Availability in CAP Theorem: Always On, Always Responding

Understanding Availability (A)

Availability (A) in the CAP theorem means that every non-failing node in a distributed system returns a response for every request. This means that the system is always operational and responsive to client requests, even if some parts of the system are experiencing failures. An available system doesn’t necessarily guarantee that the response will contain the most up-to-date data (that’s Consistency’s job), but it does guarantee that a client will receive some response in a reasonable amount of time, rather than a timeout or an error indicating the system is down.

Availability is about continuous operation. In an available system, if you send a request, you will get a reply. It prioritizes keeping the service running and accessible to users, even if it means potentially serving slightly stale data during periods of network disruption or node failure.

Real-World Examples of Availability in Action

Social Media Feeds

When you open your social media app, you expect to see your feed immediately. While seeing the absolute latest post from every single friend might be ideal, it’s often more important that you see some feed, rather than a blank screen or an error message. If a specific server hosting a user’s latest posts goes down, an available social media system might serve slightly older content from a different replica or a cached version, ensuring you can still browse your feed without interruption. The priority is to keep you engaged with content.

Online Streaming Services

When you hit “play” on a movie or TV show, you expect it to start playing without delay. Streaming services are highly available. If one content delivery network (CDN) server goes down, your request is quickly rerouted to another healthy server that can provide the content. While there might be a tiny delay in switching, the service remains operational and delivers the media, even if it means pulling from a slightly less optimal location or a replica that might not have the very latest subtitle update. The paramount goal is uninterrupted playback.

Real-time Bidding (RTB) for Advertisements

In the ad tech world, ad requests come in millions per second. For an ad exchange, availability is critical. It must respond to an ad request within milliseconds to participate in the bidding process. If a particular server responsible for a niche targeting segment is temporarily unavailable, the system might still return an ad (even a generic one) from other available segments, rather than failing to respond at all. Missing a bid window means lost revenue, so responding quickly is prioritized over always having the absolute perfect ad.

DNS (Domain Name System)

When you type a website address into your browser, DNS servers translate that human-readable name into an IP address. DNS is designed for incredibly high availability. There are multiple layers of DNS servers distributed globally. If one local DNS server fails, your request will typically be routed to another, ensuring that website resolution continues uninterrupted. It’s far more critical to resolve a domain name (even if it’s slightly stale due to a recent change) than to have the entire internet stop working because a single DNS server went down.

Stateless Web Servers

Many modern web applications use stateless web servers behind a load balancer. If one server experiences an issue, the load balancer simply directs incoming requests to another healthy server. The user might not even notice a hiccup because their session state isn’t tied to a specific server. The website remains available and responsive, even if individual server instances are failing and being replaced.

E-commerce Product Browsing

When browsing an online store, customers expect immediate access to product catalogs, search results, and recommendations. While the exact inventory count might be slightly stale, it’s more important that customers can browse products, read reviews, and add items to their cart without interruption. The true inventory validation typically happens at checkout, but the browsing experience prioritizes availability.

The User Experience Factor

In essence, availability is about resilience and responsiveness. It ensures that your users can always interact with your system, even in the face of partial system failures, by prioritizing the ability to serve some response over guaranteeing absolute data freshness.

Database Choice for High Availability

Primary Recommendation: Apache Cassandra

For systems where continuous uptime and responsiveness are paramount, even if it means accepting eventual consistency, a NoSQL database specifically designed for high availability and partition tolerance like Apache Cassandra or Amazon DynamoDB (both wide-column stores) is an excellent choice.

Why Cassandra is ideal for availability:

• Peer-to-Peer Architecture: Built from the ground up for distributed environments with no single point of failure
• Multi-Node Replication: Data is replicated across multiple nodes, often with tunable consistency levels
• Continuous Operation: Always able to accept writes and serve reads, even when individual nodes or entire data centers fail or become partitioned
• Eventual Consistency: While data might not be immediately consistent across all replicas, it will converge over time
• Tunable Consistency: Can be configured for stronger consistency when needed, but strength lies in high availability
• Fault Tolerance: Individual node failures don’t impact overall system availability
• Geographic Distribution: Can operate across multiple data centers and regions

Key Features for Availability:

• Hinted Handoffs: Temporarily stores writes for unavailable nodes
• Read Repair: Fixes inconsistencies during read operations
• Anti-Entropy: Background process that ensures eventual consistency
• Multiple Consistency Levels: From “ANY” (highest availability) to “ALL” (highest consistency)

Alternative Options

Amazon DynamoDB:

• Fully managed NoSQL service with built-in high availability
• Automatic scaling and multi-region replication
• 99.999% availability SLA
• Ideal for serverless and cloud-native applications

MongoDB (with proper configuration):

• Replica sets provide automatic failover
• Sharding for horizontal scaling
• Can be configured for high availability over consistency

Redis Cluster:

• In-memory data structure store
• Automatic partitioning across multiple nodes
• Continues operating even when some nodes fail
• Excellent for caching and session storage

Ideal Use Cases for High-Availability Systems

• Social Media Platforms: User engagement more important than perfect consistency
• Content Delivery Networks: Global content distribution with eventual synchronization
• IoT Data Collection: Continuous sensor data ingestion
• Gaming Applications: Real-time gameplay and leaderboards
• Streaming Services: Uninterrupted media delivery
• Mobile Applications: Responsive user experiences with offline capabilities
• Analytics and Logging: High-volume data ingestion systems

Trade-offs to Consider

When choosing availability-focused databases, you accept that:

• Eventual Consistency: Data might be temporarily inconsistent across nodes
• Conflict Resolution: May need mechanisms to handle conflicting updates
• Complex Querying: Limited support for complex joins and transactions compared to SQL databases
• Data Modeling: Requires different approaches to schema design
• Monitoring Complexity: More nodes and components to monitor and maintain

The Business Impact

High availability systems are crucial for:

• Revenue Protection: Downtime directly impacts sales and user engagement
• User Experience: Users expect always-on services in today’s digital world
• Competitive Advantage: Reliability becomes a differentiating factor
• Global Operations: 24/7 operations across multiple time zones
• Scalability: Ability to handle growing user bases without service interruption

The key is understanding that for many modern applications—especially consumer-facing services, social platforms, and real-time systems—user experience and continuous operation often outweigh the need for perfect data consistency at every moment.