Log Analytics is DEAD
December 03, 2015

Albert Mavashev
jKool

Log Analytics is DEAD. Did I really say that?? Yes I did. Log Analytics is a process of investigating logs and hoping to derive actionable information that might be useful to the business. Many log analytics tools are used to gain visibility into web traffic, security, application behavior, etc. But how valuable and practical is log analytics in reality?

One basic precondition for log analytics is that information to be delved into must be in log files and here lies the basic problem:

In order to derive useful analytics from logs one must have proper logging instrumentation and have it enabled everywhere, all the time.

Not only is this approach impractical and very expensive, except in a few limited cases, but it is also burdensome, imposing a significant performance overhead on the systems that produce these logs.

One must log gigabytes and gigabytes of data, store this data and then analyze it in order to detect a problem. I would call this a brute force approach. As most brute force approaches, it is expensive, slow and unwieldy. In many cases log analytics is used to catch occasional errors or exceptions. Do we really need to have all these logs to catch a few outliers?

Log analytics quickly turns into a Big Data problem – store and analyze everything, everywhere, all the time. Is that really needed? Maybe, or maybe not …

Simple Example

You deploy log analytics and it tells you've got 100 errors or exceptions in the past hour. Typically, you will want to investigate this and start with a specific exception.

Your next question would be “is what am I looking and noise or something that requires attention?” Then you will ask “what else happened” and “why?”. There is a series of questions you would ask might include the following:

■ What was my application doing?

■ What was the response time?

■ What was CPU, memory utilization?

■ What were the I/O rates and network utilization?

■ What was Java GC doing?

■ What other abnormal conditions occurred that I should be looking at?

There are so many variables. There are too many to look at and too much to analyze.

What do you do? Unfortunately this is where log analytics stops, you have to jump elsewhere. The path to root-cause becomes lengthy and painful. You may know that there is a problem, but why you have a problem in many cases is not clear.

We have all this data (big data) yet I don’t know what it means or where to look to find meaning. Of course one can say that you can parse out the log entries and extract metrics. Who will write the parsers? Who maintains the rules? Who writes complex regular expressions? What if the required metrics are not in the log files? In most cases they won’t be.

The biggest problem with log analytics is that what can be analyzed must be always logged. You need to know what information you need for root cause in advance. How often do you know what you need in advance? It is what you don’t know, have not thought about, did not instrument, did not log. It is unlikely you will have the information you will need.

Customers don’t want log analytics; customers want solutions to their problems. So what do I propose? I think log analytics is really morphing into a larger discipline.

The Post Log Analytics World

It is Application Analytics that combines logs, metrics, transactions, topology, changes, and more, along with machine learning techniques: where asking about quality of service, application performance, business and IT KPIs is a click away.

This approach must be combined with smart instrumentation, heuristics and even crowd-sourced knowledge that points to anomalies, suppresses noise and reveals important attributes without constantly collecting terabytes of data.

How do I understand what I don’t know or have not collected yet? How do I know what questions to ask?

Essentially Application Analytics is about managing risks lurking within application and IT infrastructures which are inherently complex and “broken”.

Log Analytics is dead, not because is not useful, but because it must quickly evolve into the next level.

Albert Mavashev is Chief Technology Officer at jKool.

Share this

Industry News

March 27, 2024

WaveMaker has updated its platform in response to customer demand for more sophisticated API and code management tools.

March 27, 2024

Vercara announced the launch of UltraAPI™, a product suite that protects APIs and web applications from malicious bots and fraudulent activity while ensuring regulatory compliance.

March 27, 2024

Legit Security announced the launch of its standalone enterprise secrets scanning product, which can detect, remediate, and prevent secrets exposure across the software development pipeline.

March 26, 2024

Progress announced a strategic partnership with Veeam® Software, the #1 leader by market share in Data Protection and Ransomware Recovery, to provide customers with an enterprise-ready cyber defense solution that strengthens the security of their business-critical data.

March 26, 2024

GitGuardian released its Software Composition Analysis (SCA) module.

March 26, 2024

DataStax announced a milestone in its journey to simplify enterprise retrieval-augmented generation (RAG) for developers by integrating with Microsoft Semantic Kernel.

March 25, 2024

Check Point® Software Technologies Ltd. is collaborating with NVIDIA to enhance the security of AI cloud infrastructure. Integrating NVIDIA BlueField DPUs, which feature a broad range of purpose-built, innovative security capabilities, the new Check Point AI Cloud Protect solution will help prevent threats at both the network and host levels.

March 25, 2024

Sentry announced the release of Autofix, an AI-powered feature to debug and fix code in minutes, saving important time and resources.

March 25, 2024

Apiiro announced a product integration and partnership with Secure Code Warrior, the agile developer security training platform, to extend its ASPM technology and processes to the people layer.

March 21, 2024

Progress announced that Progress® Semaphore™, its metadata management and semantic AI platform, was named a Champion in SoftwareReviews’ 2024 Metadata Management Emotional Footprint Awards.

March 21, 2024

The Cloud Native Computing Foundation® (CNCF®) has partnered with Udemy, an online skills marketplace and learning platform.

March 21, 2024

GitLab has acquired Oxeye, the provider of a cloud-native application security and risk management solution.

March 21, 2024

GitHub announced that code scanning autofix, powered by GitHub Copilot and CodeQL, is available in public beta for all GitHub Advanced Security (GHAS) customers.

March 21, 2024

NetApp is collaborating with NVIDIA to advance retrieval-augmented generation (RAG) for generative AI applications.

March 21, 2024

CalypsoAI launched the CalypsoAI Platform, an advanced SaaS-based security and enablement solution for generative AI applications within the enterprise.