🌐CAP Theorem

In very simple words,

• Consistency: Every read receives the most recent write or an error.

• Availability: All reads contain data, but it might not be the most recent.

• Partition tolerance: The system continues to operate despite network failures.

Source: Wikipedia

In practice, distributed systems must choose which two out of the three properties they will guarantee:

• CP (Consistency and Partition Tolerance): The system remains consistent and tolerates network partitions, but might not be available during a partition.

  Example: HBase, MongoDB in certain configurations, Banking Systems

• AP (Availability and Partition Tolerance): The system remains available and tolerates network partitions, but might return outdated data during a partition.

  Example: Cassandra, Couchbase, Social media platforms

• CA (Consistency and Availability): The system remains consistent and available as long as there are no network partitions.

  Example: Relational databases in a single-node setup or systems within a single data center without network partitions.

MongoDB can be configured to satisfy all the above three different implications (CP, AP, CA)

1. Consistency and Partition Tolerance (CP) Configuration:

   • Replica Sets: MongoDB uses replica sets to ensure data is replicated across multiple nodes. By default, MongoDB is designed to provide strong consistency.

   • Write Concerns: You can configure write concerns to ensure that writes are acknowledged by a majority of the nodes, ensuring strong consistency even in the presence of network partitions.

   • Read Preferences: Setting read preferences to "primary" ensures that all reads are directed to the primary node, which holds the most recent data.

   • Trade-off: This configuration might compromise availability because if the primary node is unavailable or there's a network partition, writes cannot be acknowledged until a new primary is elected.

2. Availability and Partition Tolerance (AP) Configuration:

   • Replica Sets: MongoDB's replica sets can also be configured to prioritize availability.

   • Write Concerns: Using lower write concerns (e.g., w: 1) ensures that writes are acknowledged as soon as they are written to the primary node, which increases availability but might compromise consistency during network partitions.

   • Read Preferences: Setting read preferences to "secondary" or "nearest" allows reads from secondary nodes, which might return slightly stale data but ensures high availability.

   • Trade-off: This configuration might compromise consistency because reads might not always reflect the most recent writes, especially during network partitions.

3. Consistency and Availability (CA) Configuration (Less Typical for Distributed Systems):

   • This configuration is less typical for MongoDB in a distributed setup because achieving CA requires no network partitions, which is unrealistic in most distributed systems.

   • In a single-node MongoDB setup or within a single data center with highly reliable networking, MongoDB can effectively provide CA guarantees.

   • Trade-off: This setup sacrifices partition tolerance, meaning it won't handle network partitions gracefully.

References:

https://en.wikipedia.org/wiki/CAP_theorem