6. Performance Considerations
Integrating ELabLIMS, SciForge, and PowerBI brings powerful analytics to laboratory operations, but it also introduces new performance challenges that must be addressed to ensure a smooth user experience and protect core systems. This chapter explores practical strategies for optimizing data access, scheduling, and query design, helping organizations deliver responsive dashboards without overburdening infrastructure.
A key consideration is how often PowerBI reports and dashboards refresh their data. While it may be tempting to update reports as frequently as possible, excessive polling can place unnecessary strain on both the SciForge API and the underlying ELabLIMS database. For most laboratory reporting needs, daily or hourly refreshes strike the right balance between data freshness and system stability. PowerBI Service makes it easy to configure these intervals, and by staggering refresh schedules across multiple reports, spikes in demand that might otherwise impact performance can be avoided.
Query design is equally important. SciForge’s GraphQL API empowers users to request exactly the data needed. By filtering at the source, such as specifying date ranges or statuses in queries, the volume of data transferred and processed is minimized. Selecting only the necessary fields further streamlines data retrieval, while leveraging endpoints that provide aggregated results (like monthly sample counts) can dramatically reduce the workload on both the API and PowerBI. This approach not only accelerates report refreshes but also ensures that dashboards remain responsive even as data volumes grow.
The impact of PowerBI integration on database and API infrastructure should not be underestimated. Each new dashboard or report introduces additional queries and connections. Monitoring API usage helps identify patterns that could lead to bottlenecks, and working with IT to limit concurrent connections can prevent resource contention. For organizations developing custom endpoints, reviewing SQL execution plans and ensuring proper indexing are essential steps to maintain optimal performance.
Efficient data retrieval is about more than just fast queries. For static or rarely changing datasets, caching results within PowerBI or using incremental refresh features can significantly reduce the load on systems. Before rolling out new reports, testing with production-sized data can uncover potential issues early. Persistent performance challenges should be referred to LabLynx support for advice on endpoint optimization or infrastructure scaling.
To illustrate these principles, consider a dashboard that tracks sample receiving volume by month, such as in prior examples. A purpose-built SciForge endpoint that returns monthly counts directly could provide performance benefits:
query {
getSampleRecdVolByMonth(input: {
beginDt: "2025-04-01",
endDt: "2025-10-09"
}) {
year
month
count
}
}This approach minimizes data transfer and leverages database-side aggregation.
Implementation of these performance strategies ensures that PowerBI reports and dashboards deliver timely insights while maintaining the stability and responsiveness of ELabLIMS and SciForge integration.