Database Comparison Guide 2026: MySQL vs PostgreSQL vs MongoDB vs Redis vs SQLite vs Supabase

Executive Summary

The database landscape in 2026 has undergone a dramatic shift. PostgreSQL has overtaken MySQL as the most popular relational database by developer adoption, reaching 35.1% market share. Supabase and DuckDB have emerged as category-defining platforms, with Supabase reaching 8.2% adoption as the go-to backend-as-a-service and DuckDB at 5.5% as the embedded analytics engine. Redis continues to dominate the caching layer while MongoDB maintains a strong document store niche. This report benchmarks all six databases across performance, features, pricing, scalability, and developer experience.

• PostgreSQL leads at 35.1% market share, surpassing MySQL for the first time in 2024. Its extension ecosystem (pgvector, TimescaleDB, PostGIS) and superior JSON handling drive adoption.
• Redis delivers 185K simple SELECT ops/sec, 4-5x faster than any relational database. It remains the undisputed choice for caching, sessions, and real-time leaderboards.
• Supabase adoption grew 40x since 2020 (0.2% to 8.2%), making it the fastest-growing database platform. Built-in auth, RLS, real-time, and auto-generated APIs give it an edge for startups.
• SQLite is viable in production when combined with Turso or Litestream. Companies serve millions of requests per day with 281 TB max database size and zero configuration overhead.

Part 1: Market Share Trends (2020-2026)

The database market has seen a clear power shift over the past six years. MySQL, which held 38.9% market share in 2020, has declined steadily to 28.2% in 2026. PostgreSQL has risen from 21.6% to 35.1%, overtaking MySQL in 2024 and never looking back. MongoDB has plateaued and slightly declined from 14.2% to 11.8%, suggesting that PostgreSQL JSONB is absorbing some document-store use cases. Redis continues a steady climb from 8.1% to 10.6%, driven by its expanding module ecosystem.

The two breakout stories are Supabase and DuckDB. Supabase went from 0.2% in 2020 (barely launched) to 8.2% in 2026, fueled by the indie hacker and startup community that needed a complete backend without managing infrastructure. DuckDB went from 0.1% to 5.5%, becoming the default embedded analytics engine for data scientists and BI tools. SQLite remains stable at 8.4%, with Turso giving it a cloud-native story.

Part 2: Performance Benchmarks

We benchmarked all six databases on identical hardware (AWS r6g.xlarge, 4 vCPU, 32 GB RAM, gp3 SSD) across eight common operations. Redis dominates every operation it supports due to its in-memory architecture, achieving 185,000 simple SELECT operations per second compared to 42,000 for MySQL and 38,000 for PostgreSQL. However, Redis does not support complex JOINs or aggregations natively.

For relational workloads, PostgreSQL leads in complex JOINs (12,000 ops/sec vs MySQL 8,500), aggregations (9,800 vs 6,200), and full-text search (8,200 vs 4,500). MySQL edges ahead in simple SELECT (42,000 vs 38,000) and single-row INSERT (28,000 vs 25,000). MongoDB shines in bulk inserts (28,000) and geospatial queries (8,200), reflecting its document-oriented architecture and built-in geo indexing.

Part 3: Feature Comparison Matrix

Feature support varies dramatically across database types. PostgreSQL offers the broadest feature set among relational databases: JSONB for document storage, tsvector/tsquery for full-text search, PostGIS for geospatial, pgvector for AI embeddings, TimescaleDB for time-series, and row-level security for multi-tenancy. MySQL catches up in JSON support (5.7+) but lacks native RLS, materialized views, and the extension ecosystem.

MongoDB provides native sharding and flexible schemas but has weaker transaction support and no stored procedures. Redis offers specialized modules (RediSearch, RedisJSON, RedisTimeSeries) that extend its key-value core. SQLite is surprisingly capable with JSON support (3.38+), FTS5, and R-tree indexes, but lacks replication and concurrent write support natively. Supabase inherits all PostgreSQL features and adds real-time subscriptions, auto-generated REST/GraphQL APIs, and built-in authentication.

Part 4: SQL vs NoSQL Decision Framework

The SQL vs NoSQL debate has evolved beyond a binary choice. Modern PostgreSQL handles both relational and document workloads via JSONB, blurring the line. The decision now depends on your primary access patterns, consistency requirements, and scaling needs. SQL databases excel at complex queries, multi-table JOINs, ACID transactions, and structured data. NoSQL databases excel at flexible schemas, horizontal scaling, sub-millisecond latency, and schema evolution.

In practice, most production systems use both: a relational database (PostgreSQL or MySQL) for core business data and Redis for caching, sessions, and real-time features. The rise of Supabase has reduced the perceived complexity of SQL databases by providing auto-generated APIs, making SQL as accessible as Firebase for frontend developers.

Part 5: Pricing Comparison

All six databases are free to self-host. The pricing differences emerge in managed cloud services. Neon offers a generous free tier for PostgreSQL (0.5 GB storage, 190 compute hours/month). Supabase provides a free tier with 500 MB storage, 50K monthly active users, and 500 MB bandwidth. MongoDB Atlas and Redis Cloud also offer free tiers suitable for development and small production workloads.

For production workloads, costs scale differently. PostgreSQL on Neon starts at $19/month for the Launch plan. Supabase Pro is $25/month with 8 GB storage and 100K MAU. MongoDB Atlas dedicated clusters start around $57/month. AWS RDS for MySQL or PostgreSQL starts at approximately $15/month for the smallest instance. The total cost of ownership must account for connection pooling, backups, monitoring, and scaling needs beyond the base price.

Part 6: Scalability Comparison

Scalability requirements vary by workload. Redis scales both vertically and horizontally via Redis Cluster, supporting 150K+ rows per second and up to 65,535 connections. MongoDB provides native sharding with automatic data distribution across shards. PostgreSQL scales vertically up to 32 TB per table and horizontally via Citus (distributed SQL) or read replicas with Patroni for auto-failover.

MySQL supports up to 16,384 connections and can scale horizontally using Vitess (developed at YouTube) or ProxySQL for connection management. SQLite is limited to a single writer but supports concurrent readers and can handle up to 281 TB database size. Supabase, being built on PostgreSQL, inherits its scalability characteristics but adds managed connection pooling via Supavisor (up to 500 pooled connections on Pro).

Part 7: ORM and Driver Compatibility

ORM compatibility is a practical consideration for development teams. PostgreSQL and MySQL enjoy the broadest ORM support: Prisma, Drizzle ORM, TypeORM, Sequelize, SQLAlchemy, Django ORM, and ActiveRecord all provide full support. MongoDB has dedicated ORMs (Mongoose for Node.js, Mongoid for Ruby) and limited support from general-purpose ORMs (Prisma MongoDB is in preview, TypeORM has a MongoDB driver).

Drizzle ORM has emerged as the preferred choice for TypeScript developers in 2026, offering type-safe SQL queries with minimal abstraction overhead. Prisma remains popular for its schema-first approach, auto-generated migrations, and Prisma Studio GUI. For Python, SQLAlchemy 2.0 with its modern async support and Django ORM for full-stack applications remain the standards.

Part 8: Developer Satisfaction

Developer satisfaction data from 4,200 survey respondents reveals PostgreSQL as the most-loved database overall (4.6/5.0 satisfaction, 89% would use again). Redis scores highest in performance perception (4.8/5.0) and SQLite in ease of use (4.8/5.0). Supabase, despite being the newest entrant, achieves 4.4/5.0 satisfaction with the highest ease-of-use score among managed databases (4.6/5.0).

MySQL and MongoDB show declining satisfaction trends. MySQL dropped to 3.6/5.0 satisfaction with only 68% willing to use it again, reflecting developer migration to PostgreSQL. MongoDB sits at 3.8/5.0 with 72% retention, as the document-store use case is increasingly served by PostgreSQL JSONB. Community strength correlates with satisfaction: PostgreSQL leads (4.7/5.0), followed by Redis (4.3/5.0).

Part 9: Use Case Recommendations

Choosing the right database depends on your specific use case. For a SaaS MVP or startup, Supabase provides the fastest time-to-market with built-in authentication, row-level security, real-time subscriptions, and auto-generated APIs. For high-traffic web applications, PostgreSQL is the proven choice, used by Instagram, Discord, and Notion.

For real-time caching and session management, Redis is unmatched in latency and throughput. For IoT and time-series data, PostgreSQL with TimescaleDB provides hypertable partitioning and continuous aggregates. For embedded and mobile applications, SQLite offers zero-configuration, single-file deployment with a 700 KB footprint. For analytics and OLAP workloads, DuckDB provides columnar storage with 10-100x faster analytical queries than row-oriented databases.

Part 10: PostgreSQL Deep Dive

PostgreSQL 17 (released late 2025) brought incremental backup support, enhanced JSON_TABLE, improved MERGE command performance, and better partitioning. PostgreSQL is the only open-source relational database with a truly extensible architecture: over 1,000 extensions cover every niche from vector search (pgvector) to geospatial (PostGIS), time-series (TimescaleDB), columnar storage (Citus), and graph queries (Apache AGE).

Key PostgreSQL strengths: (1) JSONB provides document-store capabilities without sacrificing relational features. (2) The query planner is more sophisticated than MySQL, producing better execution plans for complex queries. (3) Write-Ahead Logging (WAL) enables point-in-time recovery, streaming replication, and change data capture (CDC). (4) Row-Level Security (RLS) enables multi-tenant applications without application-layer filtering. (5) Advisory locks, LISTEN/NOTIFY, and SKIP LOCKED enable PostgreSQL to serve as a lightweight message queue.

PostgreSQL weaknesses: (1) Higher memory usage than MySQL for simple workloads. (2) VACUUM maintenance is required to reclaim dead tuple storage (though autovacuum handles this automatically). (3) No built-in connection pooling (requires PgBouncer or Supavisor). (4) Replication lag can occur under heavy write loads with streaming replication.

Part 11: MySQL Deep Dive

MySQL 8.4 LTS brought JavaScript stored programs, vector data type support, and improved performance for prepared statements. MySQL remains the database with the largest installed base worldwide, powering WordPress (43% of all websites), Meta, and Netflix. Its InnoDB storage engine provides ACID compliance, row-level locking, and crash recovery through redo logs.

Key MySQL strengths: (1) Fastest simple SELECT performance among relational databases (42K ops/sec in our benchmarks). (2) Group Replication provides built-in high availability with automatic failover. (3) Extensive tooling ecosystem (MySQL Workbench, phpMyAdmin, Percona Toolkit). (4) Mature replication with binlog-based primary-replica and multi-source replication. (5) PlanetScale provides a serverless MySQL experience with branching and non-blocking schema changes.

MySQL weaknesses: (1) Inferior query planner compared to PostgreSQL for complex queries. (2) No native row-level security. (3) Limited extension system. (4) JSON support is functional but less performant than PostgreSQL JSONB. (5) Oracle ownership creates licensing uncertainty (GPLv2 with commercial option). (6) No materialized views natively.

Part 12: MongoDB Deep Dive

MongoDB 8.0 introduced queryable encryption, improved aggregation pipeline performance, and enhanced time-series collection capabilities. MongoDB remains the leading document database, used by companies like Cisco, eBay, and Adobe for applications requiring flexible schemas and horizontal scaling. Its document model maps naturally to application objects, reducing impedance mismatch.

Key MongoDB strengths: (1) Native horizontal sharding with automatic data distribution. (2) Flexible schema that adapts to evolving data models without ALTER TABLE migrations. (3) Atlas Search provides Lucene-based full-text search integrated into the aggregation pipeline. (4) Atlas Vector Search enables AI/ML similarity queries. (5) Change Streams provide real-time event notification for CDC patterns. (6) MongoDB Compass and Atlas provide excellent developer tooling.

MongoDB weaknesses: (1) No stored procedures or triggers in the traditional sense. (2) JOINs across collections (via $lookup) are significantly slower than SQL JOINs. (3) SSPL license restricts cloud provider usage. (4) Higher storage usage due to field names repeated in every document. (5) Multi-document transactions, while supported since 4.0, add latency and should be minimized.

Part 13: Redis Deep Dive

Redis 7.4 brought improvements to Redis Functions, Stream consumer groups, and memory optimization. Redis is an in-memory data structure store that supports strings, hashes, lists, sets, sorted sets, streams, bitmaps, HyperLogLog, and geospatial indexes. Its single-threaded event loop processes commands sequentially, achieving sub-millisecond latency for most operations.

Key Redis strengths: (1) Sub-millisecond latency for all supported operations. (2) Rich data structures beyond simple key-value (sorted sets for leaderboards, streams for event sourcing, HyperLogLog for cardinality estimation). (3) Pub/Sub and Streams for real-time messaging. (4) Lua scripting for atomic multi-step operations. (5) Redis Cluster provides automatic sharding across nodes. (6) Modules extend functionality (RediSearch, RedisJSON, RedisTimeSeries, RedisGraph).

Redis weaknesses: (1) Data size limited by available RAM (though Redis on Flash helps). (2) Single-threaded for command processing (io-threads for network I/O only). (3) Persistence options (RDB/AOF) involve trade-offs between durability and performance. (4) License changed to RSALv2 + SSPLv1, preventing cloud providers from offering competing managed services. (5) No support for complex queries (JOINs, aggregations across keys).

Part 14: SQLite Deep Dive

SQLite 3.48 continues to be the most deployed database engine in the world, embedded in every smartphone, browser, and operating system. It is a serverless, zero-configuration, self-contained SQL database engine stored in a single file. SQLite is public domain software with no licensing restrictions whatsoever.

Key SQLite strengths: (1) Zero configuration and zero administration. (2) Single-file database simplifies deployment and backup (just copy the file). (3) Full SQL support including CTEs, window functions, JSON, FTS5, and R-tree indexes. (4) Extremely small footprint (~700 KB). (5) Turso provides managed SQLite at the edge with libSQL fork. (6) Litestream enables continuous replication to S3-compatible storage.

SQLite weaknesses: (1) Single-writer concurrency (only one write transaction at a time, though WAL mode allows concurrent reads). (2) No built-in replication or clustering. (3) No user authentication or access control. (4) Not suitable for high-write-throughput workloads. (5) Limited ALTER TABLE support (cannot drop columns in older versions, though SQLite 3.35+ added DROP COLUMN).

Part 15: Supabase Deep Dive

Supabase is an open-source Firebase alternative built on PostgreSQL. It provides a full backend-as-a-service: database (PostgreSQL), authentication (email, OAuth, magic links, phone), storage (S3-compatible), edge functions (Deno), real-time subscriptions (WebSocket), and auto-generated REST and GraphQL APIs. Supabase reached $80M ARR in 2025 and is used by companies like Mozilla, PwC, and 1Password.

Key Supabase strengths: (1) Full PostgreSQL with no vendor lock-in (you can always migrate to plain PostgreSQL). (2) Row-Level Security (RLS) policies for fine-grained access control. (3) Realtime engine for live subscriptions to database changes. (4) Auto-generated REST API via PostgREST and GraphQL via pg_graphql. (5) Supabase Auth handles 50+ OAuth providers, magic links, and phone auth. (6) Supabase Storage with image transformation and CDN.

Supabase weaknesses: (1) Performance overhead from the API layer compared to direct PostgreSQL connections. (2) Free tier limitations (500 MB storage, pauses after 1 week of inactivity). (3) No horizontal write scaling (inherits PostgreSQL limitations). (4) RLS policies can become complex in deeply nested permission models. (5) Vendor-specific features (Realtime, Auth) create soft lock-in despite the open-source core.

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