Distributed SQL
Distributed SQL (sometimes called NewSQL) refers to a class of database systems that combine traditional SQL capabilities with horizontal scalability and strong consistency guarantees. These systems are designed to handle high-throughput transaction processing while maintaining ACID compliance across distributed nodes.
Core characteristics of distributed SQL
Distributed SQL databases are built to address the limitations of both traditional relational databases and distributed time-series database systems. Key features include:
- Automatic sharding and replication
- Distributed transaction processing
- Strong consistency guarantees
- Horizontal scalability
- SQL compatibility
- High availability through redundancy
Architecture and components
A typical distributed SQL system consists of:
The system distributes data and processing across multiple nodes while maintaining transactional consistency.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Performance considerations
Distributed SQL systems must balance several performance factors:
- Network latency between nodes
- Transaction coordination overhead
- Data consistency requirements
- Query optimization across shards
- Resource utilization across the cluster
Use cases in financial systems
Financial institutions often employ distributed SQL for:
- Real-time transaction processing
- Market data management
- Trade lifecycle tracking
- Risk management systems
- Regulatory reporting
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Consistency models
Distributed SQL databases typically implement:
- Strict serializability
- Linearizable consistency
- External consistency guarantees
- Snapshot isolation levels
- Causal consistency
Scaling and partitioning
These systems handle scale through:
- Automatic data sharding
- Dynamic node addition/removal
- Intelligent load balancing
- Distributed query planning
- Parallel query execution
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Integration with time-series workloads
While distributed SQL databases excel at transactional workloads, they can complement specialized time-series databases for:
- Historical data archival
- Time-series analytics
- Real-time data processing
- Hybrid operational/analytical processing
Best practices for implementation
When deploying distributed SQL systems:
- Design for appropriate partition keys
- Consider network topology
- Plan for disaster recovery
- Monitor cluster health
- Optimize for workload patterns
- Implement proper backup strategies
Future trends
The evolution of distributed SQL continues with:
- Enhanced cloud-native capabilities
- Improved automation and self-tuning
- Advanced multi-region support
- Integration with streaming systems
- Enhanced security features