Lecture 23 Part 2 Scaling Relational Databases

Summary

The video explores the challenges of scaling transactional databases, focusing on two main strategies: data partitioning and replication. Partitioning increases throughput but complicates consistency, especially with join queries across machines. Replication boosts fault tolerance and query performance but makes writes more costly. The video then introduces NoSQL databases, which sacrifice consistency in favor of availability and scalability. With its BASE model (Basic Availability, Soft State, Eventual Consistency), NoSQL aims to simplify scaling by offloading some responsibilities to applications, in contrast to the ACID properties of traditional relational databases.

Takeaways

• 😀 Partitioning data across multiple machines improves throughput but makes reading data more expensive and complicated, especially when joining data across partitions.
• 😀 Replicating data across multiple machines increases read performance and fault tolerance but makes writes more expensive as they must be propagated to all replicas.
• 😀 Maintaining data consistency is difficult in both partitioning and replication approaches, with different challenges for reads and writes.
• 😀 Sharding involves splitting data across machines so that each machine handles part of the data, improving system throughput by serving more clients simultaneously.
• 😀 Partitioning introduces complexity when querying data that spans across multiple partitions, especially if there are concurrent writes or deadlocks.
• 😀 Replication improves read performance by allowing queries to be handled by different replicas, but it requires careful management of writes to maintain consistency.
• 😀 NoSQL databases simplify scalability by giving up certain features, such as joins and strong consistency, which are challenging to maintain in large systems.
• 😀 NoSQL systems operate on the BASE principles (Basic Availability, Soft State, Eventual Consistency) as opposed to the ACID principles used in relational databases.
• 😀 BASE principles mean that NoSQL databases offer eventual consistency, where the system may not always be fully consistent but will eventually achieve consistency over time.
• 😀 The main advantage of NoSQL over traditional relational databases is its ability to scale easily and handle large loads by sacrificing some consistency guarantees.
• 😀 Applications using NoSQL are expected to handle failure recovery and consistency issues, as the database itself provides only basic availability and eventual consistency.

Q & A

What are the two primary methods for scaling a database in transactional workloads?

The two primary methods are data partitioning (sharding) and data replication.

How does data partitioning help scale a database?

Data partitioning distributes data across multiple machines, allowing queries to be spread out, thus increasing system throughput and allowing the system to serve more clients simultaneously.

What is the main problem when reading data in a partitioned database system?

Reading data becomes expensive, especially when performing operations like join queries that require data from multiple partitions, which may involve delays and increased waiting time.

What issue arises when performing a join query across multiple partitions in a database?

A join query across multiple partitions requires data from different machines, which can cause delays, and may require waiting for data from all partitions to complete the query.

What challenge does data partitioning pose during concurrent writes?

During concurrent writes, managing consistency becomes difficult, as writes to different partitions might conflict, leading to issues such as deadlocks or the need for transaction restarts.

How does data replication improve database scalability?

Data replication creates copies of data across multiple machines, allowing read queries to be distributed and improving throughput. It also provides fault tolerance, as queries can be rerouted to other replicas if one machine fails.

What is the main drawback of data replication in terms of writes?

The main drawback is that writes become expensive because all replicas need to be updated to maintain data consistency, which can be resource-intensive and time-consuming.

Why is consistency challenging when scaling relational databases?

In partitioned systems, joins across multiple servers and concurrent writes to different partitions complicate consistency. In replicated systems, maintaining consistency across replicas becomes difficult, especially when data has not yet been updated across all machines.

How does NoSQL address the challenges of scaling databases?

NoSQL simplifies the data model by not requiring complex functions like joins or strong consistency. This allows the database to scale more easily by relying on the application to handle tasks such as joins and consistency.

What does the acronym BASE stand for in NoSQL databases?

BASE stands for Basic Availability, Soft state, and Eventually consistent, which contrasts with the ACID properties of relational databases. BASE emphasizes availability and eventual consistency rather than strong consistency.

What is meant by 'eventual consistency' in NoSQL databases?

Eventual consistency means that the database may not be immediately consistent, but it will eventually reach a consistent state over time, even if there is a temporary inconsistency.

What is the main difference between ACID and BASE in the context of databases?

ACID (Atomicity, Consistency, Isolation, Durability) ensures strict transaction properties for relational databases, while BASE (Basic Availability, Soft state, Eventually consistent) focuses on availability and flexibility in NoSQL systems, sacrificing strict consistency for scalability and performance.