jKool Blog

About Albert Mavashev

Over 20 years experience in: Application Performance, Performance Measurement & Practices, Streaming Analytics @ Scale, Clustered Computing, Big+Fast Data, Location aware transaction tracking, Complex Event Processing (CEP), Middleware, IT Service Management, Ops & DevOps.
25 02, 2016

3 Reasons Root Cause Analysis is Fundamentally Flawed

By |February 25th, 2016|devops, Java, operational analytics, prediction, Real-time Analytics|0 Comments

Always the same final answer to all WHYs

Root-cause analysis = ask WHY until the answer is ‘i don’t know’, meaning that root-cause analysis inevitably arrives @ UNKNOWN or UNKNOWABLE:

Oversimplified example:

Boss: Our application is slow and end-users are unhappy, why?
You: Database is slow
Boss: Why?
You: Because our report query is taking too long.
Boss: Why?
You: Database server is out of memory.
Boss: Why???
You: I think Bob […]

23 02, 2016

5 ways to spice up DevOps with jKool

By |February 23rd, 2016|blog, devops, Java, log analytics, operational analytics, operational intelligence, performance monitoring, SaaS, streaming analytics, transaction tracking|0 Comments

Eliminate finger pointing, reduce blame

jKool allows application owners to track and audit transactions; from browser all the way to application servers and middleware. Ability to track transactions is critical if you want to know if your app is slow; and where and why it is slow. How often is the blame placed on you or your team […]

11 02, 2016

What does APM have to do with Fast Data?

By |February 11th, 2016|Apps, Big Data, Big Data Analytics, Fast Data, Java, log analytics, operational analytics, operational intelligence, performance monitoring, Real-time Analytics, streaming analytics, transaction tracking|0 Comments

Fast Data is about processing high velocity data in real-time as it happens. Think of Fast Data as Big Data in real-time.

So what does APM have to do with Fast Data?

Everything.

APM is all about processing lots and lots of data as close as possible to real-time.
Tracking transactions, analyzing logs, sampling metrics, figuring out relationships, […]

10 02, 2016

Application Performance in 4 simple steps with jKool

By |February 10th, 2016|Apps, Big Data, Big Data Analytics, performance monitoring, Real-time Analytics, streaming analytics, transaction tracking|0 Comments

Real-time updates on application KPIs
I use “subscribe to events where … output every X seconds” to get a digest of all data streams X number of seconds. Pretty cool feature, especially for those looking for real-time updates on what is happening within your application as it runs.

Queries paired with views
Type in a query and […]

9 02, 2016

10 Reasons Your Application is Slow

By |February 9th, 2016|Apps, devops, end-user analytics, Java, log analytics, Real-time Analytics, Spark, storm, transaction tracking|0 Comments

1) Too much contention
You claim to have multi-threaded applications. Multi-threaded model does not mean faster. Your apps will not get the boost from multi-threaded model and multiple CPU/cores if you have too much contention, blocking and waiting. Review your threading model and weed out contention. Excessive contention will reduce your app performance as load […]

20 01, 2016

Why reducing MTTR is not enough

By |January 20th, 2016|Big Data, Big Data Analytics, cloud, devops, Java, log analytics|0 Comments

Reducing MTTR is a hot topic among DevOps practitioners.  MTTR measures average time for a cycle: problem occurrence, detection, response, and repair. Reducing the MTTR should greatly improve service quality right? Well, not exactly? The metric we should be looking at is this: what is the available time for repair (MATR — maximum available time […]