Data is the lifeblood of modern applications. As data volumes grow exponentially, choosing the right database to store and process that data is critical.
Open source databases offer significant advantages over proprietary solutions – they are free to use, modify and distribute. The open source community also actively develops and supports these databases, releasing frequent updates and fixes.
In this comprehensive guide, we will explore the top 13 open source database systems to consider for your next project.
1. MySQL
The most popular open source database, MySQL powers massive websites like Facebook, Twitter and YouTube. Its relational model organizes data into tables, rows and columns, with strict schemas defining the structure.
Key Features:
- Relational database management system
- ACID compliant for reliability
- Wide language support
- Mature ecosystem of tools and support
- Scales well with sharding
- Available as managed cloud database
Use Cases:
- Transactional applications
- Ecommerce platforms
- CMS like WordPress and Drupal
- Mobile apps needing a small footprint
MySQL is a great default choice for web and mobile applications due to its ease of use, performance at scale and vast community support.
2. PostgreSQL
A powerful open source database, PostgreSQL calls itself the "world‘s most advanced". It emphasizes extensibility and standards compliance.
Key Features:
- Advanced SQL implementation
- Extensible with custom data types
- JSON support
- Multi-version concurrency control
- Superior indexing and cost estimation
- Replication and failover support
Use Cases:
- Analytics and business intelligence
- Scientific data and spatial apps
- Data warehousing
- Highly concurrent workloads
PostgreSQL manages heavy OLTP and OLAP workloads with aplomb. Its enterprise-grade features rival top proprietary databases.
3. MongoDB
The leading modern database, MongoDB uses flexible JSON documents instead of rows and columns. Its intuitive development experience propelled the NoSQL movement.
Key Features:
- Document model with dynamic schemas
- Tunable consistency guarantees
- Indexing, querying and aggregation
- Horizontal scaling and replication
- Multi-data center deployment
Use Cases:
- Content management and delivery
- Mobile and social apps
- User profiles and catalogs
- Personalization and recommendations
MongoDB makes building internet-scale apps easy. Its versatile document model handles quickly evolving data schemas.
4. Redis
Redis is a blazing fast in-memory data store used as a database, cache and message broker. It supports lucid data structures like strings, hashes, lists and sets natively.
Key Features:
- Data structures with atomic operations
- Replication and high availability
- Lua scripting
- Transactions and pub/sub messaging
- Modules adding new functionality
Use Cases:
- Caching layer
- Rate limiting and throttling
- Real time apps with pub/sub
- Leaderboards and counters
- Time series data
Redis accelerates apps by orders of magnitude by keeping hot datasets in memory. Its versatility makes it a popular complement to databases like MySQL and MongoDB.
5. Apache Cassandra
A massively scalable NoSQL database, Apache Cassandra powers large scale sites handling millions of writes per second. Netflix, Apple and Comcast rely on Cassandra for mission critical data.
Key Features:
- Elastically scalable across nodes
- Fault tolerant with no single point failure
- Tunable consistency levels
- Column-oriented tables
- Table partitioning and compaction
Use Cases:
- Media streaming and IoT apps
- Retail purchases history
- Logging and messaging
- Time series data
For write heavy workloads that demand scale, Cassandra does the heavy lifting. Its linear scalability lets you easily add nodes as data volumes grow.
6. Couchbase
Couchbase combines in-memory speeds with disk persistence, scaling from mobile devices to internet clusters. Natively integrated caching, replication and cross data center features accelerate development.
Key Features:
- Memory first architecture
- Sub millisecond data operations
- SQL-compatible query language
- Schemaless JSON documents
- Geo-distributed deployments
- Multi-dimensional scaling
Use Cases:
- Gaming and session data
- Shopping cart data
- User profiles and preferences
- Product catalogs
Couchbase couples operational simplicity with jaw dropping performance. It shines when a disk-based data store feels too slow for highly dynamic data.
7. DynamoDB
DynamoDB is Amazon‘s fully managed NoSQL database offered as a service. It provides reliable performance at any scale along with built-in security, backup and in-memory caching features.
Key Features:
- Highly scalable managed service
- Single digit millisecond response
- Encryption at rest and in transit
- Automated partition management
- Point in time recovery
- Streams and triggers for apps
Use Cases:
- Mobile, web and gaming apps
- Distributed session stores
- Product catalogs and inventory
- IoT sensor data pipeline
For those leveraging AWS, DynamoDB ticks all the boxes for massively scalable, low latency database needs.
8. Elasticsearch
Built on Lucene, Elasticsearch is a wildly popular open source, real-time search and analytics engine. Its versatile REST APIs make ingesting, exploring, storing and analyzing machine data easy.
Key Features:
- Real time searching and analytics
- Scales to billions of documents
- Fast highlighting and aggregation
- Multi-tenancy and security
- Anomaly detection and forecasting
Use Cases:
- IT operational monitoring
- Application diagnostics
- Clickstream analytics
- Security analytics
- Business intelligence
Elasticsearch, along with the ELK stack, now drives security, observability and log analysis pipelines for enterprises.
9. InfluxDB
InfluxDB tackles the formidable time series data challenge – where massive amounts of time-stamped observations must be stored and analyzed efficiently. It powers real-time dashboarding and alerting for metrics from servers, devices, sensors and apps.
Key Features:
- Optimized for time stamp queries
- High availability and horizontal scaling
- Built-in retention policies
- Expressive Flux query language
- Plug in external analysis
Use Cases:
- IoT and sensor analytics
- DevOps monitoring
- Industrial telemetry
- Price tracking
For observing trends across metrics like temperature, pressure and latency over time, InfluxDB provides lightning fast time series data storage and processing.
10. ClickHouse
ClickHouse claims to be the world‘s fastest open source OLAP database. It uses cutting edge column oriented techniques to run real time analytics on billions of rows and petabytes of data.
Key Features:
- Column oriented storage
- Uses all CPU cores efficiently
- Joins and aggregations
- Table engines for integration
- SQL, instant and batch queries
Use Cases:
- Dashboards and reports
- Exploratory analytics
- Marketing analytics
- Financial data
- Network traffic logs
ClickHouse brings stunning analytical query performance to big data applications, rivaling costly commercial solutions.
11. Apache HBase
Part of Hadoop ecosystem, Apache HBase provides big data tables leveraging the HDFS file system. It brings real time, low latency random data access to datasets spanning billions of rows and columns.
Key Features:
- Linear and modular scaling
- Strong consistency
- Automatic sharding
- Real time lookups
- MapReduce integrations
Use Cases:
- Product catalogs
- Quant trading
- Social media analytics
- Telecom roaming data
For low latency querying of massive datasets, Apache HBase parallelizes queries across Hadoop clusters storing big data efficiently.
12. Apache Ignite
An in-memory computing platform, Apache Ignite spans an impressive array of database, caching and processing functionality. It can handle both transactional or analytical workloads at petabyte scale across clusters.
Key Features:
- In-memory speed
- ACID compliant transactions
- ANSI SQL, Key-Value and compute APIs
- Intermittent query architecture
- Integrated data grid
Use Cases:
- Real time analytics
- Algorithmic or quant trading
- IoT and telematics
- Risk and fraud detection
Ignite pulls off a unique synthesis, acting as an in-memory data grid and massively parallel database in one integrated package.
13. ScyllaDB
ScyllaDB is a drop in replacement for Apache Cassandra that claims 10x higher throughput, significantly lower latency and consistent high performance. It stores unstructured data efficiently and scales apps as data volumes explode.
Key Features:
- Wire compatible with Cassandra
- 10x higher throughput
- Persistent memory support
- Repair service performance
- Better disk write utilization
Use Cases:
- High performance apps needing scale
- Time series industrial data
- Financial trading platforms
- IoT infrastructure
ScyllaDB brings hyperscale performance to Cassandra‘s versatile data model to tackle demanding big data applications.
This quick guide surveys some of the most capable open source databases available today for developers. From relational mainstays to data warehousing powerhouses and distributed NoSQL innovators, incredible choice exists.
The key is first understanding application access patterns – will small, fast data be queried in real time or are pre-defined reports adequate? Are rigid schemas needed or must flexible documents or column families suffice? How large can this database scale both in storage and throughput while staying performant?
By factoring in these concerns before choosing a database technology, your next project will avoid costly rework down the line as data swells unexpectedly. Leverage the collective experience and innovation of open source communities to pick the optimal data platform.
