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There is no single best open-source database for every app. If you are deciding between PostgreSQL, MySQL, MongoDB, and SQLite, start with what the application needs to do: handle transactions, store documents, search text, analyze events, or work locally without a database server. Then weigh the data model, expected scale, operational effort, team experience, hosting, and license.
This guide groups more than 25 candidates by workload, explains when to shortlist them, and flags an important caveat: a project’s open-source history does not guarantee that its current license meets the Open Source Initiative’s definition of open source.
How to choose a database for your project
- Name the workload. Is the database serving transactions, analytics, full-text search, time-series data, a cache, or relationships? If the answer is mainly “store and retrieve application records,” begin with relational SQL.
- Choose the data model. Relational tables and constraints, documents, key-value pairs, wide-column records, and graph relationships are not interchangeable. Pick the model that makes common queries and updates clear rather than trying to fit every workload into the same shape.
- Decide whether you need a server. A separate database process brings deployment, backups, monitoring, access control, and upgrades. For local, mobile, test, edge, or small single-process applications, an embedded database may avoid unnecessary operations.
- Estimate scale from the workload, not a slogan. Consider write rate, query patterns, data volume, latency targets, availability needs, and whether horizontal scaling is actually required. Do not add a distributed system before its operational cost is justified.
- Check the surrounding ecosystem. Confirm driver and ORM support, migration tooling, backup and replication options, team familiarity, and managed-hosting availability for the exact edition and license you plan to use.
- Verify the current license and service terms. Review the project’s current license and the terms of any hosted service before adoption; licensing and product terms can change.
For a general-purpose server-side SQL application, PostgreSQL is a strong starting point. For a file-based database embedded in an application, consider SQLite. A specialized engine can be a better fit when its workload is central to the product, but running several databases adds operational complexity.
Shortlist databases by workload
| Workload | Options to investigate | Why they may fit | Trade-off to examine |
|---|---|---|---|
| General application transactions | PostgreSQL, MySQL, MariaDB | Relational SQL is a practical fit for structured records, constraints, and application queries. | Compare feature needs, compatibility, team expertise, operational tooling, hosting, and license. |
| Embedded or local SQL | SQLite, Firebird, H2, Apache Derby | Useful when a separate database server is unnecessary or the application needs a local database. | Check deployment and concurrency requirements against the specific engine’s intended use. |
| Embedded or local analytics | DuckDB | Designed for analytical work with columnar data such as CSV and Parquet. | It is not a general replacement for an application’s transactional database. |
| Distributed SQL | TiDB, CockroachDB | Worth investigating when horizontal scale and a SQL interface are both requirements. | Distributed operation adds complexity; check current licensing, especially for CockroachDB. |
| Document data | Apache CouchDB, FerretDB, MongoDB | Consider when document-shaped records or a document API match the application’s access patterns. | MongoDB’s current license needs separate scrutiny; FerretDB is a PostgreSQL-backed compatibility layer, not the same storage architecture. |
| Key-value, cache, and in-memory data | Redis, Valkey, Memcached, KeyDB, Redict | Suitable to investigate for caching, low-latency key-value access, or in-memory data structures. | Check persistence and durability needs, project activity, compatibility, and license—especially among Redis-family alternatives. |
| Distributed write-heavy data | Apache Cassandra, ScyllaDB | Wide-column systems for multi-node, high-write workloads; ScyllaDB is Cassandra-compatible. | Data modeling and operating a distributed cluster demand careful planning. |
| Relationship-heavy data | Neo4j | Graph queries can suit domains such as recommendations, identity, and network analysis. | Choose it when traversing relationships is a core workload, not merely because records have links. |
| Time-series data | InfluxDB, Timescale | Options for metrics, telemetry, and event data; Timescale is PostgreSQL-based for teams seeking SQL and PostgreSQL compatibility. | Check retention, query, and integration needs against the intended workload. |
| Search and text analytics | OpenSearch, Apache Solr, Elasticsearch | Search-oriented engines for indexing and full-text retrieval; Solr is based on Lucene. | They are not interchangeable with a transactional system of record. Elasticsearch’s current license has an OSI qualification noted below. |
| Large-scale analytical workloads | ClickHouse, Apache Druid, Apache Hadoop ecosystem components | Investigate for column-oriented analytics, aggregation-heavy event data, or distributed big-data platforms. | Match the architecture to analytical rather than ordinary transactional needs. |
| MySQL-compatible operations focus | Percona Server for MySQL | A distribution to investigate when MySQL compatibility, operational tooling, or support matters. | Compare the exact tooling and support requirements with the alternatives your team can operate. |
Relational databases for most application backends
PostgreSQL: a strong general-purpose default
PostgreSQL is a sensible first candidate for many new server applications that need relational transactions and a flexible SQL platform. Its official project overview highlights extensible data types, custom functions, and integrations with multiple programming languages. The PostgreSQL Global Development Group describes it as “the open source relational database of choice for many people and organisations.” That breadth is useful, but still compare the extensions, hosting, and operational skills your actual project requires.
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MySQL and MariaDB: compare compatibility and operations
MySQL has a mature web and application ecosystem. MariaDB is a MySQL-compatible open-source branch and may be relevant where compatibility or its particular deployment options matter. MariaDB says it can federate heterogeneous databases, including Oracle, SQL Server, and Db2. Treat compatibility as something to validate against your application and tools, not a guarantee that every feature, extension, or operational procedure is identical.
OpenLogic’s 2025 State of Open Source Support survey reported PostgreSQL at 51.06%, MySQL at 36.70%, and MariaDB at 30.85% among its respondents. These are survey respondent percentages, not global market shares or a universal ranking of database quality.
Smaller and distributed SQL candidates
Firebird is a relational SQL option for embedded and client/server deployments. H2 is a Java-oriented embedded/server SQL engine often considered for development, tests, and smaller applications; Apache Derby is another Java relational engine appearing in OpenLogic’s ecosystem survey. SQLite is the better-known embedded file-based option discussed below.
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TiDB is a distributed SQL option for teams that need horizontal scale with a SQL interface. CockroachDB is also a distributed SQL project, but do not assume its historical open-source status describes its current license. OpenLogic’s 2025 report says CockroachDB no longer meets the OSI criteria for OSS under its current license. A distributed SQL architecture is a substantial operations choice; first establish that the scale or availability requirement warrants it.
Embedded databases and analytics engines
SQLite when an application needs local SQL
SQLite is an embedded, file-based SQL engine. It suits local software, mobile apps, edge deployments, tests, and small single-process applications where a separate database server would add more machinery than value. It can also make a useful local store or development component. If the design depends on a separately managed multi-node database service, shortlist a server-based system instead.
DuckDB, ClickHouse, Druid, and Hadoop for analysis
DuckDB is an embedded analytical database suited to local analysis of columnar files such as CSV and Parquet. ClickHouse is a column-oriented analytical database for high-volume analytical queries, while Apache Druid is aimed at real-time, aggregation-heavy event data. Apache Hadoop ecosystem components belong in consideration when the project is a distributed big-data platform rather than an ordinary application database.
Analytics engines optimize for a different job from an online transaction processing (OLTP) database. If the application needs both, keep the operational records in a system suited to transactions and add an analytical store only when the reporting workload justifies its data movement and maintenance.
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Document-shaped data
MongoDB is document-oriented and may fit applications whose records are naturally represented and accessed as documents. However, OpenLogic’s 2025 report says MongoDB no longer meets the OSI definition of open source under its current license, despite its open-source history. Check the current license and any hosted-service terms before treating it as an open-source choice.
Apache CouchDB is another document database, with replication-oriented use cases. FerretDB offers a MongoDB-protocol-compatible layer backed by PostgreSQL; it may interest teams wanting a document API with PostgreSQL as the storage core. That architecture differs from adopting MongoDB itself, so test the API and feature compatibility your application needs.
Caching and low-latency key-value access
Redis is an in-memory key-value store used for caching, real-time workloads, and data structures. Valkey is a Redis-compatible open-source direction to consider for key-value and caching needs. Memcached is a simpler distributed memory cache for reducing read pressure on a database. KeyDB and Redict also appear among Redis-family alternatives in OpenLogic’s 2025 survey; verify current project activity, compatibility, and licensing rather than assuming the projects are interchangeable.
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Before using a cache as durable application storage, decide what happens when cached data is lost or evicted. A cache is most useful when the underlying source of truth and the rules for invalidating stale values are explicit.
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Wide-column databases for distributed writes
Apache Cassandra is a distributed wide-column store for high-write, multi-node workloads. ScyllaDB is a Cassandra-compatible option to investigate when latency and resource efficiency are important requirements. These systems reward data models designed around known queries; they are poor choices simply because an application might grow someday.
Graph databases for relationship traversal
Neo4j is a graph database for relationship-heavy problems such as recommendations, identity, and network analysis. It is worth considering when traversing connected entities is central to the product. OpenLogic reported Neo4j at 4.26% of respondents in its 2025 survey; that figure is survey-specific, not a measure of technical suitability.
Time-series databases for metrics and telemetry
InfluxDB targets time-series use cases such as metrics, events, and telemetry. Timescale is a PostgreSQL-based time-series option for teams that want SQL and PostgreSQL compatibility. Compare retention and query requirements, ingestion patterns, and how the chosen system fits existing monitoring and data workflows.
Search engines for indexing and retrieval
OpenSearch is a search and analytics engine; Apache Solr is a Lucene-based search platform for indexing and full-text retrieval. Elasticsearch remains widely used, but OpenLogic’s 2025 report says its current license no longer meets the OSI definition of OSS. Search engines commonly complement a primary database: decide how an index is populated, how quickly updates must appear, and how to rebuild it if needed.
What adoption surveys can—and cannot—tell you
In OpenLogic’s 2025 State of Open Source Support survey, respondents reported PostgreSQL 51.06%, MySQL 36.70%, MariaDB 30.85%, SQLite 30.32%, MongoDB 29.79%, Elasticsearch 23.94%, Redis/Valkey/KeyDB/Redict 23.40%, OpenSearch 11.17%, Cassandra 10.64%, Neo4j 4.26%, and CockroachDB 2.66%. Those percentages describe responses to that survey, not market share, project health, or a guarantee that a database will suit a new application.
MariaDB’s 2025 survey identifies PostgreSQL, SQLite, and MySQL as the leading named open-source relational responses and includes mentions of CouchDB, Elastic, Redis, Cassandra, ClickHouse, CockroachDB, InfluxDB, and DuckDB. It is another survey snapshot, not a workload-based recommendation. Use survey results to see what respondents report using, then make the choice from the application’s actual requirements.
Check whether the license fits your definition of open source
“Open source” is a meaningful licensing claim, not just a synonym for source code being visible or a project having once used an OSI-approved license. OpenLogic’s 2025 report says MongoDB, Elasticsearch, and CockroachDB no longer meet the Open Source Initiative’s criteria under their current licenses, while noting that they began as open-source projects. If OSI-approved licensing is a requirement, verify the license for the precise project version and deployment before choosing it. Also review hosted-service terms separately: a project license does not automatically settle the terms of a managed offering.
A practical decision path
- For ordinary server-side records and transactions: compare PostgreSQL, MySQL, and MariaDB. Start with PostgreSQL if you have no compatibility constraint, then validate hosting, extensions, and the team’s operational comfort.
- For a local, mobile, test, or single-process app: evaluate SQLite before deploying a server. For local analytical work over files, evaluate DuckDB.
- For a specialized workload: add a document, cache, wide-column, graph, time-series, search, or analytical engine only when the data model or workload gives it a clear job.
- For distributed scale: write down the expected load, availability target, and failure scenarios that require horizontal distribution. Compare the operational burden as well as query fit.
- Before committing: test representative reads and writes, migrations, backups, restore procedures, driver support, access controls, license, and hosting terms. A successful restore and a maintainable upgrade path matter as much as a feature checklist.
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Frequently Asked Questions
Can one database handle both application transactions and reporting?
Often it can serve both at modest scale, but analytical queries can compete with application traffic. Separate the workloads only when measured requirements or reliability targets justify the added systems and data flow.
Should I use a cache instead of a database?
A cache and a database have different roles. Use a cache to accelerate access when the system has a defined source of truth and a plan for stale or missing cached values.
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