What is a distributed database and why is it used?
A distributed database system spreads data storage and processing across multiple servers instead of relying on a single server, as a single database does. The servers communicate with each other over a network and can be in the same or different physical locations. The objective of building a distributed database is to enhance scalability, performance, and fault tolerance by spreading not only data but also workloads, across multiple servers.
How do distributed databases work?
Distributed databases work by networking multiple servers to store data. Each server in a distributed database is responsible for storing a subset of the data, which allows the database to scale out horizontally to handle large volumes of data and transactions.
Types of distributed databases
Distributed databases can be broadly classified into two categories: relational and non-relational.
Relational distributed databases
These databases follow the traditional relational model based on tables, rows, and columns. They use structured query language (SQL) for querying and maintaining atomicity, consistency, isolation, and durability (ACID) properties for transactions. Examples of relational distributed databases include Google Spanner and Amazon Aurora.
Non-relational distributed databases
Non-relational databases, also known as not only SQL (NoSQL), are designed to handle unstructured or semi-structured data and provide high scalability and flexibility. The four main types of NoSQL distributed databases are listed below.
Key-value databases use two main components to store data: a unique identifier, known as the key, and a value (e.g., a number, a string, an object) associated with the key. A key-value pair is essentially a simple mapping between the key and its corresponding value, making it ideal for simple data models. Examples include Amazon DynamoDB and Riak.
Document databases store data in documents that contain an object and its related metadata, usually in formats like JSON or BSON. They are suitable for complex data models and provide high flexibility. Examples include MongoDB and Couchbase.
Column-family databases organize columns of data into column families, which are groups of related columns stored together. Each column family is a collection of columns that are related to each other and may contain different types of data, such as strings, integers, or timestamps. They are ideal for large-scale, write-heavy workloads. Examples include Apache Cassandra and HBase.
Graph databases store data as nodes and the relationships between nodes as edges in a graph structure. This makes them ideal for handling complex relationships. Examples include Neo4j and Amazon Neptune.
Distributed database management systems (DDBMS)
A distributed database management system, or DDBMS, is software used to manage and maintain distributed databases. Some popular DDBMS solutions include:
Apache Cassandra: Cassandra is a highly scalable and distributed NoSQL database management system designed for handling large amounts of data across many servers. Cassandra provides high availability and fault tolerance
Google Spanner: Spanner is a globally distributed relational database management system that offers strong consistency, high availability, and horizontal scalability. Many large organizations, including financial institutions, telecommunications companies, and e-commerce platforms, use Spanner to store and manage their mission-critical data.
Amazon Aurora: Aurora is a relational database service that offers high performance, availability, and scalability for enterprise-level applications compatible with MySQL and PostgreSQL. It distributes data across multiple servers, making it a DDBMS. Its design focuses on providing high performance, scalability, and availability.
Transactions in distributed databases
Transactions play a crucial role in maintaining data consistency and integrity in distributed databases. They ensure that multiple operations, such as inserting or updating data, either succeed or fail as a unit. Distributed transactions maintain each of the individual ACID properties:
Atomicity: A transaction must either complete fully or have no effect at all. This means that the transaction commits either all the changes made by it to the database or none of them.
Consistency: A transaction must transition the database from one consistent state to another.
Isolation: The effects of each transaction must be isolated from all other transactions.
Durability: Once a transaction commits, its effects must be permanent.
To achieve these properties, distributed databases employ various protocols and techniques. For example, the two-phase commit (2PC) protocol ensures that all servers involved in a transaction either commit or abort the transaction. This maintains consistency across the distributed database because it ensures the same behavior for all transactions.
Another approach to handling distributed transactions is optimistic and pessimistic concurrency control. Optimistic concurrency control assumes conflicts between transactions are relatively rare and allows transactions to proceed without locking resources, validating conflicts only at commit time. Pessimistic concurrency control assumes conflicts are likely and locks resources to ensure that only one transaction at a time can access a resource.
Scalability and performance tuning in distributed databases
Scalability is a crucial property of distributed databases, enabling them to handle increased workloads without compromising performance. There are two main scaling strategies.
Horizontal scaling (sharding) involves adding more servers to the system. It scales out a database horizontally by partitioning data across an increased number of servers. Because each server stores a subset of the data, the database can handle more data and a higher processing load than a single server could handle.
Vertical scaling involves adding more resources, such as CPU, memory, and storage space, to existing servers. While this can improve performance, it has limitations such as increased cost and maintenance. Furthermore, adding more resources through vertical scaling can decrease the scalability of the system, meaning that it may become more difficult to further increase its performance beyond a certain point.
Real-world applications of distributed databases
Various industries use distributed databases to solve complex data storage and retrieval challenges. Some examples include:
Financial services: Banks and financial institutions use distributed databases to manage customer data, transactions, and risk analysis, benefiting from their high availability and fault tolerance.
E-commerce: Online retailers leverage distributed databases to manage user data, product catalogs, and orders, ensuring consistent and responsive customer experiences.
Social media: Social networking platforms use distributed databases to store and retrieve user-generated content, relationships, and interactions at scale.
Telecommunications: Telecom companies employ distributed databases to manage call records, customer data, and network configurations, ensuring high availability and fault tolerance.
Advantages and disadvantages of distributed databases
Distributed databases offer several advantages over traditional centralized databases, such as improved scalability, availability, and fault tolerance. However, when considering their use, one should take into account their disadvantages. Here are some of the advantages and disadvantages of distributed databases:
Scalability: Distributed databases can scale horizontally by adding more servers to the network. This allows them to handle large volumes of data and transactions.
Availability: By replicating data across multiple servers, distributed databases provide high availability and prevent data loss in the event of a server failure.
Fault tolerance: Distributed databases provide fault tolerance by replicating data across multiple servers, which reduces the risk of data loss or corruption.
Geographic distribution: Distributed databases enable global access to data by allowing storage and access of data from multiple geographic locations.
Reduced data transfer: Distributed databases can reduce the amount of data transfer between servers since each server stores only a subset of the data. Thus, unnecessary data transfers between servers are minimized, resulting in less network traffic and improved performance.
Complexity: Distributed databases are more complex to design, implement, and manage than centralized databases. They require coordination and communication between multiple servers.
Cost: Distributed databases can be more expensive to implement and manage than centralized databases because they require more hardware, software, and administrative resources.
Data consistency: Propagating updates to multiple servers in a timely manner to maintain data consistency can be challenging.
Security: Distributed databases require more complex security measures. They’re sometimes more vulnerable to security breaches than centralized databases because they have multiple attack vectors.
Network dependency: Distributed databases rely on network connections between servers. Each connection can be a single point of failure and impact performance.