Relational databases have been around for quite a long time and SQL is a universally understood, widely popular, simplified data retrieval and manipulation language. Still today a number of globally popular applications rely on relational databases at the back end. Traditional relational databases are monolithic in nature confined to one geographic region. This creates two unique challenges – limitations in scaling, and latency in querying from SQL servers in other geographic regions.
This is where distributed SQL becomes useful breaking the geographical barriers and limitations of scaling by leveraging the power of a cloud computing network.
What is Distributed SQL?
A Distributed SQL database is an SQL-compatible single logical relational database deployed across a cluster of network servers. They combine the core features of SQL and NoSQL systems and provide ACID transactional support across data centers, availability zones, and regions in the cloud. Examples of modern distributed SQL databases include CockroachDB, Google Cloud Spanner, and Amazon Aurora Distributed SQL.
How Does a Distributed SQL Database Function?
Imagine a cluster of servers spread across different geographies but connected over a cloud computing network to present as a single logical relational database to an external user, this is what a distributed SQL looks like in layman’s terms. The good part is since it is cloud native there is no limitation in spinning up these units of databases and in preferred geographic regions overcoming scaling and latency challenges.
The servers in a DSQL (Distributed SQL) are called nodes that communicate with each other to form a network cluster spanning across a data center or across different geographic locations. Scaling requirements can be fulfilled by just adding some nodes.
Each node can be divided into the following two layers:
The distributed data storage layer
Data in a distributed SQL database is automatically sharded across multiple nodes to prevent a single node from becoming a bottleneck. The storage layer replicates data synchronously across multiple nodes using the Raft consensus protocol. Additionally, the database storage layer actively supports distributed ACID transactions by modifying multiple rows spread across shards on different nodes. This ensures absolute data integrity and safety.
The API layer
The API/query processing layer compiles, executes, and optimizes language-specific queries and commands. The SQL API in this layer facilitates queries with the relational data which is then automatically distributed across multiple nodes of the database cluster by the distributed data storage layer.
Differences Between SQL and Distributed SQL
While traditional SQL databases have long been a staple in data management, distributed SQL databases are emerging as a powerful alternative that addresses the limitations of monolithic systems.
Resilience to Failures
Distributed SQL databases are designed with resilience in mind. Unlike monolithic SQL databases, which can be vulnerable to single points of failure, distributed SQL systems replicate data across multiple nodes. This redundancy ensures that critical data and applications remain accessible even in the event of hardware failures or network issues, providing a more robust solution for enterprises that require high availability.
Horizontal Scalability
One of the standout features of distributed SQL databases is their ability to scale horizontally. This means that as workload demands increase or decrease, organizations can easily add or remove nodes without significant reconfiguration. This flexibility supports business growth by allowing enterprises to efficiently manage resources in response to changing demands, unlike traditional SQL systems that often require vertical scaling, which can be costly and complex.
Geo-Distributed Cluster Topology
Distributed SQL databases support geo-distributed cluster topologies that span multiple regions and cloud providers. This capability enables organizations to deliver an always-on, consistent experience to users around the globe. By distributing data closer to where users are located, businesses can reduce latency and improve performance, ensuring a seamless experience regardless of geographical location.
High Level of SQL Compatibility
Despite their distributed nature, many distributed SQL databases provide a high level of compatibility with standard SQL features and functionality. This means that organizations can leverage their existing SQL knowledge and tools while benefiting from the advanced capabilities of distributed systems. This compatibility reduces the learning curve for teams transitioning from traditional databases.
Integration with Container and Kubernetes Environments
Distributed SQL databases align well with modern development practices by matching database architecture with container and Kubernetes environments. This integration enhances business agility by enabling developers to deploy applications rapidly and scale them dynamically in response to user needs, fostering a more responsive development cycle.
Improved Data Visibility and Real-Time Analysis
With distributed SQL databases, organizations gain enhanced data visibility and the ability to perform real-time data analysis across geographically dispersed datasets. This capability not only improves operational insights but also helps reduce security risks by enabling better monitoring of data access patterns and anomalies.
Differences Between NoSQL and Distributed SQL
NoSQL and distributed SQL databases are both powerful tools for managing data, but they have distinct characteristics. Let’s break down their differences in an engaging way.
Data Model
NoSQL databases thrive on flexibility. They use non-relational models like key-value, document-oriented, wide-column, or graph structures. This allows for diverse data types and formats. On the other hand, distributed SQL databases stick to a relational model. They support SQL queries, making them familiar and accessible for users who are accustomed to traditional database systems.
Scalability
Both NoSQL and distributed SQL databases can scale horizontally, meaning they can expand across multiple nodes. However, they do this differently. NoSQL databases often allow for rapid scaling without much overhead, which is great for handling large volumes of unstructured data. Distributed SQL databases also scale efficiently but do so while maintaining strong consistency and transactional integrity.
Consistency
When it comes to consistency, NoSQL databases often prioritize availability and partition tolerance over strict consistency. This means that data may not always be uniform across all nodes at any given moment. In contrast, distributed SQL databases focus on maintaining consistency. They employ distributed transactions and quorum-based replication to ensure that all nodes reflect the same data state.
Query Language
NoSQL databases typically do not support SQL. Instead, they offer unique query languages or APIs tailored to their specific models, such as CQL for Cassandra. Distributed SQL databases embrace SQL, allowing integration with existing applications and tools that rely on standard SQL syntax.
Amazon Aurora Distributed SQL
To quote Andy Jassy, President and CEO, Amazon “Most relational databases wanted a multi-regional database… low latency… high availability, a strong consistency, and zero operational burden… SQL compatible.” Amazon Aurora DSQL is a serverless distributed SQL database offering seamless scalability, high availability, and simplified operations without infrastructure management. It delivers lightning-fast SQL reads and writes, adapting effortlessly to workloads without requiring sharding or instance upgrades. Its active-active architecture ensures strong data consistency, with availability ratings of 99.99% for single-Region and 99.999% for multi-Region deployments. The serverless design eliminates maintenance tasks like patching and downtime. PostgreSQL-compatible, Aurora DSQL provides developers with a straightforward and efficient experience.
Benefits of Aurora DSQL
Aurora Distributed SQL (DSQL) is revolutionizing the way businesses manage their databases. Here’s why it stands out:
Virtually Unlimited Scale
With Aurora DSQL, scaling is a breeze. It effortlessly handles any workload demand without the hassle of database sharding or instance upgrades. You can independently scale reads, writes, storage, and compute resources. This means your database grows with your business, adapting to your needs in real-time.
Always Available Applications
Build applications that never go down. Aurora DSQL ensures application resiliency by maintaining strong consistency and durability for all reads and writes across any regional endpoint. With an impressive availability rate of up to 99.999%, you can trust that your applications will remain operational, even in the face of failures.
No Infrastructure Management
Say goodbye to the headaches of infrastructure management. Aurora DSQL removes the need for provisioning, patching, or upgrading servers. It automatically handles updates without downtime, ensuring zero impact on application performance. Focus on innovation instead of maintenance.
Easy to Use
Creating a new database has never been simpler. Aurora DSQL is PostgreSQL-compatible, providing a user-friendly experience for developers. You can set up a new database in just a few quick steps, allowing you to get your applications up and running faster than ever.
Conclusion
To meet the demands of modern applications, organizations require highly scalable and always-on database solutions. Traditional systems often fall short, especially for real-time, high-demand workloads. Distributed SQL databases provide a robust alternative, combining the strengths of relational databases with the scalability and resilience of cloud-native architectures. They enable developers, architects, and operators to handle the most demanding workloads with ease, ensuring exceptional user experiences. By adopting distributed SQL, organizations can future-proof their applications while maintaining enterprise-grade performance and reliability.
