Derived Horizontal Fragmentation

by

Derived Horizontal Fragmentation in Distributed Databases

Horizontal fragmentation in distributed databases involves dividing a database table into smaller parts called fragments, where each fragment contains a subset of the table’s rows. In derived horizontal fragmentation, the condition used to divide the data into fragments is based on derived predicates or access patterns that come from business requirements, user queries, or system usage.

This type of fragmentation improves performance by allowing the system to fetch only the relevant subset of data needed for specific queries, reducing query response time and improving overall efficiency.

Steps in Derived Horizontal Fragmentation

  1. Define the Fragmentation Predicate:
    • The first step is to identify the condition (predicate) that will be used to break the data into fragments. This predicate can be derived from access patterns, such as frequently accessed columns or data values. For example, if customers are often queried based on their age, the derived fragmentation condition might be based on age (e.g., age > 30 or age <= 30).
  2. Fragment the Data:
    • Once the fragmentation condition is defined, the database system divides the rows of the table into separate fragments based on whether they satisfy the condition. For example, using the condition age > 30, all customers older than 30 would form one fragment, and customers aged 30 or less would form another fragment.
  3. Store Fragments:
    • The resulting fragments are stored across different nodes in the distributed database. This allows for parallel processing of queries on different fragments, improving query performance.
  4. Query Processing:
    • When a query is executed, only the relevant fragments are accessed, minimizing the amount of data that needs to be retrieved and processed. For example, if a query is looking for customers aged over 30, the system will only access the fragment containing customers older than 30.

Example of Derived Horizontal Fragmentation

Consider a Customer table with the following columns: customer_id, name, age, and city.

Let’s say that the system frequently queries customers based on their age. A derived fragmentation condition based on age could be:

  • Fragment 1: Customers with age ≤ 30
  • Fragment 2: Customers with age > 30

This condition allows the data to be divided into two fragments:

  • Fragment 1: Contains all customers aged 30 or younger.
  • Fragment 2: Contains all customers older than 30.

Steps of Fragmentation:

  • Define the Fragmentation Predicate: We decide to fragment the data based on the condition age <= 30 and age > 30.
  • Fragment the Data: Customers who satisfy age <= 30 form the first fragment, and customers with age > 30 form the second fragment.
  • Store Fragments: These two fragments can be stored on different nodes or servers in a distributed system.
  • Query Processing: If a query requests customers aged over 30, only Fragment 2 will be accessed, reducing data retrieval time.

Advantages of Derived Horizontal Fragmentation

  1. Improved Performance: By breaking the data into smaller, more specific fragments, queries can be executed more efficiently. Only the relevant fragments are accessed based on the query conditions, improving performance by reducing the data scanned.
  2. Data Localization: Frequently accessed subsets of data (e.g., customers from a specific region or age group) can be stored on the same node, allowing quicker access and reduced network latency.
  3. Efficient Query Execution: Queries that need only a specific subset of the data can skip the irrelevant fragments, reducing I/O operations and speeding up query response times.
  4. Scalability: As the database grows, new fragments can be added or redistributed, making it easier to scale the system by adding more nodes to handle larger datasets.

Disadvantages of Derived Horizontal Fragmentation

  1. Complex Fragmentation Predicate: The process of defining an optimal fragmentation predicate can be complex. It requires understanding the access patterns and query behavior of the system. If the condition is poorly chosen, it may not improve query performance.
  2. Rebalancing Fragments: As data changes over time (e.g., new customers are added), the fragmentation may need to be rebalanced to maintain efficiency. This can be costly in terms of time and resources.
  3. Overlapping Access Patterns: If the predicate does not align well with actual query patterns, it could result in queries needing to access multiple fragments, reducing the intended performance benefits.

Correctness Rules for Fragmentation

When performing horizontal fragmentation, certain correctness rules should be followed to ensure that the fragmentation is effective and the system remains consistent:

  1. Completeness: Every row of the original table must appear in at least one of the fragments. No data should be lost during the fragmentation process.
  2. Disjointness: No row should appear in more than one fragment unless explicitly replicated. Each fragment should be non-overlapping.
  3. Correctness of Data Retrieval: When a query is executed that requires data from multiple fragments, the system must correctly retrieve and combine the data to produce the right result.
  4. Replication Consistency: If any fragments are replicated across multiple nodes, all copies of the fragments must be consistent. Any updates to a fragment must be reflected across all its replicas.

Derived horizontal fragmentation is a powerful technique in distributed databases to improve query performance by dividing large tables into smaller, more manageable fragments based on derived conditions. This allows for more efficient data retrieval, especially when queries frequently access specific subsets of the data. However, careful design of fragmentation predicates is crucial to ensure that the system benefits from this method. By adhering to fragmentation correctness rules, systems can achieve better performance, scalability, and data integrity.

Leave a Comment