1. Data Fragmentation and Distribution
Challenge: Views often need to aggregate or join data from multiple fragments distributed across
different nodes.
Impact: This increases query complexity and communication overhead, as data must be fetched from
multiple locations.
2. Consistency Maintenance
Challenge: Ensuring that views reflect the most up-to-date data from underlying base tables, especially
when data is updated at different nodes.
Impact: Requires synchronization mechanisms, which can introduce latency and overhead.
3. Performance Overhead
Challenge: Materialized views (stored physically) must be updated whenever base tables change, which
can be costly in a distributed environment.
Impact: Frequent updates can lead to high network traffic and processing delays, affecting system
performance.
4. Concurrency Control
Challenge: Managing concurrent access to views while ensuring data integrity, especially when base
tables are updated simultaneously at different nodes.
Impact: Requires sophisticated locking or versioning mechanisms, which can reduce system efficiency.
5. View Update Problem
Challenge: Propagating updates made to views back to the underlying base tables is non-trivial,
especially in distributed systems.
Impact: Updates may lead to ambiguities or inconsistencies, requiring complex resolution strategies.
6. Scalability
Challenge: As the system scales, managing views across a growing number of nodes becomes
increasingly difficult.
Impact: Maintaining performance and consistency while scaling requires careful design and resource
allocation.
7. Network Reliability
Challenge: Network failures or delays can disrupt view creation, maintenance, or querying.
Impact: Systems must handle partial failures and ensure view availability despite network issues.
8. Security and Privacy
Challenge: Ensuring that views comply with access control policies and do not expose sensitive data.
Impact: Requires robust security mechanisms, which can add overhead and complexity.
In a centralized system, updating a view means modifying the underlying data on a single central server,
which then reflects across all user views; whereas in a distributed system, updating a view involves
coordinating changes across multiple nodes, potentially requiring complex synchronization mechanisms
to ensure consistency across all distributed views, as data is spread across different servers.
Key differences:
1. Single point of update: In a centralized system, updates are made to a single source of truth, which then
propagates to all views, while in a distributed system, updates might need to be made to multiple nodes
to maintain data consistency across the distributed system.
2. Complexity of coordination: Centralized updates are relatively simpler to manage due to a single point of
control, while distributed updates require complex algorithms to ensure consistency across multiple
nodes, especially when dealing with concurrent updates.
3. Scalability: In a centralized system, updating views can become a bottleneck if there are a large number
of users or frequent updates, whereas distributed systems can scale better by distributing the update
workload across multiple nodes.
Example:
Centralized database: If you update a view in a centralized database, the changes are immediately
reflected in all user views because the data is stored on a single server.
Distributed database: When updating a view in a distributed database, the system needs to
communicate with multiple nodes to ensure the updated data is consistent across all distributed views,
potentially requiring additional synchronization mechanisms.
Hence, managing views in distributed databases is more complex than in centralized systems due to data fragmentation, consistency challenges, and synchronization overhead. Effective strategies are needed to balance performance, scalability, and data integrity across multiple nodes.