Patterns of Distributed Systems 1st Edition by Unmesh Joshi 0138221980 9780138221980

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ISBN 10: 0138221980 

ISBN 13: 9780138221980

Author: Unmesh Joshi 

A Patterns Approach to Designing Distributed Systems and Solving Common Implementation Problems More and more enterprises today are dependent on cloud services from providers like AWS, Microsoft Azure, and GCP. They also use products, such as Kafka and Kubernetes, or databases, such as YugabyteDB, Cassandra, MongoDB, and Neo4j, that are distributed by nature. Because these distributed systems are inherently stateful systems, enterprise architects and developers need to be prepared for all the things that can and will go wrong when data is stored on multiple servers–from process crashes to network delays and unsynchronized clocks. Patterns of Distributed Systems describes a set of patterns that have been observed in mainstream open-source distributed systems. Studying the common problems and the solutions that are embodied by the patterns in this guide will give you a better understanding of how these systems work, as well as a solid foundation in distributed system design principles. Featuring real-world code examples from systems like Kafka and Kubernetes, these patterns and solutions will prepare you to confidently traverse open-source codebases and understand implementations you encounter “in the wild.” Review the building blocks of consensus algorithms, like Paxos and Raft, for ensuring replica consistency in distributed systems Understand the use of logical timestamps in databases, a fundamental concept for data versioning Explore commonly used partitioning schemes, with an in-depth look at intricacies of two-phase-commit protocol Analyze mechanisms used in implementing cluster coordination tasks, such as group membership, failure detection, and enabling robust cluster coordination Learn techniques for establishing effective network communication between cluster nodes. Along with enterprise architects and data architects, software developers working with cloud services such as Amazon S3, Amazon EKS, and Azure CosmosDB or GCP Cloud Spanner will find this set of patterns to be indispensable.

Patterns of Distributed Systems 1st table of contents: 

Part I: Narratives
Chapter 1: The Promise and Perils of Distributed Systems
The Limits of a Single Server
Separate Business Logic and Data Layer
Partitioning Data
A Look at Failures
Replication: Masking Failures
Process Crash
Network Delay
Process Pause
Unsynchronized Clocks
Defining the Term “Distributed Systems”
The Patterns Approach
Chapter 2: Overview of the Patterns
Keeping Data Resilient on a Single Server
Competing Updates
Dealing with the Leader Failing
Multiple Failures Need a Generation Clock
Log Entries Cannot Be Committed until They Are Accepted by a Majority Quorum
Followers Commit Based on a High-Water Mark
Leaders Use a Series of Queues to Remain Responsive to Many Clients
Followers Can Handle Read Requests to Reduce Load on the Leader
A Large Amount of Data Can Be Partitioned over Multiple Nodes
Partitions Can Be Replicated for Resilience
A Minimum of Two Phases Are Needed to Maintain Consistency across Partitions
In Distributed Systems, Ordering Cannot Depend on System Timestamps
A Consistent Core Can Manage the Membership of a Data Cluster
Gossip Dissemination for Decentralized Cluster Management
Part II: Patterns of Data Replication
Chapter 3: Write-Ahead Log
Problem
Solution
Implementation Considerations
Usage in Transactional Storage
Compared to Event Sourcing
Examples
Chapter 4: Segmented Log
Problem
Solution
Examples
Chapter 5: Low-Water Mark
Problem
Solution
Snapshot-Based Low-Water Mark
Time-Based Low-Water Mark
Examples
Chapter 6: Leader and Followers
Problem
Solution
Leader Election
Why Quorum Read/Writes Are Not Enough for Strong Consistency Guarantees
Examples
Chapter 7: HeartBeat
Problem
Solution
Small Clusters: Consensus-Based Systems
Technical Considerations
Large Clusters: Gossip-Based Protocols
Examples
Chapter 8: Majority Quorum
Problem
Solution
Deciding on Number of Servers in a Cluster
Flexible Quorums
Examples
Chapter 9: Generation Clock
Problem
Solution
Examples
Chapter 10: High-Water Mark
Problem
Solution
Log Truncation
Examples
Chapter 11: Paxos
Problem
Solution
Flow of the Protocol
An Example Key-Value Store
Flexible Paxos
Examples
Chapter 12: Replicated Log
Problem
Solution
Multi-Paxos and Raft
Replicating Client Requests
Leader Election
Technical Considerations
Push vs. Pull
What Goes in the Log?
Examples
Chapter 13: Singular Update Queue
Problem
Solution
Choice of the Queue
Using Channels and Lightweight Threads
Backpressure
Other Considerations
Examples
Chapter 14: Request Waiting List
Problem
Solution
Expiring Long Pending Requests
Examples
Chapter 15: Idempotent Receiver
Problem
Solution
Expiring the Saved Client Requests
Removing the Registered Clients
At-Most-Once, At-Least-Once, and Exactly-Once Actions
Examples
Chapter 16: Follower Reads
Problem
Solution
Finding the Nearest Replica
Disconnected or Slow Followers
Read Your Own Writes
Linearizable Reads
Examples
Chapter 17: Versioned Value
Problem
Solution
Ordering of Versioned Keys
Reading Multiple Versions
MVCC and Transaction Isolation
Using RocksDB-Like Storage Engines
Examples
Chapter 18: Version Vector
Problem
Solution
Comparing Version Vectors
Using Version Vector in a Key-Value Store
Examples
Part III: Patterns of Data Partitioning
Chapter 19: Fixed Partitions
Problem
Solution
Choosing the Hash Function
Mapping Partitions to Cluster Nodes
Alternative Solution: Partitions Proportional to Number of Nodes
Examples
Chapter 20: Key-Range Partitions
Problem
Solution
Predefining Key Ranges
An Example Scenario
Auto-Splitting Ranges
Examples
Chapter 21: Two-Phase Commit
Problem
Solution
Locks and Transaction Isolation
Commit and Rollback
An Example Scenario
Using Versioned Value
Using Replicated Log
Failure Handling
Transactions across Heterogeneous Systems
Examples
Part IV: Patterns of Distributed Time
Chapter 22: Lamport Clock
Problem
Solution
Causality, Time, and Happens-Before
An Example Key-Value Store
Partial Order
A Single Leader Server Updating Values
Examples
Chapter 23: Hybrid Clock
Problem
Solution
Multiversion Storage with Hybrid Clock
Using Timestamp to Read Values
Assigning Timestamp to Distributed Transactions
Examples
Chapter 24: Clock-Bound Wait
Problem
Solution
Read Restart
Using Clock-Bound APIs
Examples
Part V: Patterns of Cluster Management
Chapter 25: Consistent Core
Problem
Solution
Metadata Storage
Handling Client Interactions
Examples
Chapter 26: Lease
Problem
Solution
Attaching the Lease to Keys in the Key-Value Storage
Handling Leader Failure
Examples
Chapter 27: State Watch
Problem
Solution
Client-Side Implementation
Server-Side Implementation
Handling Connection Failures
Examples
Chapter 28: Gossip Dissemination
Problem
Solution
Avoiding Unnecessary State Exchange
Criteria for Node Selection to Gossip
Group Membership and Failure Detection
Handling Node Restarts
Examples
Chapter 29: Emergent Leader
Problem
Solution
Sending Membership Updates to All the Existing Members
Handling Missing Membership Updates
An Example Scenario
Failure Detection
Comparison with Leader and Followers
Examples
Part VI: Patterns of Communication between Nodes
Chapter 30: Single-Socket Channel
Problem
Solution
Examples
Chapter 31: Request Batch
Problem
Solution
Technical Considerations
Examples
Chapter 32: Request Pipeline
Problem
Solution
Examples
References

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