"A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another."
Decentralized and scalable distributed systems and architectures.
Distributed Systems Architecture: Understanding the structure of distributed systems, their components, and how they interact with each other is crucial when learning scalability in distributed systems.
Scalability: This refers to the ability of a distributed system to handle more load or work without experiencing downtime or performance degradation.
Load Balancing: Load balancing refers to distributing incoming network traffic across multiple servers to improve reliability, increase capacity, and maximize uptime.
Distributed Data Storage: Distributed data storage refers to the ability of a distributed system to store data across multiple nodes or servers. It includes topics such as data sharding, replication, and consistency models.
Consensus Algorithms: Consensus algorithms help distributed systems to agree on a single value, even in the presence of network failures or other issues.
Failure Handling: Distributed systems must be able to handle failures of individual nodes, network partitions or complete cluster failures gracefully.
Message Queuing and Stream Processing: These are enabling technologies for distributed, highly-performant processing of data and events.
Distributed Transactions and Coordination: Distributed systems require coordination between different nodes in some transactions. This includes topics like two-phase commits, distributed locking, and atomicity.
Eventual Consistency: The modern era of distributed systems is moving away from strong consistency to a more relaxed conception of consistency. Eventual Consistency is becoming the norm in today's distributed systems era.
CAP Theorem: The CAP theorem is a theoretical framework that explores the tradeoffs between Consistency, Availability, and Partition tolerance in distributed systems.
Infrastructure as Code: Managing the complexity of distributed systems requires skills in infrastructure-as-code (IAC). In the context of scalability, this is often about orchestrating a large number of compute instances, containers or serverless functions.
Cloud infrastructures: Cloud services like AWS, Azure, and GCP offer a range of scaled infrastructure options that teams can use to build their own scalable distributed systems.
Microservices: Microservices architecture refers to designing modular, loosely coupled independent components of an application that can be independently deployed, optimized and scaled.
Performance Testing: When learning about scalability in distributed systems, one key area to explore is performance testing. Topics covered here include measuring performance, understanding bottlenecks, and optimizing architectures.
Monitoring and Diagnostics: Distributed systems require extensive monitoring and diagnostic tools beyond the traditional measuring of end-user performance or infrastructure assets. There are specialty monitoring and diagnostic systems that are built around distributed systems such as Dynatrace, AppDynamics or New Relic.
Horizontal Scalability: It refers to the ability to add more processing power or nodes to a system to handle an increased load.
Vertical Scalability: It involves adding more processing power, typically in the form of CPU, memory, or disk capacity, to an individual node to handle increased load.
Elastic Scalability: It refers to the ability of a system to automatically add or remove resources based on changes in the workload, without requiring manual intervention.
Dynamic Scalability: It involves changing the number of nodes or resources allocated to a system based on changing workloads, rather than relying on static configurations.
Incremental Scalability: It involves adding resources to a system in small, incremental steps rather than in large, single increments, which allows for more granular control and minimizes waste.
Load Balancing Scalability: It involves distributing incoming traffic across multiple nodes or servers to ensure that each node is operating at optimal capacity.
Transactional Scalability: It refers to the ability of a distributed system to handle a high volume of transactions concurrently without compromising performance or data integrity.
Component Scalability: It involves designing each component of a distributed system to be scalable independently of the others, which allows for more flexibility and easier maintenance.
Geographic Scalability: It refers to the ability of a distributed system to handle a high volume of transactions and data transfers across multiple geographic locations.
Network Scalability: It involves designing a distributed system to take advantage of the available network bandwidth and infrastructure, which allows for faster data transfers and better performance.
"Distributed computing is a field of computer science that studies distributed systems."
"The components of a distributed system interact with one another in order to achieve a common goal."
"Three significant challenges of distributed systems are: maintaining concurrency of components, overcoming the lack of a global clock, and managing the independent failure of components."
"When a component of one system fails, the entire system does not fail."
"Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications."
"A computer program that runs within a distributed system is called a distributed program."
"Distributed programming is the process of writing such programs."
"There are many different types of implementations for the message passing mechanism, including pure HTTP, RPC-like connectors, and message queues."
"Distributed computing also refers to the use of distributed systems to solve computational problems."
"In distributed computing, a problem is divided into many tasks."
"Each task is solved by one or more computers, which communicate with each other via message passing."
"The components of a distributed system... communicate and coordinate their actions by passing messages to one another."
"Maintaining concurrency of components" is a significant challenge in distributed systems.
"Overcoming the lack of a global clock" is a significant challenge in distributed systems.
"Managing the independent failure of components" is a significant challenge in distributed systems.
"When a component of one system fails, the entire system does not fail."
"Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications."
"A computer program that runs within a distributed system is called a distributed program."
"Computers in distributed computing... communicate with each other via message passing."