{ "id": "mongodb-vs-postgresql", "ids": [ "mongodb", "postgresql" ], "title": "MongoDB vs PostgreSQL: document and relational workflows", "heading": "MongoDB vs PostgreSQL", "kicker": "Database software comparison", "deck": "Compare document modelling and relational constraints using the application’s actual reads, writes and recovery needs.", "description": "MongoDB vs PostgreSQL: compare document and SQL modelling, JSON, transactions, hosted free access, software licences and migration requirements.", "answerHeading": "Which database fits the application?", "answer": "Shortlist MongoDB when document-shaped records and its query and aggregation workflow fit the application. Shortlist PostgreSQL when relational constraints, SQL reporting and transactions across related tables are central. Both can handle varied data shapes. Decide from representative access patterns and consistency rules rather than the slogan that one is flexible and the other is rigid.", "checkedAt": "2026-10-15", "researchTag": "Buying requirements comparison", "researchLimit": "Published capabilities and edition conditions are compared. Examples are hypothetical acceptance briefs, not hands-on tests or performance measurements.", "datasetDescription": "Seven practical differences, four illustrative buying briefs and a four-row acceptance checklist.", "type": "workflow-buying", "rows": [ [ "Primary model", "BSON documents in collections", "Relational tables with extensible types" ], [ "Query workflow", "Document queries and aggregation pipelines", "SQL queries and relational operations" ], [ "Document fields", "Documents are the primary storage model", "JSON and JSONB can sit alongside relational columns" ], [ "Integrity design", "Schema validation and application/document modelling choices", "Constraints, keys and transaction boundaries" ], [ "Self-managed terms", "Community Server uses SSPL terms", "PostgreSQL License" ], [ "Hosted entry point", "Atlas Free is a constrained managed development environment", "Managed availability depends on the selected hosting provider" ], [ "Acceptance proof", "Document structure, indexes and update behaviour", "Table structure, indexes and relational consistency" ] ], "scenarios": [ { "name": "Product catalogue", "question": "How do variable product attributes and shared category rules fit together?", "left": "Prototype document shapes for product variations and the required aggregation queries.", "right": "Prototype relational reference data with JSONB for the attributes that genuinely vary.", "note": "Proposed check: change a category rule and verify all affected products and reports remain correct." }, { "name": "Order and inventory workflow", "question": "Which updates must succeed or fail together?", "left": "Define document boundaries and test the necessary transaction and validation behaviour.", "right": "Define related tables, constraints and a transaction covering the stock and order changes.", "note": "Use cancelled payments, concurrent updates and duplicate requests as acceptance fixtures." }, { "name": "Analyst reporting", "question": "Can the reporting team produce the required business totals?", "left": "Build the actual aggregation or reporting integration rather than assuming a connector solves modelling.", "right": "Build the SQL reports and data access permissions for the intended analysts.", "note": "Reconcile totals and maintenance effort with the same sample data and update cadence." }, { "name": "Managed first deployment", "question": "What happens when the initial development environment is outgrown?", "left": "Compare Atlas Free boundaries with the paid deployment configuration you would need.", "right": "Compare the selected provider’s PostgreSQL service and its upgrade path.", "note": "Estimate production storage, recovery and support; do not equate a free learning environment with the target service." } ], "exampleTable": { "caption": "Illustrative data-model acceptance plan; these are proposed checks rather than measured outcomes.", "headers": [ "Application need", "Exercise", "Evidence of fit" ], "rows": [ [ "Changing attributes", "Add and query a new product attribute", "Results remain useful without uncontrolled duplication" ], [ "Consistency", "Replay concurrent and cancelled updates", "Written business rules still hold" ], [ "Reporting", "Produce the monthly reconciliation report", "Expected totals match and ownership is clear" ], [ "Recovery", "Restore and reconnect a staging application", "Data and application behaviour are validated" ] ] }, "sections": [ { "id": "access-patterns", "title": "Model reads and writes before choosing", "navLabel": "Data model", "paragraphs": [ "Start with a list of the application’s important reads and writes. Mark which records change together, which values are shared across many entities and which queries the business needs each month. Then sketch both a document design and a relational design for those operations.", "Avoid treating schema flexibility as an absence of structure. A document application still needs validation, update rules and indexes. A relational application can include changing document attributes. Compare the amount of duplicated data, corrective work and operational complexity in your own proposed models." ] }, { "id": "migration-proof", "title": "Prove a migration with business records", "navLabel": "Validation", "paragraphs": [ "Moving from documents to tables, or the reverse, can change how application behaviour is expressed. Choose difficult records with missing fields, nested values, historical formats and references to deleted entities. Specify how each case should appear after conversion.", "Reconcile the business outputs as well as record counts: sales totals, stock balances and customer histories may expose errors that a successful import misses. Plan the write cutover and exit export early. The winning design should give the team an understandable way to recover and maintain the application." ] } ], "faqs": [ { "question": "Does PostgreSQL support document data?", "answer": "Yes. JSON and JSONB types let it store and query documents alongside relational data. Whether that is the best design depends on the application’s access patterns." }, { "question": "Does MongoDB remove the need for a schema?", "answer": "No. Flexible document structures still require validation rules, indexing and a clear update strategy. Decide how old and new document shapes will coexist." }, { "question": "Is MongoDB free?", "answer": "Community Server is available under SSPL terms, and Atlas has a non-expiring limited Free cluster. Production hosting, enterprise services and licence obligations are separate considerations." }, { "question": "Which database scales better?", "answer": "This comparison does not establish a scale or speed winner. Topology, indexes, queries, data shape and operations need a representative workload test." } ], "sources": [ { "url": "https://www.mongodb.com/docs/manual/", "facts": "Document model, query, aggregation, transactions and validation." }, { "url": "https://www.mongodb.com/docs/atlas/tutorial/deploy-free-tier-cluster/", "facts": "Atlas Free scope and non-expiring availability." }, { "url": "https://www.mongodb.com/legal/licensing/server-side-public-license", "facts": "Community Server licence." }, { "url": "https://www.postgresql.org/docs/current/datatype-json.html", "facts": "PostgreSQL JSON and JSONB model." }, { "url": "https://www.postgresql.org/about/", "facts": "Relational constraints, transactions and extension capabilities." }, { "url": "https://www.mongodb.com/docs/manual/core/schema-validation/", "facts": "Flexible document schema and validation rules." } ], "relatedLinks": [ { "path": "/categories/database-software/", "title": "Explore database software" }, { "path": "/guides/database-managed-vs-self-hosted/", "title": "Managed databases vs self-hosted: ownership and recovery checklist" } ] }