jKool Blog

4 12, 2017

Top 5 programming languages for DevOps

By |December 4th, 2017|Java|0 Comments

Knowing how to rack and stack servers isn’t an in-demand skill at this stage. Most businesses aren’t building physical datacenters. Rather, we’re designing and building service capabilities that are hosted in public cloud environments. The infrastructure is configured, deployed, and managed through code. This is the heart of the DevOps movement—when an organization can […]

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?


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, […]

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 […]

11 11, 2015

Analyze and visualize your data with jKool on Bluemix

By |November 11th, 2015|Big Data Analytics, bluemix, Java, log analytics, Real-time Analytics, SaaS|0 Comments

Applications can behave in unexpected ways and it’s often difficult to know whether this is appropriate behavior or if something is really wrong. Sound familiar? This is the same conundrum parents face… and often just as hard to solve.

But, for Java developers and members of DevOps, the ultimate advantage in this situation would […]

3 02, 2015

A Better way to Monitor JVM Containers: StreamJMX

By |February 3rd, 2015|Java, open-source, streaming analytics|0 Comments

StreamJMX is a better way to monitor JVM containers. Typically JVMs are monitored by using remote JMX monitoring tools. There are significant problems with this approach. Examples: how do you monitor a farm of JVMs without having and administrative headache of setting up remote JMX configurations, ports, SSL, etc etc. With all the talk about cyber security, JMX remote connectivity opens up a way for hackers to exploit these administrative ports.

StreamJMX, open source JMX streaming framework, allows a developer accomplish just that. StreamJMX allows developers to stream JMX metrics from JVM out to the central location, or any other destination.


30 01, 2015

Open-Source Connectors for jKool Streaming Analytics

By |January 30th, 2015|connectors, Java, open-source|0 Comments

Open-Source is a type of technology and it’s also a philosophy. The technology is one where the source code is published for anyone interested to see and use. The philosophy is about letting the community drive the direction of a product as opposed to the traditional method where an individual vendor provides the product roadmap.

As a Product Manager, I know that it is all too easy for a product to drift away from the needs of the market either due to not listening to the market or over listening to one customer and only one customer. The beauty of open-source is that the market drives the product; thus, in theory at least the product or project is always in synchronization with the changing needs of the market.

At jKool we have embraced open-source and having attended All Things Open in October we know there is a vibrant community out there for this approach. We are actively creating new open-source connectors to jKool, our SaaS solution for Streaming Analytics. We have just released two new connectors. They are: […]

20 11, 2014

Unlock Big Data with Real-time Analytics

By |November 20th, 2014|Big Data, Java, Real-time Analytics, SaaS|0 Comments

jKool, providing real-time analytics as a service was announced at All Things Open.

During ATO, Albert Mavashev presented a session entitled, “Unlocking Big Data: Streaming Analytics as a Service”.  Albert covered how to meet the big data challenge.  He described it as a process where you capture, store, analyze, acquire insight and act.

Watch the video […]