Declarative data migrations¶

Declarative data migrations describe intended rows and transformations beside SQLAlchemy models. DBWarden compiles declarations into a canonical data specification, SQL, a checksummed plan, and a frozen .data.py artifact. Applying a migration uses those artifacts. It does not import current declarations.

Use this for reviewed changes to data that belongs with a schema release. Declarations generate versioned migrations; they cannot be placed in repeatable migration files. Keep one-off repair SQL and application backfills separate.

Configure discovery¶

model_paths contains mapped SQLAlchemy models. data_paths optionally contains modules with DataTransition declarations. DBWarden does not treat *.data.py files as live declarations.

from dbwarden import database_config

database_config(
    database_name="primary",
    default=True,
    database_type="postgresql",
    database_url_sync="postgresql://localhost/app",
    model_paths=["app/models.py"],
    data_paths=["app/data"],
    data_snapshot_dir=".dbwarden/data",
    snapshot_registry=".dbwarden/snapshots/registry.json",
)

model_paths and data_paths name Python files or directories, not dotted module names. Missing configured paths fail discovery. Symlink paths, duplicate declaration IDs, duplicate owners of a table, and reserved DBWarden tables are rejected. model_tables restricts model discovery, including model data declarations. Two database entries cannot use the same live data path unless their existing overlap_models=True opt-in permits it. data_snapshot_dir and snapshot_registry are relative paths. They hold compiled data snapshots and historical schema registry metadata.

Declaration IDs default to the project-relative module path and class name. Set declaration_id on a mapped model or transition to retain its identity when moving code. Keep an ID only for the same logical owner. Changing managed key columns or owned columns under an existing ID fails planning.

Managed rows¶

Put a Data inner class on a mapped model. rows() declares key columns, literal values, columns owned by the declaration, missing-row handling, and rollback policy.

from sqlalchemy import String
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
from dbwarden.data import DataMeta, rows

class Base(DeclarativeBase):
    pass

class Country(Base):
    __tablename__ = "countries"
    code: Mapped[str] = mapped_column(String(2), primary_key=True)
    name: Mapped[str] = mapped_column(String, nullable=False)

    class Data(DataMeta):
        managed_rows = rows(
            key="code",
            rows=[{"code": "UY", "name": "Uruguay"}],
            owned_columns=["name"],
            rollback="restore_previous",
        )

rows() accepts exactly one of rows= and source=. source= names a static CSV or JSON file. It is read during compilation and becomes canonical values in the frozen specification. Keys and owned columns must exist on the target table. on_missing="delete" requires a scope expression and acknowledge_delete=True. Choose rollback="restore_previous" when the generated migration can restore prior values, or "irreversible" when it cannot.

Omitted keys use the model's primary key. Omitted owned_columns use the declared non-key columns. DBWarden records ownership separately from application rows. Missing-row deletion only removes previously owned rows inside the explicit scope; it does not adopt every row with a matching key. A pre-existing row is accepted only under the declaration's value and ownership checks. Rollback checks current owned values before changing them, so later application edits cause a conflict instead of being overwritten.

CSV and JSON inputs must stay inside the project. CSV values are converted using column types. JSON rejects duplicate keys and non-finite numbers. Normalized strings and typed literals determine checksums, not source-file formatting. Removing a managed-row declaration does not silently delete its rows: first declare the intended deletion and generate its migration.

Derived values and expressions¶

derive() owns one target column inside its declared domain. Expressions are typed ASTs, not Python callbacks or raw SQL.

from dbwarden.data import DataMeta, col, derive, func

class Data(DataMeta):
    transformations = [
        derive("normalized_name", func.lower(col("name")), rollback="clear"),
    ]

Use col(name, table=None), literal(value), param(name), case(...), cast(...), mapping(...), and func.<name>(...) to build expressions. Arithmetic, comparisons, null tests, membership, and Boolean composition are supported. Combine predicates with &, |, and ~; Python and, or, and not cannot build expressions. The function allowlist is lower, upper, trim, concat, coalesce, json_extract, split_part, and PostgreSQL date_trunc. Arbitrary functions, Python callbacks, raw SQL, volatile values, and unsupported casts fail compilation.

The compiler checks column types, nullability, ownership, and expression domains. A partial case or mapping needs an explicit fallback or a domain that proves it complete. derive(..., when=..., on_unmatched="error" | "keep", max_rows=...) controls its domain and affected-row limit. Rollback is required: clear requires a nullable target; recompute requires a previous expression that can be restored; capture records a guarded typed preimage; irreversible blocks rollback.

date_trunc requires settings containing nonempty backend_version, timezone, collation, search_path, and encoding strings. These settings become part of expression identity and are checked against PostgreSQL before execution. A numeric version pin matches the server major version; other version strings must match exactly.

Use func.json_extract(value, "$.path") for scalar JSON extraction and func.split_part(value, delimiter, index) for one-based string splitting. JSON paths and delimiters must be nonempty literals; the path begins with $. JSON strings are decoded, numbers become text, booleans become true or false, and JSON null, missing paths, objects, and arrays become SQL null. ClickHouse rejects json_extract because its native result cannot preserve that contract. The split index is a literal integer from 1 through 64. SQL null stays null and a missing component returns an empty string. split_part works on all five backends. DBWarden freezes both canonical expressions.

Supply parameters through repeated --param NAME=VALUE options on generation and data inspection commands, or through the Python generation API's parameters mapping. Values parse as JSON when possible, otherwise as strings. Missing or duplicate names fail. Frozen artifacts contain resolved canonical values, so execution does not reread parameters.

Validation rules¶

Data.validations holds validate(expression, message=...) declarations. Each expression must hold for every target row; false and null results fail. Validation runs after relevant data writes and during check --data. Validation-only declarations need no rollback policy because they do not change rows.

from dbwarden.data import DataMeta, col, validate

class Data(DataMeta):
    validations = [validate(col("name").is_not(None), message="Name is required")]

Historical transitions¶

Use historical_table() to name a source table pinned to a snapshot. DataTransition sends source rows to one or more current mapped models with into().

from dbwarden.data import DataTransition, historical_table, into
from app.models import Customer, Person

legacy = historical_table("legacy_people", snapshot="primary__0007")

class SplitLegacyPeople(DataTransition):
    source = legacy
    source_identity = [legacy.id]
    overlap = "fan_out"
    targets = [
        into(
            Person,
            map={Person.id: legacy.id, Person.name: legacy.full_name},
            key=[Person.id],
            on_conflict="ignore_if_equivalent",
        ),
        into(
            Customer,
            map={Customer.person_id: legacy.id},
            key=[Customer.person_id],
            on_conflict="ignore_if_equivalent",
        ),
    ]

An explicit snapshot ID is recommended and required when compatible lineage is ambiguous. Omitting it invokes the generation-time latest-compatible selection described below; the frozen bundle still contains one exact ID. Current ORM models describe targets. Historical source tables come from the pinned snapshot and are not rediscovered at execution. A transition may preserve its source or drop it only with the declaration's explicit completion and rollback rules. The unified generation path removes the duplicate schema drop_table operation when the transition owns that source removal.

Python target mappings use {Target.column: source_expression}. Target keys must identify stable rows. on_conflict is error, ignore_if_equivalent, or overwrite; overwrite requires acknowledge_overwrite=True. Equivalent pre-existing rows remain unowned, so rollback does not delete them. Identity ledgers bind each source key to its target key and mapped values.

Target foreign keys determine write order. Unsupported cycles, ambiguous identities, overlapping ownership, or references that would be broken by source removal fail before execution. Nullable expansion, data writes, validation, and non-null contraction share one dependency-ordered plan. A transition owns its source removal, preventing duplicate schema drops.

The older exactly_once spelling normalizes to exactly_once_match; coverage="all" with priority normalizes to all_assigned_once. Frozen plans store the canonical mode. Priority values on a non-priority transition are rejected.

To copy from a model that remains part of desired schema, assign the mapped model class to source, set its exact source_snapshot, and select on_complete="keep". DBWarden requires complete coverage with on_unmatched="error" and leaves the source unchanged on rollback. PostgreSQL and SQLite keep the source name throughout. MySQL and MariaDB temporarily freeze it with an atomic rename, verify the target, and restore its name before success. A historical_table(...) or merged source cannot use keep.

Deterministic merges¶

Use merge_sources() when several historical tables contribute to one current target. Every source needs an exact snapshot, a stable identity, the same named projection, and a distinct priority. Every non-key output needs an explicit aggregate or winner rule.

from dbwarden.data import (
    DataTransition,
    aggregate,
    batch,
    col,
    from_source,
    historical_table,
    into,
    merge_sources,
    winner,
)
from app.models import Customer

crm = historical_table("crm_customers", snapshot="primary__0010")
billing = historical_table("billing_customers", snapshot="primary__0010")

customers = merge_sources(
    from_source(
        crm,
        identity=[crm.id],
        map={
            "customer_id": crm.id,
            "display_name": crm.name,
            "balance": crm.balance,
        },
        priority=10,
    ),
    from_source(
        billing,
        identity=[billing.customer_id],
        map={
            "customer_id": billing.customer_id,
            "display_name": billing.name,
            "balance": billing.balance,
        },
        priority=20,
    ),
    key=["customer_id"],
    values={
        "display_name": winner(col("display_name")),
        "balance": aggregate("sum", col("balance")),
    },
    allow_empty=False,
)

class MergeCustomers(DataTransition):
    source = customers
    targets = [
        into(
            Customer,
            map={
                Customer.id: col("customer_id"),
                Customer.name: col("display_name"),
                Customer.balance: col("balance"),
            },
            key=[Customer.id],
            on_conflict="ignore_if_equivalent",
        )
    ]
    execution = batch(size=1000, key=["customer_id"], max_duration=60)
    on_complete = "preserve"
    rollback = "restore_preserved_source"

Supported aggregate names are sum, avg, min, max, count, and require_equal. count counts non-null expression results. require_equal fails when contributions differ or mix null with a non-null value. For strings it compares UTF-8 bytes instead of the database's default collation. winner selects the lowest numeric source priority, then orders by the source identity's UTF-8 byte encoding. Source identities and grouping keys must be non-null integers or strings; cast other types explicitly. Source priorities must be distinct.

allow_empty=False is the default and stops before source retirement when all inputs are empty. Set allow_empty=True only when an empty merged target is a reviewed outcome. The flag changes merge identity. A merge supports preserved or acknowledged dropped sources. Archive completion belongs to a normal single-source transition.

DBWarden rejects arbitrary reducer functions, different projection fields, nullable or duplicate source identities, missing grouping keys, ambiguous field rules, repeated input relations, ambiguous qualified snapshot entries, and merge revisions that reuse the old declaration. Create a new declaration for changed merge semantics. ClickHouse rejects merges because it cannot establish the required source-write boundary.

Capture, archive, and batching¶

Use rollback="capture" on a transformation when rollback must restore the actual typed value that existed before the generated write.

from dbwarden.data import DataMeta, batch, col, derive, func

class Data(DataMeta):
    transformations = [
        derive(
            "normalized_name",
            func.lower(col("name")),
            rollback="capture",
            execution=batch(
                size=500,
                key=["id"],
                max_duration=30,
                statement_timeout=10,
                lock_timeout=5,
            ),
        )
    ]

Capture requires a target primary key. Apply stores the preimage before the write in the same consistency boundary. Rollback checks that the current value still matches the generated postimage. If application code changed it, rollback stops rather than overwriting the later value.

check --data verifies the authenticated capture receipt and recorded postimage. It does not reevaluate a captured expression or its domain on the post-write row. That would misclassify valid one-shot expressions such as derive("value", col("value") + 1, rollback="capture").

Archive policies use archive_table() and an explicit archive acknowledgement. They are available for scoped missing managed rows, unmatched transition rows, and completed transition sources. An archive move writes or verifies the archive row before removing the source row. The archive destination cannot be a current managed model or overlap another incompatible declaration.

from dbwarden.data import DataMeta, archive_table, col, rows

class Data(DataMeta):
    managed_rows = rows(
        key="code",
        rows=[{"code": "UY", "name": "Uruguay"}],
        owned_columns=["name"],
        on_missing="archive",
        scope=col("managed"),
        archive_to=archive_table("country_archive"),
        acknowledge_archive=True,
        rollback="restore_previous",
    )

For a transition, set coverage="subset", on_unmatched="archive", and unmatched_archive_to=archive_table(...) to move unmatched rows. Set on_complete="archive" and archive_to=archive_table(...) to retain the completed source under the declared archive name. Either form requires acknowledge_archive=True. rollback="capture" records overwritten target preimages; restore_preserved_source restores a preserved or archived source after verified target cleanup. Archive destinations cannot reuse an internal name, a desired model, the source or target itself, or another declaration's archive destination.

batch() uses ascending keyset ranges and never uses OFFSET. The key must be the source identity for a transition or the target primary key for a transformation, must be non-null and unique, and cannot be modified by the statement. All chunks remain in one statement transaction and ownership receipt. They do not commit or resume independently. A timeout or lag violation rolls back that statement.

max_duration is one cooperative budget across every step and chunk in the declaration attempt. A retry starts a new budget. The executor checks the budget before and after SQL and between chunks; statement_timeout interrupts supported statements independently. Duration, statement, lock, and lag limits are finite positive seconds.

Backend controls differ. PostgreSQL supports statement, lock, duration, and replication-lag limits. MariaDB supports its native statement limit; MySQL rejects a requested DML statement timeout and supports the other admitted controls. SQLite supports duration, statement interruption, and lock timeout, but rejects replication-lag limits. ClickHouse rejects keyset batching.

Historical snapshots¶

Use an explicit snapshot ID when branch intent matters. You may omit snapshot= during authoring to request the newest compatible snapshot on one unambiguous registered lineage. Generation then freezes the concrete snapshot ID, database, backend, schema checksum, and parents into the bundle. Multiple heads, multiple newest compatible candidates, a missing table, or invalid lineage fails and asks for snapshot=. Execution never performs this search or substitutes another snapshot. latest_previous is not an accepted alias.

For external snapshot import, use dbwarden.data.snapshots.register_snapshot(snapshot_id, schema_state, database=..., backend=..., parent_snapshot_ids=..., registry_path=...). resolve_snapshot(snapshot_id, database=..., backend=..., registry_path=..., schema_dir=...) reads the pinned entry without changing the registry. Reusing an ID with different content fails. Existing schema snapshot files must carry their own valid checksum before they can be resolved without a registry entry.

Registry writes use a local operating-system file lock and atomic replacement, including offline imports. Parent IDs are sorted and must identify registered snapshots in the same database and backend. Resolution rejects missing parents, cycles, and duplicate IDs. Pass both branch snapshot IDs as parents when importing a merge or squash snapshot; an imported root may have no parents.

Generate and inspect¶

Use the shared generator so schema and transition work become one migration when a source table changes.

dbwarden make-migrations "introduce country data" --offline --database primary
dbwarden data render --database primary --format operations
dbwarden data describe --database primary --format markdown
dbwarden data docs --database primary --output docs/data --diagrams mermaid
dbwarden data transition validate SplitLegacyPeople --database primary
dbwarden data transition plan SplitLegacyPeople --database primary --format sql
dbwarden data transition dry-run SplitLegacyPeople --database primary --probes
dbwarden migrate --database primary --dry-run --data
dbwarden check --database primary --data
dbwarden diff --database primary --data

data render and data describe accept --model, --include, --only-with-data, and --relationship-depth (0–5). Include sections are schema, constraints, relationships, data, and transitions. Render formats are text, json, mapping, sql, and operations; describe formats are text, json, and markdown, with optional --output. Values are redacted by default. --show-managed-values reveals declaration literals. SQL output intentionally shows SQL literals because it is for review. data docs writes an index, model and transition pages, and checksums.json.

Render, describe, and docs can read the latest non-superseded frozen bundle when configured live inputs have been removed. This preserves the stored model schema and declarations without importing a frozen Python file. Validation and generation still require live inputs; malformed live code is not silently replaced by old artifacts.

transition new --manual Name writes a disabled draft beneath a configured data_paths directory. Fill in its source, identity, and targets before enabling it. Its direct form accepts --from-table or --from-model, --source-snapshot, repeated --to-model, --map source:Model.target, --where, source and target keys, coverage, overlap, and preserve or drop-source flags. Direct predicates accept column equality with a literal; complex predicates belong in the Python declaration. Interactive authoring requires a terminal. Existing files are not overwritten.

For direct priority authoring, pass --coverage all_assigned_once --overlap priority and one --priority Model:INTEGER per target. Values must be distinct; lower numbers win.

The transition group contains audit, describe, new, render, validate, plan, dry-run, and reconcile. The compatibility group dbwarden data-transition exposes the same commands. For direct transition creation, use --source-key, --key, --coverage, --overlap, --preserve-source or --drop-source, and --acknowledge-drop when source removal needs acknowledgement. dbwarden make-data-migration and dbwarden make-migrations accept repeatable --param NAME=VALUE generation parameters.

Offline generation reads saved model state, declarations, and pinned snapshots. It does not need a target database connection. A historical source without a valid snapshot fails before SQL generation. make-data-migration uses the same generator with schema changes excluded. Use make-migrations when target tables or columns must change with the data.

Complete review and execution flow¶

The following sequence uses the same compiled semantics from declaration review through convergence.

1. Configure model_paths, data_paths, data_snapshot_dir, and snapshot_registry. Register any externally supplied historical snapshot.
2. Add the declaration beside its model or under data_paths. Give declarations explicit stable IDs before moving or renaming their Python definitions.
3. Validate and inspect the live semantics:

dbwarden data transition validate --database primary
dbwarden data render --database primary --format operations
dbwarden data transition plan SplitLegacyPeople --database primary --format json

1. Run read-only probes against the intended database. Review the database identity, snapshot, isolation level, observed counts, and accepted limits:

dbwarden data transition dry-run SplitLegacyPeople --database primary --probes

1. Generate one unified schema/data migration. Omit --offline when generation must collect live review evidence:

dbwarden make-migrations "split legacy people" --database primary

1. Review all three generated members together. Audit verifies their manifest and statically reads the frozen declaration:

dbwarden data transition audit primary__0012_split_legacy_people --database primary

1. Preview the pending frozen bundle selected by version and safety policy. This does not import the current declarations:

dbwarden migrate --database primary --dry-run --data

1. Apply under the normal migration lock. Use --force only when the reviewed severity requires destructive acknowledgement:

dbwarden migrate --database primary --force

1. Verify current state from desired declarations and applied frozen history:

dbwarden check --database primary --data
dbwarden diff --database primary --data

check --data succeeds only when the schema, pending migration SQL, and declared data converge. --force can acknowledge reviewed WARN and CRITICAL safety findings, but it cannot bypass unknown SQL classification or data drift.

If apply fails before any durable effect, the journal records a retryable failure and a later ordinary migrate may retry it. If an effect may have occurred, the state is UNKNOWN_REQUIRES_RECONCILIATION and ordinary migrate stops. Inspect first:

dbwarden data transition reconcile primary__0012_split_legacy_people --database primary

After repairing or verifying database state, record only a decision the reported state permits:

dbwarden data transition reconcile primary__0012_split_legacy_people \
  --database primary --apply --decision verified_retry

verified_retry does not ignore conflicts or infer ownership from equivalent values. --decision abandon is an audited terminal decision where supported. It does not mark the declared state converged.

Rollback uses the frozen rollback plan and refuses irreversible declarations or changed owned values:

dbwarden rollback --database primary --count 1

A completed data rollback records ROLLED_BACK. Ordinary migrate does not silently replay it. Reapply is a separate operator decision and creates a new linked epoch:

dbwarden migrate --database primary --reapply-data

Baseline records history without executing the data plan. It requires an exact target version and stores the explicit acknowledgement, reason, and skipped checks. It does not prove current data convergence:

dbwarden migrate --database primary --baseline --to-version 0012

Artifacts and integrity¶

A data migration writes three paired files:

• .sql contains reviewable upgrade and rollback SQL.
• .plan.json contains typed operations, canonical data specification, execution steps, safety information, and integrity manifest.
• .data.py contains the frozen canonical declaration data.

The manifest binds ordered member names, media types, byte lengths, and SHA-256 checksums for SQL, plan, and frozen artifact. The plan records semantic identity separately from probe evidence. Canonical JSON normalizes Unicode and rejects duplicate normalized keys, non-finite values, and unsupported literal types. Static input rows are embedded; replay does not depend on the original CSV or JSON file.

The plan member's hash and length cover its canonical JSON payload with data_bundle omitted; they do not measure the indented file that also stores the manifest. SQL and frozen Python hashes cover their UTF-8 text. This keeps each checksum outside its own hash input.

transition audit reads the frozen file with the AST reader, verifies the bundle, and does not import it. A merge that supersedes a data bundle leaves its SQL unchanged and writes a .superseded.json sidecar bound to the complete original SQL checksum. Tampering makes the marker fail closed. File publication restores prior contents on failure and publishes the plan last.

Apply, journal, rollback, and reconciliation¶

Execution verifies the complete bundle before writes and uses the existing database migration lock. Backend behavior differs:

Autocommit or concurrent schema steps cannot be combined with a data bundle. Unsupported backend operations fail during planning rather than emitting placeholder SQL.

Recorded native evidence includes six lifecycle flows on MySQL 8.4.11 and the same six independently on MariaDB 11.4.4: managed revision and reapply, two-revision archive rollback, captured derivation rollback, preserved transition rollback, batched merge round trip, and retained-source tamper refusal. Connection-loss, process-crash, and broader-version qualification remain release gates. No live ClickHouse result is claimed.

_dbwarden_data_runs records migration ID, application epoch, bundle checksum, run ID, status, last completed operation, sanitized error, baseline flag, and timestamps. _dbwarden_data_events records state transitions; nontransactional backends also persist statement checkpoints. A successful checksum-matched migration returns as already applied. Changed checksums fail. A fully rolled-back transactional failure is retryable; uncertain durable effects produce UNKNOWN_REQUIRES_RECONCILIATION and block automatic replay.

On MySQL and MariaDB, claimed insert counts must match the driver's affected-row count. The executor commits a WRITE_VERIFIED checkpoint in the same DML transaction as the inserted rows. An equivalent concurrent application insert cannot become migration-owned merely by passing a value comparison. Count mismatches produce terminal FAILED_FINAL; a crash with only a PENDING ownership insert cannot be reconciled from equivalent target values alone.

Generated MySQL/MariaDB rollback steps persist intent before acquiring row locks. Pure DML steps then keep SELECT ... FOR UPDATE locks through identity verification, guards, mutation, and the DONE checkpoint commit. Target and checkpoint tables must use InnoDB with autocommit disabled. DDL runs in separate steps because it can commit implicitly.

MySQL and MariaDB rename a historical source to a reserved internal name before capturing its rows. Writes through the original name then fail while the transition runs. A failed attempt can leave that internal source in place for explicit reconciliation. Rollback restores the original name after target cleanup succeeds.

A MySQL/MariaDB merge freezes all input sources in one multi-table rename, so no source remains writable after another source has reached the merge snapshot. Merge staging, aggregate guards, target ownership, contribution edges, and source retirement remain ordered journal steps.

Captured values, archive receipts, and merge contribution edges live in reserved internal tables and are authenticated or checksum-bound to the frozen plan. Back them up with the execution journal. Removing them can make rollback or convergence unverifiable.

Batched statements call fencing and progress hooks for each emitted chunk. The operation becomes complete only after every chunk, guard, edge record, and postcondition succeeds. Chunks never create independent application epochs.

Dry-run probes read the original source name without renaming it. Apply-only DDL existence checks run during execution. Later convergence checks use the preserved source and omit temporary rename-state checks.

ClickHouse managed-row deletion is rejected because the backend does not provide the atomic ownership receipt needed to distinguish migration inserts from concurrent application inserts. Historical transitions are also rejected: its nonblocking rename cannot establish the source-write barrier needed for complete capture and safe source retirement.

Retained identity edges bind source identity, target identity, and mapped values with HMAC authentication using a per-database key. Convergence and rollback verify those edges against current target rows. Treat the key and journal tables as part of the database backup. Baseline application records baseline=True plus an acknowledgement, reason, and skipped check identifiers in its event. The Python executor accepts baseline_reason; the CLI records that baseline was explicitly requested. This does not prove that declared data already exists.

Distinct source keys that collapse to the same normalized identity are rejected. On transactional backends, that failure rolls back the transition. On a nontransactional backend, any durable effects still require reconciliation.

Rollback follows each declaration's policy. Irreversible data operations refuse rollback. A completed rollback records ROLLED_BACK. Ordinary migrate refuses to replay that data; migrate --reapply-data explicitly starts a later epoch linked to the previous one. --force does not authorize reapply, and --reapply-data does not rerun an already successful migration. The direct Python executor uses reapply=True for the same decision.

When the same bundle creates a target table, rollback removes owned rows before checking whether the table is empty. Any remaining application row prevents the table drop. PostgreSQL and SQLite keep that check inside the locked transaction; SQLite also rejects remaining dependent views, foreign keys, and triggers. MySQL/MariaDB first rename the target into a reserved rollback table, then check and drop it. If that check fails, the renamed table retains its rows for inspection and recovery.

Use dbwarden data transition reconcile MIGRATION_ID to inspect journal state. --apply --decision abandon records an explicit abandonment where supported. --apply --decision verified_retry requires backend-specific proof: transactional rollback evidence, or matching checkpoints and verified postconditions for durable statements. An ambiguous partial statement remains blocked. The command takes the migration lock before it writes.

A partially completed MySQL/MariaDB rollback can remain blocked when it has already removed or changed target rows needed by the original identity proof. Verified checkpoints alone do not bypass that proof. Inspect and repair the affected state before retrying; DBWarden does not guess the missing before-image.

Persistent-environment dbwarden reconcile is separate: it generates a new reconciliation bundle from the declarations actually applied on that database to the merged frozen target. It retains original migration history, checks schema and data convergence, then marks branch versions reconciled. Repeating reconciliation after convergence produces no new change.

Data guards are read-only count queries run before or after a step. They enforce maximum or minimum rows, validation predicates, coverage, and conflict policies. transition dry-run --probes executes those queries and records count, scope checksum, database, isolation level, observation time, thresholds, and evidence checksum. It does not predict post-write values when the required schema or rows do not yet exist.

migrate --dry-run --data inspects pending frozen bundles after version and severity selection. It reports unavailable probes without creating a missing SQLite database. Probe success describes the observed database at that time; apply reruns guards inside its execution boundary.

Safety and convergence¶

Data operations carry INFO, WARN, or CRITICAL severity based on their declaration and rollback behavior. Destructive source handling, overwrite behavior, or irreversible work deserve CRITICAL review. A passing compile and a successful guard prove only the declared checks ran. They do not prove application semantics or data quality outside declared ownership.

category= on rows, derive, or DataTransition selects a named migration group. Groups use the existing category and CLI plugin API; they do not add severity levels. SAFE, INFO, WARN, and CRITICAL retain their ordering. Safety-scoped generation defers dependent data and schema operations together, and each generated group has its own verified bundle. See safety-scoped migrations and plugin API.

check --data and diff --data add managed-row, derived-value, validation, transition, merge, archive, and capture convergence checks to schema drift checks. Preserved sources allow source-to-target comparison. Dropped sources require authenticated retained edges and matching applied journal evidence. Merge checks validate grouped values and contribution provenance. Archive and capture checks validate their receipts and current postimages. Missing or changed evidence reports an unverifiable declaration instead of assuming convergence. Reserved ownership, edge, capture, archive, merge, and journal tables are excluded from schema drift.

Re-run data render, transition plan, and bundle audit after a declaration change. Once a migration exists, edit the live declaration and generate a new migration. Do not edit frozen data files or migration SQL.

Compared with legacy seeds¶

Seed files are useful for optional initial content and ad hoc loading. Declarative data migrations are versioned migration operations with canonical identity, ownership, rollback policy, guards, a durable journal, and frozen source artifacts. Use them when data change belongs to the same release contract as schema change.

The design specification defines the artifact and execution contract. The generated Python API reference and CLI option reference list signatures and command options.