Best open source databases in 2026: Postgres won, here's what to pair it with

Quick answer. If you are picking a data layer in 2026 H2, the radar points to one default and seven complements. The default is Postgres. The complements, in order of "you will probably need this too," are Redis (cache/queue), ClickHouse or DuckDB (analytics), SQLite / libsql (edge + mobile), Qdrant (vectors), Meilisearch (full-text search), and PocketBase (single-binary app backend).

How this list was chosen

Every database below has been measured on the GitPop radar for the full 12-week window of 2026 H2. The selection criteria are:

• Sustained PopScore ≥ 55 for the full window
• Active maintenance (commits in the last 30 days)
• Real production usage (cross-checked with HN/Reddit mention volume)
• Distinct from the others on the list (we are not picking 5 OLTP engines)

The result is a list that reflects "the right database for each workload in 2026 H2," not "the 8 most-starred databases on GitHub."

The 8 projects

1. PostgreSQL — the OLTP default

The default for OLTP. Feature-rich, mature, the lingua franca of relational databases. JSONB, CTEs, full-text search, extensions (PostGIS, pgvector, etc.). The radar's pick for any new app that needs a relational database.

• GitHub: postgres/postgres
• PopScore (W34): 88
• Best for: OLTP, the default for new apps
• Compare with: Postgres vs MySQL, Postgres vs SQLite, Postgres vs MongoDB

2. Redis — the cache + queue default

The default for cache, queue, pub/sub, and ephemeral state. Mature, fast, the lingua franca of in-memory data structures. The radar's pick for any new app that needs a cache or a job queue.

• GitHub: redis/redis
• PopScore (W34): 79
• Best for: cache, queue, pub/sub, ephemeral state
• Compare with: Redis vs Memcached

3. ClickHouse — the OLAP engine

The column-oriented analytics database. The default for "I have 10B+ rows and I need to query them in seconds." Used by Cloudflare, Uber, and most large-scale analytics platforms. The radar's pick for production-scale analytics.

• GitHub: ClickHouse/ClickHouse
• PopScore (W34): 82
• Best for: production-scale analytics, time-series, log search

4. DuckDB — the in-process analytics engine

The in-process OLAP engine. Embedded like SQLite, but for analytics workloads. The default for "I have 100M rows and I want to query them from Python without setting up a server." The fastest-growing database in 2026 H2.

• GitHub: duckdb/duckdb
• PopScore (W34): 84
• Best for: in-process analytics, data science notebooks, embedded OLAP

5. SQLite / libsql — the embedded default

The embedded database. Zero-config, single-file, the most deployed database in the world. libsql (Turso) is the fork with replication and edge-deployment. The default for mobile, CLI tools, edge functions, and any single-process app.

• GitHub: sqlite/sqlite, tursodatabase/libsql
• PopScore (W34): 72
• Best for: embedded apps, mobile, CLI tools, edge functions
• Compare with: Postgres vs SQLite, MySQL vs SQLite

6. Qdrant — the vector database

The Rust-based vector database. The radar's pick for self-hosted vector search. Strong on filtering (you can filter on payload before the vector search), single-binary deploy. The default for RAG stacks that need a self-hosted vector store.

• GitHub: qdrant/qdrant
• PopScore (W34): 67
• Best for: RAG systems, semantic search, hybrid search

7. Meilisearch — the full-text search engine

The Rust-based full-text search engine. Single-binary, typo-tolerant, fast, easy to deploy. The radar's pick for "I need full-text search without running Elasticsearch." Used by many SaaS apps for in-app search.

• GitHub: meilisearch/meilisearch
• PopScore (W34): 70
• Best for: in-app full-text search, replacing Elasticsearch for small/medium workloads

8. PocketBase — the single-binary app backend

The single-binary backend for small apps. SQLite + auth + file storage + admin UI in a 15 MB binary. The default for prototypes, side projects, and small-to-medium SaaS apps that do not want to run a separate backend service.

• GitHub: pocketbase/pocketbase
• PopScore (W34): 75
• Best for: prototypes, side projects, small-to-medium SaaS apps

Decision table

Use case	Pick
OLTP (new app)	Postgres
Cache + queue	Redis
Production analytics	ClickHouse
In-process analytics	DuckDB
Embedded / mobile / edge	SQLite / libsql
Vector search (self-hosted)	Qdrant
Full-text search (in-app)	Meilisearch
Single-binary app backend	PocketBase

The pattern: complementary, not competing

Notice that the eight databases on this list do not compete with each other — they are complementary. A modern 2026 stack is Postgres + Redis + DuckDB (or ClickHouse) + Qdrant + Meilisearch. You do not pick one; you assemble the right set. See State of GitPop H2 2026 §5 for the full data on the data layer.

What is not on this list

• MySQL — still a great database, but no momentum in 2026 H2. Postgres won the OLTP default. If you have an existing MySQL deployment, stay there. If you are picking, pick Postgres.
• MongoDB — PopScore -22% in 2026 H2. The document-store model is not winning against Postgres + JSONB. If you have an existing MongoDB deployment, do not migrate. If you are picking, pick Postgres.
• Elasticsearch — the full-text search default for the last decade, but Meilisearch is eating the small/medium workload. Elasticsearch is still the right pick for very large deployments, but it is no longer the default.
• Supabase / Neon / pgvector — these are not databases; they are Postgres hosting and extensions. They show up in the radar under the Postgres-adjacent category.

Where to go next

• State of GitPop H2 2026 — the full data layer analysis
• Compare matrix — 30 high-volume "X vs Y" decision pages
• Methodology — how PopScore is calculated
• Projects directory — every project in the radar

Every project on this list is a single click away from its full PopScore breakdown, AI snapshot, star history, and external mentions.