Ch 10 Scalable Db Q&A

<p>For applications that must support read-heavy workloads. (C)</p> Signup and view all the answers

<p>It can reduce the load on the primary database by handling all reads. (B)</p> Signup and view all the answers

<p>It happens asynchronously to minimize delays during writes. (C)</p> Signup and view all the answers

<p>Simplification of query writing by providing prejoined data. (B)</p> Signup and view all the answers

<p>Only the entry that holds the canonical reference needs to be modified. (A)</p> Signup and view all the answers

<p>Some NoSQL databases have limited support for JOIN operations, while others do not support them at all. (B)</p> Signup and view all the answers

<p>It allows for more efficient handling of writes. (C)</p> Signup and view all the answers

<p>Clients may read outdated or stale data. (A)</p> Signup and view all the answers

<p>Optimize physical storage. (D)</p> Signup and view all the answers

<p>Use the primary key with a hash function. (D)</p> Signup and view all the answers

<p>It focuses on physical rather than conceptual optimization. (D)</p> Signup and view all the answers

<p>Secondary databases can help handle read requests if the primary fails. (B)</p> Signup and view all the answers

<p>It can lead to challenges in maintaining data integrity during updates. (D)</p> Signup and view all the answers

<p>Normalization structures data to eliminate redundancy. (B)</p> Signup and view all the answers

<p>Read operations (A)</p> Signup and view all the answers

<p>Third normal form (3NF) (A)</p> Signup and view all the answers

<p>It eliminates the need for complex relational operations. (A)</p> Signup and view all the answers

<p>Time-consuming updates to maintain data consistency. (B)</p> Signup and view all the answers

<p>To increase processing capacity (B)</p> Signup and view all the answers

<p>Data availability during failures (B)</p> Signup and view all the answers

<p>Maintaining consistency across replicas (B)</p> Signup and view all the answers

<p>To enhance availability for read and write requests (A)</p> Signup and view all the answers

<p>It may lead to slower response times (B)</p> Signup and view all the answers

<p>By allowing additional nodes to handle requests (B)</p> Signup and view all the answers

Involves migrating a database to more powerful hardware to increase processing capacity.

A common approach to increasing database processing capacity by adding read replicas.

The primary database node in a read replica setup.

Nodes in a read replica setup that maintain a copy of the main database.

The primary database node is responsible for handling all write operations.

Changes made to the primary database are replicated to the read replicas asynchronously.

Read replicas are often located in different data centers or continents to support global clients.

This architecture is highly effective for applications that primarily handle read requests.

A technique for distributing a relational database across multiple independent disk partitions and database engines, allowing for better resource utilization and scalability.

A partitioning strategy that splits a logical table into multiple physical partitions based on the value of a specific row field or using a hash function on the primary key.

A partitioning strategy that splits a table based on the columns in a row, separating data into static and dynamic components for optimization purposes.

Data that is stored in a database and is not expected to change frequently.

Data that is frequently updated or changed in a database.

When data written to the primary database is replicated to secondaries with a slight delay, potentially leading to clients reading outdated information.

Involves adding more resources, such as servers or processors, to a database system to handle increased load or processing demands.

A new generation of database technologies that emerged in the early 2000s to address the limitations of traditional relational database systems in handling large datasets and providing scalability.

A database architecture where data is distributed across multiple independent nodes, each having its own storage and processing capabilities.

The process of adapting a database to handle increasing data volumes and user traffic by adding more hardware resources.

A type of database scaling that involves adding more powerful hardware to a single database instance to handle more data and requests.

A type of database scaling that involves distributing data and processing across multiple independent nodes, each handling a portion of the workload.

A family of database systems that deviate from the traditional relational database model, often offering more flexibility and scalability for handling vast amounts of data.

The process of modeling data in a database by breaking it down into smaller, related entities and defining relationships between them.

A database operation that combines data from multiple tables or collections based on common fields. This operation is often not directly supported in NoSQL systems.

A data model where each item is stored as a single entry, making it efficient for accessing and updating data. Changes to a reference affect all queries using that data.

A data model that focuses on the how data will be used (the solution), prioritizing common access patterns and pre-joining related data for fast retrieval. Often results in a denormalized structure.

The practice of combining data from multiple tables into a single structure, potentially reducing performance but simplifying query complexity.

A database design approach where different data elements are stored in separate tables based on their relationship to one another, minimizing redundancy and promoting data integrity.

A data model created to directly reflect the specific output required by a query, rather than just storing individual data items separately.

A type of database where data is stored in multiple independent tables with consistent relationships. The data integrity is maintained across tables using foreign keys, ensuring consistency and avoiding redundancy.

The process of dividing a database into smaller, independent units called partitions for better resource utilization and scalability.

A technique for improving database availability by creating multiple copies of data, stored on separate servers.

A database architecture where each replica of a partition resides on a dedicated server, allowing for parallel processing and improved performance.

Ensuring that all replicas of a data object have the same value.

A type of replica consistency that guarantees all replicas reflect the most recent update, ensuring clients always read the same value.

The process of updating all replicas when a data update request occurs, ensuring consistent data across the system.

Data model where each row represents a day a person visited a ski resort, including details like the date, resort name, skier ID, skier name, number of lifts, vertical drop, highest and lowest temperatures, and wind conditions.

The process of updating data across multiple tables to maintain consistency, often necessary when changes occur in one table that affect others.

A structure that follows the first three normalization rules, eliminating redundant data and achieving a good balance between efficiency and flexibility.

Changes in a database that affect multiple tables are performed simultaneously to maintain consistency.