As we talked about in the first blog about, we have our new Application Performance Extensibility (APEX) features that have been introduced for your Turbonomic 8 platform:
- Application Technology Definition (ATD) – brand new ability to define your business applications and services using a wide variety of dynamic criteria
- Data Ingestion Framework (DIF) – open-source declarative framework for creating customizable entities in Turbonomic ARM
The DIF is a very powerful and flexible framework which enables the ingestion of many diverse data, topology, and information sources to further DIFferentiate (see what I did there) the Turbonomic platform in what it can do for you.
Unlocking the Power of Application Data with DIF
Let’s have a look at how you the DIF works in the Turbonomic environment! Turbonomic ingests data through a variety of different probes which poll different targets to gather analytics and topology information. Using those analytics, Turbonomic automatically builds the relationships of application demand and all of the resource dependencies which is used by the analytics engine to create actionable decisions to improve application performance.
Figure 1 – Turbonomic DIF Probe Architecture
The DIF manifest is a declarative definition in a simple JSON format which can be used to describe a wide variety of resources and data sources to populate the Turbonomic platform with additional instrumentation and business logic.
Figure 2 – Turbonomic DIF Manifest Example
The technology behind it is incredibly powerful. The value that you get from this has the potential for massive business value which is where we are going to explore now.
Turbo ON Turbo
One of the most fun use-cases to show is what we call “Turbo ON Turbo” which can be used as part of the Turbonomic 8 platform. Turbonomic has a DIF definition for its own internal configuration which allows us to see everything that is powering your own Turbonomic platform.
Figure 3 – Turbo ON Turbo using DIF Definition
You see both the overall topology and dependencies defined in the supply chain as well as the specific transactional and service components which are showed in the main window and can be clicked on to see the health, resource dependencies, and actionable decisions that can be taken in cases where the environment needs to scale.
Figure 4 – Transactions and Actions Widget
I did a complete run-through of the value (and fun!) of Turbo ON Turbo in a recent Turbonomic Labs episode which you can watch here:
One-Click Workflow - Adding DIF Targets
Once you define your declarative DIF object, you just need to host the JSON manifest in an accessible server location that can be targeted by Turbonomic. Then you add the target just like you would with any other application or infrastructure target.
Figure 5 – Adding a New DIF Target
This really is a situation where the possibilities are endless. Today’s use-cases already active in customer environments includes:
- Adding KPIs (e.g. # of tickets for a resource)
- Adding custom application metrics (e.g. number of failed requests)
- Defining additional application relationships and dependencies
You now have extensibility for data and analytics integrations that open the doors to so much more that you can do. Thanks to the growing customer adoption with the DIF capabilities, we are going to be publishing many example DIF manifests and deployment examples.
The Turbonomic GitHub project contains two existing examples. Two very commonly asked for integrations were Azure Log Analytics and Jira tickets. We’ve published the DIF manifest and a simple walkthrough to implement them in your own environment. You can find those here: https://github.com/turbonomic/data-ingestion-framework/tree/master/example
Keep watching in the coming weeks as we continue to grow the example ecosystem. Got an idea for a DIF integration? You can also share your examples back with the open source community at Turbonomic by submitting a pull request directly in the GitHub project.
Find out more!
There are lots of new application examples coming which will make it even easier to get rolling with DIF for your Turbonomic deployment and if you have any requests or need some help you can always reach out to your Turbonomic support team and we can get you up and running with all of the Turbonomic core and APEX capabilities.
Visit the DIF GitHub project page here: https://github.com/turbonomic/data-ingestion-framework
Check out the Turbonomic Labs episode here: https://www.youtube.com/watch?v=C3u_2pjFvQE
We are excited to share this with you as part of the Turbonomic ARM platform update and make sure to also watch our Turbonomic Labs streaming episodes which feature more interactive deep-dives into these APEX features and much more.