15.1 Distributed database architectures
4 min read•august 6, 2024
Distributed database architectures are the backbone of modern data management systems. They allow organizations to store and access data across multiple locations, improving availability, reliability, and performance. This topic explores the different types of architectures and their key characteristics.
Understanding distributed database architectures is crucial for designing efficient and scalable systems. We'll look at client-server and peer-to-peer models, homogeneous and heterogeneous systems, and important concepts like data distribution, transparency, and autonomy. These form the foundation for building robust distributed databases.
Architecture Types
Distributed Database System Fundamentals
Top images from around the web for Distributed Database System Fundamentals
Frontiers | A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives View original
Is this image relevant?
An introduction to Apache Hadoop for big data | Opensource.com View original
Is this image relevant?
Analysis of Blockchain Architecture in File Sharing Management for Tertiary Institution View original
Is this image relevant?
Frontiers | A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives View original
Is this image relevant?
An introduction to Apache Hadoop for big data | Opensource.com View original
Is this image relevant?
1 of 3
Top images from around the web for Distributed Database System Fundamentals
Frontiers | A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives View original
Is this image relevant?
An introduction to Apache Hadoop for big data | Opensource.com View original
Is this image relevant?
Analysis of Blockchain Architecture in File Sharing Management for Tertiary Institution View original
Is this image relevant?
Frontiers | A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives View original
Is this image relevant?
An introduction to Apache Hadoop for big data | Opensource.com View original
Is this image relevant?
1 of 3
- Distributed database system consists of a single logical database that is spread physically across computers in multiple locations connected by a data communications network
- Enables data to be stored and accessed from multiple sites, providing benefits such as increased availability, reliability, and performance
- Facilitates data sharing and collaboration among geographically dispersed users and applications (global organizations with offices in different countries)
- Supports parallel processing and by distributing data and workload across multiple nodes
Client-Server and Peer-to-Peer Architectures
- Client-server architecture divides the system into two main components: clients that request services and servers that provide services
- Clients send requests to servers, which process the requests and send back responses (web browsers requesting web pages from web servers)
- Servers are responsible for managing data, processing queries, and coordinating transactions
- Provides centralized control and security but can create performance bottlenecks and single points of failure
- Peer-to-peer (P2P) architecture allows each node in the system to act as both a client and a server
- Nodes can directly communicate and share data with each other without relying on a central server (file-sharing networks like BitTorrent)
- Offers increased scalability, fault tolerance, and load balancing by distributing data and processing across multiple nodes
- Challenges include ensuring data consistency, security, and efficient query processing in a decentralized environment
Homogeneous and Heterogeneous Systems
- Homogeneous distributed database systems use the same DBMS software and hardware at all sites
- Simplifies system management, , and query processing due to consistent data models, schemas, and interfaces (chain of retail stores using the same database system across all locations)
- Enables seamless data integration and interoperability among the sites
- Heterogeneous distributed database systems integrate different DBMS software and hardware platforms
- Allows organizations to leverage existing systems and choose the best-suited DBMS for each site's requirements (company merger involving different database systems)
- Requires middleware or data integration tools to handle data translation, schema mapping, and query decomposition across the diverse systems
- Presents challenges in ensuring data consistency, query optimization, and transaction management due to the differences in data models, query languages, and concurrency control mechanisms
Data Distribution and Characteristics
Data Distribution Strategies
- Data distribution involves deciding how to partition and allocate data across the sites in a distributed database system
- Horizontal fragmentation partitions a relation by splitting it into subsets of tuples (rows) based on a criteria (customer records partitioned by region)
- Vertical fragmentation partitions a relation by splitting it into subsets of attributes (columns) based on the access patterns and requirements of applications (separating frequently accessed attributes from rarely accessed ones)
- Replication involves maintaining copies of data fragments at multiple sites to improve availability, reliability, and performance (storing customer data at both the main office and regional offices)
- Careful data distribution is crucial for optimizing query processing, minimizing data transfer, and ensuring efficient access to data
Transparency and Autonomy
- Transparency hides the details of data distribution and system architecture from users and applications
- Location transparency allows accessing data without knowing its physical location (using a global schema to query data across multiple sites)
- Replication transparency hides the existence of multiple copies of data, providing a single logical view (updating data at one site automatically updates the replicas)
- Fragmentation transparency presents a fragmented relation as a single logical relation, hiding the partitioning details (querying a global relation that is horizontally partitioned across sites)
- Autonomy refers to the ability of each site in a distributed database system to control its local data and operations
- Design autonomy allows sites to choose their own data models, schemas, and constraints (each department in an organization maintaining its own database design)
- Execution autonomy enables sites to execute transactions independently, without global coordination (local transactions accessing only local data)
- Communication autonomy allows sites to decide when and how to communicate with other sites (sites exchanging updates or synchronizing data on their own schedules)
Scalability and Reliability
- Scalability is the ability of a distributed database system to handle increasing amounts of data, users, and transactions without significant performance degradation
- Horizontal scalability (scale-out) involves adding more nodes to the system to distribute the workload (adding new servers to handle increased traffic)
- Vertical scalability (scale-up) involves increasing the capacity of individual nodes, such as upgrading hardware or optimizing software (adding more memory or faster processors to existing servers)
- Distributed database systems can scale by partitioning data across multiple nodes and leveraging parallel processing
- Reliability ensures that the distributed database system remains operational and provides correct results even in the presence of failures
- Replication helps improve reliability by maintaining multiple copies of data, allowing the system to continue functioning if some nodes fail (data is still accessible from the remaining replicas)
- Fault tolerance mechanisms, such as transaction logging and recovery protocols, help maintain data consistency and integrity in case of failures ( protocol ensures atomicity of distributed transactions)
- Distributed database systems can detect and recover from various types of failures, such as node crashes, network partitions, and data corruptions, to provide high availability and data durability
Key Terms to Review (16)
CAP Theorem: The CAP Theorem states that in a distributed data store, it's impossible to simultaneously guarantee all three of the following properties: Consistency, Availability, and Partition Tolerance. This theorem highlights the trade-offs that developers must make when designing distributed systems, particularly as databases evolved to support more complex and scalable architectures.
Client-server model: The client-server model is a computing architecture that separates tasks or workloads between service providers, known as servers, and service requesters, known as clients. This model allows clients to access shared resources and services hosted on servers, facilitating communication and resource management in distributed environments. It is foundational for many networked applications and supports scalability, as multiple clients can interact with a single server or a group of servers simultaneously.
Data fragmentation: Data fragmentation refers to the process of dividing a database into smaller, manageable pieces that can be distributed across multiple locations in a network. This approach optimizes performance and improves access speed by allowing data to be stored closer to where it is needed, which is crucial in distributed database systems. Effective data fragmentation strategies help balance workload and enhance overall system efficiency.
Data replication: Data replication is the process of storing copies of data at multiple locations to ensure consistency, reliability, and availability across a distributed database system. This technique helps in minimizing data loss and enhancing access speed for users by allowing them to retrieve data from the nearest copy. It plays a crucial role in maintaining data integrity, improving performance, and facilitating fault tolerance within distributed architectures.
Distributed query processing: Distributed query processing refers to the method of executing database queries across multiple, interconnected databases located in different physical locations. This approach helps optimize data retrieval by breaking down queries into smaller sub-queries, allowing for parallel execution and efficient data access. Distributed query processing is integral to distributed database architectures, as it ensures that users can access and manipulate data seamlessly across various nodes.
Distributed transaction management: Distributed transaction management refers to the coordination of multiple transactions that span across different databases or data sources. This process ensures that all parts of a transaction either succeed or fail as a single unit, maintaining data integrity and consistency across distributed systems. It involves complex protocols to handle communication between databases, manage locking mechanisms, and ensure proper recovery in case of failures.
Eventual consistency: Eventual consistency is a consistency model used in distributed systems that ensures that, given enough time and no new updates, all replicas of a data item will converge to the same value. This model is essential in scenarios where high availability and partition tolerance are prioritized over immediate consistency, allowing for greater flexibility in distributed database architectures. It plays a crucial role in NoSQL databases, enabling them to handle large volumes of data across various nodes while maintaining performance.
Heterogeneous distributed databases: Heterogeneous distributed databases are systems that combine multiple databases, which may differ in their data models, schemas, and underlying technologies, into a single unified database system. This approach enables organizations to leverage data from various sources while allowing them to maintain autonomy over their individual database systems. In this context, interoperability is key, as these systems must effectively communicate and share data despite their differences.
Homogeneous distributed databases: Homogeneous distributed databases are systems where all nodes share the same underlying architecture, database management system (DBMS), and data model. This uniformity allows for easier data management and interoperability among different sites, as each node operates with the same set of tools and protocols. Such databases streamline data sharing and access while maintaining consistency and simplifying administration tasks.
Load balancing: Load balancing is the process of distributing workloads across multiple computing resources to ensure optimal resource use, maximize throughput, minimize response time, and avoid overload on any single resource. This concept is crucial in maintaining the performance and reliability of systems, especially in environments where multiple databases or servers operate concurrently, as it helps manage the distribution of data requests and processing tasks efficiently.
Message passing: Message passing is a communication method used in distributed systems where processes exchange data by sending and receiving messages. This approach enables processes to work together across different nodes in a distributed database architecture, facilitating coordination, synchronization, and data sharing without requiring shared memory. It plays a critical role in ensuring that data consistency and integrity are maintained across various locations.
Partitioning: Partitioning is the process of dividing a database into smaller, more manageable segments, called partitions, to improve performance and maintainability. This technique allows for more efficient data access and management by spreading the workload across multiple servers or nodes, ultimately leading to better resource utilization and quicker query responses.
Peer-to-peer model: The peer-to-peer model is a decentralized network architecture where each participant, or 'peer', can act as both a client and a server, allowing for direct communication and resource sharing without relying on a central authority. This model enhances scalability and reliability, as peers can connect directly to one another to share data or resources, making it especially effective in distributed environments.
Remote Procedure Calls: Remote Procedure Calls (RPC) are a protocol that allows a program to execute a procedure on a remote server as if it were a local call. This mechanism simplifies the process of communication between distributed systems by enabling functions to be called remotely, making it easier to access services or resources across networked systems. RPC is critical in distributed database architectures as it facilitates seamless interaction between different database nodes and applications, streamlining data retrieval and manipulation across various locations.
Sharding: Sharding is a database architecture pattern that involves partitioning a database into smaller, more manageable pieces called shards, which can be spread across multiple servers or nodes. This approach enhances performance and scalability by allowing the system to handle larger datasets and increased loads while also enabling parallel processing of queries across shards. Sharding plays a crucial role in distributed database systems as it helps distribute the data efficiently and ensures high availability.
Two-phase commit: Two-phase commit is a distributed algorithm used to ensure that a transaction is completed successfully across multiple databases in a distributed system. It provides a way to achieve atomicity in transactions, meaning either all parts of the transaction are committed or none are, even when the databases are located in different places. This process helps maintain data consistency and integrity across the network.