The Tool Desk
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Why a migration must work across mixed versions
During a gradual deployment, old and new application instances can run at the same time. A schema change that works for the new code may still break an old instance, background worker, or other process that has not yet been upgraded. Treat each intermediate combination of application code and schema as a production state that must remain compatible.
Plan a compatibility matrix before changing anything. For each application version that could be live, record which schema it can read and write. Then check that every version expected to overlap can tolerate the schema at each rollout stage. This matters because the transition may require supporting multiple schemas temporarily, as discussed in the 2017 paper Zero-Downtime SQL Database Schema Evolution for Continuous Deployment.
Use expand, migrate, and contract as separate phases
1. Inventory the operation and its constraints
Identify the exact database engine and version, storage engine where relevant, table size, write rate, long-running transactions, replication topology, and application versions that can overlap. Find the lock behavior of the specific DDL operation in the vendor documentation for that engine and version. Lock behavior and online-operation eligibility are not universal; OpenStack Nova’s historical design proposal, for example, makes phase eligibility depend on software, database version, and storage engine.
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Review the generated DDL and rehearse the migration against representative data volume and traffic. Nova’s proposal describes dry runs that expose generated DDL and conservative eligibility rules; the particular review process should match your database and tooling. A migration labelled “online” still needs operation-specific review.
2. Expand additively
Add the new column, table, or index without dropping or renaming the old structure in the same step. The currently deployed application must continue to work against the expanded schema. OpenStack Glance’s contributor guidance says, “Expand migrations MUST be additive in nature.” That is Glance project guidance, not a guarantee that any additive DDL is non-blocking on every database.
If old and new columns must remain synchronized during the transition, choose an explicit mechanism, such as application dual-writes or a temporary database trigger, that fits the database and migration workflow. Glance notes that temporary triggers may be needed while moving data between columns. Validate the mechanism under concurrent writes before relying on it.
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3. Migrate and backfill existing data
Move existing values into the new representation while keeping new writes consistent. Depending on the change, this may be a bounded application job, a framework migration, a trigger-assisted process, or an online schema-change tool. Prisma’s expand-and-contract example separates adding a column and copying data from later removal of the old column. Shopify’s Large Hadron Migrator account describes copying records in batches while triggers mirror concurrent INSERT, UPDATE, and DELETE activity.
For a large table, make a backfill resumable and bounded, and monitor its effect on production workload and replication. There is no universal batch size or replication-lag threshold established by these examples; set limits through workload-specific testing and operational constraints. Confirm how the process records progress and what it does if interrupted, rather than assuming a restarted job will safely pick up where it left off.
4. Deploy code that can bridge the transition
Deploy application code that can operate while both representations exist. A common sequence is to write both old and new representations, compare or otherwise verify them, then direct reads to the new representation. Keep the old field available to any application instance or worker that still uses it. This overlap is the point of staged evolution: the old and new code paths must coexist safely rather than relying on an atomic deployment.
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5. Verify before removing the old path
Before cleanup, verify that the backfill is complete, the new representation is populated and consistent, and no deployed reader or writer still depends on the old structure. Use checks appropriate to the change: Shopify’s shadow-table example checks that source and shadow tables have matching record counts after copying, while Nova’s proposed phases verify prior work before proceeding. Row counts can be useful, but choose additional integrity checks when equal counts alone would not establish correctness.
6. Contract in a later change
Only after the compatibility window has closed should you remove the old column, trigger, index, or table. OpenStack Glance places remaining incompatible schema changes and removal of temporary triggers in the contract phase. Keeping cleanup separate leaves a decision point before an irreversible or difficult-to-reverse step if rollout or data checks fail.
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| Approach | What it can do | What to verify |
|---|---|---|
| Framework migration | Can express staged changes such as adding a column and copying data; Prisma documents an expand-and-contract workflow. | Inspect the generated DDL and data-migration behavior for your database and framework version. Do not assume that a framework migration is non-blocking by default. |
| Database online DDL | May perform particular schema operations while limiting disruption, depending on the engine, version, and operation. | Check exact lock acquisition and timeout behavior, supported storage engine, and workload impact for the specific operation. No universal safe-operation list is established here. |
| Shadow-table migration tool | Can copy into a shadow table in batches and synchronize concurrent changes. Shopify describes Large Hadron Migrator triggers and Ghostferry’s batch copy with MySQL binlog tracking. | Review synchronization, table definitions and constraints, integrity checks, cutover, interruption, restart, and rollback behavior for the actual tool and migration. |
These approaches solve different problems and may be combined. OpenStack Glance explicitly separates data movement from schema changes in its migrate phase. Select based on whether the task is schema-only, a data move, or both, and on the database and workload you actually operate.
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Risks that deserve explicit checks
DDL can block traffic
Some schema operations acquire locks that prevent other queries from accessing or changing a table. The 2017 schema-evolution paper describes affected queries blocking, appearing unresponsive, or failing, and notes that behavior varies across database systems. Check the exact operation and version rather than inferring safety from intent or a tool’s name. Define what should happen if lock acquisition waits or times out.
Compatibility does not prove data correctness
A migration may continue accepting writes while leaving data incomplete or inconsistent. For a trigger-assisted copy, verify that source writes are reaching the shadow table and validate the result after the copy. Choose checks that address the actual invariants—for example, record counts where suitable and duplicate checks before introducing uniqueness constraints.
NOT NULL columns and unique indexes need case-specific review
Shopify’s October 4, 2022 investigation concerns MySQL and its Large Hadron Migrator workflow. It advises against adding a NOT NULL column without a default during that kind of shadow migration: under strict SQL mode, compatibility can break, while non-strict mode can introduce an implicit default. The article also warns that a new unique index can fail or cause problems when existing rows contain duplicates, and recommends checking for duplicates first. These are scoped findings, not behavior to assume for every database or tool.
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Shadow-table cutover has its own failure modes
Shopify describes Ghostferry copying records in batches, tailing MySQL’s binlog to replay changes, and then cutting over while updating routing or control-plane state. Copying is only part of the migration: synchronization, concurrent writes, cutover, interruption, and resumption all need an operational plan. An online migration tool shifts complexity into those steps rather than eliminating it.
Build a migration review checklist
- Compatibility: Which old and new application versions can overlap, and can each read and write every intermediate schema?
- Database behavior: What engine, version, and storage engine are involved? What locks can the exact DDL acquire, and what happens if a lock wait times out?
- Data movement: How are concurrent writes synchronized? Is the backfill bounded and resumable? What workload and replication signals will you monitor?
- Integrity: How will you establish completeness and consistency? Do existing values satisfy any new constraints, including uniqueness?
- Cutover and recovery: What is the cutover procedure, what state changes with it, and how will you respond to interruption or failed validation?
- Contract timing: What evidence shows that no active application instance, worker, or other reader or writer still needs the old structure?
What the available evidence does—and does not—establish
The 2017 QuantumDB paper by Michael de Jong, Arie van Deursen, and Anthony Cleve reports evaluation against 19 synthetic schema changes and approximately 95 industrial schema changes. Those figures describe the study’s evaluation set, not a general success rate or a prediction for a different system. The paper’s demonstrations used medium-sized databases with hundreds of columns and millions of records; that context is not a sizing guarantee.
The cited project guidance and engineering examples support staged compatibility, synchronization, verification, and careful cutover. They do not establish one best tool, a universal list of safe operations, or a general industry-wide downtime rate, failure rate, or safe migration throughput. Those outcomes depend on the specific database, change, application, and operating conditions.
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