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Top 5 Most Common Ways of Using Log Data for Performance Management

Trevor Parsons

Logentries identified the top five most common ways that log data is used for monitoring and analyzing system performance.

Our research team looked across more than 25,000 log management and analytics users, tracking more than 200,000 shared patterns, and identified the top five areas that are most frequently analyzed for performance-related issues. The top five pattern categories for performance monitoring included:

1. Slow Response Times

Response times are one of the most common and useful performance measures that are available from your log data. They give you an immediate understanding of how long a request is taking to be returned.

2. Memory Issues and Garbage Collection

Out-of-memory errors can be catastrophic when they occur as they often result in an application crashing due to lack of resources.

3. Deadlocks and Threading Issues

Deadlocks can occur in many shapes and sizes and can have negative effects when they occur - from bringing your system to a complete halt to simply slowing it down.

4. High Resource Usage (CPU/Disk/ Network)

In many cases, lower system performance may not be a result of any major software flaw, but can be a simple case of system load increasing without increasing resources available to manage the increased demand.

5. Database Issues and Slow Queries

One of the most common areas for slow system performance can be a result of long-running database queries. Once identified, these queries can be optimized for significant performance improvements. Setting acceptable thresholds for query time and reporting on anything that exceeds these thresholds can help you quickly identify when your users experience is being effected.

Trevor Parsons, PhD, is Co-founder and Chief Scientist of Logentries.

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Top 5 Most Common Ways of Using Log Data for Performance Management

Trevor Parsons

Logentries identified the top five most common ways that log data is used for monitoring and analyzing system performance.

Our research team looked across more than 25,000 log management and analytics users, tracking more than 200,000 shared patterns, and identified the top five areas that are most frequently analyzed for performance-related issues. The top five pattern categories for performance monitoring included:

1. Slow Response Times

Response times are one of the most common and useful performance measures that are available from your log data. They give you an immediate understanding of how long a request is taking to be returned.

2. Memory Issues and Garbage Collection

Out-of-memory errors can be catastrophic when they occur as they often result in an application crashing due to lack of resources.

3. Deadlocks and Threading Issues

Deadlocks can occur in many shapes and sizes and can have negative effects when they occur - from bringing your system to a complete halt to simply slowing it down.

4. High Resource Usage (CPU/Disk/ Network)

In many cases, lower system performance may not be a result of any major software flaw, but can be a simple case of system load increasing without increasing resources available to manage the increased demand.

5. Database Issues and Slow Queries

One of the most common areas for slow system performance can be a result of long-running database queries. Once identified, these queries can be optimized for significant performance improvements. Setting acceptable thresholds for query time and reporting on anything that exceeds these thresholds can help you quickly identify when your users experience is being effected.

Trevor Parsons, PhD, is Co-founder and Chief Scientist of Logentries.

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For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

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My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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