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PostHeaderIcon Understanding `elastic.apm.instrument_ancient_bytecode=true` in Elastic APM

Elastic APM (Application Performance Monitoring) is a powerful tool designed to provide visibility into your application’s performance by instrumenting code at runtime. Most of the time, Elastic APM dynamically attaches itself to Java applications, weaving in the necessary instrumentation logic to capture transactions, spans, and errors. However, some applications, especially legacy systems or those running on older bytecode, may require additional configuration. This is where the parameter elastic.apm.instrument_ancient_bytecode=true becomes relevant.

What Does This Parameter Do?

By default, the Elastic APM agent is optimized for modern JVM bytecode, typically generated by more recent versions of Java compilers. However, in certain environments, applications may rely on very old Java bytecode compiled with legacy compilers, or on classes transformed in ways that deviate from expected patterns. In such cases, the default instrumentation mechanisms may fail.

Setting elastic.apm.instrument_ancient_bytecode=true explicitly tells the agent to attempt instrumentation on bytecode that does not fully conform to current JVM standards. It essentially relaxes some of the agent’s safeguards and fallback logic, allowing it to process “ancient” or non-standard bytecode.

When Is This Necessary?

Most modern Java applications do not require this parameter. However, it becomes useful in scenarios such as:

  • Legacy Applications: Systems still running on bytecode generated by Java 5, 6, or even earlier.
  • Bytecode Manipulation: Applications that make heavy use of frameworks or tools that dynamically generate or transform bytecode in unusual ways.
  • Incompatible Class Structures: Some libraries written long ago may use patterns that modern instrumentation cannot safely parse.

Examples of Differences

Without the Parameter

  • The Elastic APM agent may skip certain classes entirely, resulting in gaps in transaction traces.
  • Errors such as “class not instrumented” may appear in logs when working with older or unusual bytecode.
  • Performance metrics may look incomplete, missing critical spans in legacy code paths.

With the Parameter Enabled

  • The agent attempts a broader set of instrumentation strategies, even for outdated or malformed bytecode.
  • Legacy classes and libraries are more likely to be traced successfully, providing a fuller view of application performance.
  • Developers gain visibility into workflows that would otherwise remain opaque, such as old JDBC calls or
    proprietary frameworks compiled years ago.

Trade-offs and Risks

While enabling this parameter may seem like a straightforward fix, it should be approached with caution:

  • Stability Risks: Forcing instrumentation of very old bytecode could lead to runtime issues if the agent misinterprets structures.
  • Performance Overhead: Instrumenting non-standard classes may come with higher CPU or memory costs.
  • Support Limitations: Elastic primarily supports mainstream JVM versions, so using this
    parameter places the application in less-tested territory.

Best Practices

  • Enable elastic.apm.instrument_ancient_bytecode only if you detect missing traces or errors in the agent logs related to class instrumentation.
  • Test thoroughly in a staging environment before applying it to production.
  • Document which modules require this setting and track their eventual migration to modern Java versions.

Conclusion

The elastic.apm.instrument_ancient_bytecode=true parameter is a niche but valuable option for teams maintaining legacy Java systems. By enabling it, organizations can bridge the gap between outdated bytecode and modern observability needs, ensuring that even older applications benefit from the insights provided by Elastic APM. However, this should be viewed as a temporary measure on the journey toward modernizing application stacks, not as a permanent fix.


Hashtags:
#ElasticAPM #JavaMonitoring #ApplicationPerformance #LegacySystems #DevOps #Observability #JavaDevelopment #PerformanceMonitoring #ElasticStack #SoftwareMaintenance

PostHeaderIcon [DevoxxUA2023] Panel Discussion: AI – Friend or Foe?

Moderated by Oleg Tsal-Tsalko, Senior Solution Architect at EPAM, the Devoxx Ukraine 2023 panel discussion, AI: Friend or Foe?, brought together experts Evgeny Borisov, Mary Grygleski, Andriy Mulyar, and Sean Phillips to explore the transformative impact of AI on software development and society. The discussion delves into AI’s potential to augment or disrupt, addressing ethical concerns, practical applications, and the skills developers need to thrive in an AI-driven world. This engaging session aligns with the conference’s focus on AI’s role in shaping technology’s future.

AI’s Impact on Software Development

The panel opens with a provocative question: does AI threaten software development jobs? Evgeny and Andriy assert that AI will not replace developers but rather enhance their productivity, acting as a “third arm.” Evgeny notes that many developers, especially juniors, already use tools like ChatGPT alongside their IDEs, streamlining tasks like code generation and documentation lookup. This shift, he argues, allows developers to focus on creative problem-solving rather than rote tasks, making development more engaging and efficient.

Mary reinforces this, suggesting that AI may create new roles, such as prompt engineers, to manage and optimize AI interactions. The panel agrees that while fully autonomous AI agents are still distant, current tools empower developers to deliver higher-quality code faster, transforming the development process into a more strategic and innovative endeavor.

Ethical and Societal Implications

The discussion shifts to AI’s ethical challenges, with Andriy highlighting the risk of “hallucinations”—incorrect or fabricated outputs from LLMs due to incomplete data. Mary adds that unintentional harm, such as misusing generated content, is a significant concern, urging developers to approach AI with caution and responsibility. Sean emphasizes the need for regulation, noting that the lack of oversight could lead to misuse, such as generating misleading content or exploiting personal data.

The panelists stress the importance of transparency, with Evgeny questioning the trustworthiness of AI providers like OpenAI, which may use user inputs to improve their models. This raises concerns about data privacy and intellectual property, prompting a call for developers to be mindful of the tools they use and the data they share.

Educating for an AI-Driven Future

A key theme is the need for broader AI literacy. Andriy advocates for basic machine learning education, even for non-technical users, to demystify AI systems. He suggests resources like MIT’s introductory ML courses to help individuals understand the “black box” of AI, enabling informed interactions. Mary agrees, emphasizing that understanding AI’s implications—without needing deep technical knowledge—can prevent unintended consequences, such as misinterpreting AI outputs.

The panelists encourage developers to learn prompt engineering, as well-formulated prompts significantly improve AI outputs. Evgeny shares that a well-named class or minimal context can yield better results than overly detailed prompts, highlighting the importance of clarity and precision in AI interactions.

Preparing Developers for AI Integration

The panel concludes with practical advice for developers. Sean recommends exploring AI tools to stay competitive, echoing the sentiment that “AI will not replace you, but people using AI will.” Evgeny suggests starting with simple resources, like YouTube tutorials, to master prompt engineering and understand AI capabilities. Mary highlights emerging tools like LangStream, an open-source library for event streaming in RAG patterns, showcasing how AI can integrate with real-time data processing.

The discussion, moderated with skill by Oleg, inspires developers to embrace AI as a collaborative tool while remaining vigilant about its challenges. By fostering education, ethical awareness, and technical proficiency, the panelists envision a future where AI empowers developers to innovate responsibly.

Hashtags: #AI #SoftwareDevelopment #Ethics #MachineLearning #PromptEngineering #EPAM #DataStax #NomicAI #OlegTsalTsalko #EvgenyBorisov #MaryGrygleski #AndriyMulyar #SeanPhillips #DevoxxUkraine2023

PostHeaderIcon [DevoxxUA2023] Orchestrate Your AI with Semantic Kernel

Soham Dasgupta, a Cloud Solution Architect at Microsoft, presented an illuminating session at Devoxx Ukraine 2023, titled Orchestrate Your AI with Semantic Kernel. With over 16 years of experience in software development, Soham demystifies the complexities of integrating AI into applications using Microsoft’s Semantic Kernel SDK. His talk, featuring live coding, provides developers with practical tools to harness large language models (LLMs), aligning with the conference’s focus on AI-driven development.

Understanding Semantic Kernel

Soham introduces Semantic Kernel, an open-source SDK designed to simplify the integration of LLMs into applications. He explains that Semantic Kernel acts as an orchestrator, enabling developers to manage AI requests and responses efficiently. Unlike other frameworks like LangChain, which supports a broader range of LLMs, Semantic Kernel is optimized for Azure Open AI and select models, offering a streamlined approach for Java developers.

Through a live coding demo, Soham demonstrates how Semantic Kernel reduces code verbosity, allowing developers to focus on business logic and prompt design. He showcases a simple application connecting to an LLM, highlighting how the SDK abstracts complex interactions, making AI integration accessible even for those new to the technology.

Simplifying AI Integration

Delving deeper, Soham illustrates how Semantic Kernel enables modular application design. Developers can create objects to connect to specific LLMs, ensuring flexibility without overwhelming complexity. He advises against combining multiple LLMs in a single application, as their non-deterministic nature can introduce unpredictability. Instead, Soham recommends selecting a framework that aligns with the chosen LLM, such as Semantic Kernel for Azure Open AI or LangChain for broader compatibility, including Hugging Face or LLaMA.

His demo emphasizes practical use cases, such as generating context-aware responses for conversational applications. By leveraging Semantic Kernel, developers can orchestrate AI workflows efficiently, reducing development time and enhancing application responsiveness, a key theme of the conference.

Addressing Data Privacy Concerns

Soham addresses a critical concern raised throughout the conference: data privacy in AI applications. He explains that Azure Open AI ensures data remains within a user’s subscription, offering robust privacy controls for enterprise use. In contrast, public versions of LLMs, like Open AI’s standard endpoints, may use data for further training unless an enterprise version is selected. Soham urges developers to read the fine print before integrating LLMs, as sensitive data, such as customer contracts, could inadvertently contribute to model training if not handled properly.

This focus on privacy aligns with the conference’s discussions on ethical AI, providing developers with practical guidance to navigate enterprise requirements while leveraging AI’s capabilities.

Practical Recommendations for Developers

Soham concludes by encouraging developers to explore Semantic Kernel through Microsoft’s Azure platform, which offers resources for hands-on learning. His live coding demo, despite the pressure of a live audience, showcases the SDK’s ease of use, inspiring developers to experiment with AI orchestration. Soham invites further engagement via social platforms, sharing his enthusiasm for building AI-driven applications that are both powerful and responsible.

His presentation, delivered with clarity and technical expertise, equips developers with the tools to integrate AI seamlessly, fostering innovation while addressing practical and ethical considerations.

Hashtags: #AI #SemanticKernel #LargeLanguageModels #Azure #Java #Microsoft #SohamDasgupta #DevoxxUkraine2023