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40 Topics Every Java Backend Developer Should Understand

I found the following tweet online:

As I mentioned in my reply, I got very inspired by this list. As a senior Java engineer myself, I realised, I am probably familiar with most topics on this list but since I don’t use them daily, a refresh and a place where to store them all could benefit me and others as well. So I will share my notes on the first topic.

CAP Theorem

The CAP Theorem, also known as Brewer’s Theorem, outlines the fundamental trade-offs that must be considered when designing distributed applications and databases.

What is the CAP Theorem?

The CAP Theorem states that a distributed data system can guarantee only two of the following three properties simultaneously:

1. Consistency ( C ):

• Every read receives the most recent write or an error.
• All nodes see the same data at the same time.

2. Availability (A):

• Every request receives a (non-error) response, without the guarantee that it contains the most recent write.
• The system remains operational 100% of the time.

3. Partition Tolerance (P):

• The system continues to operate despite arbitrary partitioning due to network failures.
• The system can handle communication breakdowns between nodes.

Why Can’t We Have All Three?

In a distributed system where partitions (network failures) can occur, you must choose between consistency and availability:

• If you prioritize consistency (CP system): The system may become unavailable during network partitions to ensure all nodes have consistent data.
• If you prioritize availability (AP system): The system remains operational, but some nodes may have stale data until consistency is restored.

Applying the CAP Theorem in Java Backend Development

As a Java backend developer, you often interact with distributed systems, databases, and microservices where the CAP Theorem is highly relevant.

1. Choosing Between Consistency and Availability

Consistency Priority (CP Systems):

• Use Case: Financial transactions, inventory management.

Implementation:

• Distributed Transactions: Use technologies like JTA (Java Transaction API) to manage transactions across multiple resources.
• Two-Phase Commit Protocol: Ensure all nodes agree on a transaction before committing.
• Synchronous Replication: Write operations are replicated synchronously to all nodes.

Availability Priority (AP Systems):

• Use Case: Social media feeds, real-time analytics.

Implementation:

• Eventual Consistency: Accept that data will become consistent over time.
• Asynchronous Replication: Write operations are replicated asynchronously.
• Fallback Mechanisms: Use cached data when fresh data isn’t available.

2. Database Selection

Relational Databases (CP Systems):

• Examples: PostgreSQL, MySQL with Galera Cluster.
• Features: Strong consistency, ACID transactions.
• Java Integration: JDBC, JPA (Java Persistence API), Hibernate.

NoSQL Databases (AP Systems):

• Examples: Cassandra, DynamoDB.
• Features: High availability, eventual consistency.
• Java Integration: Using drivers provided by the database vendors.

Configurable Databases:

• Example: MongoDB can be tuned for consistency or availability.
• Java Integration: MongoDB Java Driver, allowing configuration of read/write concerns.

3. Microservices Architecture

Consistency Focused Microservices:

Synchronous Communication:

• Use RESTful APIs with HTTP calls.
• Ensure immediate consistency but risk reduced availability during network issues.

Transactions Across Services:

• Implement Saga Pattern for distributed transactions.
• Use Event Sourcing to maintain a consistent state.

Availability Focused Microservices:

Asynchronous Communication:

• Use Message Brokers like Apache Kafka or RabbitMQ.
• Employ Event-Driven Architecture to decouple services.

Resilience Patterns:

• Implement Circuit Breakers using libraries like Hystrix.
• Use Bulkheads and Fallbacks to maintain availability.

4. Handling Network Partitions

Design Strategies:

Timeouts and Retries:

• Configure appropriate timeouts for network calls.
• Implement retry logic with exponential backoff.

Idempotent Operations:

• Design APIs to handle retries without side effects.

Data Replication and Sharding:

• Distribute data across nodes to minimize the impact of partitions.
• Use consistent hashing to route requests.

5. Consistency Models in Java

Strong Consistency:

Implementation:

• Use synchronized blocks or Reentrant Locks for thread safety.
• Employ atomic operations with java.util.concurrent.atomic package.

Eventual Consistency:

Implementation:

• Use Caching Mechanisms like Ehcache or Caffeine.
• Implement Data Versioning and Conflict Resolution strategies.

Design Patterns

Implement patterns like Circuit Breaker, Bulkhead, and Retry to handle failures gracefully.

Conclusion

I will keep track of my notes and my paper implementations related to each topic (where it fits) in my GitHub repo.

Thank you for reading. I would love to hear your thoughts in the comments section.

UPDATE: Part 2 – Consistency Models