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Grokking the Principles and Practices of Advanced System Design
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Q: What are the principles of System Design?

The main seven principles of System Design are as follows: Availability: Ensuring the system is operational and accessible to users at all times, even during failures or high demand. Scalability: Designing the system to handle increasing loads by efficiently adding resources without compromising performance. Reliability and fault tolerance: Building the system to continue functioning correctly even when some components fail, ensuring seamless recovery and minimal downtime. Consistency: Ensuring all users see the same data, maintaining uniformity across distributed systems even in the presence of replication or partitioning. Performance and low latency: Optimizing the system to deliver quick responses and process requests efficiently, reducing delays and enhancing the user experience. Maintainability: Designing the system in a way that it can be easily updated, debugged, and enhanced over time. Security: Implementing measures to protect the system from unauthorized access and ensuring data integrity.

Q: Which System Design principles do you consider when you implement solutions and why?

When implementing solutions in System Design, some key principles to consider include horizontal and vertical scaling, load balancing, fault tolerance, data integrity, availability architectures, modularity, authentication and authorization, data encryption, and resource management.

Q: What is the meaning of an advanced system?

An advanced system generally refers to a complex system that exhibits enhanced capabilities beyond its basic functions. These can include using the latest technology to increase efficiency or include features that support automation, scalability, and fault tolerance.

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Overview

Content

Reviews

This course teaches you how large, real-world systems are built and operated to meet strict service-level agreements. You’ll learn the many building blocks of a modern system’s design by picking and combining the right pieces and understanding the trade-offs between them. You’ll learn about some great systems from hyperscalers such as Google, Facebook, and Amazon. This course has hand-picked seminal work in system design that has stood the test of time and is grounded on strong principles. You will learn a...Show More

This course teaches you how large, real-world systems are built and operated to meet strict service-level agreements. You’ll lea...Show More

WHAT YOU'LL LEARN

Working knowledge of building large-scale systems

Ability to evaluate common system design trade-offs

Ability to map interview questions and on-job design tasks to well-known systems

Familiarity with the complexity of real-world systems behind a seemingly simple system

Understanding of large cloud service providers hosted in geographically dispersed data centers

Working knowledge of building large-scale systems

TAKEAWAY SKILLS

System Design

Prepare for Interview

Content

158 Lessons111 Quizzes

Prologue

1 Lessons

This chapter sets the stage for the course, emphasizing learning from historical systems and balancing innovation with established design practices.

Case Studies: Standing on the Shoulders of Giants

File Systems

1 Lessons

This chapter sets the stage for exploring distributed file systems, focusing on advancements in data management with systems like GFS, Colossus, and Tectonic.

Introduction to Distributed File Systems

Google File System (GFS)

11 Lessons

This chapter covers the Google File System (GFS), focusing on efficient management of large data files with scalability, fault tolerance, and high throughput.

Introduction to GFS

GFS File Operations

Detailed Design of GFS

Workflow of Create and Read File Operations in GFS

Workflow of Write Operations in GFS

Workflow of Delete and Snapshot Operations in GFS

Relaxed Data Consistency Model

Dealing with Data Inconsistencies in GFS

Metadata Consistency Model of GFS

Evaluation of GFS

Quiz on GFS

Google Colossus File System

3 Lessons

This chapter covers Colossus, which improves scalability and performance over GFS using a distributed metadata model for better data management and low latency.

Introduction to Colossus

Design and Evaluation of Colossus

Quiz on Colossus

Facebook's Tectonic File System

8 Lessons

This chapter discusses Tectonic File System, providing scalable storage with performance isolation and optimized resource management for diverse workloads.

Introduction to Tectonic

ZippyDB Design

Detailed Design of Tectonic

Multitenancy in Tectonic

Tenant-specific Optimization in Tectonic

Empirical Evaluation of Tectonic's Functional Requirements

Evaluation of Tectonic

Quiz on Tectonic

Databases

1 Lessons

This chapter covers the evolution from relational to NoSQL databases, highlighting the balance between scalability, availability, and consistency.

Introduction to Distributed Databases

Google Bigtable

7 Lessons

This chapter covers Bigtable, a scalable storage solution for managing large datasets, enhancing performance and availability with its unique design.

Introduction to Bigtable

Data Model of Bigtable

Detailed Design of Bigtable: Part I

Detailed Design of Bigtable: Part II

Design Refinements in Bigtable

Evaluation of Bigtable

Quiz on Bigtable

Google Megastore

6 Lessons

This chapter covers Megastore, blending NoSQL scalability with relational features for high availability, ACID transactions, and optimized cloud performance.

Introduction to Megastore

High-level Design for Better Availability and Scalability

Data Model of Megastore

Replication in Megastore

Evaluation of Megastore

Quiz on Megastore

Google Spanner

9 Lessons

This chapter covers Google Spanner, combining relational features with NoSQL scalability for strong consistency, high availability, and global data management.

Introduction to Spanner

Detailed Design of Spanner

Database Buckets and Data Model of Spanner

TrueTime API in Spanner

Spanner, TrueTime, and the CAP Theorem

Concurrency Control in Spanner

Database Operations in Spanner

Evaluation of Spanner

Quiz on Spanner

10.

Key-value Stores

1 Lessons

This chapter introduces key-value stores, crucial for caching, NoSQL databases, and enhancing scalability and availability in modern distributed applications.

Introduction to Key-value Stores

11.

Many-core Key-value Store

5 Lessons

This chapter covers the many-core key-value store, enhancing efficiency and scalability while addressing power consumption and performance challenges.

Motivation and Requirements for a Many-core Approach

Estimations and Limitations of a Many-core System

Detailed Design of a Many-core System

Evaluation of the Many-core System

Quiz on Many-core Systems

12.

Scaling Memcache

7 Lessons

This chapter explores Memcache scaling strategies, addressing performance, consistency, and network efficiency challenges across various operational levels.

Introduction to Scaling Memcache

Single-server Level of Memcache

Cluster Level of Memcache

Regional Level of Memcache

Cross-regional Level of Memcache

Evaluation of Memcache

Quiz on Memcache

13.

SILT

12 Lessons

This chapter covers SILT, which optimizes key-value storage with a multi-store architecture, focusing on memory efficiency, low latency, and data management.

Introduction to SILT

High-level Design of SILT

A Write-friendly Store for SILT: Part I

A Write-friendly Store for SILT: Part II

A Write-friendly Store for SILT: Part III

Intermediary Store(s) in SILT

A Memory-efficient Store for SILT: Part I

A Memory-efficient Store for SILT: Part II

A Memory-efficient Store for SILT: Part III

Request Flows in SILT

Evaluating and Extending the Design of SILT

Quiz on SILT

14.

Amazon DynamoDB

8 Lessons

This chapter covers DynamoDB, a managed NoSQL service designed for high availability, strong durability, and scalability, meeting diverse data management needs.

Introduction to DynamoDB

High-level Design of DynamoDB

No Fixed Schema in DynamoDB

Partitioning and Replication in DynamoDB

Adapting to Traffic Patterns in DynamoDB

Durability and Correctness in DynamoDB

Ensuring High Availability in DynamoDB

Quiz on DynamoDB

15.

Concurrency Management

1 Lessons

This chapter introduces concurrency management methods for efficiently handling simultaneous client requests in distributed systems.

Introduction to Concurrency Management

16.

Two-phase Locking (2PL)

3 Lessons

This chapter covers 2PL, a concurrency control mechanism ensuring data integrity, while addressing challenges like deadlocks and throughput issues.

Introduction to Two-Phase Locking (2PL)

Analysis and Evaluation of Two-Phase Locking (2PL)

Quiz on 2PL

17.

Google Chubby Locking Service

8 Lessons

This chapter covers Chubby, a distributed locking service that enhances coordination, availability, and fault tolerance in Google’s systems with robust design.

Introduction to Chubby

Detailed Design of Chubby: Part I

Detailed Design of Chubby: Part II

Detailed Design of Chubby: Part III

Detailed Design of Chubby: Part IV

The Rationale Behind Chubby’s Design

Evaluation of Chubby

Quiz on Chubby

18.

ZooKeeper

5 Lessons

This chapter covers ZooKeeper, a coordination system for distributed environments, offering efficient resource management and high availability.

Introduction to ZooKeeper

Detailed Design of ZooKeeper

Primitives of ZooKeeper

Evaluation of ZooKeeper

Quiz on ZooKeeper

19.

Big Data Processing: Batch to Stream Processing

1 Lessons

This chapter explores the evolution and significance of big data processing systems like MapReduce, Spark, and Kafka in data handling and management.

Introduction to Big Data Processing Systems

20.

MapReduce

8 Lessons

This chapter covers MapReduce, which simplifies processing large datasets with a user-friendly model that enables efficient parallelization and fault tolerance.

System Design: MapReduce

High-level Design of MapReduce

MapReduce: Detailed Design

Design Refinements in MapReduce: Part I

Design Refinements in MapReduce: Part II

MapReduce: Evaluation

Concluding MapReduce

Quiz on MapReduce

21.

Spark

10 Lessons

This chapter covers Spark's architecture, focusing on in-memory processing, RDDs, and features for low latency and fault tolerance.

Introduction to Spark

Requirements of Spark

High-level Design of Spark

Resilient Distributed Datasets of Spark

Parallel Operations in Spark

Shared Variables in Spark

Detailed Design of Spark

Refinements in Spark

Evaluation of Spark

Quiz on Spark

22.

Kafka

8 Lessons

This chapter introduces Kafka, a powerful messaging system for real-time event streaming, known for high scalability, efficiency, and reliable data delivery.

Introduction to Kafka

High-level Design of Kafka

Detailed Design of Kafka

Efficiency of Kafka

Distributed Coordination in Kafka

Delivery Guarantees of Kafka

Evaluation of Kafka

Quiz on Kafka

23.

Consensus

1 Lessons

This chapter introduces consensus in distributed systems, covering algorithms like Paxos and Raft, and key concepts like FLP and Byzantine faults.

Introduction to Consensus in Distributed Systems

24.

Understanding Consensus: Two Generals, FLP, & Byzantine Generals

4 Lessons

This chapter explores consensus challenges in distributed systems, focusing on the Two Generals problem, FLP impossibility, and Byzantine Generals problem.

Consensus Prerequisites and Two Generals' Problem

FLP Impossibility

The Byzantine Generals Problem

Let AI Evaluate Your Understanding of Consensus Fundamentals

25.

Two-phase Commit

4 Lessons

This chapter explains 2PC, a consensus protocol to ensure atomicity in distributed transactions by coordinating across nodes and handling failure challenges.

Introduction to Two-Phase Commit (2PC)

Working of the Two-Phase Commit Protocol

Failures in the Two-Phase Commit Protocol

Quiz on Two-Phase Commit

26.

State Machine Replication

10 Lessons

This chapter covers State Machine Replication, which ensures fault tolerance by using replicated state machines to maintain consistency despite failures.

Introduction to State Machine Replication

State Machines

Replication and Coordination of State Machines

Ordering Requests: Part I

Ordering Requests: Part II

Fault Tolerance for Outputs and Clients

Protocols for Maintaining Fault Tolerance: Part I

Protocols for Maintaining Fault Tolerance: Part II

SMR in Practice Via a Log

Quiz on State Machine Replication

27.

Paxos

6 Lessons

This chapter explores the Paxos consensus algorithm, detailing its design, operation, and use in achieving reliable distributed consensus.

Introduction to Paxos

Basic Paxos Protocol Design

Basic Paxos in Action

The Rationale behind Paxos Design Choices

Multi-Paxos

Quiz on Paxos

28.

Raft

8 Lessons

This chapter covers Raft, a consensus algorithm ensuring consistency and fault tolerance through leader election, log replication, and cluster management.

Introduction to Raft

Raft's Basics and High-Level Workflow

Raft's Leader Election Protocol

Raft's Log Replication Protocol

Raft's Safety, Fault-Tolerance, and Availability Protocols

Raft's Cluster Membership Changes

Log Compaction and Client Interaction in Raft

Quiz on Raft

29.

Epilogue

1 Lessons

This chapter concludes the course by emphasizing applying system design principles to real-world challenges while encouraging ongoing exploration and learning.

Conclusion

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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Free Resources

blog

Components of System Design

blog

System Design Interview Questions

cheatsheet

What is Availability in System Design?

blog

System Design fundamentals

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Amazon System design Interview Questions

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System Design Building Blocks

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What is Performance in System Design?

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Distributed Systems

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Scalable Systems

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FAANG/MAANG System Design Interview

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Facebook System Design Interview Questions

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Solve Any System Design Interview Question

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Microsoft System Design Interview

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Uber Backend System Design

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Google System Design Interview Questions

Frequently Asked Questions

What are the principles of System Design?

The main seven principles of System Design are as follows:

Availability: Ensuring the system is operational and accessible to users at all times, even during failures or high demand.
Scalability: Designing the system to handle increasing loads by efficiently adding resources without compromising performance.
Reliability and fault tolerance: Building the system to continue functioning correctly even when some components fail, ensuring seamless recovery and minimal downtime.
Consistency: Ensuring all users see the same data, maintaining uniformity across distributed systems even in the presence of replication or partitioning.
Performance and low latency: Optimizing the system to deliver quick responses and process requests efficiently, reducing delays and enhancing the user experience.
Maintainability: Designing the system in a way that it can be easily updated, debugged, and enhanced over time.
Security: Implementing measures to protect the system from unauthorized access and ensuring data integrity.

Which System Design principles do you consider when you implement solutions and why?

When implementing solutions in System Design, some key principles to consider include horizontal and vertical scaling, load balancing, fault tolerance, data integrity, availability architectures, modularity, authentication and authorization, data encryption, and resource management.

What is the meaning of an advanced system?

An advanced system generally refers to a complex system that exhibits enhanced capabilities beyond its basic functions. These can include using the latest technology to increase efficiency or include features that support automation, scalability, and fault tolerance.

Grokking the Principles and Practices of Advanced System Design Save

Grokking the Principles and Practices of Advanced System Design
Save