Since its introduction to the marketplace in 2000, the consistency, availability, and partition theorem, or CAP theorem, has been a guiding principle in database management. Computer scientist Eric Brewer presented the CAP theorem in a talk about distributed systems that provide web services. Two MIT professors later proved the theorem. It states that a database can be strong in one or two of three areas – database consistency, availability, and partition tolerance – but not in all three simultaneously. For instance, traditional SQL databases prioritize strong consistency but may compromise on availability during network failures. In contrast, NoSQL databases prioritize availability and partition tolerance but may accept eventual lapses in consistency. The CAP theorem describes an innate limitation of distributed systems and applies to various databases. It is important for designers to carefully consider which two CAP guarantees are most crucial for their organizations before implementing a database.
Distributed systems are spread across multiple computers and servers, offering a solution for dealing with massive amounts of data. Consistency in a distributed system refers to the degree to which data appears correctly and identically across nodes. It can be achieved by way of locks that prevent multiple users from making changes at once. Systems that prioritize consistency are reliable and robust. In a consistent system, each server delivers a response appropriate to the specified request. The meaning of consistency varies based on the type of service requested. Trivial and weakly consistent services, which require no coordination between servers or only minor coordination between servers, respectively, do not fall within the scope of the CAP theorem and generally avoid sacrificing availability and partition tolerance. However, any service that requires significant coordination between servers will incur CAP tradeoffs.
Availability refers to the ability of all nodes in a system to consistently be read or written. In an available system, all requests from the user reliably receive a response. Even if some nodes malfunction, an available system will continue to respond to user requests. However, systems that prioritize availability are often unable to guarantee that the data returned is fully up to date.
In partition-tolerant systems, data is distributed among multiple servers, increasing robustness in cases of partial failures and network splits. In a network partition, nodes are divided into multiple sub-nets that cannot easily communicate with one another. Partitions are generally viewed as inevitable in systems distributed over a wide area. A partition-tolerant system has the ability to recover quickly and maintain function in the face of these splits.
Finding the Right Database
Databases that prioritize consistency and availability, including Oracle and MySQL, are ideal for use cases like banking applications and transaction processing. In the past, systems prioritized consistency and availability, but as data systems and storage evolve, consistency is starting to recede in importance. Often, newer systems have use cases in which it is permissible for multiple users to make changes at once. In these cases, partition tolerance is the priority.
Databases that are consistent and partition tolerant, including MongoDB, Redis, and Google Spanner, are ideal for storing documents. Google Drive, for instance, utilizes Google Spanner, a consistency and partition tolerance (CP) database. The drawback to CP databases is that they may become unavailable during a network partition. Users of Google Drive, for example, occasionally lose access to their documents for short periods of time.
Meanwhile, databases that prioritize availability and partition tolerance are ideal for use cases where speed is most important, like data analytics operations. Netflix uses an availability and partition tolerance (AP) database called Cassandra, while Airbnb uses one known as Riak. AP databases sacrifice some consistency; a read operation may return an outdated value if the database is partitioned at the time of the read.
Because each database has unique strengths and weaknesses, selecting the best one requires a thorough understanding of an organization’s requirements and specific application. It is vital to establish clear service-level objectives (SLOs) ahead of time and regularly track service-level indicators (SLIs). Database scale, both at the time of implementation and in terms of potential for further growth, is a crucial consideration.
Another consideration is data sharding, in which data is broken up into segments and shared across servers. This can be advantageous for specific databases since it increases availability and partition tolerance and can make disaster recovery and backup easier. Sharding involves some sacrifices when it comes to consistency. Determining whether data sharding is appropriate is an important part of planning database design.
How to Minimize CAP Trade-Offs
Though no one database can provide perfect consistency, availability, and partition tolerance, there are several ways to mitigate CAP trade-offs. Database replication, in which data is continuously copied from a source database to others, improves availability and partition tolerance, even in databases that prioritize consistency. Hybrid architecture involves combining two different databases – for example, a relational database with a NoSQL database – to take advantage of the benefits of both designs while minimizing their drawbacks.
Partitioning a distributed system into segments allows systems to prioritize the elements of CAP that are most important for certain data or operations. Some architectures incorporate multiple separate databases for different use cases. For example, the online marketplace Etsy uses a MySQL database for strong consistency; high-availability Redis for in-memory caching; and Apache Kafka, which prioritizes partition tolerance, for streaming data.
Many new databases attempt to overcome the limitations described by the CAP theorem. CockroachDB is a distributed SQL database that uses a Raft consensus algorithm to ensure that all replicas of the database agree on the order of writes so that the database remains available even if some replicas fail. This allows CockroachDB to offer strong consistency and availability, even in the face of network partitions. TiDB, another newer distributed SQL database, utilizes a Raft consensus algorithm to provide strong consistency and availability for large-scale applications.
The Future of Database Design
New technologies and trends have the potential to further address the tradeoffs of the CAP theorem. Many database designers are shifting to cloud-based architectures, which have a number of advantages over on-premise databases, including scalability, elasticity, and fault tolerance. Multi-cloud deployments are capable of making hybrid database architectures easier to implement. Machine learning (ML) algorithms can be employed to dynamically adjust the balance between consistency and availability, based on workload patterns, application requirements, and data access patterns. The Tunable Availability and Consistency Trade-offs (TACT) system, developed by Haifeng Yu and Amin Vahdat, enables applications to continuously update the level of required consistency. Finally, as quantum computing matures, databases may incorporate quantum-resistant cryptographic techniques to ensure data integrity, confidentiality, and consistency.
The CAP theorem provides a useful framework for understanding the strengths and limitations of various databases. While no database is perfect, some are better suited than others for specific applications. Too often, businesses select databases based on availability or convenience. This can incur unnecessary costs without meeting the needs of the organization. Instead, it is important for developers to have a thorough understanding of database fundamentals and the details of their specific use cases. It is crucial to determine which of the three database properties is most important to the application and define specific service-level objectives and indicators before implementation. Although it may not be possible for a single database to deliver consistency, partition tolerance, and availability, hybrid and multi-database architecture can mitigate a database’s weaknesses. As emerging technologies and developments such as cloud computing and ML continue to impact the field of database design, the ways developers address CAP tradeoffs will also continue to evolve.