Dissecting SolarWinds (Part One): SQL Server Dependency: The Hidden Bottleneck in Scalability

Before entering the discussion, it is necessary to emphasize that the goal of this series of articles is not to question the value or capability of the prominent IT infrastructure monitoring tools. Many of these solutions have been used in various organizations for years and play an important role in monitoring and managing IT environments. What is examined in this series of articles is more of an analytical look at some limitations and challenges that can become problematic for organizations. Therefore, if you intend to purchase SolarWinds software, we suggest that before making a decision, you also study the series of articles on dissecting SolarWinds.

In a medium-sized environment containing several thousand nodes and tens of thousands of interfaces, the simultaneous occurrence of entirely ordinary events—such as increased network traffic, a link status change, the execution of a scheduled report, and multiple users accessing dashboards—can quickly lead to resource saturation and performance degradation in SolarWinds.

The primary cause of this behavior does not lie within the data collection engines themselves, but rather in the database upon which nearly every system operation depends.

At the exact moment when the data collection modules are writing information into large database tables, the alerting engine is reading from those same tables to evaluate conditions, while dashboards simultaneously execute their own visualization queries. This intense concurrency of read and write operations turns SQL Server into an inherent bottleneck.

This situation is not merely an isolated flaw or an occasional implementation issue. Instead, it can be viewed as a consequence of SolarWinds' centralized architectural design, in which data ingestion, alert processing, reporting, and visualization rely heavily on a central data infrastructure.

The conclusion is straightforward:

Even under entirely normal workloads, SQL Server can reach its processing limits and affect the performance of the entire platform. This behavior is not primarily the result of insufficient hardware resources; it is an inherent characteristic of SolarWinds' centralized architecture.

The Single Foundation of Operations: SQL Server

SolarWinds consists of several components, but in practice, almost all read operations, write operations, and processing activities ultimately converge on SQL Server.

Although Orion consists of multiple components, its significant reliance on SQL Server can create a central bottleneck at scale.

The three primary operational flows within SolarWinds are:

• Write operations: Continuous insertion of metrics by data collection modules.
• Read operations: Dashboards, reports, and tools such as PerfStack.
• Processing combined with reads: Evaluation and execution of alerting rules.

These activities occur simultaneously on SQL Server without a dedicated queuing mechanism or independent processing path. Consequently, whenever raw data ingestion overlaps with intensive dashboard queries or alert evaluations, SQL Server can rapidly approach saturation.

Modern monitoring architectures take a different approach by separating data ingestion, event processing, and visualization into independent layers, each running on technologies optimized for its specific purpose.

In SolarWinds, however, SQL Server effectively supports the time-series data storage, alert processing, reporting, and dashboard workloads. This is a role that SQL Server was fundamentally not designed to fulfill.

The Limitations of Orion's Relational Data Model

The challenge with SolarWinds is not merely its reliance on SQL Server; the design of its data schema also creates limitations at scale.

Orion's relational model depends heavily on multiple joins between tables, and this approach introduces significant computational costs in large environments.

Three major limitations stand out:

• Heavy aggregation workloads: Raw data is initially stored in detailed tables and later transformed into hourly and daily summaries. This process imposes substantial processing overhead directly on SQL Server.
• Multi-layer table joins: Even a relatively simple dashboard often requires a chain of joins, and as data volume increases, both complexity and execution cost grow exponentially.
• Poor suitability for time-series workloads: Traditional SQL databases were not originally designed for trend analysis, baseline calculations, or Top-N queries. These operations often become computationally expensive and time-consuming.

As a result, any increase in data collection rates or dashboard usage is transferred directly to SQL Server without any protective abstraction layer. This component becomes the central bottleneck of the platform, and the problem cannot be solved sustainably by simply adding more hardware.

Aging Alerting Architecture and the Alert Rule Explosion Problem

SolarWinds' alerting architecture is built on an older design philosophy that remains heavily dependent on SQL Server. As the number of monitored resources increases, its behavior can grow exponentially and become increasingly difficult to manage.

Every alert rule must continuously evaluate status tables and summarized datasets. As the number of nodes and interfaces grows, the number of evaluations increases dramatically.

Because the alerting engine, processing layer, and database all operate within the same chain, increased load in one area immediately propagates to the others.

The result is:

• Delays in alert processing
• Increased SQL Server load
• False-positive alerts
• Overall platform slowdowns

The phenomenon commonly referred to as alert rule explosion is also prevalent in large environments. To cover different operational scenarios, teams often create large numbers of alert rules, and each additional rule introduces several more expensive queries against SQL Server.

The absence of a dedicated time-series processing engine causes alerting to move away from real-time behavior and become more similar to a batch-processing system.

Under heavy load conditions, even critical alerts may be significantly delayed or fail to execute altogether.

Orion's Architectural Limitations in Microservices and Cloud-Native Environments

Orion was designed during an era in which infrastructures were relatively static and predictable, and periodic polling adequately met operational requirements.

Modern environments, however, are fundamentally different:

• Pods and containers can be created and destroyed within seconds.
• Service behavior changes continuously.
• Organizations increasingly require real-time and event-driven data.

SolarWinds' centralized polling model, heavily dependent on SQL Server, struggles to keep pace with this rate of change.

Even integration with Kubernetes often introduces latency, overhead, and visibility gaps due to the platform's reliance on a relational database and centralized core architecture.

An even greater challenge is that SolarWinds lacks a genuinely service-centric model.

From Orion's perspective, a Pod, a Deployment, and an Endpoint are merely collections of individual objects rather than components of a unified service.

The absence of continuous telemetry, the lack of native distributed tracing capabilities, and the absence of a dedicated event aggregation layer mean that SolarWinds can become not only inefficient in microservices environments but can also rapidly approach operational pressure limits even at moderate scale.

Conclusion

An analysis of SolarWinds' architecture reveals that many of its challenges stem from the era in which it was originally designed—an era characterized by slower infrastructure change and environments where concentrating functionality around a central core seemed entirely reasonable.

Today, with continuously changing infrastructures, massive scale requirements, and the need for real-time processing, it is natural that this traditional model cannot fully satisfy the requirements of modern environments.

Alongside SolarWinds, newer platforms such as Moein Monitoring Platform have been designed around next-generation architectural principles and incorporate capabilities that address some of the requirements of dynamic environments at their foundational layer.

This difference should not be interpreted as evidence of an absolute weakness in one platform or definitive superiority in another. Rather, it reflects two fundamentally different design philosophies that were created to solve different sets of problems.

Series of Articles on Dissecting SolarWinds