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.
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