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Distributed systems refer to computer systems composed of multiple interconnected nodes that work together to achieve a common goal. These nodes can be located in different physical locations and communicate with each other over a network. The goal of distributed systems is to provide increased scalability, reliability, and performance compared to centralized systems by distributing tasks across multiple machines.
One of the key challenges in distributed systems is achieving coordination and synchronization among nodes, especially in the face of failures and network partitions. Various techniques and protocols, such as consensus algorithms like Paxos and Raft, are used to ensure consistency and fault tolerance in distributed systems. These algorithms enable nodes to agree on a common state even in the presence of failures.
Distributed systems often rely on message passing as the primary means of communication between nodes. Messages can be sent asynchronously or synchronously, depending on the requirements of the application. Asynchronous communication allows nodes to continue processing tasks without waiting for a response, while synchronous communication requires nodes to wait for a response before proceeding.
One of the fundamental concepts in distributed systems is the client-server model, where clients request services from servers over a network. This model is commonly used in web applications, where clients interact with servers to retrieve and update data. Load balancing techniques, such as round-robin and least-connections, are used to distribute client requests across multiple servers to improve scalability and fault tolerance.
Another important concept in distributed systems is fault tolerance, which involves designing systems that can continue to operate correctly even in the presence of failures. Techniques such as replication, redundancy, and error detection and recovery are used to ensure that distributed systems can withstand failures of individual components.
Distributed systems often rely on distributed databases to store and manage data across multiple nodes. These databases are designed to provide high availability, fault tolerance, and scalability by replicating data across multiple nodes and distributing query processing. Examples of distributed databases include Apache Cassandra, Amazon DynamoDB, and Google Spanner.
In addition to databases, distributed systems also rely on distributed file systems to store and manage large amounts of data across multiple nodes. These file systems provide features such as scalability, fault tolerance, and consistency by distributing data across multiple nodes and replicating it for redundancy. Examples of distributed file systems include Hadoop Distributed File System (HDFS) and Google File System (GFS).
Distributed systems often rely on middleware to provide abstractions and services that simplify the development of distributed applications. Middleware provides features such as communication protocols, message queuing, and distributed transactions, allowing developers to focus on application logic rather than low-level networking details.
One of the challenges in distributed systems is ensuring security and privacy of data transmitted over the network. Techniques such as encryption, authentication, and access control are used to protect data from unauthorized access and tampering. Secure communication protocols such as SSL/TLS are used to encrypt data transmitted over the network.
Another challenge in distributed systems is managing consistency and concurrency control in distributed databases. Techniques such as distributed locking, optimistic concurrency control, and multi-version concurrency control are used to ensure that multiple nodes can access and update data concurrently without violating consistency constraints.
Distributed systems often rely on consensus algorithms to achieve agreement among nodes on a shared state or decision. Consensus algorithms ensure that all nodes in a distributed system reach a consistent view of the system state even in the presence of failures or network partitions. Examples of consensus algorithms include Paxos, Raft, and ZAB (ZooKeeper Atomic Broadcast).
Distributed systems are used in a wide range of applications, including web services, cloud computing, and IoT (Internet of Things). They provide the foundation for building scalable, reliable, and high-performance systems that can handle large volumes of data and support millions of users simultaneously.
Overall, distributed systems play a crucial role in modern computing by enabling the development of scalable, reliable, and high-performance applications. They provide the infrastructure for building distributed applications that can operate across multiple nodes and withstand failures and network partitions.
For further details and resources on distributed systems, you can explore the following links: - [Distributed Systems Principles and Paradigms (Book)](https://www.amazon.com/Distributed-Systems-Principles-Paradigms-Tanenbaum/dp/0132143011) - [Distributed Systems for Fun and Profit (Online Book)](https://book.mixu.net/distsys/single-page.html) - [MIT Distributed Systems Course Materials](https://pdos.csail.mit.edu/6.824/) - [Google File System Paper](https://research.google/pubs/pub51/) - [Amazon DynamoDB Documentation](https://docs.aws.amazon.com/amazondynamodb/) - [Apache Cassandra Documentation](https://cassandra.apache.org/doc/latest/) - [Introduction to Distributed Systems (Video Lecture)](https://www.youtube.com/watch?v=7d2I8uu0prk) - [Distributed Systems Theory for the Distributed Systems Engineer (Blog Post)](https://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/) - [Consistency in Distributed Systems (Blog Post)](https://martin.kleppmann.com/2015/05/11/please-stop-calling-databases-cp-or-ap.html)
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers.
The components of a distributed system communicate and coordinate their actions by passing messages to 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 microservices to massively multiplayer online games to peer-to-peer applications. Distributed systems cost significantly more than monolithic architectures, primarily due to increased needs for additional hardware, servers, gateways, firewalls, new subnets, proxies, and so on. Also, distributed systems are prone to fallacies of distributed computing. On the other hand, a well designed distributed system is more scalable, more durable, more changeable and more fine-tuned than a monolithic application deployed on a single machine. According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant." Serverless technologies fit this definition but the total cost of ownership, and not just the infra cost must be considered.
A computer program that runs within a distributed system is called a distributed program, and 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 of which is solved by one or more computers, which communicate with each other via message passing.
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