Version – SQL Server 2014
Client based in – US
We achieved a great deal of client satisfaction by accomplishing this task.
Business Intelligence & Reporting
Linked Server and SSRS report performance
Recently we had to use linked servers to fetch data for some SSRS reports. Our initial approach degraded the performance and the reports were getting loaded very slow which was not at all acceptable. We had to come up with a solution that would use linked servers yet decrease the report loading time.
The Challenge
What Was Holding the Business Back?
Fetching remote data through Linked Servers caused slow report execution, resource-intensive queries, and concurrency issues that impacted user experience.
Slow Report Performance
SSRS reports experienced long loading times because Linked Server queries retrieved excessive remote data before applying filters.
Inefficient Query Execution
Complex queries using OpenQuery became difficult to maintain and repeatedly executed remote requests.
Concurrency Issues
Global temporary tables introduced deadlocks when multiple users accessed reports simultaneously.
Scalability Constraints
Sequential dataset execution reduced report throughput and prevented efficient parallel processing.
Objective
What We Set Out to Achieve
Improve SSRS report performance while continuing to use Linked Servers by reducing remote query overhead, eliminating concurrency bottlenecks, and enabling scalable report execution.
Our Approach & Solution
How We Delivered Results
Redesigned the data retrieval process using table-valued functions, table variables, and optimized SSRS execution to deliver faster and more scalable reporting.
Performance Assessment
Analyzed Linked Server query execution and identified unnecessary remote data retrieval as the primary performance bottleneck.
Query Optimization
Replaced repetitive remote calls with a table-valued function that retrieved only the required dataset efficiently.
Local Data Processing
Loaded remote data into local table variables, allowing multiple report operations to reuse the same dataset without repeated queries.
SSRS Optimization
Enabled parallel dataset execution and removed sequential transaction dependencies to improve report scalability and response times.
Linked Server Optimization
Minimized unnecessary remote data transfers by fetching data only once during report execution.
Reusable Data Layer
Implemented table-valued functions and local table variables to simplify query maintenance and improve performance.
Deadlock Elimination
Removed dependency on global temporary tables, eliminating concurrency conflicts during simultaneous report access.
Parallel Processing
Configured SSRS datasets to execute independently, maximizing throughput and reducing report wait times.
Results & Impact
The Outcome
The optimized reporting architecture significantly improved SSRS performance while maintaining scalability for multiple concurrent users.
Fast
Report Loading
Reduce
Remote Queries
Parallel
Dataset Execution
High
User Satisfaction
Conclusion
The Bigger Picture
By redesigning the Linked Server data retrieval strategy, replacing inefficient query patterns with table-valued functions and local table variables, and enabling parallel SSRS dataset execution, the solution transformed report performance. The client achieved faster report loading, eliminated concurrency issues, simplified maintenance, and delivered a significantly better reporting experience for end users.
Additional Details
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