Distributed Database Systems Concepts

A database system where data is stored and managed across multiple physical locations connected by a network.

A database system where all data is stored and managed in a single physical location.

The ability for users to access data from a distributed database as if it were stored locally.

The process of defining relationships and mapping data between different databases in a distributed system.

The process of identifying if two database objects (like tables) have similar semantics or meaning.

A distributed database system where all sites use the same database software and schema.

A distributed database system where different sites can use different database software and schemas.

A database system where data is replicated across multiple sites to ensure high availability and fault tolerance.

In a tree-like data structure, nodes that have no children, representing the end of a branch.

In distributed query processing, a semi-join operator efficiently reduces data transmission by only sending necessary data.

Rebuilding a table from vertical fragments involves combining the fragments via a join operation.

Concatenating horizontal fragments into a single table can be achieved with a union operation.

In logical integration, a global conceptual schema is virtual, not physically materialized.

Data allocation is the process of assigning data fragments to specific sites in a distributed system.

Intelligent data fragmentation for better performance and availability for end-users.

A replication schema describes how fragments are replicated in a distributed database.

The process of dividing a database into smaller, independent sub-tables or relations, allowing data to be distributed and stored efficiently across different systems.

When performing fragmentation, it's crucial to ensure that the sub-tables can be recombined to recover the original database structure.

The process of dividing a complete database into smaller sub-tables or relations for efficient storage and management across multiple systems.

Query processing in distributed database systems is inherently different from centralized systems because the need to exchange data across physically separated locations adds communication overhead and complexity.

In physical data integration, the source databases are combined into a single, integrated database that is physically stored. This integrated database is materialized, meaning it exists as a concrete data structure.

Extract-Transform-Load (ETL) tools are crucial for integrating data in distributed database systems. They extract data from source systems, transform it into a consistent format, and load it into the target database.

In the query trading algorithm, the site initiating a distributed query is referred to as the requester, while the sites processing the query locally are considered sellers.

When performing operations like joins or unions on fragments located across multiple sites, it's more efficient to transfer the fragmented data to a single site where most of the data resides and execute the operation there.

Query optimization that takes into account the characteristics and resources of each individual database instance (DBE) in a distributed system.

Query optimization that considers the overall distribution of data across all DBEs in a distributed network, optimizing data transfer and communication between sites

The time it takes to process a query at a local database instance, including CPU and disk I/O operations.

The cost associated with transmitting data between different database instances in a distributed system.

A data structure that visually represents the sequence of operations involved in processing a relational database query.

The process of finding the most efficient way to execute a query in a distributed database environment, by considering both local and global optimization strategies.

The aim of distributed query optimization: finding a solution that is good enough, but not necessarily the absolute best, to optimize query execution.

Ensures all replicas in a distributed system eventually converge to the same value, but doesn't guarantee real-time consistency.

Guarantees that all replicas in a distributed system have the same data at all times.

A type of consistency in distributed databases where data is guaranteed to be accurate and up-to-date across all nodes.

The property of a transaction that ensures all changes happen atomically, meaning they either all succeed or all fail.

A technique used to manage locks in a distributed database system where a central lock manager is responsible for granting and releasing locks.

A deadlock prevention method that uses timestamps to prioritize transactions, resolving deadlocks by potentially aborting lower-priority transactions.

A mechanism used to recover from a deadlock situation in a distributed database system.

The probability that a system will be operational according to its specifications at a given point in time.

Data that is organized in a structured format, often in rows and columns, making it easy to analyze and process. Think spreadsheets or relational databases.

Data that lacks a predefined structure or organization, making it challenging to analyze and process. Examples include text files, images, audio files, and videos.

Data that has a semi-structured format, meaning it has some organization but not as rigid as structured data. Examples include XML and JSON documents.

Unstructured data is challenging to analyze and process because it requires different algorithms and can take longer time to analyze.

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