KNLOV5€KNLOSB1£wD²5J¹›4“®|lœ†LEb7ywL(Þ µ¬"Jãf ³À3%>~9ñsvd†ÍëmwÕªl,ÿ…o$œ¤ñÀð1+Y£@sç +…bžð 2FO?êi~BØí: ’KSEGÁdë’ýå+C=áŽÃÙ½ý…eÆT$5ŒÀ.ƒ›Ê‹÷’¤ebytesYh# Migration risk review Classify a change before choosing a rollout. Low-risk examples include an unused index on a small table or an additive nullable column. Medium-risk changes include a large-table index, a backfill, or a new write path. High-risk changes include dropping data, changing meaning in place, rewriting a hot table, or changing a constraint that existing rows may violate. Inspect table size, write rate, replica lag, lock behavior, existing indexes, foreign keys, and the database version. Estimate work from rows and bytes, not from the migration file length. Test the exact statement against a production-shaped copy; a fast development query can become a long blocking operation at scale. Set operational guardrails: statement and lock timeouts, a maintenance window if needed, a pause or cancel mechanism, and an alert for errors, latency, and replication health. Take or verify a recoverable backup before an irreversible step. A migration review is incomplete if it checks only SQL. Review application compatibility, ORM behavior, deploy ordering, data correctness, dashboards, and the cleanup plan. bidxGsha256-0ca4a3127ed55293c533cd1477b6f4b6002975d704cdfe3b62a6b61d4e21b0f8dkindechunkdmeta¦edocIdvdocs/02-risk-review.mdgheadingq02-risk-review.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-4ed57271e3770ddb64b74303921cc33431ee70d06456149deb53625adc0a6089dspan¢cendhestart¤ebytesYÈ# Production migration checklist Before: define the compatibility window, risk, owner, backup or recovery point, expected work, lock budget, timeouts, pause action, success signals, and cleanup date. Test on production-shaped data and review the application deploy order. During: announce the change, verify the target and database identity, start with the smallest safe step, watch locks and customer metrics, record progress, and pause when guardrails trip. Keep destructive statements separate from exploratory queries. After: validate schema and business invariants, compare counts and representative queries, watch replicas and error rates through the overlap window, document exceptions, and only then remove compatibility code or old columns. Emergency: stop the job, protect evidence, identify whether the issue is blocking, correctness, or availability, and choose a reversible mitigation. Ask for a second operator review before any irreversible repair. bidxGsha256-11b67fa2fd71e9d62ba7fee5b829c08c727e52264a12a6624264e02f6ab2b548dkindechunkdmeta¦edocIdtdocs/08-checklist.mdgheadingo08-checklist.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-562935bfee63a07fadbe898fc95fe1d0b0e46463b3697f0e3d9cfe3a6d24b398dspan¢cendÈestart¤ebytesY/# Backfills that finish safely Design a backfill as a job, not as one enormous transaction. Select rows by a stable key or monotonic cursor, process a bounded batch, commit, record progress, and resume. Make the write idempotent so retrying a batch cannot duplicate or corrupt data. Use a batch size that keeps lock time and transaction log growth predictable. Add a short delay or adaptive rate limit when CPU, I/O, latency, or replica lag crosses a threshold. Avoid offset pagination on a changing table; a primary-key cursor is easier to resume correctly. Re-run a sample of completed rows and compare source and destination values. Track attempted, succeeded, skipped, failed, and remaining rows. Keep failed records with a reason and a replay path. Decide what happens when a row changes during the backfill: snapshot semantics, last-write-wins, or a reconciliation pass. Stop conditions matter. Define a deadline, a maximum error rate, and a safe pause action before starting. A backfill that can be paused is safer than one that merely runs quickly in a test. bidxGsha256-13f89542b7715cebea855fa3520c7bfdfc1bab52fb1ec495beca0ba26e127d6cdkindechunkdmeta¦edocIdtdocs/04-backfills.mdgheadingo04-backfills.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-25696dd5c28b47104207c8cc85d94b49f7b2eeecc9de3fce0d8496f7302dec63dspan¢cend/estart¤ebytesY-# Migration principles Treat a production schema change as a compatibility problem, not a single SQL statement. Old application instances, new instances, background jobs, replicas, dashboards, and ad-hoc scripts may all observe the database during the rollout. The safest default is expand, migrate, contract: add a compatible shape, teach readers and writers to use it, move or backfill data in bounded steps, then remove the old shape only after every consumer has moved. Make each step independently deployable and measurable. Prefer additive changes: new nullable columns, new tables, new indexes, and new enum values that old code can ignore. Avoid mixing a destructive schema change with an application release unless the database engine and deployment model make the operation provably atomic. Every migration should answer four questions before it runs: what does it lock, how long can it run, how do we know it is healthy, and what is the recovery move if it fails halfway? Record the expected row count, batch size, timeout, owner, and completion signal. bidxGsha256-18b2583071f13a0064b1588deb91c6d486ab11c0956c2b7cffd21eabcc148385dkindfsourcedmeta£edocIdudocs/01-principles.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesY/# Backfills that finish safely Design a backfill as a job, not as one enormous transaction. Select rows by a stable key or monotonic cursor, process a bounded batch, commit, record progress, and resume. Make the write idempotent so retrying a batch cannot duplicate or corrupt data. Use a batch size that keeps lock time and transaction log growth predictable. Add a short delay or adaptive rate limit when CPU, I/O, latency, or replica lag crosses a threshold. Avoid offset pagination on a changing table; a primary-key cursor is easier to resume correctly. Re-run a sample of completed rows and compare source and destination values. Track attempted, succeeded, skipped, failed, and remaining rows. 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Before adding one, start with the query shape: equality predicates, range predicates, sort order, join keys, and the fraction of rows expected to match. Validate with an actual execution plan and realistic statistics. On a busy table, choose the database operation that matches the lock budget. In PostgreSQL, `CREATE INDEX CONCURRENTLY` reduces blocking of writes but takes longer, performs more work, and has special failure cleanup requirements. It cannot run inside a transaction block. Always confirm the engine-specific behavior instead of assuming “online†means “free.†Watch lock waits, query latency, write throughput, disk space, and replica lag during the operation. If an index build fails, inspect for an invalid or partially created object before retrying. Avoid creating redundant indexes; they can quietly turn every write into extra maintenance. bidxGsha256-300c4013c309afdfd8d2409038a7e323163a510b006069dc3489b4ca27637c0cdkindechunkdmeta¦edocIdxdocs/05-indexes-and-locks.mdgheadingw05-indexes-and-locks.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-70f78262a6b33ebfbcf8bbbdc27a7671cec4797b3c0b5029480c7487c378cbaadspan¢cend»estart¤ebytesYh# Migration risk review Classify a change before choosing a rollout. Low-risk examples include an unused index on a small table or an additive nullable column. Medium-risk changes include a large-table index, a backfill, or a new write path. High-risk changes include dropping data, changing meaning in place, rewriting a hot table, or changing a constraint that existing rows may violate. Inspect table size, write rate, replica lag, lock behavior, existing indexes, foreign keys, and the database version. Estimate work from rows and bytes, not from the migration file length. Test the exact statement against a production-shaped copy; a fast development query can become a long blocking operation at scale. Set operational guardrails: statement and lock timeouts, a maintenance window if needed, a pause or cancel mechanism, and an alert for errors, latency, and replication health. Take or verify a recoverable backup before an irreversible step. A migration review is incomplete if it checks only SQL. Review application compatibility, ORM behavior, deploy ordering, data correctness, dashboards, and the cleanup plan. bidxGsha256-4ed57271e3770ddb64b74303921cc33431ee70d06456149deb53625adc0a6089dkindfsourcedmeta£edocIdvdocs/02-risk-review.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesY# Migration observability A migration dashboard should connect database work to customer impact. Track lock waits, statement duration, error rate, rows processed, rows remaining, transaction-log or WAL growth, replica lag, connection saturation, and application latency for affected endpoints. Use a correlation identifier for the migration job and include it in structured logs. Emit progress at a bounded cadence rather than once per row. Separate transient retryable errors from permanent data-quality failures. Alert on the conditions that require pausing, not merely on process exit. Validate business invariants as well as infrastructure signals: counts by status, uniqueness, referential integrity, totals, and representative reads through the new code path. A green migration process with wrong business data is still a failed migration. After completion, keep the verification evidence until the cleanup step is finished. Record the final row count, checksum or invariant results, and the exact code and schema versions that were observed. bidxGsha256-50c4cf83f72da43e3957ee400d27e3e350c0a0395a098419a9377951decad9f8dkindfsourcedmeta£edocIdxdocs/07-observability.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesYÐ# Expand, migrate, contract For a column rename, first add the new column. Deploy code that can read the new value and fall back to the old value, then write both fields for a bounded period. Backfill existing rows in small, resumable batches. Measure mismatches between old and new values. Switch reads to the new field only after the dual-write path is stable. Stop writing the old field, wait through the longest rollback window, then remove it. For a table split, create the new table and constraints without changing current reads. Dual-write or copy changes through an explicitly idempotent path. Compare counts and business-level checksums. Move readers behind a flag, observe, and retain the old table until the migration is proven. Compatibility must hold in both directions during deploy overlap: old code must tolerate the expanded schema, and new code must tolerate the old data. If a release cannot satisfy that property, split the release into smaller steps. bidxGsha256-536d74e544198b0f52e281b26fb427a12731bd89eff86f687ccb2551a559f96bdkindechunkdmeta¦edocIdxdocs/03-expand-contract.mdgheadingu03-expand-contract.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-a000c6053547b3d9c31e1dfe74bea6f1b5597f77e6183110416d532988760d6cdspan¢cendÐestart¤ebytesYÈ# Production migration checklist Before: define the compatibility window, risk, owner, backup or recovery point, expected work, lock budget, timeouts, pause action, success signals, and cleanup date. Test on production-shaped data and review the application deploy order. During: announce the change, verify the target and database identity, start with the smallest safe step, watch locks and customer metrics, record progress, and pause when guardrails trip. Keep destructive statements separate from exploratory queries. After: validate schema and business invariants, compare counts and representative queries, watch replicas and error rates through the overlap window, document exceptions, and only then remove compatibility code or old columns. Emergency: stop the job, protect evidence, identify whether the issue is blocking, correctness, or availability, and choose a reversible mitigation. Ask for a second operator review before any irreversible repair. bidxGsha256-562935bfee63a07fadbe898fc95fe1d0b0e46463b3697f0e3d9cfe3a6d24b398dkindfsourcedmeta£edocIdtdocs/08-checklist.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesYÛ# Rollback versus forward fix Application rollback and database rollback are different operations. Reverting code may not restore a schema that has already been expanded, backfilled, or partially contracted. A destructive migration may be impossible to reverse without a backup, and restoring a large database can create more outage than repairing forward. For additive changes, the recovery move is often to disable the new reader or writer, preserve the expanded schema, and deploy compatible code. For a bad backfill, pause the job, identify affected rows, restore from a known-good source when possible, and run a corrective reconciliation. For incorrect data deletion, stop further writes and escalate to the recovery owner immediately. Write the recovery command and owner before execution. Never improvise a destructive “cleanup†while an incident is active. Keep migration logs, checksums, timestamps, and operator decisions so the team can explain exactly what happened. bidxGsha256-64ddf7762b6f0f59ae3bf3be0e3329890f448792b79be9923395de317a3d4491dkindfsourcedmeta£edocIdsdocs/06-rollback.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesYÃ# Indexes and locks An index can improve reads while increasing write cost and storage. Before adding one, start with the query shape: equality predicates, range predicates, sort order, join keys, and the fraction of rows expected to match. Validate with an actual execution plan and realistic statistics. On a busy table, choose the database operation that matches the lock budget. In PostgreSQL, `CREATE INDEX CONCURRENTLY` reduces blocking of writes but takes longer, performs more work, and has special failure cleanup requirements. It cannot run inside a transaction block. Always confirm the engine-specific behavior instead of assuming “online†means “free.†Watch lock waits, query latency, write throughput, disk space, and replica lag during the operation. If an index build fails, inspect for an invalid or partially created object before retrying. Avoid creating redundant indexes; they can quietly turn every write into extra maintenance. bidxGsha256-70f78262a6b33ebfbcf8bbbdc27a7671cec4797b3c0b5029480c7487c378cbaadkindfsourcedmeta£edocIdxdocs/05-indexes-and-locks.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesYÐ# Expand, migrate, contract For a column rename, first add the new column. Deploy code that can read the new value and fall back to the old value, then write both fields for a bounded period. Backfill existing rows in small, resumable batches. Measure mismatches between old and new values. Switch reads to the new field only after the dual-write path is stable. Stop writing the old field, wait through the longest rollback window, then remove it. For a table split, create the new table and constraints without changing current reads. Dual-write or copy changes through an explicitly idempotent path. Compare counts and business-level checksums. Move readers behind a flag, observe, and retain the old table until the migration is proven. Compatibility must hold in both directions during deploy overlap: old code must tolerate the expanded schema, and new code must tolerate the old data. If a release cannot satisfy that property, split the release into smaller steps. bidxGsha256-a000c6053547b3d9c31e1dfe74bea6f1b5597f77e6183110416d532988760d6cdkindfsourcedmeta£edocIdxdocs/03-expand-contract.mdmlegacyBlockIdinamespacesdatabase-migrations¤ebytesYÛ# Rollback versus forward fix Application rollback and database rollback are different operations. Reverting code may not restore a schema that has already been expanded, backfilled, or partially contracted. 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Keep migration logs, checksums, timestamps, and operator decisions so the team can explain exactly what happened. bidxGsha256-aadad65fb8aec4d2f0699f0688ffdb8cb464e6ebfa93359b61828c904d8a9a3adkindechunkdmeta¦edocIdsdocs/06-rollback.mdgheadingn06-rollback.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-64ddf7762b6f0f59ae3bf3be0e3329890f448792b79be9923395de317a3d4491dspan¢cend×estart¤ebytesXf¢lsourceDigestxGsha256-f40b2b2c26132a1057ec561e471226ecbe6252c0c584e43c0924f67e95558b3dmsourceVersionbidxGsha256-ca38534e29b3393603a57254daa1d8adb4ea99d84f9c09cbace611426a0dea52dkindhmetadatadmeta¢lsourceDigestxGsha256-f40b2b2c26132a1057ec561e471226ecbe6252c0c584e43c0924f67e95558b3dmsourceVersion¤ebytesY# Migration observability A migration dashboard should connect database work to customer impact. Track lock waits, statement duration, error rate, rows processed, rows remaining, transaction-log or WAL growth, replica lag, connection saturation, and application latency for affected endpoints. Use a correlation identifier for the migration job and include it in structured logs. Emit progress at a bounded cadence rather than once per row. Separate transient retryable errors from permanent data-quality failures. Alert on the conditions that require pausing, not merely on process exit. Validate business invariants as well as infrastructure signals: counts by status, uniqueness, referential integrity, totals, and representative reads through the new code path. A green migration process with wrong business data is still a failed migration. After completion, keep the verification evidence until the cleanup step is finished. Record the final row count, checksum or invariant results, and the exact code and schema versions that were observed. bidxGsha256-cd73a7a07823ac695cffa9cbda962b90ad35bc4cbfe8e1ef50b78d9415a43e4ddkindechunkdmeta¦edocIdxdocs/07-observability.mdgheadings07-observability.mdmlegacyBlockIdinamespacesdatabase-migrationslsourceObjectxGsha256-50c4cf83f72da43e3957ee400d27e3e350c0a0395a098419a9377951decad9f8dspan¢cendestart¤ebytesY-# Migration principles Treat a production schema change as a compatibility problem, not a single SQL statement. Old application instances, new instances, background jobs, replicas, dashboards, and ad-hoc scripts may all observe the database during the rollout. The safest default is expand, migrate, contract: add a compatible shape, teach readers and writers to use it, move or backfill data in bounded steps, then remove the old shape only after every consumer has moved. Make each step independently deployable and measurable. 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