Applications used to be so simple, some self-contained code linked with a few system libraries that accessed local data: a distinct bundle where everything required was on a single machine. Client-server software complicated things a bit, but the demarcation was clean and the hub-spoke design pattern straightforward. No longer. Apps are now composite mashups, often mixing custom and packaged code, accessing both public and private data sources and using APIs to access multiple Web services that each run on highly distributed cloud infrastructure. Gone are the boundaries between an application, associated infrastructure and data. Of course, this fusion of apps and infrastructure is transparent and irrelevant to users, but to developers, release managers, business owners and IT it's a nightmare of complexity that makes monitoring, troubleshooting and maintaining application performance frustratingly difficult. Indeed, the situation requires a new approach that mixes traditional application performance monitoring (APM) and IT operational intelligence (OI).
Maintaining application performance is a looming problem since as I wrote last year, applications are central to business success. As CA's CEO put it at the time, "applications now define a business’ relationship with its customers and fuel the productivity of its employees. We now live in a world where customers are no longer just loyal to the brand or product or service. Instead, they are loyal to the complete experience a brand delivers. And that experience is delivered by software." No one cares if the code on their smartphone is working flawlessly if an application can't access the data sources or remote cloud services required to view information or complete a transaction. Furthermore, the composite nature of modern apps means there's a wider array of stakeholders in application performance that most importantly includes customers. What's the first thing many Gmail or Google Drive users do when they can't access the service? Hit the Google Apps Status Dashboard. This means APM is no longer just the domain of developers.
Ironically, traditional application modules, the code on a smartphone, PC or front-end Web server, has less and less an impact on overall performance. The amalgamated nature mobile and Web apps means the customer experience is more often defined by dependencies on cloud infrastructure (IaaS), software (SaaS), access to open APIs and Internet performance that aren't integral to the core application code and not under the direct control of developers, application managers or IT. Just as slow or no response from a Web page is rarely the result of bugs in the browser, performance and reliability problems increasingly don't stem from the application per se, but external systems and networks. Mobile apps present additional challenges since they introduce other variables like various client OS versions, a wide variety of hardware, unpredictable edge connections over carrier networks or Wi-Fi and even greater use of external service APIs.
Next-gen APM: the next big data opportunity
The blurring line between applications and infrastructure presents an opportunity for companies that can collect and make sense of data from multiple sources. This includes specialists in IT operations monitoring like Loggly, LogLogic, LogRhythm, Scalyr, Splunk and Sumo Logic, but also traditional APM leaders like AppDynamics, CA and New Relic. As with operations and security monitoring software, the goal of next-generation APM is to facilitate data analysis, summarization, correlation and interrogation from every element that affects application performance. These include logs and telemetry from application code, server and network infrastructure, cloud services, API calls and even PC and mobile clients.
Systems built to analyze large event databases collected from the myriad sources of relevant information have initially targeted IT operations, however these are also well-suited to next-gen APM. Given the diversity of stakeholders, including business executives wanting dashboards summarizing KPIs, developers needing deep analysis of code and network performance and support personnel using forensic investigation of correlated events to troubleshoot problems, the APM feature set must go both wide and deep. Furthermore, with mobile apps exploding the potential number of simultaneous users and the concomitant increase in both the total volume and rate of new data, systems must have databases tuned for rapid data ingestion and search.
Modern operational monitoring systems are designed to provide just this ability to serve dual roles, equally adept at macro-scale summarization and micro-scale forensic analysis. Many, like Loggly, Splunk and Sumo Logic with open APIs have fostered an ecosystem of add-on modules designed for specific applications like Exchange, ServiceNow or VMware.This same extensibility could accommodate the unique characteristics and subtleties of any distributed, composite application with modules to analyze their performance, anomalies and failures.
Today's applications are woven from a variety of custom and commercial modules, cloud services, databases and infrastructure into an extensible fabric that can be easily tailored for mobile or Web delivery for potentially millions of users. Next-gen APM products must be able to smoothly handle large data volumes from many sources and provide both high-level summarization of service-level performance and granular access to data from each constituent.
As the lines between applications and infrastructure have blurred, the worlds of APM and operational intelligence (OI) have collided, there are a couple obvious strategies for meeting the needs of next-gen APM. Some, like Splunk will add features to address application-specific needs without necessarily duplicating traditional developer-focused APM tasks. Others, like Loggly and Sumo Logic will take a hybrid approach by integrating with existing APM products like New Relic.
Given the dynamic nature of the problem and technology, it's unclear how the specifics will evolve, however it's obvious that organizations need a new, comprehensive approach to APM that reflects the same blending of development and operations that spawned the DevOps movement.
Disclosure: Like all independent technology analysts, I provide research, analysis, consulting, and/or content to many companies in a variety of high-tech markets. The column mentions several companies, but does not endorse any specific products, however it is informed by prior work done for Splunk. I hold no direct equity position in Splunk or its competitors.
