntimeException("Interrupted during retry backoff", e); } }} /** * Spring-based implementation using @Retryable */@Servicepublic class OrderService { /** * Declarative retry with Spring Retry */ @Retryable( value = {DeadlockLoserDataAccessException.class, OptimisticLockingFailureException.class, CannotAcquireLockException.class}, maxAttempts = 3, backoff = @Backoff(delay = 100, multiplier = 2, maxDelay = 1000) ) @Transactional public Order placeOrder(OrderRequest request) { // If this fails with a transient error, entire method is retried // Note: Transaction is completely new on each retry Order order = createOrder(request); reserveInventory(order); chargePayment(order); return order; } /** * Recovery method called when all retries exhausted */ @Recover public Order placeOrderFallback(RuntimeException e, OrderRequest request) { logger.error("Order placement failed after all retries", e); metrics.incrementCounter("orders.failed.retry_exhausted"); throw new OrderPlacementException( "Unable to place order. Please try again later.", e ); }}Always retry the ENTIRE transaction, from BEGIN to COMMIT. Retrying just the failed statement leaves the database in an undefined state. When a transaction fails, the database rolls back ALL changes—your next attempt must start fresh.
A deadlock occurs when two or more transactions are waiting for each other to release locks, creating a cycle that can never complete. Databases detect deadlocks and terminate one transaction (the "victim") to allow others to proceed.
How deadlocks happen:
T1: Locks row A, waits for row B
T2: Locks row B, waits for row A
→ Neither can proceed → Deadlock detected → T2 chosen as victim → T2 rolled back
Deadlocks are transient by nature—the victim transaction can usually succeed if retried immediately (possibly with a brief delay to let the other transaction complete).
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/** * Deadlock-resistant transaction implementation */public class DeadlockAwareTransactionTemplate { private static final int MAX_DEADLOCK_RETRIES = 5; private static final long DEADLOCK_RETRY_DELAY_MS = 50; /** * Execute transaction with deadlock retry */ @Transactional public <T> T executeWithDeadlockRetry(TransactionCallback<T> callback) { for (int attempt = 1; attempt <= MAX_DEADLOCK_RETRIES; attempt++) { try { return transactionTemplate.execute(callback); } catch (DeadlockLoserDataAccessException e) { if (attempt == MAX_DEADLOCK_RETRIES) { logger.error("Deadlock persisted after {} attempts", attempt); throw e; } logger.info("Deadlock detected (attempt {}), retrying...", attempt); metrics.incrementCounter("database.deadlocks"); // Brief sleep to let the winner complete sleep(DEADLOCK_RETRY_DELAY_MS); } } throw new IllegalStateException("Should not reach here"); } /** * Prevent deadlocks by locking in consistent order */ @Transactional public void transferFunds(UUID fromAccount, UUID toAccount, BigDecimal amount) { // CRITICAL: Always lock accounts in a deterministic order (e.g., by ID) // This prevents the deadlock where T1 locks A→B while T2 locks B→A UUID firstLock = fromAccount.compareTo(toAccount) < 0 ? fromAccount : toAccount; UUID secondLock = fromAccount.compareTo(toAccount) < 0 ? toAccount : fromAccount; // Acquire locks in consistent order Account first = accountRepository.findByIdForUpdate(firstLock) .orElseThrow(); Account second = accountRepository.findByIdForUpdate(secondLock) .orElseThrow(); // Now perform the actual transfer Account source = fromAccount.equals(firstLock) ? first : second; Account destination = fromAccount.equals(firstLock) ? second : first; if (source.getBalance().compareTo(amount) < 0) { throw new InsufficientFundsException(); } source.debit(amount); destination.credit(amount); accountRepository.save(source); accountRepository.save(destination); }} /** * Alternative: Use NOWAIT to detect potential deadlocks early */@Repositorypublic class AccountRepository { @Query("SELECT a FROM Account a WHERE a.id = :id") @Lock(LockModeType.PESSIMISTIC_WRITE) @QueryHints(@QueryHint(name = "javax.persistence.lock.timeout", value = "0")) Optional<Account> findByIdWithNoWait(@Param("id") UUID id); // If lock cannot be acquired immediately, throws exception // Prevents waiting that could lead to deadlock}Track deadlock frequency as a metric. Occasional deadlocks are normal in high-concurrency systems. Frequent deadlocks indicate a design problem—usually inconsistent locking order or overly broad transactions. Most databases can log deadlock details to help debug.
Constraint violations—unique key conflicts, foreign key failures, check constraint rejections—indicate that the data being written violates the database schema's rules. Unlike transient errors, retrying will always fail unless the data changes.
Proper handling requires identifying the specific constraint that failed and translating it into meaningful user feedback or application logic.
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/** * Sophisticated constraint violation handling */@Servicepublic class UserRegistrationService { @Transactional public User registerUser(RegistrationRequest request) { try { User user = new User(); user.setEmail(request.getEmail()); user.setUsername(request.getUsername()); user.setPasswordHash(passwordEncoder.encode(request.getPassword())); return userRepository.save(user); } catch (DataIntegrityViolationException e) { // Parse the underlying cause to identify which constraint failed String message = extractConstraintName(e); if (message.contains("users_email_key")) { throw new RegistrationException( "Email already registered. Please use a different email or log in.", RegistrationErrorCode.EMAIL_TAKEN ); } if (message.contains("users_username_key")) { throw new RegistrationException( "Username already taken. Please choose a different username.", RegistrationErrorCode.USERNAME_TAKEN ); } // Unknown constraint - log for debugging, return generic message logger.error("Unexpected constraint violation during registration", e); throw new RegistrationException( "Registration failed. Please try again.", RegistrationErrorCode.UNKNOWN ); } } private String extractConstraintName(DataIntegrityViolationException e) { Throwable cause = e.getCause(); if (cause instanceof ConstraintViolationException cve) { return cve.getConstraintName() != null ? cve.getConstraintName() : cause.getMessage(); } return e.getMessage(); } /** * Alternative: Check before insert (has race condition but better UX) */ @Transactional public ValidationResult validateRegistration(RegistrationRequest request) { List<String> errors = new ArrayList<>(); // Pre-check (may have false negatives due to race, but improves UX) if (userRepository.existsByEmail(request.getEmail())) { errors.add("Email already registered"); } if (userRepository.existsByUsername(request.getUsername())) { errors.add("Username already taken"); } if (!errors.isEmpty()) { return ValidationResult.failed(errors); } // Note: Even if pre-checks pass, the actual insert might still fail // due to race condition. Handle constraint violation as fallback. return ValidationResult.passed(); }} /** * Handle unique constraint as upsert (insert or update) */@Servicepublic class SettingsService { @Transactional public UserSettings saveSettings(UUID userId, Map<String, String> settings) { for (Map.Entry<String, String> entry : settings.entrySet()) { // Use database's upsert capability settingsRepository.upsert(userId, entry.getKey(), entry.getValue()); } return settingsRepository.findByUserId(userId); }} @Repositorypublic interface SettingsRepository extends JpaRepository<Setting, UUID> { // PostgreSQL upsert using native query @Modifying @Query(value = "INSERT INTO user_settings (user_id, key, value) " + "VALUES (:userId, :key, :value) " + "ON CONFLICT (user_id, key) DO UPDATE SET value = :value", nativeQuery = true) void upsert( @Param("userId") UUID userId, @Param("key") String key, @Param("value") String value );}Pre-checking (SELECT before INSERT) improves UX by catching conflicts early, but has a race condition window. Handle constraint violations anyway as a safety net. For high-concurrency scenarios, rely primarily on constraint handling; for low-concurrency with complex validation, pre-checks provide better UX.
Connection failures and timeouts represent infrastructure problems rather than logical errors. They require careful handling because the state of the transaction may be unknown.
| Scenario | Transaction State | Appropriate Response |
|---|---|---|
| Cannot acquire connection | Transaction never started | Retry after backoff, apply back-pressure |
| Connection lost during transaction | Unknown - may have committed | Check for completion, then retry or fail |
| Query timeout | Transaction rolled back | Retry with larger timeout or break into chunks |
| Lock wait timeout | Transaction rolled back | Retry with NOWAIT or reduce contention |
| Idle connection timeout | Connection stale/closed | Pool should validate; get new connection |
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/** * Robust connection and timeout handling */@Configurationpublic class DataSourceConfiguration { @Bean public DataSource dataSource() { HikariConfig config = new HikariConfig(); // Connection pool settings config.setMaximumPoolSize(20); config.setMinimumIdle(5); // Connection validation config.setConnectionTestQuery("SELECT 1"); config.setValidationTimeout(5000); // 5 seconds to validate // Acquire timeout - fail fast if pool exhausted config.setConnectionTimeout(10000); // 10s to acquire connection // Idle connections closed after this time config.setIdleTimeout(300000); // 5 minutes // Connections closed after this time regardless of activity config.setMaxLifetime(1800000); // 30 minutes // Leak detection config.setLeakDetectionThreshold(60000); // Warn if connection held > 60s return new HikariDataSource(config); }} @Servicepublic class RobustDataAccessService { /** * Handle connection acquisition failures */ public Order placeOrderWithConnectionRetry(OrderRequest request) { int attempts = 0; while (attempts < 3) { try { return orderService.placeOrder(request); } catch (CannotGetJdbcConnectionException e) { attempts++; logger.warn("Connection pool exhausted, attempt {}/3", attempts); if (attempts >= 3) { throw new ServiceUnavailableException( "Database temporarily unavailable. Please try again." ); } // Back-pressure: wait before retry sleep(500 * attempts); } } throw new IllegalStateException("Should not reach here"); } /** * Handle query timeouts with chunking */ @Transactional(timeout = 30) // 30 second timeout public void processLargeBatch(List<Record> records) { // If this times out, break into smaller chunks for (Record record : records) { processRecord(record); } } /** * Alternative: Process in smaller transactions */ public BatchResult processInChunks(List<Record> records) { List<List<Record>> chunks = Lists.partition(records, 100); int processed = 0; List<String> errors = new ArrayList<>(); for (List<Record> chunk : chunks) { try { // Each chunk is a separate transaction processChunk(chunk); // @Transactional with shorter timeout processed += chunk.size(); } catch (TransactionTimedOutException e) { logger.error("Chunk timed out at offset {}", processed); errors.add("Timeout at record " + processed); // Continue with next chunk or break depending on requirements } } return new BatchResult(processed, records.size(), errors); } @Transactional(timeout = 10) // Smaller timeout per chunk public void processChunk(List<Record> records) { records.forEach(this::processRecord); }}When a connection is lost during a transaction, you may not know if the transaction committed. For critical operations, use idempotency keys or check for completion before retrying. Never assume rollback—the commit might have succeeded just before the connection dropped.
When a database is experiencing persistent issues, continuing to send requests makes things worse—both for the database (adding load) and for the application (wasting resources on doomed requests). The Circuit Breaker pattern stops the bleeding.
How it works:
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/** * Database circuit breaker implementation */@Servicepublic class DatabaseCircuitBreaker { private final AtomicInteger failureCount = new AtomicInteger(0); private final AtomicReference<State> state = new AtomicReference<>(State.CLOSED); private volatile long lastFailureTime = 0; private static final int FAILURE_THRESHOLD = 5; private static final long OPEN_TIMEOUT_MS = 30000; // 30 seconds public <T> T execute(Supplier<T> operation, Supplier<T> fallback) { if (state.get() == State.OPEN) { if (shouldAttemptReset()) { return attemptHalfOpen(operation, fallback); } return fallback.get(); } try { T result = operation.get(); onSuccess(); return result; } catch (Exception e) { if (isCircuitBreakerException(e)) { onFailure(); } throw e; } } private boolean shouldAttemptReset() { return System.currentTimeMillis() - lastFailureTime > OPEN_TIMEOUT_MS; } private <T> T attemptHalfOpen(Supplier<T> operation, Supplier<T> fallback) { state.set(State.HALF_OPEN); try { T result = operation.get(); reset(); // Success! Close the circuit return result; } catch (Exception e) { tripBreaker(); // Still failing, reopen return fallback.get(); } } private void onSuccess() { failureCount.set(0); } private void onFailure() { int failures = failureCount.incrementAndGet(); lastFailureTime = System.currentTimeMillis(); if (failures >= FAILURE_THRESHOLD) { tripBreaker(); } } private void tripBreaker() { state.set(State.OPEN); logger.warn("Circuit breaker OPENED - database failures exceeded threshold"); alertOps("Database circuit breaker opened"); } private void reset() { state.set(State.CLOSED); failureCount.set(0); logger.info("Circuit breaker CLOSED - database recovered"); } private boolean isCircuitBreakerException(Exception e) { // Only trip for infrastructure failures, not business errors return e instanceof CannotGetJdbcConnectionException || e instanceof QueryTimeoutException || e instanceof DataAccessResourceFailureException; } enum State { CLOSED, OPEN, HALF_OPEN }} /** * Using Resilience4j for production circuit breaker */@Configurationpublic class CircuitBreakerConfig { @Bean public CircuitBreakerRegistry circuitBreakerRegistry() { CircuitBreakerConfig config = CircuitBreakerConfig.custom() .failureRateThreshold(50) // Open when 50% of calls fail .waitDurationInOpenState(Duration.ofSeconds(30)) .slidingWindowSize(10) // Evaluate last 10 calls .permittedNumberOfCallsInHalfOpenState(3) // Test with 3 calls .recordExceptions( CannotGetJdbcConnectionException.class, DataAccessResourceFailureException.class ) .build(); return CircuitBreakerRegistry.of(config); }} @Servicepublic class OrderServiceWithCircuitBreaker { private final CircuitBreaker circuitBreaker; private final CachedOrderService cachedOrderService; @Autowired public OrderServiceWithCircuitBreaker( CircuitBreakerRegistry registry, CachedOrderService cachedOrderService) { this.circuitBreaker = registry.circuitBreaker("database"); this.cachedOrderService = cachedOrderService; } public Order getOrder(UUID orderId) { return circuitBreaker.executeSupplier(() -> orderRepository.findById(orderId).orElseThrow() ); } // With fallback public List<Order> getRecentOrders(UUID customerId) { try { return circuitBreaker.executeSupplier(() -> orderRepository.findRecentByCustomerId(customerId) ); } catch (CallNotPermittedException e) { // Circuit is open - use fallback return cachedOrderService.getCachedOrders(customerId); } }}For read operations, you can often fallback to cached data. For write operations, you might queue the write for later retry (requires idempotency) or fail fast with a user-friendly message. Never silently drop writes—either queue them or make the failure visible.
For retries to be safe, operations must be idempotent: executing the same operation multiple times produces the same result as executing it once. Without idempotency, retries can cause duplicate charges, duplicate records, or other data corruption.
The problem:
T1: BEGIN; INSERT INTO orders (...); COMMIT; // Network error after commit
Client: Didn't receive response, retries
T2: BEGIN; INSERT INTO orders (...); COMMIT; // Duplicate order!
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/** * Idempotency key pattern for safe retries */@Servicepublic class IdempotentOrderService { private final IdempotencyKeyRepository idempotencyRepo; private final OrderRepository orderRepository; /** * Client generates a unique key per logical operation. * If key exists, return cached result instead of re-executing. */ @Transactional public OrderResult placeOrder(String idempotencyKey, OrderRequest request) { // Check if we've already processed this request Optional<IdempotencyRecord> existing = idempotencyRepo.findByKey(idempotencyKey); if (existing.isPresent()) { IdempotencyRecord record = existing.get(); if (record.isCompleted()) { // Already completed - return cached result logger.info("Returning cached result for idempotency key: {}", idempotencyKey); return record.getCachedResult(); } if (record.isInProgress()) { // Another request is processing - tell client to wait throw new ConflictException( "Request in progress. Please retry in a few seconds." ); } } // Mark this key as in-progress IdempotencyRecord record = new IdempotencyRecord( idempotencyKey, IdempotencyStatus.IN_PROGRESS ); idempotencyRepo.save(record); try { // Execute the actual operation Order order = createOrder(request); reserveInventory(order); // Cache the result OrderResult result = new OrderResult(order); record.setStatus(IdempotencyStatus.COMPLETED); record.setCachedResult(result); idempotencyRepo.save(record); return result; } catch (Exception e) { // Mark failed - could retry or leave for investigation record.setStatus(IdempotencyStatus.FAILED); record.setError(e.getMessage()); idempotencyRepo.save(record); throw e; } } /** * Alternative: Use natural keys for implicit idempotency * Works when operations can be uniquely identified by content */ @Transactional public void recordPayment(PaymentNotification notification) { String paymentId = notification.getPaymentProviderTransactionId(); // Check if payment already recorded (using unique constraint) if (paymentRepository.existsByExternalId(paymentId)) { logger.info("Payment {} already recorded, skipping", paymentId); return; // Idempotent - no error, no duplicate } Payment payment = new Payment(); payment.setExternalId(paymentId); // Has unique constraint payment.setAmount(notification.getAmount()); payment.setOrderId(notification.getOrderId()); try { paymentRepository.save(payment); } catch (DataIntegrityViolationException e) { // Race condition: another thread saved first // This is fine - payment is recorded if (e.getMessage().contains("payments_external_id_key")) { logger.info("Payment {} recorded by concurrent request", paymentId); return; } throw e; } }} /** * IdempotencyRecord entity */@Entity@Table(name = "idempotency_keys")public class IdempotencyRecord { @Id @Column(name = "idempotency_key") private String key; @Enumerated(EnumType.STRING) private IdempotencyStatus status; @Column(columnDefinition = "jsonb") @Type(JsonType.class) private OrderResult cachedResult; private String error; @CreationTimestamp private Instant createdAt; // Expire old keys @Column(name = "expires_at") private Instant expiresAt;}Idempotency keys should have an expiration (e.g., 24 hours). After expiration, the same key can be used again. This prevents the table from growing indefinitely while still protecting against immediate retries. Clean up expired keys periodically.
Robust failure handling is what separates production-ready systems from prototypes. Let's consolidate the essential strategies:
Module Complete:
You've now completed the comprehensive study of database transactions at the LLD level. You understand ACID properties deeply, can draw correct transaction boundaries, select appropriate isolation levels, and handle failures gracefully. This knowledge forms the foundation for building data systems that are both correct and resilient in production environments.
Congratulations! You've mastered the essential aspects of database transaction management: ACID properties, transaction boundaries, isolation levels, and failure handling. You're now equipped to build persistence layers that maintain data integrity under real-world conditions—concurrent access, network failures, and system crashes.