PostgreSQL vs MongoDB

PostgreSQL and MongoDB solve the same problem (storing application data) in opposite ways. PostgreSQL structures data in tables with predefined schemas and SQL queries. MongoDB structures data as documents with flexible schemas and JavaScript-like queries.

This difference runs deep. PostgreSQL validates data types, enforces relationships through foreign keys, and requires migrations to change structures. MongoDB accepts any document structure, handles validation in application code, and lets you modify schemas without database changes.

Developers picked MongoDB when rapid iteration mattered more than data validation. You could deploy schema changes by modifying application code, not by coordinating database migrations across environments. PostgreSQL's strictness felt like friction during early development.

PostgreSQL 18 challenges this narrative. The September 2025 release added JSONB features that match MongoDB's flexibility while keeping PostgreSQL's advantages: referential integrity, SQL joins, and ACID transactions across tables. The choice between them is less obvious than it used to be

What is PostgreSQL?

PostgreSQL is an object-relational database that emphasizes SQL standards compliance and data integrity. Originally developed at UC Berkeley in 1986, the database treats correctness as non-negotiable. Foreign keys, check constraints, and ACID transactions prevent invalid data from entering your system.

The database added JSONB support in version 9.4, allowing structured and unstructured data to coexist. PostgreSQL 18's improvements to JSON handling, combined with its traditional relational features, make it viable for workloads that previously required document databases.

PostgreSQL's extension system adds capabilities like full-text search, geographic data types, and time-series functions. You're not choosing between relational and document storage—you can use both in the same database.

What is MongoDB?

MongoDB is a document database that stores data as JSON-like documents. Created by MongoDB Inc. in 2009, it prioritizes developer experience and schema flexibility. Documents in the same collection can have completely different structures, eliminating the need for migrations when requirements change.

The database uses its own query language instead of SQL. MongoDB Query Language (MQL) uses JavaScript-like syntax for filtering, aggregating, and transforming data. This approach feels natural to JavaScript developers but requires learning a new language.

MongoDB's distributed architecture makes horizontal scaling straightforward. Sharding distributes data across multiple servers automatically, and replica sets provide high availability. These features work out of the box without complex configuration.

PostgreSQL 18 vs MongoDB: quick comparison

Installation and getting started

I installed both databases on a fresh Ubuntu server to compare the initial setup experience. The philosophies behind each database showed up immediately.

PostgreSQL follows traditional database patterns:

sudo apt install postgresql postgresql-contrib
sudo -i -u postgres psql

CREATE USER myapp WITH PASSWORD 'secure_password';
CREATE DATABASE myapp_db OWNER myapp;

PostgreSQL required explicit user and database creation. The database started with peer authentication for local connections and needed configuration for network access. This setup felt familiar if you'd used traditional databases before.

MongoDB took a simpler approach:

# Add MongoDB repository and install
sudo apt install mongodb-org
sudo systemctl start mongod

# Connect and start using immediately
mongosh
use myapp_db

MongoDB created databases and collections automatically when you first wrote to them. No explicit user setup was needed for local development. This "just works" approach got you started faster but meant you had to configure security manually later.

Storing and querying documents

I built a simple blog to test how each database handled document-like data. Posts had titles, content, tags, and nested author information—typical semi-structured data that could change over time.

PostgreSQL 18 used JSONB for flexible fields:

CREATE TABLE posts (
    id SERIAL PRIMARY KEY,
    title TEXT NOT NULL,
    metadata JSONB NOT NULL
);

INSERT INTO posts (title, metadata) VALUES (
    'Getting Started with PostgreSQL',
    '{"author": {"name": "John"}, "tags": ["postgresql", "database"]}'
);

-- Query by nested field
SELECT title FROM posts 
WHERE metadata->'tags' @> '["postgresql"]'::jsonb;

PostgreSQL treated JSONB as a first-class type with operators for querying and updating. The @> operator checked if arrays contained specific values. I could index JSON fields for fast queries while keeping structured data in regular columns.

MongoDB stored entire documents natively:

db.posts.insertOne({
    title: 'Getting Started with MongoDB',
    author: { name: 'Jane' },
    tags: ['mongodb', 'database']
});

// Query by nested field
db.posts.find({ tags: 'mongodb' });

MongoDB's native document format felt more natural for this use case. The query syntax matched the document structure directly.

Schema flexibility versus data integrity

While testing those basic operations, I wanted to see what happened when I inserted invalid data. This revealed the fundamental difference between these databases.

PostgreSQL enforced what I defined:

CREATE TABLE posts (
    title TEXT NOT NULL CHECK (LENGTH(title) > 0),
    metadata JSONB NOT NULL,
    CONSTRAINT valid_tags CHECK (jsonb_typeof(metadata->'tags') = 'array')
);

-- Try invalid data
INSERT INTO posts (title, metadata) VALUES ('', '{"tags": "string"}');
-- ERROR: check constraint violated

PostgreSQL caught errors at insert time. Check constraints validated that title wasn't empty and tags was an array. This prevented bad data from entering the database.

MongoDB allowed anything by default:

// Missing fields and wrong types both succeed
db.posts.insertOne({ title: 'Incomplete' });
db.posts.insertOne({ tags: 'not-an-array', views: 'zero' });

MongoDB's flexibility meant it accepted anything. This was great during early development but pushed validation into application code.

You could add validation to MongoDB:

db.createCollection('posts', {
    validator: {
        $jsonSchema: {
            required: ['title', 'tags'],
            properties: {
                title: { bsonType: 'string', minLength: 1 },
                tags: { bsonType: 'array' }
            }
        }
    }
});

The validation worked, but it was optional and applied at the collection level. Existing documents weren't validated when you changed the schema.

Joins and relationships

My blog needed to track comments on posts. This required relating documents across collections, and the approaches differed dramatically.

PostgreSQL used standard foreign keys:

CREATE TABLE comments (
    id SERIAL PRIMARY KEY,
    post_id INTEGER REFERENCES posts(id) ON DELETE CASCADE,
    content TEXT NOT NULL
);

-- Join posts with comments
SELECT p.title, c.content
FROM posts p
LEFT JOIN comments c ON p.id = c.post_id;

The foreign key enforced referential integrity. I couldn't create comments for non-existent posts, and deleting a post automatically deleted its comments.

MongoDB didn't enforce relationships:

// Nothing prevents invalid post_id
db.comments.insertOne({
    post_id: ObjectId('nonexistent'),
    content: 'Great post!'
});

// Join using $lookup
db.posts.aggregate([{
    $lookup: {
        from: 'comments',
        localField: '_id',
        foreignField: 'post_id',
        as: 'comments'
    }
}]);

MongoDB's $lookup performed a left join, but it couldn't enforce referential integrity. Many MongoDB applications embedded comments instead:

db.posts.updateOne(
    { _id: ObjectId('...') },
    { $push: { comments: { content: 'Great post!' } } }
);

Embedding worked well for small, bounded lists. It eliminated joins but documents had a 16MB size limit.

Complex queries and aggregations

I needed to generate analytics showing popular posts by tag. This required complex aggregations that revealed how each database handled analytical queries.

PostgreSQL 18 used SQL with window functions:

WITH tag_stats AS (
    SELECT jsonb_array_elements_text(metadata->'tags') as tag,
           title, views
    FROM posts
)
SELECT tag, COUNT(*) as post_count,
       json_agg(title ORDER BY views DESC) FILTER (WHERE rank <= 5) as top_posts
FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY tag ORDER BY views DESC) as rank
    FROM tag_stats
) ranked
GROUP BY tag;

PostgreSQL's SQL handled the complex aggregation naturally. Window functions partitioned data by tag and ranked posts within each partition.

MongoDB used its aggregation pipeline:

db.posts.aggregate([
    { $unwind: '$tags' },
    { $sort: { tags: 1, views: -1 } },
    { $group: {
        _id: '$tags',
        post_count: { $sum: 1 },
        all_posts: { $push: '$title' }
    }},
    { $project: {
        top_posts: { $slice: ['$all_posts', 5] }
    }}
]);

MongoDB's pipeline broke the operation into stages. Each stage transformed data and passed it to the next. The pipeline syntax was more verbose than SQL for this type of query.

Indexing strategies

Those aggregation queries ran slowly until I added proper indexes. The indexing approaches revealed different performance characteristics.

PostgreSQL 18 indexed JSONB efficiently:

-- GIN index on JSON array
CREATE INDEX idx_tags ON posts USING GIN ((metadata->'tags'));

-- Partial index for common query
CREATE INDEX idx_published_tags ON posts USING GIN ((metadata->'tags'))
WHERE metadata->>'published' = 'true';

The GIN index on the tags array made containment queries fast. The partial index combined tag and published filters for common query patterns.

MongoDB created similar indexes:

db.posts.createIndex({ tags: 1 });
db.posts.createIndex({ published: 1, tags: 1 });

MongoDB's compound index handled the query efficiently, but required more careful planning because performance degraded quickly without the right indexes.

Transactions and consistency

I needed to transfer view counts between posts atomically. This required transactions that affected multiple documents.

PostgreSQL guaranteed ACID transactions naturally:

BEGIN;
UPDATE posts SET views = views - 100 WHERE id = 1;
UPDATE posts SET views = views + 100 WHERE id = 2;
COMMIT;

The transaction affected multiple rows and guaranteed all changes committed together or rolled back completely.

MongoDB added multi-document transactions in version 4.0:

const session = db.getMongo().startSession();
session.startTransaction();

try {
    db.posts.updateOne({ _id: id1 }, { $inc: { views: -100 } }, { session });
    db.posts.updateOne({ _id: id2 }, { $inc: { views: 100 } }, { session });
    session.commitTransaction();
} catch (error) {
    session.abortTransaction();
}

Multi-document transactions worked, but MongoDB's documentation warned they impacted performance. The database was optimized for single-document atomicity.

Full-text search capabilities

My blog needed search functionality. Both databases offered full-text search, but with different sophistication.

PostgreSQL included language-aware search:

ALTER TABLE posts ADD COLUMN search_vector TSVECTOR
GENERATED ALWAYS AS (to_tsvector('english', title || ' ' || content)) STORED;

CREATE INDEX idx_search ON posts USING GIN (search_vector);

SELECT title, ts_rank(search_vector, query) AS rank
FROM posts, to_tsquery('english', 'postgresql & database') AS query
WHERE search_vector @@ query
ORDER BY rank DESC;

PostgreSQL handled stemming, stop words, and relevance ranking. The search understood English grammar and returned results in relevance order.

MongoDB offered basic text indexes:

db.posts.createIndex({ title: 'text', content: 'text' });

db.posts.find(
    { $text: { $search: 'postgresql database' } }
).sort({ score: { $meta: 'textScore' } });

MongoDB's text search worked for basic use cases but lacked PostgreSQL's sophistication. For advanced search, MongoDB recommended Atlas Search or integrating with Elasticsearch.

Replication and scaling

Production deployments need replication for high availability and potentially sharding for horizontal scaling.

PostgreSQL set up streaming replication:

# Primary: Enable replication in postgresql.conf
wal_level = replica

# Replica: Take base backup and configure
pg_basebackup -h primary_ip -D /data -U replicator
echo "primary_conninfo = 'host=primary_ip'" >> postgresql.conf

PostgreSQL's streaming replication copied all changes to replicas. For horizontal scaling, PostgreSQL required extensions like Citus.

MongoDB's replica sets were simpler:

rs.initiate({
    _id: 'myReplicaSet',
    members: [
        { _id: 0, host: 'mongo1:27017' },
        { _id: 1, host: 'mongo2:27017' }
    ]
});

Replica sets provided automatic failover. For horizontal scaling, MongoDB's built-in sharding distributed data automatically:

sh.enableSharding('myapp_db');
sh.shardCollection('myapp_db.posts', { author_id: 1 });

Final thoughts

PostgreSQL 18 added improvements that made it a strong alternative to MongoDB for storing documents, while still keeping features MongoDB doesn’t have. If your app needs reliable data links, advanced queries, or strict data consistency, PostgreSQL is the better choice. Its JSONB columns offer the same flexible data structure MongoDB is known for, without losing PostgreSQL’s accuracy and reliability.

MongoDB is still a good fit for apps that need automatic sharding, don’t often join data between collections, and care more about flexibility than strict data rules. In the end, the choice depends on whether you value data accuracy and powerful SQL queries more, or flexibility and easy scaling.

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