Data Migration Guide to Amazon RDS | How to Choose a Migration Method, Design for Downtime, and Avoid Failure

The most important thing when migrating data to RDS is not "which tool to use" but "which migration method to choose."

The appropriate procedures and verification process will vary depending on how you perceive the tolerability of downtime, the amount of data, and the impact on business. While many articles focus solely on the "implementation procedure," this article will go into detail and explain "why that method is used" and "in what cases risks become apparent."

The big picture you need to understand when migrating data to RDS

Migrating to RDS begins with determining the appropriate method before considering tool selection and execution procedures. The best migration pattern will vary depending on factors such as tolerance for downtime, data characteristics, and operational and security requirements. Here, we will outline the underlying concepts for selecting a method and the overall migration flow.

The success of RDS migration depends on the "method selection" rather than the "procedure"

At first glance, migrating to RDS appears to be a matter of choosing a procedure such as "using DMS" or "taking a dump." However, what actually determines success or failure is "clarifying the prerequisites" before deciding on a migration method. Factors such as how much downtime you can tolerate, how much data you will be handling, and whether there are any compatibility issues with the existing database will determine the appropriate method to choose.

For example, if you have a relatively small system and can secure a period of downtime, a logical dump may be sufficient. On the other hand, if your system operates nearly 24 hours a day, you will need continuous synchronization (CDC) or DMS replication. As such, deciding which method to choose is the starting point for your migration plan.

Items to be identified before starting the study (data volume, availability, business impact, compatibility)

Before selecting a method, some essential information must be gathered.

The first thing to check is "To what extent can operations be stopped?" By understanding the impact on operations, such as peak hours and closing processing timing, you can see the range of methods that can be realistically adopted.

You should also check the scale of your data and the frequency of updates. By understanding the total number of rows in the table, the amount of data generated daily, the load of batch processing, etc., you can estimate the migration time and the required bandwidth.

Compatibility issues such as character encoding/collation order, differences in DB engine versions, and handling of function extensions (stored/trigger/extension packages) must also be verified at an early stage, as this will determine the extent of conversion required, i.e., the amount of rework required.

PoC → Test environment → Production migration → Standard cutover process

Once the prerequisites are in place, the migration proceeds in the following stages:

First, we conduct a small-scale PoC to confirm the applicability of the method itself and understand data consistency and the estimated time required.In the verification environment, we conduct index, schema conversion, and performance tests under conditions close to the actual data scale to ensure reproducibility.

For the production migration, procedures, permission setting changes, rollback procedures, and communication systems are documented as a runbook, and the final cutover (switchover) is carried out. After the cutover, we strengthen performance monitoring and log monitoring, while maintaining a system that allows for immediate rollback if necessary.

By planning with this process in mind, you can consistently design everything from selecting the migration tool to deciding when to stop operations, minimizing the risks during the actual migration.

How to choose a migration method

The difficulty and risk of migrating to RDS will vary greatly depending on which method you choose. Each method has different strengths and prerequisites, so you need to determine where the bottlenecks lie in terms of downtime, data volume, compatibility, and existing operations. Here we will organize four common methods and the criteria for deciding which to use.

1. Physical/logical dump (for small to medium scale, stoppage OK)

This method is suitable for small-scale systems or when a clear shutdown time can be secured. It is a simple method of obtaining a dump from the existing DB and then verifying consistency after importing, and it has little dependency on settings or tools, which helps keep migration costs down. However, the larger the amount of data, the longer the downtime tends to be, making it practically difficult to apply in large-scale environments.

2. DMS (continuous replication/minimal downtime)

AWS Database Migration Service (DMS) is a method that performs differential synchronization and stops only at the time of final switchover. It is the most commonly chosen method because it allows for relatively safe migration even for services that are difficult to stop for long periods of time. However, DMS is not an all-purpose solution; it is important to note that schema conversion is a separate process and that replication performance needs to be tuned.

3. Snapshot replication (including within AWS/Aurora)

If you are already using a managed database on AWS, such as RDS or Aurora, replication based on a snapshot is the most efficient method. Because the environmental differences are small, the cost of consistency checks is low, and it is easy to imagine how operations will be performed after the switchover. However, this method cannot be applied directly to migrations from on-premises or other clouds, and there are cases where it is necessary to combine it with another method as pre-processing.

4. CDC (cases where real-time synchronization is required)

CDC (Change Data Capture) is used when real-time performance is required or when two-way data integrity must be maintained during the migration period. This method is often chosen for financial and mission-critical systems where business interruption is not permitted. While it requires advanced operational design and tool implementation costs tend to be high, its strength is that the migration can be completed by "changing the physical logistics."

Selection chart (mapping by downtime/data volume/DB type)

When actually selecting a method, it is more efficient to visualize and decide "which method should be adopted" rather than "which methods are available." In particular, if you consider the three points of allowable downtime (RTO), tolerable data loss (RPO), and the scale of the migration target, the methods will naturally be narrowed down.

Benefits, restrictions, and application boundaries of each method

Design points to minimize downtime

After deciding on the migration method, you need to plan where you can stop the migration and when to switch over. The key to reducing downtime is not in the execution of the data migration itself, but in planning the timing of the switchover.

No matter how good the tools you use, if you misjudge "moments when you cannot stop operations," such as closing or batch processing, downtime will end up being prolonged. Below we will summarize three practical perspectives for achieving minimal outages.

Determining the "moment of stopping" that should be confirmed by the business side

First of all, it's important not to let the IT side decide "when it's okay to stop" alone. In reality, there are surprisingly many moments when the business side is "involved," such as closing processing, report generation, nighttime batch processing, and external API integration. Therefore, even if a migration requires a technical shutdown of just a few minutes, it's not uncommon for an entire day of downtime to be required if the business cycle is ignored.

The first thing to do is to confirm the time periods when business cutover is possible based on the actual operation, not just "holidays and late nights" on the calendar. If you decide on the method without sorting this out, your plan will be delayed.

Data integrity check flow just before cutover

The key to minimizing outages is to apply the differences in advance and then send only the remaining differences in a short time. However, if consistency is not achieved immediately before the switchover, differences will need to be re-applied or retries will occur, which will actually prolong the outage.

Therefore, before the cutover, make sure to pay attention to the following two points:

1. Hash/count comparison after differential synchronization

2. Check after making it read-only (to see if any new updates have been added)

If this "last-minute verification" is neglected, the worst case scenario could occur where inconsistencies are discovered after the switchover and a rollback is required.

Decide how to roll back

In a migration plan, rollback planning is more important than cutover planning. If you can guarantee that you can return to the original environment within a certain amount of time even if inconsistencies or performance degradation are discovered after the switchover, decision-making will be smoother.

The important thing here is to be specific about "how far back you want to go."

• Is it a snapshot before differential synchronization?

• Is this a backup made just before the switch?

• Should we return to a two-way sync environment?

Only by actually trying out the rollback procedure rather than just reading about it in writing can you realistically reduce the risk.

Technical constraints to check before migration

Before proceeding with procedures and tool selection, it's important to confirm the technical prerequisites. While RDS is a managed service, there are cases where the available extensions and versions are limited. To avoid situations such as "the behavior is different from what was expected" or "you run into unintended constraints" after migration, it's important to clarify in advance which constraints will affect your migration plan.

Version/Engine Compatibility

The first thing you want to check is the version compatibility between your existing database and RDS. Even minor version differences, especially with PostgreSQL and MySQL, can change behavior, and there are cases where extension modules and permission settings become unusable after migration. If you choose Aurora, it's safe to consider the differences in specifications with RDS and the timing of version releases.

By checking early on "up to what version your managed environment can accommodate," you can avoid miscalculating the costs of schema conversion and modifying dependent libraries.

Character code, collation order, and time system

Differences in character codes and collation order are the areas where problems are most likely to occur after migration. In particular, when migrating from Shift-JIS to UTF-8, not only can garbled characters appear, but search and collation results can also change.

In addition, time systems (timezone / timestamp with time zone, etc.) can affect the calculation logic of batch processing and external integration, making them an issue that can be easily detected late as "hidden compatibility issues." By exposing these differences early in the PoC stage, you can reduce the cost of reversal.

Encryption, IAM, and Network

RDS differs from manually constructed databases in that it has environment-related constraints such as encryption (KMS) and connection methods (VPC/PrivateLink/Security Groups). In addition, when combined with IAM permission management, cases can arise where connections and operations that were possible on-premises are not permitted in RDS.

If network and security considerations are lacking, the data transfer itself may be completed, but problems may arise with the final application connection.

Schema Differences and Conversion Costs

Areas such as stored procedures, triggers, extension modules, and user-defined functions may not always work the same after migration. Functions not provided by RDS must be replaced with a different method, and the cost of this conversion may even be a more significant issue than the method selection.

For this reason, schema differences are not something to be "discovered and addressed later," but something to "estimate before deciding on a method." From the perspective of avoiding rework, it is important to ensure reproducibility close to the pseudo-production at the time of verification.

Common mistakes in practice and how to avoid them

Even if the migration method and constraints have been sorted out, unexpected problems can occur just before the actual migration. The cause is often not "technical failure," but rather a mismatch between the assumptions of the migration plan and the actual operation.

Relying on DMS leads to performance degradation/delays

DMS is merely a "differential synchronization tool" and is not an all-purpose migration platform. In particular, with systems that have large volumes of tables or are frequently updated, delays in the initial load and backlogs due to delays in differential synchronization can easily occur, resulting in the risk of not meeting the scheduled switchover time.

To avoid performance degradation, it is essential to design "table-level priorities," "synchronization target division," and "reservation of execution resources" when selecting a DMS.

Cutover fails due to insufficient verification

There have been cases where the migration itself went smoothly, but an inconsistency was discovered just before the cutover, forcing a quick restart. A common pattern is when people only checked the differential migration and neglected to re-check the consistency after making the data read-only.

It's easy to get anxious just before the actual event, so if you define the verification items as "judgment criteria" rather than at the procedure manual level, your Go/No-Go decisions will not waver.

Monitoring, logging, and permissions are different only for production

There were no problems in the test environment, but the connection failed or the application crashed in the production environment - this is often due to differences in permissions and auditing. Because RDS involves a complex combination of encryption, IAM, SG/ACL settings, it is common in practice for the database to be transferred successfully, but the application cannot connect.

An effective measure is to apply monitoring and logging settings equivalent to those used in production operations at an early stage and complete the "pseudo-production" phase during the verification phase.

Performance testing is completed under the assumption of "no load"

Migration verification tends to end with a "consistency test," but in production, reads and writes are constantly occurring. If the load during peak hours and batch execution is not taken into consideration, performance degradation may become apparent after the migration, resulting in the need to roll back or re-verify.

Performance testing is based on the "running workload" rather than the "actual data volume," making it possible to confirm stable operation after migration for the first time.

How to increase the probability of success

While the migration itself is a technical task, the key to success lies in the design and verification. Success or failure depends on the extent to which reproducibility and business impact can be eliminated ahead of schedule before the actual migration begins.

The importance of "front-loaded verification" to eliminate environmental differences

The most common reason for failures just before going live is differences between the testing environment and the production environment. RDS involves a complex set of operational constraints, including encryption, IAM, networking, and storage configuration, so simply reproducing the "data migration" is insufficient. It's important to create a "pseudo-production" environment from the migration verification stage, apply a configuration equivalent to the production environment, and check authorization, audit, and connection requirements ahead of time.

Points to consider during the PoC period

The purpose of a PoC is not to determine whether something works, but whether this method will actually work. Therefore, the key is to confirm the following three points:

• Method compatibility: required time, granularity of differential synchronization, compatibility

• Presence or absence of reworking: Areas that need conversion and dependent functions that need modification

• Visualization of risks: resynchronization during switchover, fluctuations in bandwidth and load, audit requirements

Simply "trying it" at the PoC stage is not enough; you can prevent yourself from going back by using "can we continue on to the real thing?" as your evaluation criteria.

Granularity of production migration planning

The key to the production migration is not a "work plan" but a "decision plan." Specifically, in addition to a procedure manual and task breakdown, the following two points are required at a granular level:

• Go/No-Go criteria (what must be met to proceed/what must not be met to return)

• Rollback boundaries (how far can you go back/at what point is the line of no return)

By combining it with RACI (Responsibility Analysis and Integration), it becomes clear who will make decisions and on what basis, significantly reducing uncertainty on the day of the transition.

Post-migration perspective, including monitoring and operational design

After the migration is complete, the key is to maintain normal operation. While RDS is a managed service, operational aspects such as monitoring, backups, and maintenance windows must be planned in advance or they will affect the operation after going live.

By not separating the monitoring design from the migration plan, and by deciding on performance monitoring, slow queries, error notifications, and even recovery times in advance, you can avoid the feeling of "worrying about what will happen after the migration."

My Feelings, Then and Now

When migrating to RDS, the most important factors are "which method to choose" and "how to design downtime." By organizing both technical constraints and business requirements in advance and solidifying prerequisites at each stage of PoC, verification, production migration, and cutover, you can reduce uncertainty on the day of migration.

Migration is not a matter of implementing procedures, but rather a matter of decision-making and design. The key to success is to plan everything from method selection, consistency checks, and rollback design to operational design.