What are the trade-offs between strong consistency and eventual consistency in NoSQL databases?
- Balanced latency and availability
- High latency and low availability
- Low latency and high availability
- No impact on latency or availability
The trade-offs between strong consistency and eventual consistency in NoSQL databases involve choosing between low latency and high availability versus high consistency. Strong consistency ensures that all nodes see the same data at the same time, introducing higher latency and potential lower availability. On the other hand, eventual consistency prioritizes low latency and high availability, allowing nodes to have temporarily inconsistent data that will eventually converge.
Scenario: A financial institution needs to maintain a vast amount of transaction records while ensuring fast access to recent data. How would you implement partitioning to optimize data retrieval and storage?
- Partitioning based on account numbers
- Partitioning based on transaction dates
- Partitioning based on transaction types
- Randomized partitioning
Partitioning based on transaction dates is a recommended strategy in this scenario. It allows for segregating data based on time, making it easier to manage and retrieve recent transactions quickly. This enhances query performance and ensures that the most relevant data is readily accessible.
_______ is the process of reorganizing table and index data to improve query performance and reduce contention in a database.
- Data Replication
- Data Sharding
- Database Partitioning
- Database Tuning
Database Tuning is the process of reorganizing table and index data to enhance query performance and reduce contention in a database. It involves optimizing queries, indexing, and other database structures to achieve better efficiency.
Star Schema often leads to _______ query performance compared to Snowflake Schema.
- Better
- Similar
- Unpredictable
- Worse
Star Schema often leads to Better query performance compared to Snowflake Schema. The denormalized structure of Star Schema simplifies query execution by minimizing joins, resulting in faster analytical query performance.
Which type of consistency model ensures that all reads reflect the most recent write for a given data item in a distributed system?
- Causal Consistency
- Eventual Consistency
- Strong Consistency
- Weak Consistency
Strong Consistency ensures that all reads reflect the most recent write for a given data item in a distributed system. This model guarantees that any read operation will return the most recent write, providing a high level of data consistency but often at the cost of increased latency and reduced availability.
Scenario: A large e-commerce platform stores millions of product records in its database. To improve query performance, the database administrator decides to implement data partitioning based on product categories. What benefits can the company expect from this storage optimization strategy?
- Enhanced data normalization
- Improved data integrity
- Increased data redundancy
- Reduced query response time
By implementing data partitioning based on product categories, the e-commerce platform can expect reduced query response time. This is because data related to specific product categories is stored together, allowing for more efficient retrieval and improved overall system performance.
In data partitioning, _______ is the process of redistributing data across partitions to ensure balanced loads.
- Data fragmentation
- Data migration
- Data reallocation
- Data shuffling
In data partitioning, data reallocation is the process of redistributing data across partitions to maintain balanced loads. This ensures that no single partition becomes a bottleneck, optimizing performance in distributed systems.
Partitioning helps in _______ the data across different storage devices or servers.
- Clustering
- Distributing
- Indexing
- Normalizing
Partitioning helps in Distributing the data across different storage devices or servers. This distribution enhances parallel processing capabilities and can lead to better utilization of resources in a database system.
In clustering, data is organized into _______ based on shared characteristics.
- Categories
- Clusters
- Hierarchies
- Sets
In clustering, data is organized into clusters based on shared characteristics. Clusters are groups of data points that are similar to each other and dissimilar to data points in other clusters. This helps in discovering patterns and relationships within the data.
Which property of graph databases makes them particularly suitable for recommendation systems and social networks?
- Ability to store large volumes of structured data
- Native representation of relationships
- Support for ACID transactions
- Use of SQL as the query language
Graph databases are particularly suitable for recommendation systems and social networks due to their native representation of relationships. In a graph database, relationships are first-class citizens, making it efficient to traverse and analyze connections between entities, a key requirement in recommendation systems and social networks.