Beyond CAP: Unveiling the PACELC Theorem for Modern Systems

Distributed systems are the backbone of modern computing, powering everything from cloud platforms to e-commerce applications. While the CAP theorem provided a foundational understanding of trade-offs in distributed systems, it left out critical considerations for normal operations. The PACELC theorem, introduced by Daniel J. Abadi, fills this gap by addressing trade-offs not only during network partitions but also during regular operation. This blog dives deep into PACELC, its implications, and its real-world applications.

---

The Limitation of CAP

The CAP theorem states that in the event of a network partition (P), distributed systems must choose between Consistency (C) and Availability (A). However, CAP does not address trade-offs when there is no partition, leaving out a critical aspect of system design—performance under normal conditions.

Below table summarizes where popular databases stand w.r.to CAP theorem

Database	Consistency	Availability	Partition Tolerance	Comments
MongoDB	Eventual	✅	✅	Popular for its flexibility and scalability
Cassandra	Eventual	✅	✅	Designed for high availability and scalability
Redis	Strong	✅	❌	Often used for caching and real-time analytics
Couchbase	Eventual	✅	✅	Combines the best of SQL and NoSQL
HBase	Strong	✅	✅	Built on top of Hadoop for big data
Amazon DynamoDB	Eventual	✅	✅	Fully managed, serverless key-value database
MySQL Cluster	Strong	✅	✅	High availability and scalability
PostgreSQL	Strong	✅	❌	Known for its robustness and feature set
Neo4j	Strong	✅	❌	Graph database for connected data
Riak	Eventual	✅	✅	Designed for fault tolerance and availability
VoltDB	Strong	✅	❌	In-memory database for high-speed transactions
CouchDB	Eventual	✅	✅	Focuses on ease of use and replication
Zookeeper	Strong	✅	❌	Coordination service for distributed systems

Why Is This Problematic?

• Latency Matters: In real-world applications, latency (response time) is often as critical as availability and consistency.
• Everyday Trade-offs: Even without partitions, distributed systems must balance consistency and latency to meet user expectations.

---

PACELC’s Solution

PACELC extends CAP by introducing a second trade-off: when there is no partition (Else mode), systems must choose between Latency (L) and Consistency (C). This dual-layered approach ensures that both failure scenarios and normal operations are considered.

---

How does PACELC work?

The PACELC theorem expands on CAP by introducing two operational modes:

1. Partition Mode (PAC): During network partitions, systems face the same trade-off as CAP—availability vs. consistency.
2. Else Mode (ELC): When there are no partitions, systems face a trade-off between latency and consistency.

Key Components

• P: Partition Tolerance
• A: Availability
• C: Consistency
• E: Else (no partition)
• L: Latency This framework categorizes distributed systems into four configurations:
  1. PA/EL: Prioritize availability during partitions; prioritize low latency otherwise.
  2. PA/EC: Prioritize availability during partitions; prioritize strong consistency otherwise.
  3. PC/EL: Prioritize consistency during partitions; prioritize low latency otherwise.
  4. PC/EC: Prioritize consistency at all times.

---

PACELC vs CAP: A Comparison

Aspect	CAP Theorem	PACELC Theorem
Focus	Trade-offs during network partitions	Trade-offs during both partitions and normal operations
Properties	Consistency (C), Availability (A), Partition Tolerance (P)	Consistency (C), Availability (A), Latency (L), Partition Tolerance (P)
Modes	Single mode: Partition scenarios	Dual mode: Partition scenarios + Normal operations
Example Systems	DynamoDB, Cassandra	DynamoDB, BigTable, MongoDB

Key Difference:

While CAP focuses exclusively on handling failures due to partitions, PACELC adds nuance by addressing performance trade-offs under normal conditions, making it more comprehensive for modern distributed systems.

---

Trade-Offs Between Latency and Consistency in Real-World Applications

In distributed systems operating without partitions, the primary trade-off is between latency and consistency:

1. Consistency Requires Coordination:
   • Strong consistency ensures that all users see the same data simultaneously.
   • Achieving this requires coordination between nodes, which increases response time.
   • Example: Financial systems like stock trading platforms prioritize consistency to ensure accurate data but accept higher latency.
2. Low Latency Relaxes Consistency:
   • Low-latency systems prioritize speed by allowing eventual consistency.
   • These systems respond quickly but may return stale or inconsistent data.
   • Example: Social media platforms like Twitter often prioritize low latency to deliver fast user experiences.

Use Cases for Each Trade-Off:

• Applications requiring accurate data (e.g., banking) lean toward strong consistency.
• Applications prioritizing user experience (e.g., gaming) lean toward low latency. By explicitly incorporating these trade-offs into system design, PACELC enables architects to optimize for specific application requirements.

---

Real-World Applications of PACELC

1. Cloud Computing Cloud providers like AWS design their services using PACELC principles:
2. DynamoDB operates as a PA/EL system to ensure high availability and low latency for global-scale applications.
3. Google Spanner follows PC/EC principles to maintain strong consistency across geographically distributed nodes.
4. E-Commerce Platforms E-commerce platforms like Amazon prioritize availability to ensure uninterrupted user access but balance this with consistent inventory records using PA/EC configurations.
5. Online Gaming Gaming platforms often prioritize low latency over strict consistency to provide seamless gameplay experiences under normal conditions.
6. Financial Services Financial databases prioritize strong consistency over availability or latency to ensure compliance with regulations and accurate transaction records.

Below is a table showing where some popular databases stand with respect to the PACELC theorem:

Database	Partition Tolerance	Availability	Consistency	Else Latency	Comments
MongoDB	✅	✅	Eventual	Latency	Prioritizes availability and latency over consistency
Cassandra	✅	✅	Eventual	Latency	Optimized for availability and low latency
Redis	❌	✅	Strong	Consistency	Prefers consistency over partition tolerance
Couchbase	✅	✅	Eventual	Latency	Balances availability and latency, less focus on consistency
HBase	✅	✅	Strong	Consistency	Ensures strong consistency, even at the cost of latency
Amazon DynamoDB	✅	✅	Eventual	Latency	Focuses on availability and low latency
MySQL Cluster	✅	✅	Strong	Consistency	Maintains strong consistency, may impact latency
PostgreSQL	❌	✅	Strong	Consistency	Prioritizes consistency, not designed for partition tolerance
Neo4j	❌	✅	Strong	Consistency	Prefers consistency, not optimized for partition tolerance
Riak	✅	✅	Eventual	Latency	Designed for high availability and low latency
VoltDB	❌	✅	Strong	Consistency	Focuses on strong consistency, less on partition tolerance
CouchDB	✅	✅	Eventual	Latency	Emphasizes availability and latency over consistency
Zookeeper	❌	✅	Strong	Consistency	Ensures strong consistency, not designed for partition tolerance

---

Trade-off Scenarios

PACELC categorizes distributed systems based on their operational priorities:

1. PA/EL Systems: Examples include DynamoDB and Cassandra, which prioritize availability during partitions and low latency otherwise.
2. PC/EC Systems: Examples include Google Spanner and CockroachDB, which prioritize strong consistency at all times.
3. Other configurations like PA/EC or PC/EL are less common but still viable based on specific use cases.

---

References

1. Dremio Wiki on PACELC
2. Luminous Men Blog
3. The Coder Cafe
4. Daniel J. Abadi’s Paper
5. ScyllaDB Glossary