AWS DMS Continuous Replication vs Full Load

AWS Database Migration Service offers multiple approaches to moving data between databases, each optimized for different scenarios and constraints. The choice between full load and continuous replication fundamentally shapes your migration architecture, operational complexity, and business continuity capabilities. Understanding these patterns deeply—not just what they do but when each excels and where each struggles—enables you to design migration strategies that align with your actual requirements rather than defaulting to what seems most sophisticated.

This article examines the practical differences between full load and continuous replication in AWS DMS, exploring the architectural implications, performance characteristics, operational considerations, and decision criteria that determine which approach suits specific migration scenarios. By understanding these patterns in depth, you’ll make informed choices that balance complexity, risk, and business needs.

Understanding Full Load: The Foundation of Data Migration

Full load represents the simplest DMS migration pattern—copying all existing data from source to target databases in a single operation. This approach mirrors traditional database backup-restore patterns but with the added intelligence of handling schema conversion, data type mapping, and cross-engine migrations that manual approaches struggle with.

The full load process follows a straightforward workflow. DMS reads all tables from the source database, transforms data according to mapping rules, and writes to the target database. For each table, DMS queries the source, processes rows in batches, and inserts them into the target. The process continues until all tables are fully replicated, at which point the task completes and stops.

Full load characteristics and behavior:

• Point-in-time snapshot: Full load captures source database state at task start time. Changes occurring during migration aren’t captured unless you enable CDC (change data capture) for continuous replication afterward.
• Table-by-table processing: DMS processes tables independently, potentially in parallel based on task configuration. Large tables might still be actively loading while smaller tables complete.
• Resource intensity: Full loads consume significant source database resources—reading entire tables generates substantial I/O and CPU load. Target databases face intensive write operations as millions or billions of rows are inserted.
• Deterministic completion: Full load tasks have clear completion—when all tables finish loading, the migration task ends. There’s no ongoing maintenance or monitoring requirement.

The full load pattern excels for one-time migrations where ongoing synchronization isn’t required. Migrating a development database to a new instance, creating analytics replicas for point-in-time reporting, or performing periodic bulk data refreshes all suit full load approaches perfectly. The migration happens, completes, and you’re done.

Common full load use cases:

• Database engine migrations: Moving from commercial databases like Oracle or SQL Server to open-source alternatives like PostgreSQL or MySQL. These migrations often occur during maintenance windows where applications are offline, eliminating the need for ongoing change capture.
• Cross-region replication for disaster recovery setup: Creating initial replica databases in different regions before establishing ongoing replication. Full load establishes the baseline, then continuous replication maintains synchronization.
• Data warehouse loading: Periodic bulk loads into analytical databases where real-time synchronization isn’t necessary. Nightly or weekly full loads refresh data warehouse tables with current operational data.
• Database version upgrades: Migrating between major versions of the same database engine where in-place upgrades are risky. Full load copies data to a new version instance, allowing validation before cutover.

However, full load has significant limitations that make it unsuitable for many production scenarios. During the load process, the source and target databases diverge as changes accumulate in the source that aren’t reflected in the target. The longer the load takes—potentially hours or days for large databases—the more stale the target becomes. Applications cannot safely switch to the target without data loss unless you implement application downtime during the final cutover.

Understanding Continuous Replication: Real-Time Data Synchronization

Continuous replication extends beyond simple data copying to maintain ongoing synchronization between source and target databases. This pattern captures changes as they occur in the source database and applies them to the target with minimal latency, keeping both databases in near-real-time alignment.

AWS DMS implements continuous replication through change data capture technology, monitoring database transaction logs for insert, update, and delete operations. Rather than periodically querying tables to detect changes, CDC reads database logs—PostgreSQL’s write-ahead log, MySQL’s binlog, Oracle’s redo logs—capturing changes at the transaction level with minimal source database performance impact.

Continuous replication characteristics:

• Ongoing synchronization: Unlike full load’s one-time execution, continuous replication runs indefinitely, constantly monitoring for and applying changes. The DMS task remains active, consuming resources and requiring operational monitoring.
• Low-latency change propagation: Changes appear in target databases typically within seconds to minutes of occurring in source databases, depending on network latency, transformation complexity, and target write capacity.
• Transactional consistency: DMS maintains transaction boundaries where possible, ensuring related changes commit together in the target. However, cross-table transaction guarantees aren’t absolute due to parallel processing optimizations.
• Minimal source impact: Log-based CDC imposes negligible overhead on source databases compared to query-based change detection. Source databases continue serving production workloads while changes stream to targets.

The continuous replication architecture supports migration patterns impossible with full load alone. You can migrate production databases with near-zero downtime by running full load to establish the initial baseline, then switching to continuous replication to capture ongoing changes while applications continue operating. When target replication lag drops to acceptable levels, you perform a brief cutover—stopping applications, allowing final changes to replicate, and redirecting applications to the target database.

Continuous replication architectural patterns:

• Zero-downtime migrations: The most common use case for continuous replication. Applications continue operating against the source database while DMS replicates changes to the target. Once synchronized, you cutover during a brief maintenance window measured in minutes rather than the hours or days required for full load migrations.
• Read replica creation: Maintaining real-time replicas for reporting, analytics, or read scaling without impacting production databases. Continuous replication keeps replicas current while production databases handle write traffic.
• Cross-region active standby: Creating geographically distributed standby databases for disaster recovery. Continuous replication maintains standby currency, enabling rapid failover if primary regions experience outages.
• Hybrid cloud database synchronization: Keeping on-premises databases synchronized with cloud replicas during gradual cloud migrations. Applications can partially migrate while maintaining access to synchronized data in both environments.

The Combined Approach: Full Load Plus CDC

The most powerful DMS pattern combines full load and continuous replication in a single task, leveraging each pattern’s strengths while mitigating weaknesses. This combined approach represents the gold standard for production database migrations requiring minimal downtime.

The combined workflow proceeds in phases. First, DMS performs full load of all tables, creating the initial target database state. Simultaneously or immediately after full load begins, CDC activates to capture changes occurring in the source database. These changes accumulate in DMS’s internal cache or storage. Once full load completes, DMS applies the cached changes to the target database, bringing it current with the source. Then continuous replication takes over, applying new changes as they occur.

Why the combined approach excels:

• Minimized cutover window: Applications can continue operating during full load, which might take hours or days. Only brief downtime is needed for final synchronization and cutover, measured in minutes rather than the hours required for full load-only migrations.
• Data consistency: CDC captures all changes from full load start, preventing data loss. Without CDC, changes during full load would be lost, requiring applications to remain offline or accepting data loss.
• Validation opportunities: While continuous replication maintains synchronization, you have time to validate target database performance, test application compatibility, and plan cutover without time pressure. The synchronized databases allow running parallel application testing.
• Rollback capability: If issues emerge with the target database, applications remain on the source with minimal impact. You can troubleshoot, fix problems, and retry cutover without data loss since continuous replication continues tracking changes.

Configuring combined full load and CDC requires understanding task settings that control the transition between phases. The FullLoadCompleted state indicates full load has finished and cached changes are being applied. The OngoingReplication state shows continuous replication is active and maintaining synchronization. Monitoring these states helps you understand migration progress and readiness for cutover.

Operational considerations for combined replication:

• CDC start point: Configure whether CDC begins when the task starts (capturing changes during full load) or after full load completes. For zero-downtime migrations, always start CDC with the task to capture all changes.
• Cache requirements: Changes accumulating during long full loads require sufficient cache capacity. DMS uses local storage and optionally S3 for overflow. Monitor CDCIncomingChanges and CDCChangesInMemorySource metrics to ensure cache doesn’t exhaust capacity.
• Replication lag monitoring: Track how far behind the target database lags from the source using the CDCLatencySource and CDCLatencyTarget metrics. Lag must drop below threshold values (typically under 60 seconds) before cutover is safe.
• Table-by-table CDC activation: DMS begins CDC for each table only after that table’s full load completes. For databases with mixed table sizes, this staged CDC activation means some tables are already synchronizing while others are still in full load.

Performance Implications and Optimization Strategies

The performance characteristics of full load and continuous replication differ substantially, requiring different optimization approaches and resource allocation strategies.

Full load performance factors:

Full load speed depends primarily on source database read throughput and target database write capacity. Source databases must scan entire tables, generating significant I/O. Indexes on large tables slow reads as row-by-row access patterns prevent efficient sequential scans. Target databases face insert-intensive workloads that can overwhelm write capacity, particularly on storage systems without adequate IOPS provisioning.

Optimization strategies for full load include adjusting parallel table loading—DMS can load multiple tables simultaneously, but too much parallelism overwhelms source or target resources. The MaxFullLoadSubTasks parameter controls concurrency. Start conservatively with 8-16 parallel loads for large migrations, monitoring source and target resource utilization and adjusting based on bottlenecks.

Consider LOB (large object) handling carefully. By default, DMS loads LOB columns inline, but very large LOBs can cause memory issues and slow throughput. The MaxLobSize parameter controls maximum LOB size loaded inline, with larger values going to separate lookup operations. For tables with many large LOBs, this dramatically impacts performance.

Partitioning large tables improves full load parallelism and reduces task failure risk. Rather than loading a 500 million row table as a single operation prone to failure and difficult to resume, partition it into segments loaded independently. DMS supports automatic table partitioning for sources with appropriate partition keys or manual segmentation through table mappings.

Continuous replication performance factors:

CDC performance depends on source change rate, network bandwidth between source and target, transformation complexity, and target write capacity. Unlike full load’s consistent resource usage, continuous replication experiences variable load based on application activity patterns—low during quiet periods, high during peak transaction times.

The primary bottleneck in continuous replication is usually target write capacity. DMS must apply changes to the target as fast as the source generates them plus any accumulated lag. If source databases generate 10,000 transactions per second but target databases can only apply 5,000, lag accumulates indefinitely. Target database tuning—increasing write IOPS, optimizing indexes, adjusting buffer pool sizes—often provides the largest performance improvements.

CDC-specific optimizations:

• Batch apply mode: By default, DMS applies changes transactionally one-by-one. Batch apply mode accumulates changes and applies them in larger batches, dramatically improving throughput for high-change-rate scenarios. Enable through task settings, choosing batch sizes that balance latency and throughput.
• Task memory allocation: CDC requires substantial memory for caching changes and managing replication state. Underpowered replication instances cause frequent spilling to disk, degrading performance. Use compute-optimized instance types like C5 or C6g for replication tasks.
• Parallel apply: DMS can apply changes to multiple tables simultaneously, improving throughput for workloads with changes distributed across many tables. The ParallelApplyThreads parameter controls apply parallelism. However, parallel apply can cause transactional consistency issues if related changes apply out of order.
• Target table preparation: Disable foreign key constraints and triggers during replication, re-enabling after cutover. These constraints cause apply operations to slow dramatically as the database validates each change. Similarly, reduce index count during bulk load phases, rebuilding afterward.

Operational Challenges and Failure Scenarios

Both full load and continuous replication face operational challenges requiring monitoring, alerting, and incident response capabilities. Understanding failure modes helps you build resilient migration architectures.

Full load failure scenarios:

The most common full load failures involve resource exhaustion—source or target databases running out of storage, memory, or connections. Large table loads consume database connections for extended periods, potentially exhausting connection pools and affecting other workloads. Monitor connection usage and allocate sufficient connections for migration tasks.

Storage exhaustion during full load causes cascading failures. Target databases need sufficient space for incoming data plus index structures. Calculate expected target size accounting for index overhead and provision 30-50% extra space as buffer. Monitor target storage utilization and expand before exhaustion occurs.

Network interruptions during full load can cause task failures requiring restart. DMS supports resumable full loads for many scenarios, continuing from the last completed table rather than restarting entirely. However, this behavior isn’t guaranteed for all database engines and configurations. Design migration plans assuming full load might need to restart from the beginning.

Continuous replication failure scenarios:

CDC failures are more subtle and often manifest as increasing replication lag rather than obvious errors. Source database log retention policies commonly cause CDC failures—if transaction logs rotate before DMS reads them, changes are lost permanently. Configure source log retention to exceed expected lag by significant margins, typically retaining logs for 24-48 hours even if expected lag is under an hour.

Target write capacity exhaustion causes lag accumulation. Unlike full load failures that halt progress obviously, CDC tasks continue running while lag grows from seconds to minutes to hours. Monitor the CDCLatencySource metric and alert when it exceeds thresholds. Investigate whether target databases are bottlenecked or if DMS instance types need upgrading.

Transformation logic errors create subtle data corruption during replication. A misconfigured data type mapping might truncate values, character set conversions might corrupt international characters, or date format transformations might shift timestamps by timezone offsets. These errors don’t fail the task but silently corrupt data. Implement validation queries comparing source and target row counts, checksums, or sample data to detect corruption early.

DMS task state management:

Understanding DMS task states helps you operate migrations effectively. Tasks transition through creating, starting, running, stopping, and stopped states during normal operation. The failed state indicates errors requiring investigation. The modifying state appears when changing task configuration.

For continuous replication, the replication ongoing status indicates healthy operation with the target synchronized. The replication stopped status means CDC has halted, often due to errors or manual intervention. The replication pending status appears when changes are being applied but synchronization isn’t complete.

Monitoring task status programmatically through CloudWatch Events or polling DMS APIs enables automated alerting and remediation. Build dashboards showing task status, replication lag, and resource utilization for all active migrations, providing operations teams visibility into migration health.

Decision Framework: Choosing Your Replication Strategy

Selecting between full load, continuous replication, or combined approaches requires evaluating multiple dimensions of your migration requirements and constraints.

When full load alone suffices:

• Acceptable application downtime exists for migration duration
• No ongoing synchronization is needed after migration
• Source and target databases are offline or read-only during migration
• Migration is one-time rather than establishing permanent replication
• Simplicity is prioritized over migration sophistication

Full load alone works perfectly for non-production environments, disaster recovery exercises, data warehouse loading, and migrations during extended maintenance windows. The operational simplicity of full load—it completes and requires no ongoing monitoring—makes it attractive when downtime isn’t prohibitive.

When continuous replication is necessary:

• Application downtime must be minimized to minutes rather than hours
• Real-time data synchronization is required between databases
• Migration timeline is uncertain and extended validation is needed
• Read replicas for reporting or analytics are required
• Multi-region deployments need synchronized databases

Continuous replication’s complexity is justified when business requirements demand minimal downtime or ongoing synchronization. The operational overhead of maintaining active replication tasks, monitoring lag, and managing CDC is worthwhile when alternatives create unacceptable business impact.

When combined full load and CDC is optimal:

Most production database migrations benefit from the combined approach. It balances migration speed (full load’s bulk transfer) with operational flexibility (continuous replication’s ongoing synchronization). Unless you have specific constraints making one approach clearly superior, default to combined full load and CDC for production migrations.

Cost considerations:

Full load tasks run only during migration, incurring costs for hours or days. Continuous replication tasks run continuously, incurring ongoing costs for replication instance compute and data transfer. For long-running replications, these costs can become significant. Calculate expected monthly costs for continuous replication—a dms.c5.large instance costs approximately $140/month, plus data transfer charges.

For scenarios requiring temporary continuous replication (like migrations), plan to shut down tasks after cutover completes. For permanent replication scenarios (like cross-region active standby), budget for ongoing operational costs including instance compute, data transfer, and monitoring infrastructure.

Conclusion

The choice between AWS DMS full load and continuous replication fundamentally shapes your migration architecture, operational complexity, and business continuity capabilities. Full load provides simplicity and deterministic completion but requires downtime and doesn’t maintain ongoing synchronization. Continuous replication enables zero-downtime migrations and real-time data synchronization but introduces operational complexity and ongoing costs. The combined approach—full load establishing baselines plus continuous replication maintaining currency—represents the optimal pattern for most production migrations.

Successful migrations require understanding not just these patterns but also the performance characteristics, failure modes, and operational requirements each entails. By carefully evaluating your specific requirements against these patterns’ strengths and limitations, you’ll design migration strategies that balance technical capability with business constraints, delivering successful database migrations that maintain data integrity while minimizing business disruption.