SAN FRANCISCO – JUNE 23, 2015 – AppDynamics, a leading application intelligence company, today announced the general availability of application performance monitoring for Python applications. The AppDynamics APM Solution for Python brings all the capabilities of the AppDynamics Application Intelligence Platform to Python applications, including automatic application flow-mapping; real-time, end-to-end, code-level visibility to enable rapid problem resolution; and database and infrastructure monitoring. The solution monitors Python applications in both development and live production environments.
First introduced in the early 1990s, Python today is used by hundreds of thousands of programmers. The Python community has a reputation for valuing clarity, simplicity, readability and extensibility — qualities that align well with the AppDynamics Application Intelligence Platform’s automatic instrumentation, clear and comprehensive flow mapping, rapid time-to-value, and extensive library of community-contributed extensions.
As the newest language in the AppDynamics APM family, Python applications now benefit from the tremendous scalability of the platform and the ability to see application and transaction performance across complex and highly distributed environments. AppDynamics APM for Python automatically discovers and maps application topology and dependencies, including other applications, web services, databases, and underlying infrastructure, providing visibility into the components and processes that can impact Python performance.
Specific functionality for Python developers and operations teams includes:
Code-level application monitoring: Visual drill-down on transaction snapshots enables rapid identification and resolution of hot spots and slow methods to minimize impact on users.
Key business transactions monitoring: The platform is able to correlate and trace key business transactions end-to-end across complex, distributed environments to understand how application and infrastructure performance impact business outcomes.
Errors and exceptions detection in real time: The platform detects and shows errors so they can be quickly resolved, and enables proactive addressing of exceptions via policy-based runbook automation.
Heterogenous database performance management: Unique agentless database monitoring technology shows how database performance impacts Python applications.
Infrastructure performance correlation: Visibility into infrastructure behavior shows its impact on application performance, with the ability to drill down to virtual machines, containers, servers, network, or storage, whether in the cloud or on-premises.
“With the growing popularity of Python, developers and IT ops teams need the kind of visibility that AppDynamics delivers,” said Bhaskar Sunkara, chief technology officer and senior vice president of product management at AppDynamics. “With our solution, you can monitor Python applications in real time, drill down into call stacks, correlate transactions as they traverse distributed environments, and diagnose bottlenecks in development and in production. These are the necessary capabilities to keep Python applications — and all other applications — performing at their best.”
For more details about today’s news please read the AppDynamics blog.
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AppDynamics is the Application Intelligence company. With AppDynamics, enterprises have real-time insights into application performance, user performance and business performance so they can move faster in an increasingly sophisticated, software-driven world. AppDynamics’ integrated suite of applications is built on its innovative, enterprise-grade App iQ Platform that enables its customers to make faster decisions that enhance customer engagement and improve operational and business performance. AppDynamics is uniquely positioned to enable enterprises to accelerate their digital transformations by actively monitoring, analyzing and optimizing complex application environments at scale.AppDynamics reserves the right to change any planned features at any time before making them generally available as well as never making them generally available. Any buying decisions should be made based on features that are currently generally available.
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