CAP and PACELC theorems

CAP theorem

The CAP theorem, also known as Brewer's theorem, is a fundamental concept in distributed systems design. It was introduced by Eric Brewer in 2000. The CAP theorem provides a framework for understanding the trade-offs between three essential properties of distributed systems: consistency, availability, and partition tolerance.

Before the introduction of the CAP theorem, distributed systems were primarily designed with a focus on consistency and availability, often ignoring partition tolerance. However, as distributed systems grew in scale and complexity, it became evident that addressing network partitions was crucial for ensuring reliable and fault-tolerant operation.

The CAP theorem states that it is impossible for a distributed system to simultaneously provide all three properties: consistency, availability, and partition tolerance. In other words, a distributed system can only guarantee two out of these three properties at any given time. The theorem highlights the inherent trade-offs that system designers must consider when building distributed systems.

• Consistency

  • A system is considered consistent if all nodes see the same data at the same time. This means that any read operation should return the most recent write operation's result, ensuring that the system maintains a single, up-to-date version of the data.

• Availability

  • A system is considered highly available if it continues to operate and respond to requests despite failures, ensuring that every request receives a response, either a success or an error.

• Partition Tolerance

  • A system is considered partition-tolerant if it can continue to operate and maintain its guarantees despite network partitions, which are situations where communication between nodes in the system is interrupted or lost.

The CAP theorem provides a useful guideline for understanding the limitations of distributed systems and making informed design decisions that balance the needs for consistency, availability, and partition tolerance.

We cannot build a general data store that is continually available, sequentially consistent, and tolerant to any partition failures. We can only build a system that has any two of these three properties. Because, to be consistent, all nodes should see the same set of updates in the same order. But if the network loses a partition, updates in one partition might not make it to the other partitions before a client reads from the out-of-date partition after having read from the up-to-date one. The only thing that can be done to cope with this possibility is to stop serving requests from the out-of-date partition, but then the service is no longer 100% available.

CAP theorem components

The CAP theorem revolves around three key properties of distributed systems: Consistency, Availability, and Partition Tolerance. Each of these properties plays a vital role in determining the behavior of a distributed system under various conditions.

Consistency

Consistency in distributed systems refers to the degree to which the system maintains a single, up-to-date version of the data. There are different levels of consistency, depending on the requirements of the system.

• Strong consistency

  • In a strongly consistent system, all nodes see the same data at the same time. Any read operation returns the most recent write operation's result, ensuring that the system maintains a single, coherent version of the data. Strong consistency is desirable for applications that require accurate and up-to-date information at all times, such as financial transactions or inventory management systems.

• Eventual consistency

  • In an eventually consistent system, nodes may temporarily have different versions of the data, but they will eventually converge to the same, consistent state. Eventual consistency is suitable for applications where slight inconsistencies can be tolerated for short periods, such as social media updates or user profiles. This model offers better availability and performance compared to strong consistency, at the cost of temporary data inconsistencies.

Availability

Availability refers to the ability of a distributed system to continue operating and responding to requests despite failures or partial outages. Highly available systems ensure that every request receives a response, either a success or an error, without significant delays.

• High availability
  • High availability is achieved by replicating data across multiple nodes and designing the system to handle failures gracefully. In a highly available system, the loss of individual nodes does not cause a significant impact on the overall operation, as other nodes can continue to serve requests.

Partition tolerance

Partition tolerance is a critical property of distributed systems, as it determines the system's ability to handle network partitions and communication failures.

• Network partitioning

  • In a distributed system, nodes communicate with each other over a network. Network partitions occur when communication between some or all nodes is interrupted or lost. This can be caused by various reasons, such as hardware failures, network congestion, or configuration issues.

• Handling partition failures

  • Partition-tolerant systems are designed to handle network partitions gracefully and continue to operate without compromising their guarantees. This often involves strategies such as data replication, fallback mechanisms, and automatic recovery processes. However, as the CAP theorem states, it is impossible to guarantee consistency, availability, and partition tolerance simultaneously, so system designers must make trade-offs based on the specific requirements of their application.

CAP theorem tradeoffs

When designing distributed systems, architects and engineers need to make informed decisions about the trade-offs between the three properties of the CAP theorem: consistency, availability, and partition tolerance. Understanding these trade-offs is crucial for building systems that meet the desired performance, reliability, and user experience goals.

As the CAP theorem states, it is impossible for a distributed system to simultaneously provide consistency, availability, and partition tolerance. This means that system designers must choose which two of these properties are most important for their specific application and make compromises on the third property. For example, some systems may prioritize consistency and partition tolerance (CP) over availability, while others may prioritize availability and partition tolerance (AP) over consistency.

CAP theorem and distributed systems

• Partition Tolerance

  • This is a necessity in any distributed system. It refers to the system's ability to continue operating despite network partitions or communication breakdowns between nodes in the system.

• Trade-offs

  • If a system chooses Consistency and Partition Tolerance (CP), it may sacrifice availability, meaning that if a network partition happens, some users might not be able to access the data until the partition is resolved.
  • If a system chooses Availability and Partition Tolerance (AP), it can lead to temporary inconsistencies in the system, where not all nodes have the same data at the same time.

Selecting the right trade-offs

To make the best decisions regarding the trade-offs in a distributed system, consider the following factors:

• Application requirements

  • What are the specific needs of your application? Does it require real-time data consistency, or can it tolerate eventual consistency? Is high availability a critical requirement, or can the system afford to experience occasional downtime?

• Data access patterns

  • How is the data accessed and updated in your system? Are read operations more frequent than write operations, or vice versa? Understanding the data access patterns can help you optimize the system for performance and consistency.

• Failure scenarios

  • Consider the possible failure scenarios and their impact on your system. What are the risks associated with network partitions, node failures, or data corruption? How can your system handle these failures while maintaining its guarantees?

• Scalability

  • How will your system scale as the number of users, requests, and data volume grows? Consider the scalability implications of your chosen trade-offs and how they will impact the system's performance and reliability.

Examples of CAP theorem

Consistency and partition tolerance (CP) systems

Some distributed systems prioritize consistency and partition tolerance over availability. In these systems, the focus is on ensuring that all nodes have the same data at the same time, even if it means sacrificing some availability during network partitions or node failures.

Example: Google's Bigtable

Bigtable is a distributed storage system used by Google to manage structured data. It is designed to provide strong consistency, ensuring that all nodes see the same data at the same time. To achieve this, Bigtable uses a single-master architecture, where a master node coordinates all write operations. During network partitions or master node failures, the system sacrifices availability to maintain consistency and partition tolerance.

Availability and partition tolerance (AP) systems

Some distributed systems prioritize availability and partition tolerance over consistency. These systems are designed to remain operational and responsive to user requests even during network partitions or node failures, at the cost of potentially serving stale or inconsistent data.

Example: Amazon's DynamoDB

DynamoDB is a managed NoSQL database service provided by Amazon Web Services (AWS). It is designed to provide high availability and partition tolerance by using a multi-master architecture and allowing eventual consistency. In this system, nodes can accept write operations independently, even during network partitions. However, this design may lead to temporary inconsistencies as data eventually converges across nodes.

Consistency and availability (CA) systems

While the CAP theorem implies that a distributed system must sacrifice either consistency or availability in the presence of network partitions, some systems prioritize consistency and availability in environments where network partitions are rare or can be quickly resolved.

Example: Traditional Relational Databases

Traditional relational databases, such as MySQL or PostgreSQL, are often designed with a focus on consistency and availability. These systems use transactions and ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure data consistency. However, they are typically not built to handle network partitions gracefully and may experience reduced availability or performance during such events.

Beyond CAP theorem

While the CAP theorem has been fundamental to distributed systems design, it is essential to recognize that it represents a simplified view of the trade-offs in distributed systems. In recent years, researchers and practitioners have started exploring more nuanced ways of understanding and addressing the challenges in distributed systems.

ECAP model (Extended CAP)

The Extended CAP model expands the original CAP theorem by considering latency as a fourth dimension. The ECAP model posits that it is impossible to optimize for all four properties—consistency, availability, partition tolerance, and latency—simultaneously. In this model, system designers must choose which three properties to prioritize, based on the requirements and constraints of their specific application.

PACELC theorem

We cannot avoid partition in a distributed system, therefore, according to the CAP theorem, a distributed system should choose between consistency or availability. ACID (Atomicity, Consistency, Isolation, Durability) databases, such as RDBMSs like MySQL, Oracle, and Microsoft SQL Server, chose consistency (refuse response if it cannot check with peers), while BASE (Basically Available, Soft-state, Eventually consistent) databases, such as NoSQL databases like MongoDB, Cassandra, and Redis, chose availability (respond with local data without ensuring it is the latest with its peers).

The PACELC theorem is another extension of the CAP theorem that takes latency and consistency trade-offs into account. PACELC stands for "Partition (P), Availability (A), Consistency (C), Else (E), Latency (L), Consistency (C)." This theorem states that in case of a network partition, a system must choose between availability and consistency (similar to CAP), but when the system is operating normally (no partitions), it must choose between latency and consistency. This highlights the fact that trade-offs exist even in the absence of network partitions.

Examples:

• Dynamo and Cassandra are PA/EL systems
  • They choose availability over consistency when a partition occurs; otherwise, they choose lower latency.
• BigTable and HBase are PC/EC systems
  • They will always choose consistency, giving up availability and lower latency.
• MongoDB can be considered PA/EC (default configuration)
  • MongoDB works in a primary/secondaries configuration. In the default configuration, all writes and reads are performed on the primary. As all replication is done asynchronously (from primary to secondaries), when there is a network partition in which primary is lost or becomes isolated on the minority side, there is a chance of losing data that is unreplicated to secondaries, hence there is a loss of consistency during partitions. Therefore it can be concluded that in the case of a network partition, MongoDB chooses availability, but otherwise guarantees consistency. Alternately, when MongoDB is configured to write on majority replicas and read from the primary, it could be categorized as PC/EC.

CRDTs and hybrid systems

Convergent Replicated Data Types (CRDTs) are data structures designed to allow multiple replicas to be updated independently and converge to a consistent state without requiring coordination. CRDTs can help system designers achieve both strong eventual consistency and high availability. By combining CRDTs with other techniques, it is possible to build hybrid systems that provide tunable consistency guarantees, enabling applications to make trade-offs based on their specific requirements.

Application-specific trade-offs

The CAP theorem and its extensions provide valuable insights into the fundamental trade-offs in distributed systems design. However, it is crucial to remember that real-world systems often involve more complex and application-specific trade-offs. As a system designer, it is important to understand the unique requirements and constraints of your application and make informed decisions about the trade-offs that best meet those needs.

Summary

While the CAP theorem has been foundational to understanding the trade-offs in distributed systems, it is essential to explore and consider more nuanced models and techniques to design systems that effectively address the challenges and requirements of modern applications.