Distributed Database

Enhanced Definition

A distributed database is a database system where data is stored across multiple physical locations (nodes or sites) but is logically presented as a single database to the user or application. In the mainframe context, this typically involves data residing on a z/OS system (e.g., DB2 for z/OS) and also on other platforms, with mechanisms for data access, synchronization, and transaction coordination across the network. A distributed database is a collection of logically interrelated databases distributed across multiple interconnected computer systems, often located at different physical sites. In the mainframe context, this typically involves a z/OS system hosting part of the data (e.g., via DB2 for z/OS) and interacting with other databases, which might be on other z/OS systems or heterogeneous platforms, to provide a unified view of data to applications. Its primary purpose is to allow applications to access data transparently, regardless of its physical location.

Key Characteristics

- Data Distribution: Data is partitioned, fragmented, or replicated across different physical locations, which can include both mainframe and distributed servers.
- Location Transparency: Applications and users interact with the database without needing to know the physical location of the data, simplifying development and data access.
- Network Dependency: Relies heavily on robust and reliable network connectivity (e.g., TCP/IP) for communication between the mainframe and distributed database nodes.
- Distributed Transaction Management: Requires sophisticated protocols, such as two-phase commit (2PC), to ensure atomicity and consistency of transactions that span multiple database sites.
- Heterogeneous Environments: Often involves different database management systems (DBMS) and operating systems across the distributed nodes, requiring interoperability solutions like DRDA.
- Scalability and Availability: Can offer improved scalability by distributing workload and enhanced availability through data replication and failover capabilities across sites.

Use Cases

- Enterprise-wide Data Integration: Connecting critical mainframe data (e.g., customer master data in DB2 for z/OS) with data residing on distributed departmental servers or web application databases to provide a unified enterprise view.
- Business Intelligence and Data Warehousing: Extracting and distributing subsets of mainframe operational data to distributed data marts or data warehouses for analytical processing, minimizing impact on mainframe OLTP.
- Geographically Dispersed Operations: Supporting applications where users or business units are spread across different locations, needing local access to relevant data while maintaining central control and synchronization with mainframe systems.
- Disaster Recovery and High Availability: Replicating critical mainframe data to remote distributed sites, allowing for quicker recovery or failover in the event of a localized outage.
- Workload Offloading: Moving read-intensive or less critical data access from the mainframe to distributed systems to reduce mainframe CPU consumption, while still maintaining data synchronization with the system of record on z/OS.

Related Concepts

The Distributed Data Facility (DDF) is a core component of DB2 for z/OS that enables it to participate in distributed database environments, allowing remote applications to access mainframe DB2 data and mainframe applications to access remote DB2 data. DB2 Connect acts as a crucial gateway, enabling distributed applications (e.g., Java, .NET) on various platforms to seamlessly access DB2 for z/OS databases. Two-Phase Commit (2PC) is a vital protocol used by DDF and other distributed transaction managers to ensure the atomicity of transactions spanning multiple database systems, guaranteeing that all changes are either committed or rolled back together. Data Replication technologies, such as IBM Data Replication (Q Replication or SQL Replication), are frequently employed to keep data synchronized between mainframe DB2 and distributed databases, supporting various distributed architectures.

Best Practices:

Strategic Data Placement: Design data distribution based on access patterns, performance requirements, and data consistency needs to minimize network traffic and optimize query response times.
Robust Network Infrastructure: Ensure high-bandwidth, low-latency, and highly available network connectivity between all participating mainframe and distributed database nodes.
Implement Two-Phase Commit (2PC) Judiciously: Use 2PC for critical transactions requiring absolute atomicity across multiple databases, but be aware of its performance overhead and consider alternatives like eventual consistency for less critical data.
Monitor Performance and Latency: Continuously monitor network latency, transaction response times, and resource utilization across all distributed nodes, including the mainframe, to proactively identify and address bottlenecks.
Standardize Data Access Protocols: Utilize industry-standard protocols like DRDA and tools like DB2 Connect to simplify application development, ensure interoperability, and manage distributed connections efficiently.
Comprehensive Security Model: Implement a unified security model across all distributed components, including network encryption, robust authentication, and granular authorization, to protect sensitive data in transit and at rest.