AWS Cuts Database Prices by Nearly 50% and Adds Distributed Scaling Capabilities

AWS expands the capabilities of its cloud database portfolio, while reducing costs for businesses.

During a session at AWS re:invents 2024 Today, the cloud giant introduced a series of cloud database innovations. These include the new Amazon Aurora DSQL distributed SQL database, global tables for the Amazon DynamoDB NoSQL database, and new multi-region capabilities for Amazon MemoryDB. AWS also detailed its overall database strategy and explained how the vector database functionally integrates to help enable generative AI applications. Along with the updates, AWS also revealed a series of price reductions, including reducing Amazon DynamoDB's on-demand prices by up to 50%.

Although the functionality of databases is attractive to database administrators, it is the practical utility that cloud databases offer that motivates AWS innovations. The new features are all part of an overall strategy to enable increasingly large and sophisticated workloads across distributed deployments. The AWS cloud database portfolio is also very focused on enabling demanding workloads in real time. During today's keynote, several AWS users, including United Airlines, BMW and the National Football League, explained how they use AWS cloud databases.

“We are committed to innovating and making databases effortless for you, the builders, so you can focus your time and energy on building the next generation of applications,” said Ganapathy (G2) Krishnamoorthy , vice president of database services at AWS, during the session. “The database is a critical part of your applications and is part of the bigger picture of our vision for data analytics and AI.

How AWS is rethinking distributed SQL with Amazon Aurora DSQL

The concept of a distributed SQL database is not new. With distributed SQL, a relational database can be replicated across multiple servers, or even across multiple geographic areas, to enable better availability and scalability. Several providers, including Google, Microsoft, CockroachDB, Yugabyte and ScyllaDB all have distributed SQL offerings.

AWS is now rethinking how distributed SQL architecture works with the goal of accelerating reads and writes for always-on applications. Krishnamoorthy explained that unlike traditional distributed databases that often rely on partitioning and leader assignment, Aurora DSQL implements a single leaderless architecture, allowing unlimited scaling.

The new database is built on Firecracker micro-virtual machine technology which powers AWS Lambda serverless technology. Amazon Aurora DSQL operates as a small ephemeral microservice that enables independent scaling of each system component: query processor, transaction system, and storage system.

Optimistic concurrency comes to distributed SQL cloud databases

With any distributed database technologythere is always a concern for consistency between authorities. The concept of eventual consistency is common in the database space, meaning there can be some latency in maintaining consistency.

It's a challenge that AWS aims to solve with an approach Krishnamoorthy calls “optimistic concurrency.” In this approach, all database actions run locally and only the transaction commit crosses the region. This ensures that a single transaction can never disrupt the entire application by keeping too many logs.

“We designed Aurora DSQL with optimistic concurrency at its core, no locks are needed for consistency or isolation,” Krishnamoorthy said.

How Amazon DynamoDB global tables improve consistency

AWS also brings strong consistency and global distribution to its DynamoDB NoSQL database.

DynamoDB global tables with strong consistency allow data written to a DynamoDB table to be persisted synchronously across multiple regions. Data written to the global table is written synchronously to at least two regions, and applications can read the most recent data from any region. This allows mission-critical applications to be deployed across multiple regions without any changes to the application code.

Among the many AWS users particularly excited about this new feature is United Airlines. In a video testimonial on AWS re:invent, the airline's CEO Sanjay Nayar explained how his organization uses AWS with more than 2,500 database clusters storing more than 15 petabytes of data, running millions of transactions per second. These databases power several critical aspects of the airline's operations.

United Airlines uses Amazon DynamoDB global tables as part of the company's headquarters system.

“We opted for DynamoDB global tables as our primary system for seat assignment due to its exceptional scalability and active-active, multi-region high availability, which delivers single-digit millisecond latency,” said Nayar. “This allows us to write and read seat assignments quickly and reliably, ensuring we always have the most up-to-date information. »

Amazon MemoryDB goes multi-region and helps NFL build gen AI apps

The Amazon MemoryDB in-memory database also gets a distribution update with new multi-region capabilities.

While AWS offers vector support in a series of its cloud databases, according to Jeff Carter, vice president of relational databases, non-relational databases and migration services at AWS, Amazon MemoryDB offers the most high level of performance. This is why the NFL (National Football League) uses the database to help create applications based on AI generation.

“We use MemoryDB for both short-term memory when executing queries and long-term memory to save successful queries to the vector store for use in future searches,” said Eric Peters, NFL director of media administration and post-production. “We can use these saved memories to guide new queries to get the next generation Statistics API results faster and more accurately. Over time, these memories of successful users accumulate to make the system smarter, faster and, ultimately, much cheaper.”