Multi-Dimensional Database Performance Analysis

While standard databases, like relational databases, are two-dimensional, cross- or multi-dimensional databases offer a variety of dimensions for data—three dimensions or more.

Conceptually, the multi-dimensional database structure is based around the idea of a data cube. Within this cube, every point of data is accessible by multiple indexes. This access is referred to as “online analytical processing (OLAP) access.” In practical terms, this means using a multi-dimensional database structure, as opposed to a standard database, gives you fast access to top-level, summarized data you can then drill down into if you want more detail.

Some of the advantages of using a multi-dimensional, cross-platform database structure as compared to a relational database are:

• A multi-dimensional database can generate intuitive spreadsheet-like views of data that are more difficult to create with a relational database.
• Because data is both stored and viewed according to its fundamental attributes, there’s no extra computational overhead needed for database queries.
• Multi-dimensional database structures can achieve significantly higher performance levels than those of relational databases facing the same data storage requirements.

Multi-dimensional database structure performance analysis is important for optimizing database performance and reducing bottlenecks. More specifically, it’s important because it helps take the abstraction out of standard database monitoring by correlating metrics and putting them in context, so troubleshooting and optimization efforts can be more targeted and effective.

Many factors contribute to database performance, which means troubleshooting can be problematic without the right performance analysis tools. This is especially true since metrics are relative, and traditional monitoring tools often simply display dashboards with countless health metrics without any context, resulting in information overload and confusion. When you engage in multi-dimensional database structure performance analysis with a proper tool, like SolarWinds DPA, you can get data and insights in a format designed to help you address the core issues.

When you engage in multi-dimensional database structure performance analysis, you can better prevent problems caused by inefficient queries and resource contention, thanks to detailed views into your database performance. When you use a tool like DPA to collect and analyze database metrics, you can get actionable insights into the links between resource contention and response times, the wait times with the greatest impact on response time, and how database response times are impacting end users. This information can help you optimize your database performance, leading to faster response times, fewer bottlenecks, and happier end users.

A multi-dimensional database performance system offers 24/7 monitoring of the elements that can impact database performance. As it monitors those elements, the tool collects and analyzes the metrics, correlates relevant data, and provides analysis of the main elements impacting database performance.

One key metric for any multi-dimensional database structure performance analysis tool is response time, a critical aspect of performance. The tool may also monitor queries, top users, programs, wait times, and more. As with most database monitoring solutions, the tool will alert you when any of its monitored metrics is near or crosses a critical threshold.

In addition, a multi-dimensional database structure performance analysis tool lets you use context filters for the tool’s countless collected metrics. This lets you quickly filter down data to a close level of detail and then approach it from multiple angles.

Cross-platform database software also correlates resource metrics with database response time to help you see exactly how resource contention is impacting the database and how it eventually impacts application performance. The tool also monitors wait times to identify those with the greatest impact on database performance. It then offers explanations of the wait, how to resolve it, and who would typically deal with this kind of wait to help guide you through the problem-solving process.

SolarWinds Database Performance Analyzer is a multi-dimensional database performance analysis tool with cross-platform support for both on-premises and cloud databases. It uses machine learning for anomaly detection, meaning the tool is built to provide increasingly more accurate, system-aware insights into bottlenecks.

DPA collects and analyzes metrics related to the performance of your cross/multi database structure and provides views to help you see both top-level information and detailed data depending on your specific needs. For example, the Database Instance View provides a detailed view of what exactly your database is doing and how your resources are affected by that activity. If you want a broader view of your multi-dimensional database structure, you can use DPA’s standard dashboard, which gives you a broader view of the tool’s collected metrics with the option to click on specific data to see it in greater detail.

Specifically, DPA comes with context filters to let you filter down the data as much as you would like to, even to a specific instance of a query. The tool also automatically correlates database response time and resource metrics, so you can get to the root of resource contention. Additionally, DPA monitors wait times to better identify the precise cause behind slow SQL response times.

Most importantly, DPA is built to go beyond simply collecting metrics and analyzing them in isolation—the tool can correlate the data, providing context and analysis for the data in terms of what it means for the larger database and its performance.