Query decomposition and data localization, key steps in distributed query processing, involve breaking
down a complex query into smaller, manageable subqueries and then mapping those subqueries to the
specific data fragments where the relevant data resides across a distributed database, essentially
optimizing query execution by minimizing data movement across the network.
Steps involved:
1. Query Decomposition:
i. Parsing and Normalization: The initial query is parsed to understand its structure and then
converted into a normalized form, often using relational algebra operations like select, project,
and join.
ii. Analysis and Redundancy Elimination: The query is analyzed to identify redundant predicates or
conditions which can be removed to simplify the query.
iii. Algebraic Rewriting: The query is rewritten in terms of relational algebra expressions, allowing
for manipulation and optimization based on data distribution information.
iv. Subquery Generation: The complex query is decomposed into smaller, independent subqueries
that can be executed in parallel across different nodes in the distributed system.
2. Data Localization:
i. Fragmentation Information: The system utilizes information about how data is fragmented
across different database sites (fragmentation schema) to determine which data fragments are
relevant to each subquery.
ii. Mapping to Local Fragments: Each subquery is mapped to the specific data fragments where the
necessary data is stored, allowing for local execution on the relevant nodes.
iii. Query Optimization: Based on the fragmentation information, the subqueries may be further
optimized to minimize data transfer and improve query performance.
Query decomposition and data localization address the challenges of querying fragmented and
replicated data by breaking down a complex query into smaller, manageable subqueries that can
be executed on the specific nodes where the relevant data fragments reside, minimizing network
traffic and optimizing query performance by leveraging the distributed nature of the data
storage; essentially, they allow the system to efficiently target only the necessary data portions
across multiple nodes instead of retrieving all data from everywhere.
Key aspects of how they work:
1. Query Decomposition:
i. Breaking down the query: A complex query is broken down into smaller subqueries
based on the logical structure and data relationships, allowing each subquery to be
executed on a specific set of data fragments.
ii. Identifying relevant fragments: The system analyzes the query to determine which data
fragments are needed to answer each subquery, thereby directing the processing to the
appropriate nodes.
iii. Optimizing execution plan: By considering factors like data locality, network bandwidth,
and processing power, the system can choose the most efficient way to execute each
subquery on the relevant nodes.
2. Data Localization:
i. Mapping query to data fragments: The query is translated into operations that can be
performed directly on the distributed data fragments based on the fragmentation
scheme.
ii. Fragmentation information: The system utilizes information about how the data is
fragmented across different nodes to determine which nodes need to be accessed to
retrieve the necessary data.
iii. Local execution: Once the query is localized, each subquery is executed on the specific
node where the relevant data fragments are stored, minimizing data transfer over the
network.
Thus, query decomposition and data localization optimize distributed query processing by breaking complex queries into smaller subqueries and executing them on relevant data fragments. This minimizes data movement, reduces network overhead, and enhances query performance in distributed databases.