Distributed SQL represents a significant evolution in database technology, designed to meet the needs of modern cloud applications. Traditional SQL databases were built for data consistency, vertical scalability, and tight integration, which worked well for monolithic applications on single-server environments. However, as the paradigm shifted to distributed applications in the cloud, these traditional databases began to show limitations.
SQL (Structured Query Language) and NoSQL (Not Only SQL) databases serve different purposes and have distinct characteristics. SQL databases, such as CockroachDB, MySQL, and PostgreSQL, are relational databases that use structured query language for defining and manipulating data. They are known for their ACID (Atomicity, Consistency, Isolation, Durability) properties, which ensure reliable transactions and data integrity. SQL databases are ideal for complex queries and transactions, supporting structured data with predefined schemas and relationships.
On the other hand, NoSQL databases, like MongoDB and Cassandra, are designed to handle unstructured or semi-structured data. They may offer flexible schemas, allowing for rapid development and iteration. NoSQL databases are often used for large-scale data storage and real-time web applications due to their ability to scale horizontally and handle high volumes of read and write operations. They typically provide eventual consistency rather than the strong consistency guaranteed by SQL databases.
Modern applications require horizontal scalability, elasticity, and support for microservices. Traditional single-node relational databases, with their fixed schemas and lack of support for distributed data models, are not suited for these needs. Distributed SQL databases address these challenges by combining the consistency and structure of early relational databases with the scalability, survivability, and performance first pioneered in NoSQL databases.
Horizontally scalable: Distributed SQL databases can seamlessly scale to mirror the capabilities of cloud environments without introducing operational complexity. They distribute data across multiple nodes, ensuring efficient resource utilization.
Consistency: These databases deliver high levels of isolation in distributed environments, mediating contention and ensuring transactional consistency across multiple operators.
Resilience: Distributed SQL databases provide inherent resilience, reducing recovery times to near zero and replicating data naturally without external configuration.
Geo-replication: They allow for the distribution of data across geographically dispersed environments, ensuring low latency access and compliance with data sovereignty requirements.
In addition to the unique capabilities of distributed SQL, these databases must also meet foundational requirements such as:
Operational efficiency: Easy installation, configuration, and control of the database environment.
Optimization: Advanced features like cost-based optimizers for query performance.
Security: Key capabilities for authentication, authorization, and accountability.
Integration: Compatibility with existing applications, ORMs, ETL tools, and more.
Distributed SQL databases address these challenges by combining the consistency and structure of relational databases with the scalability, survivability, and performance first pioneered in NoSQL databases. They distribute data evenly across multiple nodes, ensuring efficient resource utilization and high availability. These databases deliver high levels of isolation in distributed environments, mediating contention and ensuring transactional consistency across multiple operators. Additionally, they provide inherent resilience, reducing recovery times to near zero and replicating data naturally without external configuration.
CockroachDB, for example, is a distributed SQL database that uses a transactional and strongly-consistent key-value store. It scales horizontally, is incredibly resilient against various kinds of outages, including disk, machine, rack, and even datacenter failures with minimal latency disruption and no manual intervention. CockroachDB supports strongly-consistent ACID transactions and provides a familiar SQL API for structuring, manipulating, and querying data. It ensures data consistency in a distributed environment by guaranteeing serializable SQL transactions, the highest isolation level defined by the SQL standard, using the Raft consensus algorithm for writes and a custom time-based synchronization algorithm for reads to guarantee strong data consistency.
Several databases meet the requirements of distributed SQL, including Google Spanner, Amazon Aurora, Yugabyte, FaunaDB, and CockroachDB. These databases offer various levels of support for the core capabilities mentioned above, and therefore are more appropriate for different use cases.
Amazon Aurora: Amazon Aurora is a cloud-based relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora is often described as a distributed database, but it does not scale for writes, and therefore is not truly distributed. Aurora is designed to provide high availability and durability, with automatic failover and replication across multiple Availability Zones. It supports MySQL and PostgreSQL and offers features like automatic scaling and serverless options.
CockroachDB: CockroachDB is a distributed SQL database designed for global, cloud-native applications. CockroachDB is PostgreSQL compatible, so that most applications built on PostgreSQL can be migrated without changing the application code. CockroachDB provides strong consistency, horizontal scalability, and high availability. CockroachDB supports ACID transactions and a familiar SQL interface, making it easy to use for developers. It survives various types of failures, including mechanical, data center, region, and even cloud failures, with minimal latency disruption.
FaunaDB: FaunaDB is a distributed, multi-model database that provides strong consistency, ACID transactions, and a flexible data model. It is designed for serverless applications and offers a globally distributed architecture that ensures low-latency access to data. FaunaDB supports GraphQL and FQL (Fauna Query Language) for querying data.
Google Spanner: Google Spanner is a globally distributed database service that provides strong consistency and horizontal scalability. It uses a combination of hardware (atomic clocks) and software to achieve global consistency and high availability. Spanner supports SQL queries and is designed to handle large-scale, mission-critical applications.
YugabyteDB: YugabyteDB is an open-source, distributed SQL database. It supports both SQL and NoSQL workloads, making it versatile for various use cases.
When evaluating a distributed SQL database, it is essential to consider several core requirements to ensure it meets the needs of modern applications. First, assess the database's scalability. A distributed SQL database should be able to scale horizontally, distributing data evenly across multiple nodes to handle increased loads without compromising performance. This capability is crucial for applications that experience variable or growing workloads, and can support businesses at any stage of growth, scaling as demand scales. Additionally, the database should support strong consistency, ensuring that all nodes reflect the same data state, which is vital for maintaining data integrity across distributed environments.
Another critical factor is high availability and resilience. The database should be designed to handle failures gracefully, whether they occur at the disk, machine, rack, or even datacenter level, with minimal disruption to operations. This includes features like automatic failover, data replication, and quick recovery times.
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To learn more about inherently resilient systems, check out this webinar hosted by Cockroach Labs’ CTO and CPO, Peter Mattis and Technical Evangelist, Rob Reid: “The Always-On Dilemma: Disaster Recovery vs. Inherent Resilience.”
Geo-replication is also important, allowing data to be distributed across multiple geographic locations to reduce latency and comply with data sovereignty requirements.
Finally, consider operational efficiency, security, and integration capabilities. The database should be easy to install, configure, and manage, offer robust security features for authentication and authorization, and integrate seamlessly with existing applications and tools.
A mature distributed SQL database should meet all these requirements, ensuring it is suitable for business-critical applications.
Distributed SQL databases are utilized across various verticals to address specific industry challenges and requirements.
For example, check out this presentation by Netflix engineers who provide CockroachDB-as-a-Service (DBaaS) to Netflix developers with a variety of use-cases:
Here are some examples of vertical use cases for distributed SQL databases:
In the financial sector, distributed SQL databases are crucial for applications such as payment processing, trading platforms, and identity management. These applications require strong consistency, high availability, and the ability to handle high transaction volumes. For instance, payment systems must ensure accurate and timely transactions, while trading platforms need to process large volumes of trades with minimal latency. Identity management systems benefit from distributed SQL databases by providing secure and consistent access to user data across multiple regions.
Retail and eCommerce companies leverage distributed SQL databases for order and inventory management. These systems must handle large spikes in traffic, such as during Black Friday or Cyber Monday, without compromising performance (or overselling products). Distributed SQL databases provide horizontal scalability, ensuring that the system can manage increased demand. They also offer strong consistency, which is essential for maintaining accurate inventory levels and processing transactions reliably. Additionally, these databases support multi-region deployments, allowing retailers to provide low-latency access to customers worldwide.
The gaming industry uses distributed SQL databases to manage user accounts, in-game transactions, and real-time data processing. Gaming platforms often experience high concurrency with thousands of players interacting simultaneously. Distributed SQL databases ensure that user data is consistent and available, even during peak usage times. They also support the scalability needed to accommodate growing user bases and the resilience required to maintain uptime during unexpected failures.
In logistics and supply chain management, distributed SQL databases are used for scheduling, workflow management, and tracking systems. These applications require precise coordination and data integrity to ensure timely deliveries and efficient operations. Distributed SQL databases provide the high availability and fault tolerance needed to prevent disruptions in logistics workflows. They also support geo-replication, which helps in maintaining data consistency across different geographic locations.
These examples illustrate how distributed SQL databases can be tailored to meet the specific needs of various industries, providing the scalability, consistency, and resilience required for modern applications.
Distributed SQL is the future of database management in the cloud, offering the scalability, consistency, and resilience needed for modern applications. CockroachDB, among others, exemplifies these capabilities, making it a strong candidate for organizations looking to transition to cloud-native distributed SQL databases.
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Distributed SQL refers to a class of relational databases that distribute data across multiple nodes to ensure high availability, fault tolerance, and scalability. These databases maintain SQL capabilities while leveraging a distributed architecture to handle large-scale, geographically dispersed data.
Traditional SQL databases are typically monolithic, meaning they run on a single server. Distributed SQL databases, on the other hand, spread data across multiple servers or nodes. This distribution allows for better performance, scalability, and resilience against failures.
Distributed SQL databases offer several benefits, including scalability, high availability, and resilience against mechanical failures. Distributed SQL databases combine the consistency and structure of early relational databases with the scalability, survivability, and performance first pioneered in NoSQL databases.
CockroachDB is a distributed SQL database that uses a transactional and strongly-consistent key-value store. It scales horizontally, survives various types of failures, including mechanical, data center, region, and even cloud failures with minimal latency disruption and no manual intervention. CockroachDB supports strongly-consistent ACID transactions and provides a familiar SQL API for structuring, manipulating, and querying data.
CockroachDB guarantees serializable SQL transactions, the highest isolation level defined by the SQL standard. It uses the Raft consensus algorithm for writes and a custom time-based synchronization algorithm for reads to ensure consistency between replicas.
Distributed SQL is ideal for many use cases including for distributed customer bases as you can bring the data closer to the customer, businesses that handle a high transaction volume, and businesses that handle spiky workloads. For example, this can include financial institutions, retailers or eCommerce businesses, and gaming platforms.
CockroachDB supports SQL syntax and scales easily without the manual complexity of sharding, rebalances and repairs itself automatically, and distributes transactions seamlessly across your cluster. It provides strong consistency and supports distributed transactions, unlike many other distributed databases.