PostgreSQL: The Database That Conquered the World

In the world of databases, PostgreSQL stands alone. It is not the oldest database. It is not backed by a trillion-dollar corporation. It does not have the marketing budget of Oracle or the enterprise sales force of Microsoft. Yet somehow, PostgreSQL has become the database of choice for organizations ranging from startups to Fortune 500 companies, from government agencies to technology giants, from financial institutions to healthcare systems.

Stack Overflow’s developer surveys consistently rank PostgreSQL as the most loved and most wanted database. DB-Engines, which tracks database popularity, shows PostgreSQL steadily climbing while competitors stagnate or decline. Major cloud providers—Amazon, Google, Microsoft, and dozens of smaller players—all offer managed PostgreSQL services, often as their flagship relational database offering.

How did an open-source project born in a university research lab become the database that conquered the world? What makes PostgreSQL so special that developers actively choose it over well-funded commercial alternatives? And perhaps most importantly, is PostgreSQL the right choice for your next project?

This article is a comprehensive exploration of PostgreSQL. We will trace its fascinating history from academic origins to global dominance. We will examine its architecture and understand what makes it technically excellent. We will explore its ecosystem of extensions and integrations. We will compare it honestly against competitors—both relational and NoSQL. And we will discuss when PostgreSQL is the perfect choice and when you might want something else.

No code. Just the understanding you need to appreciate why PostgreSQL matters and how to think about it for your projects.

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The Fascinating History of PostgreSQL

Understanding PostgreSQL requires understanding where it came from. Its history explains many of its design decisions, its culture, and its remarkable longevity.

The INGRES Era: Where It All Began

The story begins in 1973 at the University of California, Berkeley. Michael Stonebraker, a computer science professor, started a project called INGRES (Interactive Graphics and Retrieval System). INGRES was one of the earliest relational database management systems, implementing the revolutionary ideas that Edgar Codd had published about relational databases.

INGRES was influential beyond measure. It proved that relational databases could work in practice, not just in theory. The project trained a generation of database researchers and practitioners. It spawned a commercial company (later acquired by Computer Associates) and influenced the design of countless subsequent systems.

But by the early 1980s, Stonebraker saw limitations in INGRES and relational databases in general. He envisioned something more powerful—a database that could handle complex data types, support user-defined functions, and go beyond the rigid structures of pure relational theory.

POSTGRES: The Post-INGRES Vision

In 1986, Stonebraker started a new project at Berkeley called POSTGRES (POST inGRES). The name was a play on “after INGRES,” signaling that this was meant to be the next generation.

POSTGRES was revolutionary. It introduced concepts that were decades ahead of their time:

Object-Relational Features: POSTGRES could store complex data types, not just simple numbers and strings. Users could define their own types and the operations on them.

Extensibility: The database was designed from the ground up to be extended. New data types, new operators, new index types—all could be added without modifying the core database.

Rules System: POSTGRES introduced a powerful rules system that could trigger actions based on data changes, enabling sophisticated business logic in the database.

Time Travel: POSTGRES supported queries against historical data—you could ask “what was this value last Tuesday?” This feature, while later removed for performance reasons, showed the project’s ambitious vision.

The academic POSTGRES project ran from 1986 to 1994, producing several versions and numerous research papers. But academic projects have limited lifespans. Funding ends. Graduate students graduate. Professors move on to new research.

timeline
    title PostgreSQL Evolution
    1973 : INGRES project begins at UC Berkeley
    1986 : POSTGRES project starts
    1994 : POSTGRES project ends at Berkeley
    1995 : Postgres95 adds SQL support
    1996 : Renamed to PostgreSQL
    2000 : Version 7.0 - Major stability improvements
    2005 : Version 8.0 - Windows support, PITR
    2010 : Version 9.0 - Streaming replication
    2016 : Version 10 - Logical replication, partitioning
    2020 : Version 13 - Major performance improvements
    2023 : Version 16 - Logical replication enhancements
    2024 : Version 17 - Vacuum improvements
    2025 : Version 18 - Async I/O, OAuth, UUIDv7

Postgres95: The SQL Transformation

In 1994, two Berkeley graduate students, Andrew Yu and Jolly Chen, made a crucial decision. They took the POSTGRES codebase and replaced its original query language (called POSTQUEL) with SQL, the industry-standard query language. They called this new version Postgres95.

This was the transformation that made PostgreSQL possible. SQL was becoming the universal language of databases. By speaking SQL, Postgres95 could be adopted by anyone familiar with other databases. The barriers to entry dropped dramatically.

Postgres95 was released as open-source software. Anyone could use it, modify it, and contribute to it. This decision would prove more important than anyone realized at the time.

The Birth of PostgreSQL

In 1996, the project was renamed PostgreSQL to emphasize its SQL capabilities. A global community of developers began forming around it. This community—not a company, not a university, but a distributed group of volunteers and contributors—would guide PostgreSQL’s development for the next three decades.

The PostgreSQL Global Development Group emerged as the steward of the project. There was no single company in control, no single person who could sell or abandon the project. PostgreSQL belonged to everyone and no one.

This governance model proved remarkably resilient. While commercial databases rose and fell, were acquired and abandoned, PostgreSQL kept steadily improving, release after release, year after year.

The Modern Era

The 2000s saw PostgreSQL mature from a promising alternative into a serious enterprise database. Key milestones included:

Windows Support (2005): Version 8.0 added native Windows support, opening PostgreSQL to a huge new audience.

Point-in-Time Recovery: Enterprise features like continuous archiving and recovery made PostgreSQL viable for mission-critical applications.

Streaming Replication (2010): Version 9.0 introduced built-in replication, eliminating the need for third-party tools.

JSON Support: PostgreSQL embraced the NoSQL movement by adding native JSON and later JSONB support, allowing it to handle document-style data.

Logical Replication: Fine-grained replication capabilities enabled complex multi-datacenter architectures.

Each release brought new features, performance improvements, and reliability enhancements. The pace of development actually accelerated as more companies invested in PostgreSQL and more developers contributed.

PostgreSQL 18: The Current State

In September 2025, PostgreSQL 18 was released, representing the current pinnacle of decades of development. This version introduced:

Asynchronous I/O Subsystem: A new AIO subsystem that can dramatically improve performance of sequential scans, bitmap heap scans, vacuums, and other operations.

UUIDv7 Support: Native generation of timestamp-ordered UUIDs through the uuidv7() function, addressing a long-standing need for time-sortable identifiers.

OAuth Authentication: Modern authentication support through OAuth, reflecting the evolution of security standards.

Virtual Generated Columns: Columns that compute their values at read time rather than write time, now the default for generated columns.

Skip Scan for B-tree Indexes: More efficient use of multicolumn indexes when early columns lack restrictions.

Temporal Constraints: Non-overlapping constraints for primary keys, unique constraints, and foreign keys.

OLD/NEW in RETURNING: The ability to return both old and new values from INSERT, UPDATE, DELETE, and MERGE operations.

The release notes list hundreds of additional improvements, from optimizer enhancements to monitoring capabilities to replication features. PostgreSQL 18 represents not just incremental improvement but continued innovation.

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Understanding PostgreSQL Architecture

What makes PostgreSQL technically excellent? To answer this question, we need to examine its architecture—how it stores data, processes queries, and manages concurrency.

Process-Based Architecture

PostgreSQL uses a multi-process architecture rather than a multi-threaded one. Each client connection is handled by a separate operating system process called a “backend.” A main “postmaster” process manages the server, starting new backend processes as clients connect.

This design has important implications:

Stability: A crash in one backend process does not bring down the entire server. Other connections continue operating normally.

Memory Protection: Operating system process isolation prevents one connection from corrupting another’s memory.

Simplicity: Process-based programming is simpler and more portable than multi-threaded programming, reducing bugs.

The tradeoff is overhead. Creating a new process is more expensive than creating a new thread. PostgreSQL addresses this with connection pooling—tools like PgBouncer sit in front of PostgreSQL, maintaining a pool of backend connections that are reused across client requests.

flowchart TB
    subgraph Clients["Client Applications"]
        C1[Client 1]
        C2[Client 2]
        C3[Client 3]
    end

    subgraph Optional["Optional: Connection Pool"]
        Pool[PgBouncer/pgpool]
    end

    subgraph PostgreSQL["PostgreSQL Server"]
        Postmaster[Postmaster Process]

        subgraph Backends["Backend Processes"]
            B1[Backend 1]
            B2[Backend 2]
            B3[Backend 3]
        end

        subgraph Background["Background Processes"]
            BW[Background Writer]
            WW[WAL Writer]
            CP[Checkpointer]
            AV[Autovacuum]
            SA[Stats Collector]
        end

        subgraph Memory["Shared Memory"]
            SB[Shared Buffers]
            WB[WAL Buffers]
            Lock[Lock Tables]
        end
    end

    C1 & C2 & C3 --> Pool
    Pool --> Postmaster
    Postmaster --> B1 & B2 & B3
    B1 & B2 & B3 --> Memory
    Background --> Memory

MVCC: The Concurrency Secret

PostgreSQL uses Multi-Version Concurrency Control (MVCC) to handle concurrent access without locking conflicts. This is perhaps its most important architectural feature.

In traditional locking systems, readers block writers and writers block readers. If one transaction is reading a row, another transaction trying to modify it must wait. This creates bottlenecks in high-concurrency systems.

MVCC takes a different approach. Instead of modifying data in place, PostgreSQL creates new versions of rows when they are updated. Readers see a consistent snapshot of the database at their transaction’s start time. Writers create new versions without disturbing ongoing reads.

The result: readers never block writers, and writers never block readers. Conflicts only occur when two transactions try to modify the same row simultaneously.

flowchart TB
    subgraph MVCC["MVCC in Action"]
        direction TB

        subgraph Original["Original State"]
            R1["Row: ID=1, Value='A'<br/>xmin=100, xmax=∞"]
        end

        subgraph Transaction["Transaction 150 Updates Row"]
            R2["Row: ID=1, Value='A'<br/>xmin=100, xmax=150"]
            R3["Row: ID=1, Value='B'<br/>xmin=150, xmax=∞"]
        end

        subgraph Visibility["Visibility Rules"]
            T1["Transaction 120 sees: 'A'<br/>(started before 150)"]
            T2["Transaction 200 sees: 'B'<br/>(started after 150 commits)"]
        end

        Original --> Transaction
        Transaction --> Visibility
    end

MVCC does have costs. Old row versions accumulate and must eventually be cleaned up—this is what the VACUUM process does. And the visibility checks that determine which version each transaction sees add overhead. But for most workloads, the benefits of reduced lock contention far outweigh these costs.

Write-Ahead Logging (WAL)

PostgreSQL guarantees durability through Write-Ahead Logging. Before any change is applied to the actual data files, it is first written to the WAL—a sequential log of all changes.

This has several benefits:

Crash Recovery: After a crash, PostgreSQL replays the WAL to restore the database to its last consistent state. Nothing committed is ever lost.

Streaming Replication: The WAL can be streamed to replica servers, which replay it to stay synchronized with the primary.

Point-in-Time Recovery: By archiving WAL segments, you can restore the database to any point in time, not just the last backup.

Logical Replication: The WAL contains enough information to extract logical changes for replication to other systems.

The WAL is designed for sequential writes, which are very fast even on spinning disks. This allows PostgreSQL to acknowledge commits quickly while deferring the more expensive random writes to data files.

The Query Planner

PostgreSQL’s query planner (optimizer) analyzes SQL queries and determines the most efficient execution strategy. It considers:

Statistics: PostgreSQL maintains detailed statistics about table contents—row counts, value distributions, correlations. These guide cost estimates.

Index Availability: The planner knows what indexes exist and can use them for efficient lookups.

Join Strategies: Nested loop, hash join, merge join—the planner chooses based on data sizes and available memory.

Parallelism: Modern PostgreSQL can parallelize queries across multiple CPU cores.

The planner’s job is to choose the plan with the lowest estimated cost. It uses a sophisticated cost model that accounts for disk I/O, CPU usage, and memory consumption.

flowchart LR
    subgraph QueryProcessing["Query Processing Pipeline"]
        direction LR

        SQL[SQL Query] --> Parser[Parser]
        Parser --> |"Parse Tree"| Analyzer[Analyzer]
        Analyzer --> |"Query Tree"| Rewriter[Rewriter]
        Rewriter --> |"Rewritten Tree"| Planner[Planner/Optimizer]
        Planner --> |"Query Plan"| Executor[Executor]
        Executor --> Results[Results]

        Stats[(Statistics)] --> Planner
        Catalog[(System Catalog)] --> Analyzer
        Rules[(Rules)] --> Rewriter
    end

PostgreSQL 18 introduced several planner improvements, including skip scans for B-tree indexes, automatic removal of unnecessary self-joins, and better handling of OR-clauses. Each release makes the planner smarter and queries faster.

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What Makes PostgreSQL Special

Many databases are technically competent. What elevates PostgreSQL above the competition?

Extensibility: The Ultimate Differentiator

PostgreSQL was designed from the beginning to be extended. This is not just a nice feature—it is fundamental to PostgreSQL’s success.

You can add to PostgreSQL:

Custom Data Types: Not just new types, but full integration with the query language. Your custom type can be indexed, sorted, compared, and used in expressions just like built-in types.

Custom Functions: Functions in SQL, PL/pgSQL, Python, Perl, JavaScript, and other languages. Functions can be inlined by the optimizer for better performance.

Custom Operators: Define new operators for your types. The geometric type’s @> (contains) operator is no different from user-defined operators.

Custom Index Types: The B-tree, hash, GiST, GIN, and BRIN index types are all implemented as extensions. You can create new index types.

Custom Foreign Data Wrappers: Access external data sources as if they were local tables. There are wrappers for other databases, file formats, APIs, and more.

This extensibility enabled an ecosystem of powerful extensions that would be impossible with a locked-down database.

The Extension Ecosystem

PostgreSQL’s extensions have become crucial infrastructure for countless applications:

PostGIS: Geographic information system support. PostGIS adds geometric types, spatial indexes, and hundreds of functions for geographic analysis. It is so comprehensive that some consider it the world’s best GIS database.

TimescaleDB: Time-series database built on PostgreSQL. Automatic partitioning, compression, and continuous aggregates for time-series workloads.

Citus: Distributed PostgreSQL. Shards tables across multiple nodes for horizontal scaling.

pg_vector: Vector similarity search for AI/ML applications. Store embeddings and find similar items efficiently.

pg_stat_statements: Query performance tracking. Identifies slow queries and helps optimization.

pg_trgm: Trigram-based text search. Fast similarity matching for fuzzy search.

uuid-ossp: UUID generation. Essential for distributed systems.

hstore: Key-value storage within a column. Predates JSONB but still useful.

flowchart TB
    subgraph Core["PostgreSQL Core"]
        Engine[Database Engine]
        SQL[SQL Processing]
        Storage[Storage Engine]
    end

    subgraph Extensions["Extension Ecosystem"]
        direction TB

        subgraph Data["Data Type Extensions"]
            PostGIS[PostGIS<br/>Geospatial]
            Timescale[TimescaleDB<br/>Time-series]
            Vector[pg_vector<br/>AI/ML]
        end

        subgraph Scale["Scaling Extensions"]
            Citus[Citus<br/>Distributed]
            PgPool[pg_pool<br/>Load Balance]
            Patroni[Patroni<br/>HA]
        end

        subgraph Analysis["Analysis Extensions"]
            Stats[pg_stat_statements<br/>Query Stats]
            Trgm[pg_trgm<br/>Text Search]
            Crypto[pgcrypto<br/>Encryption]
        end
    end

    Core --> Extensions

SQL Standards Compliance

PostgreSQL is one of the most SQL-compliant databases available. This matters more than you might think.

SQL is a standard. When you write standard SQL, your queries work across different databases. You are not locked into a vendor’s proprietary dialect. Your skills transfer. Your code is portable.

PostgreSQL implements:

• Common Table Expressions (WITH clauses)
• Window functions
• LATERAL joins
• MERGE statement (as of PostgreSQL 15)
• JSON/Path queries
• Recursive queries
• And much more

When PostgreSQL extends SQL, it does so carefully, often anticipating future standards. Features like UPSERT (INSERT … ON CONFLICT) were added in ways compatible with emerging SQL standards.

The JSONB Superpower

PostgreSQL’s JSONB type deserves special attention. It allows storing JSON documents in a binary format that supports:

Indexing: GIN indexes on JSONB allow fast searches within documents.

Operators: Query into JSON structures with intuitive operators.

Functions: Transform, aggregate, and manipulate JSON data.

Constraints: Validate JSON structure with CHECK constraints or JSON Schema.

JSONB gives you document database capabilities within a relational database. You can mix structured and semi-structured data in the same database, the same table, even the same row. This eliminates the need for a separate document database in many scenarios.

Reliability and Durability

PostgreSQL takes data integrity seriously—perhaps more seriously than any other database.

ACID Guarantees: Transactions are atomic, consistent, isolated, and durable. Period. There are no settings to disable durability, no “fast and loose” modes that risk data loss.

Write-Ahead Logging: Every committed transaction is durable. Crashes do not lose data.

Data Corruption Detection: Checksums detect storage corruption before it causes silent data corruption.

Referential Integrity: Foreign key constraints are enforced, always.

This reliability is not accidental. It is a core design principle that has guided PostgreSQL development for decades. The PostgreSQL community has rejected features that would compromise data integrity, even when those features would improve benchmarks.

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PostgreSQL in the Cloud

The cloud has transformed how we deploy databases. How does PostgreSQL fit into this new world?

Managed PostgreSQL Services

Every major cloud provider offers managed PostgreSQL:

Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL: AWS offers both standard PostgreSQL on RDS and Aurora, their cloud-native reimplementation with enhanced performance and availability.

Google Cloud SQL for PostgreSQL and AlloyDB: Google provides standard managed PostgreSQL plus AlloyDB, an enhanced version optimized for their infrastructure.

Azure Database for PostgreSQL: Microsoft’s managed PostgreSQL offering with flexible and hyperscale options.

Many Others: Heroku, DigitalOcean, Supabase, Neon, CrunchyData, and dozens of other providers offer managed PostgreSQL.

These services handle backups, patching, scaling, and high availability. You focus on your data and queries while the provider manages the infrastructure.

flowchart TB
    subgraph CloudProviders["Major Cloud PostgreSQL Offerings"]
        direction TB

        subgraph AWS["Amazon Web Services"]
            RDS[RDS PostgreSQL<br/>Standard managed]
            Aurora[Aurora PostgreSQL<br/>Cloud-native]
        end

        subgraph GCP["Google Cloud Platform"]
            CloudSQL[Cloud SQL PostgreSQL<br/>Standard managed]
            AlloyDB[AlloyDB<br/>Enhanced]
        end

        subgraph Azure["Microsoft Azure"]
            AzureDB[Azure Database<br/>Flexible Server]
            Cosmos[Cosmos DB<br/>PostgreSQL API]
        end

        subgraph Specialized["Specialized Providers"]
            Supabase[Supabase<br/>Developer Platform]
            Neon[Neon<br/>Serverless]
            Crunchy[Crunchy Bridge<br/>Enterprise]
            Railway[Railway<br/>Simple Deploy]
        end
    end

Serverless PostgreSQL

The latest evolution is serverless PostgreSQL. Services like Neon and Aurora Serverless v2 can:

Scale to Zero: Stop billing when the database is not in use.

Instant Scaling: Add capacity automatically as load increases.

Branching: Create instant copies of databases for development and testing.

Point-in-Time: Restore to any second in history.

Serverless PostgreSQL is particularly attractive for development environments, testing, and applications with variable or unpredictable load.

Kubernetes and PostgreSQL

For organizations running their own infrastructure, PostgreSQL on Kubernetes has become standard. Operators like:

CloudNativePG: A comprehensive operator for running PostgreSQL on Kubernetes.

Zalando PostgreSQL Operator: Handles high availability and operations.

CrunchyData PGO: Enterprise-grade operator with extensive features.

These operators automate the complex tasks of running PostgreSQL: failover, backups, scaling, monitoring, and upgrades.

The Compatibility Advantage

PostgreSQL’s wire protocol and SQL dialect are consistent across all these platforms. An application written for PostgreSQL on your laptop will work on RDS, Cloud SQL, Azure, or any other PostgreSQL service. This portability gives you freedom—you are not locked into any single cloud provider.

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PostgreSQL vs. The Competition

How does PostgreSQL compare to alternatives? Let us examine the major competitors honestly.

PostgreSQL vs. MySQL/MariaDB

MySQL was once PostgreSQL’s main open-source competitor. The comparison has shifted over the years.

Historical Differences: MySQL prioritized speed over features. It had faster performance but weaker SQL compliance, limited transactions, and fewer data types. PostgreSQL was slower but more capable.

Modern Reality: Both databases have improved dramatically. MySQL added proper transactions (InnoDB), better replication, and JSON support. PostgreSQL improved performance significantly. The performance gap has largely closed.

Where PostgreSQL Wins:

• More advanced SQL features (window functions, CTEs, complex queries)
• Better JSONB implementation
• Superior extensibility
• More data types
• Better geographic support (PostGIS)
• More sophisticated query planner

Where MySQL Wins:

• Simpler to set up and operate
• More widely deployed (legacy advantage)
• Some specific workloads (very simple reads)
• Larger pool of MySQL DBAs

Verdict: For new projects, PostgreSQL is almost always the better choice. Its capabilities exceed MySQL’s, and the performance difference is negligible. MySQL primarily wins when you have existing MySQL expertise or are integrating with MySQL-centric systems.

PostgreSQL vs. Oracle

Oracle Database is the enterprise incumbent—expensive, powerful, and entrenched.

Where PostgreSQL Wins:

• Cost (free vs. extremely expensive)
• Open source (no vendor lock-in)
• Modern features (PostgreSQL often adds features before Oracle)
• Easier development experience
• Cloud portability

Where Oracle Wins:

• RAC clustering (real multi-node)
• Enterprise support contracts
• Specific enterprise features
• Existing Oracle investments
• Some extreme performance scenarios

The Migration Reality: Thousands of organizations have migrated from Oracle to PostgreSQL successfully. AWS and other vendors actively support and encourage these migrations. The technical gap has closed considerably; the licensing cost gap remains enormous.

Verdict: PostgreSQL can replace Oracle for the vast majority of workloads. Oracle’s remaining advantages are narrow and rarely justify the cost difference.

PostgreSQL vs. SQL Server

Microsoft SQL Server is PostgreSQL’s main commercial competitor in many markets.

Where PostgreSQL Wins:

• Cost (free vs. expensive licenses)
• Cross-platform (runs everywhere, not just Windows)
• Extensions and customization
• Open source transparency
• Cloud portability

Where SQL Server Wins:

• .NET integration
• Business Intelligence tools (SSRS, SSAS)
• Enterprise support from Microsoft
• Familiarity in Windows-centric shops
• Some specific enterprise features

Verdict: For new projects, especially outside the Microsoft ecosystem, PostgreSQL is typically superior. SQL Server’s advantages center on Microsoft integration and existing investments.

PostgreSQL vs. NoSQL Databases

The PostgreSQL vs. NoSQL comparison requires nuance because NoSQL is not a single technology.

flowchart TB
    subgraph Comparison["PostgreSQL vs NoSQL Categories"]
        direction TB

        subgraph Document["Document Databases (MongoDB)"]
            M1[Schema flexibility]
            M2[Simple scaling]
            M3[Developer familiarity]

            PM1[PostgreSQL JSONB provides<br/>document storage]
            PM2[Relational + Document<br/>in one database]
        end

        subgraph KeyValue["Key-Value (Redis, DynamoDB)"]
            K1[Extreme speed]
            K2[Simple model]
            K3[Cache use cases]

            PK1[Not a PostgreSQL<br/>replacement]
            PK2[Use alongside<br/>PostgreSQL]
        end

        subgraph Wide["Wide Column (Cassandra)"]
            C1[Massive write scale]
            C2[Geo-distribution]
            C3[Time-series at scale]

            PC1[Different use case]
            PC2[PostgreSQL for<br/>consistency needs]
        end
    end

vs. MongoDB: MongoDB is a document database. PostgreSQL’s JSONB provides similar document capabilities while also offering relational features, joins, transactions, and SQL. For new projects, PostgreSQL’s JSONB often eliminates the need for MongoDB. MongoDB’s advantages are in specific scenarios requiring extreme document flexibility or existing MongoDB expertise.

vs. Redis: Redis is an in-memory data structure store. It is not a general-purpose database. Use Redis alongside PostgreSQL—Redis for caching and fast data structures, PostgreSQL for persistent storage.

vs. Cassandra: Cassandra is designed for massive write throughput across many nodes. If you need to write millions of rows per second across global data centers, Cassandra is designed for that. PostgreSQL (even with Citus) targets different workloads.

vs. DynamoDB: DynamoDB is AWS’s key-value/document service. It offers infinite scale with minimal operations. The tradeoff is limited query capabilities and AWS lock-in. PostgreSQL offers far richer queries at the cost of more operational complexity.

The Reality: NoSQL databases are specialized tools for specific problems. PostgreSQL is a general-purpose database that handles most workloads. Choose NoSQL when you have specific requirements that PostgreSQL cannot meet—but be honest about whether you actually have those requirements.

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When to Use PostgreSQL

PostgreSQL is excellent for most applications, but let us be specific about where it shines.

Perfect Use Cases

Transactional Applications: Financial systems, e-commerce, anything requiring ACID guarantees. PostgreSQL’s reliability is unmatched.

Complex Queries: Business intelligence, analytics, reporting. PostgreSQL’s query planner handles complex queries efficiently.

Geographic Applications: Mapping, logistics, location-based services. PostGIS makes PostgreSQL the best open-source GIS database.

Mixed Workloads: Applications combining structured and semi-structured data. JSONB + relational in one database.

API Backends: REST and GraphQL APIs backed by PostgreSQL benefit from its rich features and reliability.

Time-Series Data: With TimescaleDB extension, PostgreSQL handles time-series workloads efficiently.

AI/ML Applications: The pg_vector extension enables similarity search for embeddings and machine learning features.

Multi-Tenant SaaS: Row-level security and schemas support complex multi-tenancy patterns.

Good Use Cases

OLAP/Analytics: PostgreSQL handles analytical workloads reasonably well, though dedicated analytics databases may be faster for large-scale analysis.

Content Management: WordPress, Drupal, and other CMS can use PostgreSQL, though MySQL is more common.

Moderate Scale: PostgreSQL scales well to hundreds of terabytes and thousands of connections with proper configuration.

Challenging Use Cases

Extreme Write Throughput: If you need to write millions of rows per second with global distribution, consider Cassandra or similar.

Massive Horizontal Scale: While Citus extends PostgreSQL’s scaling, some scenarios require purpose-built distributed databases.

Simple Key-Value at Scale: If your entire data model is just key-value lookups, DynamoDB or similar might be simpler.

Real-Time Streaming: For real-time stream processing, Kafka or similar is more appropriate. PostgreSQL is a database, not a streaming platform.

flowchart TB
    subgraph Decision["PostgreSQL Decision Tree"]
        Start[New Project?]

        Start --> Q1{Need ACID<br/>transactions?}
        Q1 -->|Yes| Q2{Complex queries<br/>or joins?}
        Q1 -->|No| Maybe[Consider NoSQL<br/>or streaming]

        Q2 -->|Yes| Q3{Need relational<br/>+ document?}
        Q2 -->|No| SimpleDB[PostgreSQL works,<br/>but simpler options exist]

        Q3 -->|Yes| Perfect[PostgreSQL<br/>is perfect]
        Q3 -->|No| Q4{Geographic<br/>data?}

        Q4 -->|Yes| PostGIS[PostgreSQL + PostGIS]
        Q4 -->|No| Q5{Time-series?}

        Q5 -->|Yes| Timescale[PostgreSQL + TimescaleDB]
        Q5 -->|No| Still[PostgreSQL<br/>still excellent]
    end

---

When NOT to Use PostgreSQL

Honesty requires discussing scenarios where PostgreSQL is not the best choice.

Better Alternatives Exist

Pure Key-Value at Massive Scale: DynamoDB, Redis, or similar are purpose-built for this and simpler to operate at scale.

Streaming and Event Processing: Apache Kafka, Apache Flink, or cloud streaming services are designed for continuous data streams. PostgreSQL is not a streaming platform.

Machine Learning Training: You do not store training datasets in PostgreSQL. Use data lakes, object storage, or specialized ML platforms.

Binary Blob Storage: Large files do not belong in PostgreSQL. Use object storage (S3, GCS, Azure Blob) and store references in PostgreSQL.

Global Distribution with Strong Consistency: CockroachDB, Spanner, or YugabyteDB are designed specifically for globally distributed databases with strong consistency.

Operational Considerations

Extreme Simplicity Required: If you want zero operations and cannot afford any database expertise, managed NoSQL services like DynamoDB might be simpler.

Legacy Integration: If your organization is deeply invested in Oracle or SQL Server with extensive existing tooling, migration costs may not justify the switch.

Specialized Workloads: Graph databases (Neo4j), search engines (Elasticsearch), or time-series databases (InfluxDB) may be better for highly specialized workloads.

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Best Practices for PostgreSQL

After decades of PostgreSQL experience, the community has developed clear best practices.

Configuration Best Practices

shared_buffers: Set to about 25% of available RAM. This is PostgreSQL’s main memory cache.

effective_cache_size: Set to about 50-75% of available RAM. This tells the planner how much the OS can cache.

work_mem: Memory per query operation. Start conservative (64MB-256MB) and increase for complex queries.

maintenance_work_mem: Memory for maintenance operations. Can be higher since these run less frequently.

max_connections: Keep low. Use connection pooling (PgBouncer) rather than increasing this.

wal_level: Set to replica or logical to enable replication options.

checkpoint_completion_target: Set to 0.9 to spread checkpoint I/O.

flowchart LR
    subgraph Memory["Memory Configuration"]
        direction TB
        RAM[Total RAM: 64GB]

        SB[shared_buffers<br/>16GB (25%)]
        EC[effective_cache_size<br/>48GB (75%)]
        WM[work_mem<br/>256MB per query]
        MM[maintenance_work_mem<br/>2GB]

        RAM --> SB & EC & WM & MM
    end

Schema Design Best Practices

Use Appropriate Types: PostgreSQL has rich types. Use them. UUID for identifiers, TIMESTAMP WITH TIME ZONE for times, NUMERIC for money, JSONB for flexible data.

Normalize Appropriately: Normalize for data integrity, but denormalize strategically for performance. JSONB allows selective denormalization.

Index Thoughtfully: Indexes speed reads but slow writes. Create indexes for actual query patterns, not hypothetical ones.

Partition Large Tables: Tables over 100GB often benefit from partitioning by date or other natural keys.

Use Foreign Keys: Referential integrity prevents orphaned data. The performance cost is minimal.

Operational Best Practices

Connection Pooling: Always use PgBouncer or similar for applications with many connections.

Regular Backups: Use pg_dump for logical backups, pg_basebackup for physical backups. Test restores regularly.

Monitor Everything: Use pg_stat_statements, pg_stat_activity, and monitoring tools. Know your database’s behavior.

Vacuum Regularly: Autovacuum is usually sufficient, but monitor for bloat. Manual VACUUM may be needed for very active tables.

Keep Statistics Updated: ANALYZE keeps statistics fresh. Autovacuum does this, but verify.

Test Upgrades: Before major version upgrades, test with production data in a staging environment.

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The Future of PostgreSQL

Where is PostgreSQL heading? Several trends are clear.

Performance Evolution

PostgreSQL 18’s asynchronous I/O subsystem represents a new approach to performance. Future versions will likely expand on this with:

• More parallel query capabilities
• Better use of modern hardware (NVMe, large memory)
• Improved compression
• More efficient storage formats

Cloud Integration

PostgreSQL will continue integrating with cloud-native architectures:

• Better Kubernetes support
• More serverless capabilities
• Improved elasticity
• Tighter integration with cloud services

AI and Machine Learning

The pg_vector extension is just the beginning. Expect:

• More vector operations
• Better embedding support
• Integration with ML platforms
• AI-assisted query optimization

Enhanced Extensibility

The extension ecosystem will continue growing:

• More specialized extensions
• Better extension management
• Improved extension security
• Standardized extension interfaces

Horizontal Scaling

While Citus provides horizontal scaling today, native distributed capabilities may emerge:

• Built-in sharding
• Better multi-node coordination
• Improved distributed transactions
• Easier scale-out operations

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Conclusion

PostgreSQL’s journey from a Berkeley research project to the world’s most advanced open-source database is remarkable. It succeeded not through marketing or corporate backing, but through technical excellence, community dedication, and an unwavering commitment to data integrity.

Today, PostgreSQL is the database that can do almost everything. Need relational? Check. Documents? Check. Geographic? Check. Time-series? Check. Vector search? Check. And it does all of these while maintaining the reliability and consistency that enterprise applications demand.

The PostgreSQL community continues to innovate. Version 18 brings asynchronous I/O, OAuth authentication, UUIDv7, and hundreds of other improvements. The pace of development shows no sign of slowing—if anything, it accelerates as more organizations invest in PostgreSQL.

For new projects, PostgreSQL should be the default choice. Its capabilities exceed virtually any alternative, its ecosystem is vast, its community is helpful, and its future is bright. The cost is zero, but the value is immense.

There are cases where specialized databases are better. Massive write-heavy workloads might need Cassandra. Pure key-value might be simpler with DynamoDB. Real-time streaming needs Kafka. But for the vast majority of applications, PostgreSQL is not just adequate—it is excellent.

The database that conquered the world did so by being genuinely, demonstrably better than the alternatives. That is a story worth understanding, and PostgreSQL is a technology worth knowing.

Whether you are building a startup, modernizing an enterprise, or just exploring databases, PostgreSQL deserves your attention. It is open source, so you can try it freely. It runs everywhere, so you can use it anywhere. And it keeps getting better, so you can trust it for the long term.

PostgreSQL did not conquer the world through conquest. It earned its position through excellence. That is the PostgreSQL way.