MySQL to Aurora — the homogeneous migration that's almost a free upgrade.

IWhy Aurora MySQL — what you actually get over community MySQL

Aurora is not a fork of MySQL with a faster query planner. It is a re-architecture that keeps the MySQL engine on top but replaces the storage layer underneath with a purpose-built distributed one. Almost every Aurora advantage traces back to that single design decision, so it is worth understanding before you weigh the migration.

In community MySQL — self-managed or RDS MySQL — the engine and its storage are coupled: the instance owns its disk, and a replica is a second full copy kept in sync by replaying the binary log. Aurora breaks that apart. The compute instances run the MySQL-compatible engine, but the data lives in a separate distributed storage volume shared by every instance in the cluster, which automatically grows in 10 GB increments up to 128 TiB and replicates each block six ways across three Availability Zones. You never provision or extend storage, and you never run out mid-incident.

Because storage is shared and durable on its own, read replicas are cheap and fast. A community-MySQL replica needs its own full copy of the data and lags by however long it takes to ship and replay the binlog. An Aurora read replica (Aurora calls them Reader instances) attaches to the same storage volume the writer already uses, so there is no data to copy at provision time and replica lag is typically in the low milliseconds rather than seconds. You can run up to 15 Readers per cluster behind the built-in reader endpoint to load-balance read traffic.

That same shared-storage design is why failover is fast. When the writer fails, Aurora does not rebuild or resync a replica — the storage is already intact and shared, so it promotes an existing Reader and repoints the cluster endpoint, typically in under 30 seconds versus the minutes a traditional promotion can take. The storage layer also self-heals, continuously repairing bad blocks against the other copies so a single disk fault is invisible to you.

On throughput, Aurora MySQL generally outperforms community MySQL on the same instance class for high-concurrency OLTP, because the engine offloads work like redo logging into the storage layer instead of doing it on the instance. AWS's headline figure is up to ~5× stock MySQL; honestly the multiple depends on the workload, but write-heavy, high-concurrency apps see the biggest lift and almost nobody regresses. You also get features community MySQL lacks: Aurora Backtrack (rewind the cluster to a point in time in seconds, no restore), Fast Clone (a copy-on-write clone of a multi-TB database in minutes), and Aurora Serverless v2 (fine-grained compute autoscaling for spiky load).

IIWhy MySQL→Aurora is a homogeneous migration — and why that matters

Every database migration sits somewhere on a difficulty spectrum, and the position is set by one question: does the database engine change? MySQL→Aurora MySQL is the friendly end of that spectrum, and understanding why tells you how much risk and effort to budget.

A homogeneous migration keeps the same engine. Aurora MySQL is built to be drop-in compatible with community MySQL — it speaks the same wire protocol, runs the same SQL dialect, and is compatible with a specific MySQL version (Aurora MySQL version 3 is MySQL 8.0-compatible). Your existing MySQL drivers, connection libraries, ORM, SQL queries, stored procedures, and schema all work against Aurora unchanged. In most cases the only thing your application changes is the database endpoint in its connection string. That is the defining property of a homogeneous move and the reason this migration is low-risk.

Contrast that with a heterogeneous migration — Oracle→Aurora PostgreSQL, SQL Server→Aurora — where the engine and SQL dialect both change. There you need the AWS Schema Conversion Tool to translate schema, data types, and stored procedures, plus an engineer to hand-rewrite everything the tool cannot auto-convert. That manual remainder is weeks of work and the real schedule risk. MySQL→Aurora has none of it: there is no schema-conversion step at all, because the schema is already in the target dialect.

Practically: you do not need SCT, you do not budget for a schema-conversion phase, and the effort collapses to the data move and the cutover. The risks that remain are operational, not structural — getting the data across cleanly, validating it, and flipping the app with minimal downtime. Those are well-tooled steps. It is the difference between a one-to-three-week engagement and a multi-month one.

One caveat up front: "drop-in compatible" holds for the overwhelming majority of workloads but not literally every edge case, and Aurora is not the right home for every MySQL deployment. Section IV covers the compatibility differences to check; Section VII covers when to stay on RDS MySQL instead. But as a baseline expectation, this is the migration where the answer to "will my app just work?" is almost always yes.

IIIThe three migration methods — and which one fits your source

There is no single "migrate to Aurora" button; there are three distinct methods, and the right one is decided by where you are starting from and how much downtime you can tolerate. Picking the wrong method is the most common way to make an easy migration harder than it needs to be.

The decision tree is short. If you are already on RDS MySQL and can take a maintenance window, snapshot restore is the simplest path. If you are on RDS MySQL and cannot take a real window, the Aurora read-replica promotion path gets you to near-zero downtime with almost no moving parts. If your source is self-managed MySQL (on EC2, on-prem, or another cloud) or you need near-zero downtime from any source, AWS DMS is the general answer. Here is each one in detail.

A migration partner runs whichever method fits as a rehearsed sequence: the data-copy and sync stages are reversible and repeatable, so they run them against a staging Aurora cluster first to measure timing and validate the data, then execute the production cutover with known numbers. The source stays intact and writable as the rollback target until validation passes. The method choice is the first thing they confirm on the scoping call, because it sets the entire downtime story.

IVCompatibility — what carries over cleanly, and the few things to check

Aurora MySQL is compatible with community MySQL by design, and for most applications that means a clean lift. But "compatible" is not "identical," and a short list of differences is worth checking before cutover so nothing surprises you in production.

Start from the right baseline: the schema, data, SQL queries, MySQL wire protocol, drivers, connection poolers, and the vast majority of features and behaviors are the same. Aurora MySQL version 3 targets MySQL 8.0 compatibility, so if your source is MySQL 8.0 (RDS or self-managed) the parity is very high. The items below are the known places where Aurora and community MySQL diverge — none of them are deal-breakers for typical OLTP applications, but each is something to confirm rather than assume.

• Storage engine — InnoDB only: Aurora MySQL runs on InnoDB. If any of your tables use MyISAM, MEMORY, or another engine, they must be converted to InnoDB before or during migration. Most modern MySQL schemas are already all-InnoDB, so this is usually a non-issue — but legacy schemas with MyISAM tables need a conversion pass first.
• Configuration parameters: Aurora manages some MySQL parameters itself because of its distributed storage, so a handful of community-MySQL settings are not configurable or behave differently (anything tied to local storage, the binlog for internal replication, the buffer-pool/IO tuning Aurora handles automatically). You set Aurora parameters through a DB cluster parameter group; most application-relevant parameters are still there, but do a parameter-by-parameter review rather than assuming a 1:1 copy of your my.cnf.
• Replication semantics: Inside an Aurora cluster, Readers stay in sync through the shared storage layer, not through binlog replication — so traditional MySQL replication topologies map differently. You can still use binlog replication to or from external MySQL (that is exactly what the read-replica and DMS methods use), but in-cluster read scaling is a different, faster mechanism. If you rely on specific replication internals, review how they translate.
• A small set of statements/features behave differently: A few MySQL features tied to the underlying storage or filesystem are unsupported or altered on Aurora (for example, certain operations that assume local file access). These are rare in application code. The practical move is to run your test suite and a representative workload against a staging Aurora cluster — the homogeneous nature means this validation is cheap and catches the rare edge case before production.
• Version alignment: Match the Aurora MySQL major version to your source MySQL major version where possible (e.g. MySQL 8.0 → Aurora MySQL v3). Migrating across a major version (say MySQL 5.7 → Aurora MySQL v3 / 8.0) is doable but layers a MySQL major-version upgrade on top of the platform move, which adds its own compatibility checks (deprecated syntax, charset/collation changes). Decide deliberately whether to upgrade-then-migrate or migrate-then-upgrade.

The honest summary: for a MySQL 8.0 application that is all-InnoDB and does not depend on exotic replication internals — which describes most modern MySQL workloads — the compatibility checklist is a formality and the app simply works. The cases that need real attention are legacy MyISAM tables and a major-version jump done at the same time as the migration. A partner who has done the move runs this checklist as standard intake rather than discovering an issue at cutover.

VCutover — how the switch to Aurora actually happens

Cutover is the moment of truth: the few minutes when traffic moves from the old MySQL to the new Aurora cluster. For the near-zero-downtime methods it follows the same disciplined arc, and the discipline is what keeps it boring instead of dramatic.

For the read-replica and DMS methods, by the time you reach cutover the Aurora cluster is already a near-live copy of the source — fully loaded and kept current by replication. The cutover itself is a short, rehearsed sequence, ideally run in a low-traffic window:

• 1 — Freeze writes on the source: Put the application into read-only or maintenance mode, or stop the write path, so no new transactions land on the old MySQL. This starts the downtime clock. Reads can often keep serving from the source during this step if you want to shrink perceived impact.
• 2 — Let replication drain to zero: Wait for replication lag (binlog replication for the read-replica path, CDC lag for DMS) to reach zero, so every committed transaction on the source has been applied to Aurora. This is usually seconds to a couple of minutes depending on the final write burst.
• 3 — Reconcile sequences and validate: Confirm AUTO_INCREMENT counters on Aurora are at or past the source's highest values so the first new INSERT does not collide, re-enable any constraints or triggers that were deferred, and run final validation — DMS data validation (row counts + checksums) and/or application-level smoke checks.
• 4 — Repoint the application: Change the application's database endpoint to the Aurora cluster endpoint (the writer endpoint for writes; the reader endpoint if you are also moving read traffic to Readers). Using DNS or a config flag here makes the flip — and the rollback — a single fast change.
• 5 — Resume writes and soak: Take the app out of maintenance mode, resume writes against Aurora, and monitor closely: error rates, latency, replica lag across Readers, connection counts. Keep the old MySQL source intact and untouched until the soak confirms Aurora is healthy.

VICost — Aurora vs self-managed MySQL vs RDS MySQL

Aurora is not automatically cheaper than RDS MySQL on the sticker, and it is rarely cheaper than self-managed MySQL on raw instance cost alone. The cost case is about total cost of ownership and what you stop doing, not the hourly rate — and that distinction is where teams either justify the move or talk themselves out of it for the wrong reasons.

Against self-managed MySQL (on EC2 or on-prem), the comparison is rarely close once you count everything. Self-managed looks cheap because you only see the instance bill — but you also pay, in engineer-hours, for patching, version upgrades, backup tooling, replication setup and monitoring, failover automation, storage capacity planning, and the on-call burden when any of it breaks at 3am. Aurora absorbs all of that: automated backups and minor-version patching, automatic failover, no storage provisioning, a self-healing storage layer. For most teams the operational time recovered dwarfs the difference in compute price.

Against RDS MySQL, the two are much closer because both are managed. Aurora's compute per instance-hour is in a similar band, but its storage and I/O use a different model — you pay for storage consumed (auto-growing, no over-provisioning) and, on the standard configuration, for I/O. For I/O-heavy workloads that variable I/O charge can make Aurora pricier than a comparable RDS MySQL setup; Aurora I/O-Optimized removes per-I/O charges for a higher compute/storage rate and wins above a certain I/O intensity. Model your actual I/O profile rather than assuming Aurora is cheaper or pricier in the abstract.

What tips the RDS-vs-Aurora math toward Aurora is usually not the bill — it is the capabilities. If you need fast low-lag read replicas, sub-30-second failover, a storage layer that never needs capacity planning, or features like Backtrack and Fast Clone, Aurora earns its price. For a steady, modest, single-instance workload with no read-scaling or fast-failover requirement, RDS MySQL may be the more economical home and the migration is not worth doing for cost reasons alone.

VIIWhen Aurora MySQL — and when to just stay on RDS MySQL

A good migration page tells you when not to migrate. Aurora is the stronger platform on most axes, but "stronger" is not "necessary," and there are real workloads where RDS MySQL is the right answer and moving would be effort spent for marginal gain.

Choose Aurora MySQL when one or more of these is true: you need to scale reads with multiple low-lag replicas (Aurora's Readers share storage, so they are faster and cheaper to add than RDS read replicas); you have tighter availability requirements that benefit from sub-30-second failover; your data is growing and you do not want to manage storage capacity; your workload is write-heavy or high-concurrency and would benefit from Aurora's throughput; you want Aurora-only capabilities like Backtrack, Fast Clone, the global database option for multi-region, or Serverless v2 for spiky load. The majority of growing production MySQL workloads check at least one of these boxes, which is why Aurora is the common destination.

Stay on RDS MySQL when your workload is steady and modest, single-instance or with one simple replica, with no read-scaling or fast-failover requirement, where the I/O profile would make Aurora pricier with no offsetting benefit, or where you need strict parity with a community MySQL behavior Aurora handles differently. RDS MySQL is still fully managed — automated backups, patching, Multi-AZ failover — so "stay on RDS MySQL" is not "stay on something worse to operate"; it is picking the managed option whose feature set matches your needs without paying for capabilities you will not use.

And on the source question: if you are on self-managed MySQL today, the choice is rarely "self-managed vs Aurora" in isolation — it is "which managed service," because nearly everyone moving off self-managed wants to stop carrying the operational load. There the decision collapses to Aurora MySQL vs RDS MySQL on the criteria above. The migration into either is homogeneous and low-risk; the only real decision is which managed target.

VIIIHow CloudRoute fits: a vetted partner runs the Aurora migration

A MySQL to Aurora move is low-risk, but it still rewards someone who has done it before. They pick the right method, run the cutover rehearsals and handle the compatibility edge cases as routine. CloudRoute matches you with a vetted AWS partner who has done your exact source to Aurora migration and runs it end to end.

CloudRoute is a routing layer, not an agency. You tell us your source (RDS MySQL, MySQL on EC2, on-prem MySQL, MySQL on another cloud), your rough data size, and your downtime tolerance. We match you to a partner with a track record on MySQL→Aurora moves and the regions you operate in, and the partner runs the full workflow — pick the method (snapshot restore, read-replica promotion, or DMS), run the compatibility checklist, stand up and size the Aurora cluster, do the data move and validation, rehearse the cutover on staging, execute the production flip, and soak it — with you.

On funding: MAP is for moves onto AWS. If your source is on-premises MySQL or MySQL on another cloud, the partner checks whether the move qualifies and files the MAP request. AWS approves. If your source is RDS MySQL, the move stays inside AWS and MAP does not apply, so plan to pay the partner for the work unless it comes with new AWS work that qualifies for incremental workload (IW) funding. Either way you get a senior team that already knows the InnoDB conversion gotcha, the AUTO_INCREMENT reset at cutover, the Aurora I/O-Optimized cost decision and the rollback rehearsal. CloudRoute is free for you. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding.

If you want the persona-level detail on how migrations are scoped, funded, and run, see the CloudRoute migration track. For the broader toolset a partner uses across migration types, the AWS migration services overview and the AWS DMS guide cover the mechanics, and the AWS Migration Acceleration Program page explains the funding in full.