Databases migration

MongoDB → PostgreSQL

Move MongoDB documents into PostgreSQL while designing a relational or JSONB model, preserving BSON types and IDs, rebuilding references, indexes, validation, transactions, change streams, application queries, and recovery.

Typical timeline30–75 business days160–320 hours active work

Statusneeds review

Source testedMongoDB documentation reviewed 2026-07-19

Destination testedPostgreSQL documentation reviewed 2026-07-19

Last reviewed2026-07-19

Sources3

Before you migrate

Should you make this move?

Both platforms have a case. Compare what you gain with what you give up before scheduling the cutover.

Current platform

MongoDB

Reasons to stay

Flexible documents and horizontal scaling suit rapidly evolving application data
A mature data model and query ecosystem supports serious production workloads

Reasons to leave

Relational integrity, joins, and transactional reporting require extra design care
Schema design, migration, tuning, and operations require specialist attention

New platform

PostgreSQL

What gets better

Powerful SQL, extensions, integrity, and open governance support demanding workloads
A mature data model and query ecosystem supports serious production workloads

What gets worse

Tuning, upgrades, and sophisticated features require experienced operators
Schema design, migration, tuning, and operations require specialist attention

Best of the move

PostgreSQL: Powerful SQL, extensions, integrity, and open governance support demanding workloads. This removes a major source-side concern: Relational integrity, joins, and transactional reporting require extra design care.

Worst of the move

What you lose: Flexible documents and horizontal scaling suit rapidly evolving application data. What you inherit: Tuning, upgrades, and sophisticated features require experienced operators.

01At a glance

Know the shape of the move.

Transfer outcome12 features audited

Clean0

Partial6

Manual5

Lost1

Mapping route9 of 9 fields have a destination path

SourceMongoDB9 mapped fields

Automatic0

Transform5

Manual4

Unsupported0

DestinationPostgreSQL9 supported paths

This timeline assumes

Up to 100 collections, 500 million documents, 1 TB of data, and 200 critical queries
Application and data owners approve a versioned target schema and consistency strategy.
A production-shaped snapshot and write workload are available for rehearsal.
The source remains authoritative until lag reaches zero and all invariants pass.
The migration team records evidence for every blocking verification check.

02Loss matrix

What survives the move.

“Partial” and “manual” are not footnotes. They are work that must be scheduled and verified.

Item	Outcome	Impact	What happens	Mitigation
Documents, nesting, and polymorphism	partial	critical	Collections require an explicit relational, JSONB, or hybrid schema rather than automatic one-to-one tables.	Model production query and integrity needs before transformation.
BSON types and field names	partial	critical	Relaxed JSON can lose type fidelity and MongoDB documents can contain names that export tools handle poorly.	Use canonical Extended JSON where appropriate and test every BSON type and problematic key.
mongoexport as backup	lost	critical	MongoDB explicitly states mongoexport is not a database backup tool.	Create a native restorable backup in addition to logical transformation exports.
Indexes, validation, transactions, and change streams	manual	critical	MongoDB runtime behavior must be redesigned in PostgreSQL and application code.	Build constraints and indexes, rewrite queries, and rehearse final change capture.
Schemas, collections, and tables	partial	critical	Structures require explicit relational, document, type, and naming decisions.	Version a signed target schema before loading production data.
Rows, documents, and identifiers	partial	critical	Data can move while IDs, generated values, precision, encoding, and null semantics differ.	Preserve stable source IDs and compare deterministic checksums and aggregates.
Types and nested values	partial	critical	Source types may coerce, flatten, normalize, or move into JSON columns.	Create type-level fixtures and reject silent truncation.
Relationships and references	partial	critical	Foreign keys, embedded documents, polymorphism, and cascades require redesign.	Load parents first and verify orphan and cascade behavior.
Indexes and query behavior	manual	critical	Index syntax and query planners differ.	Design from production query evidence and benchmark representative workloads.
Constraints, defaults, and generated values	manual	critical	Validation and automatic behavior do not transfer with raw data.	Recreate and test every business invariant.
Views, functions, triggers, and procedures	manual	critical	SQL dialects and database runtimes differ.	Port reviewed logic and test side effects and failure paths.
Users, roles, and permissions	manual	critical	Authentication and grants are platform-specific.	Apply least privilege before application access.

03Field and feature mapping

Where each thing goes.

Source	Destination	Method	Notes
MongoDB database or base	PostgreSQL database or document	manual	Record owner, region, collation, and source ID.
Schema, collection, or table	Destination schema and table	transform	Approve relational and naming semantics.
Row or document ID	Primary key and source ID	transform	Preserve stable identity.
Source field or column	Destination column	transform	Map type, null, default, precision, and encoding.
Nested object or array	Relation, array, or JSONB	transform	Choose query and integrity behavior explicitly.
Reference or foreign key	Destination foreign key or relation	transform	Create after parent records exist.
Index	Destination index	manual	Design from query evidence.
View, formula, or generated value	Destination view, expression, or generated column	manual	Rewrite and test output.
Trigger, automation, or integration	Destination trigger or application workflow	manual	Port idempotently and test failure behavior.

04Before you begin

Make the move recoverable.

Backup procedure

Create the source-of-truth backup

Preserve MongoDB data, configuration, and operating evidence before any destination write.

Create a native, restorable MongoDB backup plus logical schema and data exports.
Inventory schemas, types, relationships, indexes, logic, users, permissions, replication, queries, jobs, and integrations.
Record counts, checksums, aggregates, sizes, sequences, lag, performance, and recovery baselines.
Hash all immutable backup and migration artifacts.

Proof to capture: A signed manifest reconciles every scoped record class, runtime dependency, export file, count, and hash.

Transformation · Schema migration files and ETL specification

Target schema and transformation registry

Define every structural, type, relationship, and logic decision.

Inventory source values and exceptions.
Define explicit destination mappings.
Reject unmapped critical records.

Proof to capture: Save the input, output, command or tool settings, warnings, and final item counts.

Transformation · Native backup tools, pgloader or ETL, CDC, and verification SQL

Repeatable copy and change-capture pipeline

Load, catch up, verify, and cut over deterministically.

Normalize encoding, dates, identifiers, and blanks.
Run a representative pilot.
Reconcile accepted, rejected, and transformed rows.

Proof to capture: Save the input, output, command or tool settings, warnings, and final item counts.

05Handle with care

The things most likely to hurt.

These are operating limits. Treat every “Stop if” condition as a blocked migration, not a suggestion.

Transform

Silent type coercion corrupts data

criticalpossible likelihood

Precision, timezones, encodings, booleans, nested values, or nulls change without a load error.

Consequence: Applications make incorrect decisions.
Mitigation: Use type fixtures, checksums, aggregates, and invariant queries.

Stop if: Any critical invariant or precision check fails.

Cutover

Final delta misses writes

criticalpossible likelihood

Change capture lags, drops unsupported operations, or stops before writers freeze.

Consequence: Source and destination diverge permanently.
Mitigation: Monitor lag and reconcile a final consistency point.

Stop if: Lag is nonzero or an unexplained delta exists.

Performance

Queries pass functionally but fail under load

criticalpossible likelihood

Indexes and planners differ from the source.

Consequence: Cutover causes timeouts or lock contention.
Mitigation: Benchmark production-shaped queries and concurrency.

Stop if: Critical latency, error, or lock thresholds are exceeded.

06Precise timeline

Do the work in this order.

Estimate forUp to 100 collections, 500 million documents, 1 TB of data, and 200 critical queries

Total elapsed30–75 business days

Active work160–320 hours

BufferAdd time for deeply polymorphic documents, sharding, large arrays, geospatial queries, change streams, or strict zero-downtime requirements.

Days 1–3Inventory and decisions2–3 days

Days 3–5Backup and reconcile1–3 days

Days 5–12Map and pilot3–8 days

Days 10–20Final delta and switch1–5 days

Days 12–40Observe and close3–10 days

1
Days 1–3 · inventory
Inventory and decisions
6–12 hours active2–3 days elapsedOwner review waiting
- Inventory MongoDB data, features, users, domains, and integrations.
- Approve scope, owners, mappings, and exclusions.
Depends on: MongoDB and PostgreSQL administrator access
Stop / go checkpoint
Export?
Go when: Every critical item has an owner and disposition.
Stop when: Consent, billing, access, or system ownership is unclear.
2
Days 3–5 · backup
Backup and reconcile
5–10 hours active1–3 days elapsedExport processing waiting
- Create immutable exports and configuration evidence.
- Reconcile counts, totals, and hashes.
Depends on: Approved inventory
Stop / go checkpoint
Transform?
Go when: Source totals and export manifests agree.
Stop when: Any critical dataset or configuration is absent.
3
Days 5–12 · pilot
Map and pilot
10–35 hours active3–8 days elapsedDestination processing and review waiting
- Configure PostgreSQL and transform representative data.
- Run a pilot containing normal records and every critical edge case.
Depends on: Verified backup
Stop / go checkpoint
Scale?
Go when: Pilot mappings, behavior, access, and evidence pass.
Stop when: Any critical check fails or produces unexplained variance.
4
Days 10–20 · cutover
Final delta and switch
5–25 hours active1–5 days elapsedDNS, import, or sync propagation waiting
- Freeze production writes in MongoDB.
- Apply the final delta, switch ownership, and run blocking checks.
Depends on: Passed pilot and approved rollback
Stop / go checkpoint
Open production?
Go when: Counts reconcile and one destination system owns production.
Stop when: A source writer remains active or a blocking check fails.
5
Days 12–40 · observe
Observe and close
3–18 hours active3–10 days elapsedOperating-cycle evidence waiting
- Monitor one complete operating cycle.
- Sign the verification report and close rollback only after stability.
Depends on: Verified cutover
Stop / go checkpoint
Close rollback?
Go when: No trigger occurs during the agreed observation period.
Stop when: Data, access, delivery, routing, or business results regress.

07The point of change

Cut over with a way back.

Go live

Cutover

Make PostgreSQL the only production system without losing the final MongoDB delta.

Recommended window: A low-volume weekday morning with platform, data, DNS, and business owners available.

Freeze production writes and scheduled actions in MongoDB.
Export, transform, and reconcile the final delta.
Apply the approved delta to PostgreSQL.
Switch domains, forms, integrations, sending, or sync ownership as applicable.
Run every blocking verification check and keep the source intact.

Proof to capture: PostgreSQL owns production, totals reconcile, and every blocking check has durable evidence.

Return to safety

Rollback

Return production ownership to MongoDB without losing destination-era changes.

Deadline: Within seven days and before source data, plans, credentials, domains, or billing are changed.

Stop new writes and scheduled actions in PostgreSQL.
Restore the prior MongoDB routing, forms, integrations, sending, or sync ownership.
Export the PostgreSQL post-cutover delta.
Review and apply safe destination-era changes to MongoDB.
Run the same blocking checks against the restored source.

Proof to capture: MongoDB again owns production with current data and no duplicate destination action.

Rollback immediately when

Unexplained critical count or value variance
Missing or exposed critical data
Duplicate production action
Failed access, routing, delivery, or integration check
A critical feature has no safe destination replacement

08Verification report

Prove the migration worked.

Every blocking check must pass. Capture the evidence before cleanup begins.

ID	Check	Method	Expected result	Evidence
`V-01`Blocking	Record reconciliation	Compare counts, checksums, aggregates, and stratified records.	Every scoped record is accounted for.	Data ledger.
`V-02`Blocking	Value fidelity	Run type fixtures covering bounds, nulls, precision, Unicode, and timezones.	No silent coercion or truncation.	Type report.
`V-03`Blocking	Reference and constraint integrity	Query parents, children, orphans, duplicates, and cascades.	Every critical invariant holds.	Integrity report.
`V-04`Blocking	Views, formulas, triggers, and procedures	Run approved input and failure cases.	Outputs and side effects match acceptance.	Logic suite.
`V-05`Blocking	Functional and performance parity	Run production-shaped reads and writes under concurrency.	Results and service objectives pass.	Benchmark report.
`V-06`Blocking	Roles and data access	Run allowed and denied operations for every role.	Least privilege matches policy.	Access evidence.
`V-07`Blocking	Backup, restore, and rollback	Restore a destination backup and execute the rollback rehearsal.	Recovery objectives and integrity pass.	Recovery report.
`V-08`Blocking	Replication and write authority	Inspect lag, writers, jobs, and application connections.	PostgreSQL alone accepts approved writes with zero unexplained delta.	Cutover checklist.

09Post-migration cleanup

Remove the scaffolding safely.

Safe after: One complete operating cycle, at least seven stable days, and owner sign-off on every blocking check.

Create final MongoDB exports and archive verification evidence.
Revoke temporary credentials, API keys, webhooks, and elevated roles.
Remove obsolete embeds, forms, jobs, integrations, and DNS records.
Keep the source intact through the approved retention window.
Cancel paid plans only after billing, legal, and recovery review.
Schedule the next PostgreSQL backup, access, and migration-playbook review.

Sources and maintenance

Built to be reviewed.

Tested 2026-07-19. Next scheduled review: 2026-10-19.

MongoDB: mongoexportAccessed 2026-07-19
MongoDB: export behavior and BSON fidelityAccessed 2026-07-19
PostgreSQL: JSON typesAccessed 2026-07-19

Know the shape of the move.

This timeline assumes

What survives the move.

Where each thing goes.

Make the move recoverable.

Create the source-of-truth backup

Target schema and transformation registry

Repeatable copy and change-capture pipeline

The things most likely to hurt.

Silent type coercion corrupts data

Final delta misses writes

Queries pass functionally but fail under load

Do the work in this order.

Inventory and decisions

Export?

Backup and reconcile

Transform?

Map and pilot

Scale?

Final delta and switch

Open production?

Observe and close

Close rollback?

Cut over with a way back.

Cutover

Rollback

Prove the migration worked.

Remove the scaffolding safely.

Built to be reviewed.