You’ve probably come across the term LM Ops before. Across the web, it’s often tied to machine learning operations or large language model operations (LLMOps). Think model deployment, prompt tuning, and pipeline management.
But here at LogicMonitor, LM Ops means something different. It’s about how IT operations teams use our observability platform, specifically LM Logs, to streamline monitoring, speed up troubleshooting, and reduce downtime.
So if you’re part of an ITOps, DevOps, or SRE team, trying to get better at spotting issues before they snowball? You’re absolutely in the right place.
With LM Logs, you don’t have to sift through data blindly. This article digs into how LM Ops helps ITOps, DevOps, or SRE pro cut through noise, identify anomalies in real time, and answer the critical questions: What’s happening, where is it happening, and why?
The mission of the TechOps team at LogicMonitor is to build, scale, and maintain a reliable computing platform, which provides the LogicMonitor service for customers. Their work can be classified into two major categories:
#1- Making the customer experience better by improving service availability.
#2- Making their co-workers’ jobs easier by scaling or automating processes, actively tuning alerts, and fulfilling requests from other departments.
Goals include providing LogicMonitor service reliably for customers, achieving awareness of disruptions either potential or before customers notice them, spending time in the proactive zone versus reactive zone, avoiding the accumulation of technical debt, and utilizing LogicMonitor to ensure the reliability of LM servers.
Logs are records of events that happen in computing systems, either by a person or by a running process. Logs help track what happened and troubleshoot problems — they are a critical part of monitoring coverage for any application or time series metrics.
Reviewing logs is challenging, time-consuming, and oftentimes requires expertise to understand them. Insert LM Logs. LM Logs utilizes a powerful patented algorithm to automatically analyze every element of a log, data, and event at the time of ingestion without the need to learn proprietary search query languages. LM Logs takes a centralized log aggregation approach a step further by proactively identifying anomalies in logs and escalating these events automatically. In addition, log-in metric data is truly integrated – meaning log data can be analyzed in context immediately from any performance metric dashboard or graph.
This diagram shows how LM Logs collects data from across your environment (including cloud providers, servers, containers, and SaaS apps) and feeds it into the LogicMonitor platform for real-time anomaly detection. Logs and metrics flow through a single system, so your team can troubleshoot faster without switching tools.
Before LM Logs, finding the root cause of an incident or post-mortem often took hours. Engineers had to switch between tools, interpret logs without context, and manually connect metrics to events. This slowed investigations and made it harder to act under pressure.
With LM Logs, your logs and performance data live side-by-side in one place, so you can instantly answer the three questions every Ops team needs to know:
What’s happening, where is it happening, and why.
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LogicMonitor believes logs should be accessible and painless for every Ops team, accordingly, LM Logs is the first solution that has been built with IT Operations and DevOps teams in mind. Let’s dive deeper into examples of how the LM TechOps team has benefitted from integrating LM Logs that has ultimately led to a decrease in MTTR, a decrease in the amount of time required to determine a root cause analysis, and a focus on performance or availability impacting log events immediately without having to decipher what is normal or abnormal.
The team encountered a server that was still functioning but the Kafka broker had lost leadership and was not properly replicating data. Before LM Logs, the application logs from Kafka itself were useless in determining the cause of the problem and many hours were spent by multiple people trying to figure out exactly what the problem was. After LM Logs, the team received an alert for the same issue on a different server. This time, they discovered the problem immediately, not from institutional knowledge but rather from LM Logs surfacing the log message along with the alert. Having metric-based alerts alongside anomalous log messages surrounding the event timeline brought clarity to the situation.
A multi-tenant TSDB server had stopped consuming messages from Kafka. This server provided service for over a hundred and fifty different customers. Normally, diagnosing a situation like this would require sophisticated log aggregation queries by someone well versed in the application server logs. Before LM Logs, this issue could take hours or days to investigate if a root cause could be surfaced at all. After LM logs, the root cause analysis process was able to be tracked down to a single customer. Over the course of only 80 minutes, 91 anomalies were found, out of 6.2 million log lines, meaning only .0000145% of the logs during this time period were relevant to the problem. Thanks, LM Logs.
Out of 6.2 million log lines, LM Logs surfaced just 91 anomalies – identifying the issue in under 90 minutes.
Operations teams must be able to proactively identify issues when customers experience a performance issue related to a capacity constraint. In this instance, an on-call engineer received a metric-based alert indicating response time had degraded. Occasionally, reading through the actual logs is required to surface an issue and before LM Logs, this was not feasible with 800,000+ messages to scroll through. After LM Logs and upon fielding the alert, the on-call engineer was able to quickly view the related log anomalies. The log anomaly revealed a bug that required manual intervention to resolve in the short term, but it also provided the data required to create a follow-up action for preventing this issue in the future. In this case, log anomaly represented .04% of the total log volume during the 90 minutes, where the performance issue was discovered.
All in all, LM Logs centralizes all of your log data and IT infrastructure performance metrics in a single, unified status-based platform. It automatically detects and surfaces log anomalies utilizing powerful algorithms and removes labor-intensive analysis. LM Logs significantly reduces troubleshooting and root cause analysis times, avoiding incident war room scenarios, and helps to not only understand what the cause is, but what needs to be fixed to resolve the issue for good.
To learn more about how LM Logs can help your teams decrease troubleshooting, streamline IT workflows, and increase control to reduce risk, check out this on-demand webinar.
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