Open Source Databases: SQL and NoSQL Compared
In This Guide
What Are Open Source Databases
An open source database is a database management system whose source code is publicly available under a license that allows anyone to study, modify, and distribute it. Unlike proprietary databases such as Oracle Database or Microsoft SQL Server, open source databases carry no per-seat or per-core licensing fees, which makes them especially attractive for startups, small businesses, and organizations that need to scale without unpredictable cost increases.
The open source database ecosystem has matured considerably over the past two decades. PostgreSQL and MySQL have been in active development since the mid-1990s and now rival or exceed their proprietary counterparts in features, performance, and reliability. On the NoSQL side, projects like Apache Cassandra, MongoDB, and Redis emerged in the late 2000s and early 2010s to address workloads that traditional relational databases handled poorly, such as high-velocity writes, unstructured data, and horizontal scaling across commodity hardware.
Today, more than 80 percent of organizations use at least one open source database in production. Many deploy multiple databases together, pairing a relational system like PostgreSQL for transactional data with a caching layer like Redis or Valkey and an analytics engine like ClickHouse. Understanding the strengths, limitations, and licensing terms of each option is essential for making sound architectural decisions.
The economic impact is significant. Organizations that migrate from proprietary databases to open source alternatives routinely report 60 to 90 percent reductions in licensing costs alone. When factoring in the flexibility to run on any infrastructure, avoid vendor lock-in, and hire from a larger pool of developers who already know the technology, the total cost of ownership advantage becomes even more pronounced. This is why open source databases now dominate new application development across virtually every industry.
SQL Databases: Structured and Relational
SQL databases organize data into tables with predefined schemas, enforce relationships through foreign keys, and guarantee consistency through ACID transactions (Atomicity, Consistency, Isolation, Durability). They are the default choice for applications where data integrity is critical, such as financial systems, e-commerce platforms, inventory management, and any workload that involves complex joins across multiple related entities.
PostgreSQL
PostgreSQL is widely regarded as the most advanced open source relational database available. It supports a rich set of data types including JSON, JSONB, arrays, hstore, geometric types, range types, and full-text search vectors. Its extensibility model allows developers to create custom data types, operators, index methods, and procedural languages. Core features include Multi-Version Concurrency Control (MVCC) for high-concurrency reads without locking writers, declarative table partitioning, logical replication, materialized views, and parallel query execution.
PostgreSQL's extension ecosystem is one of its greatest strengths. PostGIS adds geographic information system capabilities that make it the standard database for spatial applications. TimescaleDB turns PostgreSQL into a time-series database optimized for sensor data, metrics, and event streams. Citus distributes queries across multiple nodes for horizontal scaling of analytical and multi-tenant workloads. The pgvector extension supports vector similarity search, which has become essential for AI and machine learning applications that need to store and query embeddings.
PostgreSQL is released under the PostgreSQL License, a permissive open source license similar to BSD and MIT. This means there are no restrictions on how it can be used, modified, or distributed, even in proprietary commercial products. Every major cloud provider offers a managed PostgreSQL service, and the database runs on every major operating system including Linux, macOS, Windows, and FreeBSD.
MySQL
MySQL is the most widely deployed open source database in the world, used by an estimated 51 percent of organizations globally. It is the "M" in the classic LAMP stack (Linux, Apache, MySQL, PHP) and remains the default database for WordPress, Drupal, Joomla, and many other content management systems and web frameworks. MySQL is known for its speed on read-heavy workloads, its straightforward administration, and its extensive documentation and community support.
MySQL 8.4 LTS, the current long-term support release, includes window functions, common table expressions (CTEs), JSON table functions, invisible indexes, descending indexes, and an improved cost-based optimizer. The InnoDB storage engine provides full ACID compliance, row-level locking, crash recovery via redo logs, and online DDL operations that allow schema changes without locking the table. MySQL also supports Group Replication for synchronous multi-master high availability and InnoDB Cluster for automated failover with MySQL Router handling connection routing.
MySQL is owned by Oracle Corporation and released under a dual license: the GNU General Public License (GPL) for open source use and a commercial license for organizations that need to embed MySQL in proprietary products without GPL obligations. This dual licensing model, combined with concerns about Oracle's stewardship, is a key factor that led to the creation of MariaDB as a community-driven alternative.
MariaDB
MariaDB was created in 2009 by Michael "Monty" Widenius, the original creator of MySQL, after Oracle acquired Sun Microsystems and with it the MySQL project. MariaDB is designed as a drop-in replacement for MySQL, maintaining wire-level compatibility with the MySQL protocol and supporting the same SQL syntax, connectors, and tools. It adds capabilities that MySQL lacks, including the Aria storage engine for crash-safe temporary tables, the ColumnStore engine for columnar analytics workloads, and Galera Cluster for synchronous multi-master replication across data centers.
MariaDB has diverged from MySQL more significantly in recent versions, introducing its own optimizer improvements, system-versioned (temporal) tables that track all historical changes to a row, Oracle compatibility mode for migrating PL/SQL applications, and sequence objects. It is released under the GPL and has become the default MySQL-compatible database in most Linux distributions, including Debian, Ubuntu, Red Hat Enterprise Linux, Fedora, Arch Linux, and openSUSE. For teams already invested in the MySQL ecosystem, MariaDB provides a familiar experience with stronger community governance and additional features.
SQLite
SQLite is a unique entry in the database landscape. Rather than running as a separate server process, SQLite is an embedded database that lives inside the application as a linked library. It stores the entire database in a single cross-platform file, requires zero configuration, and has no external dependencies. SQLite is the most widely deployed database engine in the world by a wide margin, running on every Android and iOS device, in every web browser, inside Python, PHP, and dozens of other language runtimes, and within thousands of desktop and embedded applications.
SQLite is ideal for mobile apps, desktop software, IoT devices, edge computing, small to medium websites, and as a local data format for applications that need structured storage without the overhead of a client-server database. It handles read-heavy workloads well and supports concurrent reads, though only one writer can operate at a time. For applications that need multi-user concurrent writes from separate network clients, a client-server database like PostgreSQL or MySQL is more appropriate. SQLite is released into the public domain, making it completely free of any licensing restrictions whatsoever.
NoSQL Databases: Flexibility at Scale
NoSQL databases move away from the rigid table-and-schema model of relational databases in favor of more flexible data structures that are optimized for specific access patterns and scale characteristics. The term "NoSQL" originally meant "not SQL" but is now commonly interpreted as "not only SQL," reflecting the fact that many NoSQL databases have added query languages with SQL-like syntax. The major categories are document stores, key-value stores, wide-column stores, graph databases, and time-series databases.
Document Databases
Document databases store data as JSON-like documents (or BSON, MessagePack, or similar formats), allowing each record to have a different structure within the same collection. This flexibility makes them popular for applications where the data model evolves frequently, where different records naturally have different attributes, or where hierarchical data fits poorly into flat relational tables.
MongoDB is the most popular document database globally. It stores BSON (binary JSON) documents in collections, supports rich queries including aggregation pipelines with dozens of stages, provides horizontal scaling through automatic sharding, and offers change streams for real-time event processing. MongoDB Atlas, its managed cloud service, is available on AWS, Google Cloud, and Azure. However, MongoDB changed its license from the GNU AGPL to the Server Side Public License (SSPL) in 2018, which the Open Source Initiative does not recognize as a genuine open source license. This licensing shift prompted the creation of alternatives like FerretDB, which provides MongoDB wire protocol compatibility on top of PostgreSQL or SQLite.
Apache CouchDB is another notable document database, distinguished by its RESTful HTTP API, built-in multi-master replication, and design for offline-first applications that sync data between edge devices and central servers. CouchDB uses the Apache 2.0 license and is governed by the Apache Software Foundation.
Key-Value Stores
Key-value stores provide the simplest data model in the database world: each record is a key paired with a value. Despite this simplicity, they are extraordinarily useful for caching, session management, rate limiting, leaderboards, job queues, real-time counters, and any workload where data is accessed primarily by a known key rather than queried by attributes or relationships.
Redis dominated the open source key-value space for over a decade, offering in-memory storage with optional disk persistence, pub/sub messaging, Lua scripting, transactions, and a rich set of data structures including strings, lists, sets, sorted sets, hashes, streams, bitmaps, and hyperloglogs. In March 2024, Redis Ltd changed the license from the permissive BSD 3-Clause to a dual-license model combining RSALv2 and SSPLv1, which restricts cloud providers from offering Redis as a managed service without a commercial agreement from Redis Ltd.
This license change triggered the creation of Valkey, a community-driven fork of Redis 7.2.4 hosted by the Linux Foundation. Valkey retains full compatibility with the Redis API and protocol while staying under the permissive BSD 3-Clause license. It has attracted engineering contributions from Amazon Web Services, Google Cloud, Oracle, Ericsson, Snap, and other major organizations. For new deployments that require a genuinely open source Redis-compatible datastore, Valkey has rapidly become the recommended choice. DragonflyDB is another notable alternative that provides Redis and Memcached compatibility with a modern multi-threaded architecture.
Wide-Column Stores
Wide-column databases organize data into rows and dynamic column families, optimized for distributed writes across large clusters of commodity hardware. They excel at write-heavy workloads that need to scale horizontally to thousands of nodes across multiple data centers and geographic regions.
Apache Cassandra is the leading open source wide-column database, known for its ability to handle massive write volumes with tunable consistency levels. Cassandra uses a peer-to-peer architecture with no single point of failure, supports multi-datacenter replication natively, and provides linear horizontal scaling where doubling the number of nodes roughly doubles throughput. It powers systems at Apple (over 100,000 nodes), Netflix, Discord, Instagram, and many other companies that need extreme write scalability. Cassandra uses the Apache 2.0 license.
ScyllaDB is a Cassandra-compatible alternative written in C++ using the Seastar framework. It delivers significantly lower tail latencies by using a shard-per-core architecture that eliminates garbage collection pauses entirely, a persistent problem with Cassandra's Java runtime. ScyllaDB also provides DynamoDB-compatible API support. The open source edition uses the AGPL license, while the Enterprise edition requires a commercial license.
Graph Databases
Graph databases model data as nodes (entities) and edges (relationships), making them ideal for workloads where the connections between data points are as important as the data itself. Social networks, recommendation engines, fraud detection systems, identity and access management, knowledge graphs, and network topology analysis are all natural fits for graph databases.
Neo4j is the most mature and widely adopted graph database. Its Community Edition is released under the GPL and supports the Cypher query language, which has been standardized as GQL (Graph Query Language) by ISO. For teams that prefer to stay within the PostgreSQL ecosystem, Apache AGE is an extension that adds openCypher graph query capabilities directly to PostgreSQL, allowing graph queries alongside traditional SQL without deploying a separate database system.
Time-Series and Analytics Databases
Time-series databases are purpose-built for timestamped data from IoT sensors, application metrics, financial market data, event logs, and observability platforms. They optimize for high-ingest write rates, time-range queries, downsampling, and automatic data retention policies that expire old data.
InfluxDB is one of the most popular time-series databases, with its open source core available under the MIT license. TimescaleDB takes a different approach by extending PostgreSQL with time-series optimizations, letting teams use standard SQL for time-series queries without learning a new database. QuestDB focuses on extreme ingestion performance using a column-oriented storage engine and SIMD-accelerated query processing.
ClickHouse, originally developed at Yandex, is an open source columnar database designed for real-time analytical queries over billions of rows. It is not a time-series database specifically, but its column-oriented storage, vectorized query execution, and compression make it exceptional for analytics workloads, log analysis, and data warehousing. ClickHouse uses the Apache 2.0 license and has a rapidly growing community.
SQL vs NoSQL: How They Compare
The choice between SQL and NoSQL is not a matter of one being inherently better than the other. Each model is optimized for different workloads, and most production systems combine multiple database types to handle different parts of the application. Understanding the fundamental tradeoffs helps you match each database to the workload it handles best.
Data Model and Schema
SQL databases enforce a schema before data is written. Every row in a table has the same columns, and relationships between tables are explicit through foreign keys and constraints. This rigidity is a strength when data consistency matters, because the database itself prevents malformed or inconsistent records from being stored. NoSQL databases allow flexible or schema-less records, which speeds up initial development when requirements are still evolving, but shifts the responsibility for data validation and consistency entirely to the application layer.
Scaling Approach
SQL databases traditionally scale vertically by adding more CPU, memory, and storage to a single server. While PostgreSQL and MySQL support read replicas and some forms of sharding (through extensions like Citus or middleware like Vitess and ProxySQL), distributing writes across multiple nodes is inherently more complex with relational databases because of the need to maintain transactional consistency. NoSQL databases like Cassandra, MongoDB, and ScyllaDB were designed from the ground up for horizontal scaling, distributing data across many commodity servers with automatic sharding, replication, and rebalancing.
Consistency Guarantees
SQL databases default to strong consistency: once a transaction commits, every subsequent read from any connection sees the updated data. Many NoSQL databases offer tunable consistency, where you can choose between strong consistency (at the cost of higher latency and reduced availability during network partitions) and eventual consistency (where replicas may briefly serve stale data but availability remains high). This tradeoff is described by the CAP theorem, which states that a distributed system can guarantee at most two of three properties: Consistency, Availability, and Partition tolerance.
Query Capabilities
SQL provides a standardized, declarative query language that supports complex joins across multiple tables, subqueries, aggregations, window functions, recursive queries, and set operations. The query optimizer figures out how to execute the query efficiently. NoSQL query languages vary by database and are typically more limited, optimized for the specific access patterns the database was designed around. MongoDB's query language is powerful for document operations, and Cassandra Query Language (CQL) looks like SQL, but neither can match the full expressiveness of SQL for multi-table analytical queries.
When to Choose SQL
Use a relational SQL database when your data has clear relationships between entities, when you need ACID transactions that span multiple tables, when your queries involve complex joins and aggregations, or when regulatory requirements demand strict data integrity and auditability. Financial applications, customer relationship management systems, e-commerce catalogs with inventory tracking, human resources platforms, and content management systems are classic SQL workloads.
When to Choose NoSQL
Use a NoSQL database when your data model is hierarchical or varies significantly between records, when you need to scale writes horizontally across dozens or hundreds of servers, when your primary access pattern is simple lookups by key or ID, or when you need sub-millisecond response times for caching and session storage. Real-time analytics dashboards, IoT data ingestion pipelines, user session and preference storage, content delivery metadata, gaming leaderboards, and social activity feeds are common NoSQL use cases.
Licensing and Community Health
Not all databases that publish their source code carry the same freedoms. Understanding the license is critical because it determines what you can do with the software, whether you can modify and redistribute it, whether cloud providers can offer it as a managed service, and what obligations you take on by using it.
The most permissive licenses in the database world are the PostgreSQL License, MIT License, BSD licenses, and Apache License 2.0. These allow unrestricted use, modification, and distribution, including embedding the code in proprietary commercial products. PostgreSQL, SQLite, ClickHouse, Valkey, and QuestDB use these permissive licenses. If licensing simplicity is a priority, these are the safest choices.
The GNU General Public License (GPL) requires that derivative works, meaning software that incorporates GPL-licensed code, also be released under the GPL. MySQL, MariaDB, and Neo4j Community Edition use the GPL. For most application developers, this is not a concern because simply connecting to a GPL database from your application does not make your application a derivative work. However, it does mean you cannot take the database source code and embed it into a proprietary product without obtaining a separate commercial license.
The Server Side Public License (SSPL) and Business Source License (BSL) are newer licenses designed to restrict cloud providers from offering the software as a hosted service without contributing back or purchasing a commercial license. MongoDB uses SSPL. Redis (since 2024) uses a dual RSALv2/SSPLv1 license. Elasticsearch switched to SSPL before later moving to AGPL. The Open Source Initiative does not recognize SSPL or BSL as open source licenses, so databases under these terms are more accurately described as "source available" rather than "open source."
Community health is just as important as the license text. A database with an active, diverse community receives frequent security patches, performance improvements, bug fixes, and ecosystem tooling. PostgreSQL has one of the healthiest communities in all of open source software, with dozens of companies contributing engineering resources and an independent governance structure that prevents any single organization from controlling the project. MySQL benefits from Oracle's significant engineering investment but has a more centralized development model. Valkey, despite being created only in 2024, has already attracted a broad base of corporate contributors and is governed by the Linux Foundation, which provides neutral stewardship.
Self-Hosting vs Managed Cloud Services
Every open source database can be self-hosted on your own servers, virtual machines, or container orchestration platforms like Kubernetes. Self-hosting gives you full control over configuration, performance tuning, security policies, data residency, and upgrade schedules. It eliminates per-query or per-connection pricing, avoids vendor lock-in, and ensures that your data never leaves infrastructure you control. The tradeoff is operational responsibility: your team must handle installation, version upgrades, backup and restore procedures, replication setup, monitoring, security patching, and disaster recovery planning.
Managed cloud database services like Amazon RDS, Google Cloud SQL, Azure Database for PostgreSQL, Supabase, PlanetScale, and Aiven handle these operational tasks for you. They provide automated daily backups with point-in-time recovery, one-click read replica creation, automatic failover, storage auto-scaling, and monitoring dashboards through a web console or API. The tradeoff is cost: managed services charge premiums of 2x to 5x over the raw infrastructure cost, which adds up significantly at scale. They may also offer modified versions of the database with vendor-specific extensions or missing upstream features.
For smaller teams without dedicated database administrators, managed services dramatically reduce operational burden and let developers focus on application logic rather than database maintenance. For organizations with database expertise and significant scale, self-hosting on Kubernetes using database operators (such as CloudNativePG for PostgreSQL, Percona Operator for MySQL, or the K8ssandra operator for Cassandra) can deliver better performance characteristics at substantially lower cost, particularly for high-throughput and data-intensive workloads.
A hybrid approach is common and often practical. Many teams self-host their primary production databases for cost control and performance tuning while using managed services for development environments, staging clusters, proof-of-concept evaluations, or secondary analytics databases that do not justify the overhead of dedicated operational attention. The flexibility of open source databases makes this kind of mixed deployment straightforward, since the same database engine runs identically regardless of where it is hosted.
Building a Modern Database Stack
Most production applications today use more than one database, a practice sometimes called polyglot persistence. A typical modern stack might include PostgreSQL as the primary transactional database for structured business data, Valkey or Redis for caching frequently accessed data and managing user sessions, Elasticsearch or Meilisearch for full-text search and autocomplete, and ClickHouse or TimescaleDB for analytics queries and time-series data. Each database handles the workload category it was purpose-built for, and the application layer routes queries to the appropriate system.
This approach delivers better performance and scalability than forcing a single database to handle every workload type, but it introduces real architectural complexity. Data must flow between systems reliably. Consistency across databases requires careful design. Operational overhead multiplies with each additional system your team must monitor, backup, upgrade, and troubleshoot. Change Data Capture (CDC) tools like Debezium can stream row-level changes from a primary database to downstream systems in near-real-time, reducing the need for error-prone dual writes and keeping data eventually consistent across the entire stack.
When evaluating databases for your stack, consider these factors roughly in this order: data model fit (does the database naturally represent your data?), query pattern match (can it efficiently serve your most common queries?), operational maturity (how battle-tested is it in production?), community health (will it still be maintained in five years?), licensing terms (are there any restrictions that affect your business model?), hosting options (self-hosted, managed, or both?), and finally raw performance benchmarks. Performance matters, but it is rarely the primary deciding factor because most open source databases perform well for most workloads. The database that fits your data model and your team's expertise will almost always serve you better than the theoretically fastest option that requires unfamiliar operational practices.
Getting Started
If you are new to open source databases and need to choose one for a new project, PostgreSQL is the strongest general-purpose default. It handles relational data, document data (via its JSONB type with indexing), full-text search (via tsvector and tsquery), geospatial queries (via PostGIS), time-series data (via TimescaleDB), and even vector similarity search (via pgvector) within a single database system. This versatility reduces the number of databases you need to deploy and operate, which is a significant advantage for smaller teams. PostgreSQL runs on every major operating system, every cloud provider offers a managed version, and its community produces documentation that is among the best in the entire database industry.
For simpler applications, embedded software, mobile development, and scenarios where a separate database server would be overkill, SQLite is worth serious consideration. It requires no server process, no configuration files, no network setup, and no administration. A SQLite database is just a single file on disk. Many applications that start with SQLite during prototyping or early development later migrate to PostgreSQL or MySQL when they need concurrent write access from multiple networked users.
If your workload genuinely requires NoSQL characteristics, such as massive distributed write throughput, highly flexible schemas with no common structure between records, or sub-millisecond caching performance, start by identifying your primary access pattern and choose the database engineered for that pattern. Apache Cassandra for linearly scalable distributed writes across data centers, Valkey for in-memory caching and data structure operations, MongoDB or CouchDB for document-oriented flexibility with rich querying, Neo4j for traversing deeply connected graph data, and ClickHouse for real-time analytical queries over billions of rows. The sub-pages below explore each of these options in depth, with practical guidance for evaluation, installation, and production deployment.