Pairing Splunk and AppDynamics
We now live in the era of the “software-defined enterprise”. Software applications represent the key enablers for commercial businesses and public sector organizations. Applications are no longer just enablers for back-office processes. Today, software applications are now the “face of the organization” to customers, partners, and also internal co-workers.
The era when customers would tolerate application failures being fixed in hours, days, or weeks are long gone. Today’s constituencies expect applications to be “always on”, and problems identified and resolved in minutes (if not quicker).
The ability to leverage analytics to support critical applications within the software-defined enterprise will define the winners and losers in the market. The power of IT operations analytics holds promise as the enabler for dramatically reducing Mean Time to Repair metrics for critical applications, regardless of where a problem exists.
The paragraphs that follow will provide insights into a proven approach for leveraging the power of analytics to identify and solve application problems quickly and to win in the market as a software defined enterprise.
A One-Two Approach: Winning Against Problematic Application Stacks
Pinpointing problems with large, distributed, and often legacy application stacks is difficult. Troubleshooting and identifying the underlying cause of internal and external customer facing problems can often take weeks or months. The result for organizations unable to solve applications problems is negative. End-user satisfaction goes down and precious customers can be lost forever. Organizations feel the pressure of hectic customer support war rooms, missed goals, and upset leadership and investors. Time is money; inefficiency and downtime for mission critical systems means lost revenue and angry customers.
But, there is hope. It’s a new day in analytics, and several solutions have entered the market recently that attempt to reduce Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR) metrics for application troubleshooting with varying levels of success.
The bottom-line: in order for organizations to get the full picture and achieve holistic application stack monitoring, they need to use Splunk and AppDynamics for a cohesive view of their entire application stack. Splunk can natively see across the application stack to point to an issue. Then, AppDynamics can drill down and see into the proverbial “black box” (as illustrated in Figure 1), which is typically a database layer, the application layer, and UI/ Web layer.
Splunk can see around the “black box”, and AppDynamics can see into it.
Where Splunk Ends AppDynamics Begins, and Vice Versa
Splunk and AppDynamics can artistically be woven together to build a cohesive analytics solution for end-to-end application visibility. Here’s how.
Splunk Pros and Cons
Arguably, the most flexible tool to address application stack monitoring is a platform called Splunk. Entering the market in 2005 initially as a type of “Google” for monitoring, Splunk software quickly evolved into a flexible and scalable platform for solving application problems. It also emerged as a platform with a robust and configurable user interface, touting sleek data visualization capabilities. Those qualities have allowed it to become a standardized platform in application stack monitoring teams. How is Splunk better than the rest? There are two main reasons.
First, Splunk’s ability to correlate disparate log sources allows it to identify and find issues in tiered applications. Applications are commonly written in very different languages. Thus, they have few logging similarities in structure, content, or methodologies. For most traditional monitoring tools, configuring data source setups is labor intensive and needs to be aggressively maintained if the application or its environment changes. On the other hand, Splunk is elite in dealing with these differences “on the fly”, as it is able to monitor these disparate log sources in real-time as the data is consumed. Splunk’s advantage is because it can provide very flexible, reporting driven schemas as the data is searched. This is important with legacy applications due to limited standardization, especially in the application layer where most of the business logic and “glue” code resides for an application to work.
Second, Splunk is easy to use for monitoring around an application, particularly in the networking, infrastructure, and Operating System (OS) layers, it has standard configurations which are fast to implement and where one can start deriving technical and business insights quickly. The areas where Splunk is the straightforward solution in IT Operations Analytics includes networking, operating system, storage, hypervisor, compute, load balancers, and firewalls.
Where does Splunk need assistance? With deep application performance monitoring in complex, highly distributed environments. This is because many mission-critical applications cannot be easily updated, and doing so is often too labor intensive (or impossible) to use the application logs to derive insights into problems. While legacy approaches to solving these monitoring problems are under siege, their existence is a reality as organizations transform. Splunk’s answer to this issue is in Splunkbase, the community for certified apps and add-ons. There is the Splunk App for Stream to monitor, ingress, and egress communicate points between the layers in the application stacks, database to application, and then application to UI/ Web layer. Still, with Splunk App for Stream this is deficient when compared to AppDynamics because monitoring “around” a problem only describes the downstream impacts, it cannot pinpoint the actual problem quickly.
AppDynamics Pros and Cons
AppDynamics entered the marketplace in 2009 with a simple purpose: be the best for addressing deficiencies in application stack monitoring options, particularly for large, distributed, and often legacy application stack monitoring. They monitor the business transactions, which are the backbone of any application. In doing so, they found a common auditing language that transcends database, application, and UI/ Web layers, including full support for legacy applications, provided the application language is one that AppDynamics supports. Here is a list of languages and system requirements: http://bit.ly/appdsystemrequirements.
A primary AppDynamics differentiator is that it has the innate ability to understand what “normal” looks like in an environment. The platform automatically accounts for all of the discrete calls that run through an application. Then, it can find bottlenecks by identifying application segments, machines, application calls, and even lines of code that are problematic. Unlike other Application Monitoring Tools (APMs), AppDynamics can monitor the application from the end user point of view.
Regarding business value, what does AppDynamics bring that Splunk cannot? As the application is updated as part of a normal software development cadence, AppDynamics agents will then autodiscover again, saving time on professional services and money on re-customizing monitoring. Conversely, the Splunk App for Stream can require re-customization as application code and topology is updated.
AppDynamics does need some augmentation its counterpart, Splunk, in looking outside of an application at the full stack. If the underlying problem is not with the code, but with the functionality of the environment, such as storage, networking, compute, or the operating system, AppDynamics cannot do in-depth problem diagnosis on broader infrastructure components. Instead, the traditional approach is that APM teams use several, narrowly focused “point tools” to monitor each layer, which causes silos within teams. To skip the silos, cue Splunk. Its sweet spot is as a “Single Pane of Glass” where it can tie together its own visibility and the visibility provided by AppDynamics to identify where in the massive environment the problem lies.
So, where Splunk ends AppDynamics begins, and vice versa.
Skip the Silos: Splunk and AppDynamics Synergize for a Holistic Approach
Splunk and AppDynamics both interact with the application infrastructure in a way that is straightforward to setup, easy to maintain, and can deliver fast time-to-value. By visualizing the output of these two platforms in Splunk, teams achieve a “single pane of glass” monitoring approach that gives the business a real-time, holistic view into distributed, complex application stacks.
Pairing together the analytics platform synergies of Splunk and AppDynamics to achieve holistic application stack monitoring for the mission will reduce MTTI and MTTR. The organization will observe reliable, sustainable ROI as applications and the environment evolve with the inevitable business transformation. Leveraging machine data in real-time is the cutting edge in analytics and empowers organizations to creatively scrutinize all their data in an automated, continuous, and contextual way to maximize insights and opportunities.
About Kinney Group
Kinney Group is a cloud solutions integrator harnessing the power of IT in the cloud to improve lives. Automation is in Kinney Group’s DNA, enabling the company to integrate the most advanced security, analytics, and infrastructure technologies. We deliver an optimized solution powering IT-driven business processes in the cloud for federal agencies and Fortune 1000 companies.