This document discusses tools and techniques for profiling Java applications, including the Solaris Studio Performance Analyzer and NetBeans IDE Profiler. It provides details on collecting profiling data, viewing results through the analyzer GUI or command line, and analyzing metrics like method execution times, memory usage, and thread activity to identify performance bottlenecks.
Spring 3.1 introduces several eagerly awaited features including bean definition profiles (a.k.a., environment-specific beans), enhanced Java-based application and infrastructure configuration (a la XML namespaces), a new cache abstraction, and MVC improvements. This session will provide attendees an in-depth look at these major new features as well as an overview of additional enhancements to the framework such as the new c: namespace for constructor arguments, updates regarding Servlet 3.0, and improvements to Spring's integration testing support.
This session will give attendees an overview of the new testing features in Spring 3.1 as well the new Spring MVC test support. Sam Brannen will demonstrate how to use the Spring TestContext Framework to write integration tests for Java-based Spring configuration using @Configuration classes. He'll then compare and contrast this approach with XML-based configuration and follow up with a discussion of the new testing support for bean definition profiles. Next, Rossen Stoyanchev will show attendees how testing server-side code with annotated controllers and client-side code with the RestTemplate just got a whole lot easier with the new Spring MVC test support. Come to this session to see these new Spring testing features in action and learn how you can get involved in the Spring MVC Test Support project.
This document summarizes changes to the Java programming language from JDK 9 to JDK 16, including new features and modules. Some key points:
- Java has moved to a six-month release cycle, delivering more features faster than before.
- Modules were introduced in JDK 9 to improve modularity. Modules group related code and dependencies.
- Incubator modules and preview features allow testing of non-final APIs before inclusion in the Java SE platform.
- Local variable type inference using 'var' was added in JDK 10 for simpler declaration of local variables when types can be inferred.
- Modules, the module system, and tools like jlink and jdeps help manage dependencies
This document summarizes the major changes and new features introduced in each version of Java from Java 8 to Java 17. It discusses key enhancements like modules in Java 9, switch expressions in Java 12, text blocks in Java 13, records and pattern matching in Java 14, sealed classes in Java 15 and strong encapsulation in Java 16. It also provides code examples to illustrate many of the new Java features.
This document summarizes different types of tests for Spring Boot applications, including unit tests, integration tests, and sliced tests. It discusses tools for mocking dependencies like MockRestServiceServer, Testcontainers for integration with Docker, and annotations like @MockBean and @SpyBean. Context caching, properties, and dirty context handling are also covered. The document concludes with an upcoming talk on JUnit 5.
This document describes how to virtualize Java applications in Java by hosting multiple "pseudo JVMs" within a single JVM. This allows deploying distributed applications for testing purposes. Key points covered include:
- Using custom classloaders and system properties to isolate "pseudo JVMs" and simulate distributed environments.
- Frameworks like GridKit that enable starting whole application topologies within JUnit tests for behaviors testing.
- Techniques for testing features like serialization, data routing, and cross-version compatibility.
- Later extensions to deploy virtual nodes across real servers using SSH for performance and deployment testing of distributed systems.
QUICK TEST PROFESSIONAL 8.2
Mercury Quick Test Professional 8.2 provides the industry’s best solution for functional test and regression test automation - addressing every major software application and environment
Spring 3.1 introduces several eagerly awaited features including bean definition profiles (a.k.a., environment-specific beans), enhanced Java-based application and infrastructure configuration (a la XML namespaces), a new cache abstraction, and MVC improvements. This session will provide attendees an in-depth look at these major new features as well as an overview of additional enhancements to the framework such as the new c: namespace for constructor arguments, updates regarding Servlet 3.0, and improvements to Spring's integration testing support.
This session will give attendees an overview of the new testing features in Spring 3.1 as well the new Spring MVC test support. Sam Brannen will demonstrate how to use the Spring TestContext Framework to write integration tests for Java-based Spring configuration using @Configuration classes. He'll then compare and contrast this approach with XML-based configuration and follow up with a discussion of the new testing support for bean definition profiles. Next, Rossen Stoyanchev will show attendees how testing server-side code with annotated controllers and client-side code with the RestTemplate just got a whole lot easier with the new Spring MVC test support. Come to this session to see these new Spring testing features in action and learn how you can get involved in the Spring MVC Test Support project.
This document summarizes changes to the Java programming language from JDK 9 to JDK 16, including new features and modules. Some key points:
- Java has moved to a six-month release cycle, delivering more features faster than before.
- Modules were introduced in JDK 9 to improve modularity. Modules group related code and dependencies.
- Incubator modules and preview features allow testing of non-final APIs before inclusion in the Java SE platform.
- Local variable type inference using 'var' was added in JDK 10 for simpler declaration of local variables when types can be inferred.
- Modules, the module system, and tools like jlink and jdeps help manage dependencies
This document summarizes the major changes and new features introduced in each version of Java from Java 8 to Java 17. It discusses key enhancements like modules in Java 9, switch expressions in Java 12, text blocks in Java 13, records and pattern matching in Java 14, sealed classes in Java 15 and strong encapsulation in Java 16. It also provides code examples to illustrate many of the new Java features.
This document summarizes different types of tests for Spring Boot applications, including unit tests, integration tests, and sliced tests. It discusses tools for mocking dependencies like MockRestServiceServer, Testcontainers for integration with Docker, and annotations like @MockBean and @SpyBean. Context caching, properties, and dirty context handling are also covered. The document concludes with an upcoming talk on JUnit 5.
This document describes how to virtualize Java applications in Java by hosting multiple "pseudo JVMs" within a single JVM. This allows deploying distributed applications for testing purposes. Key points covered include:
- Using custom classloaders and system properties to isolate "pseudo JVMs" and simulate distributed environments.
- Frameworks like GridKit that enable starting whole application topologies within JUnit tests for behaviors testing.
- Techniques for testing features like serialization, data routing, and cross-version compatibility.
- Later extensions to deploy virtual nodes across real servers using SSH for performance and deployment testing of distributed systems.
QUICK TEST PROFESSIONAL 8.2
Mercury Quick Test Professional 8.2 provides the industry’s best solution for functional test and regression test automation - addressing every major software application and environment
The Java Virtual Machine (JVM) is an abstract computing machine that executes Java bytecode. It has several core components including a class loader, memory areas like the heap and stack, and an execution engine. The execution engine initially interprets bytecode instructions but can optimize performance by just-in-time compiling frequently used bytecode into native machine code. The JVM provides a layer of abstraction between Java applications and the underlying hardware or operating system.
This document provides an overview of implementing a keyword-driven test automation framework in HP QuickTest Professional (QTP). It describes the necessary test settings in QTP including associating framework files, object repositories, recovery scenarios, and environment variables. It also explains how to call the framework and use keywords in a test data table to execute test steps. Test results can then be viewed and analyzed after a keyword-driven test run completes.
The document describes NexOS's test automation framework, which takes a hybrid approach combining test script modularity, library-driven testing, and data-driven testing. It utilizes VBScript library files for different application partitions and generic functions. Test data is stored in Excel files and imported into QTP. Results are recorded in an Access database and Quality Center. Scripts follow guidelines for components and are executed from Quality Center or manually from the test lab.
This document discusses setting up a Selenium project with Maven and Jenkins for continuous integration. It describes installing and configuring Maven, creating a Maven project structure for Selenium, and setting up Jenkins to automatically run test cases and generate XSLT reports on the test results. Key steps include downloading and configuring Maven, creating a Maven project with dependencies in the pom.xml, and configuring Jenkins with the project details to enable continuous integration and reporting.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
OrientDB - The 2nd generation of (multi-model) NoSQLRoberto Franchini
This document provides an overview of OrientDB, a multi-model database that combines features of document, graph, and other databases. It discusses data modeling and schema, querying and traversing graph data, full-text and spatial search, deployment scenarios, and APIs. Examples show creating classes and properties, inserting and querying graph data, and live reactive queries in OrientDB.
The document discusses Java SE 7 adoption rates over time, the Java SE 7 and 8 roadmap, new features in Java SE 8 like lambda expressions and streams, and the Java Mission Control toolset. It also summarizes Java FX, Java EE 7 themes, and the future of Java including Project Jigsaw.
Performance testing of a system will be done using YourKit Java profiler and Jmeter load testing tools. YourKit is a Java profiler that can analyze CPU usage, threads, memory, garbage collection, exceptions, and detect deadlocks. It helps find performance and scalability issues early in development. The presentation will cover the functionality of YourKit and how to integrate it into Eclipse. Requirements for using YourKit like downloading and ensuring client and agent versions match will also be discussed.
This document provides an overview and agenda for the "Busy Java Developer's Guide to WebSphere Debugging & Troubleshooting" presentation. The presentation covers various WebSphere Application Server components, troubleshooting tools like IBM Support Assistant, JVM troubleshooting tools, problem determination tools, common problem scenarios, how customers run into trouble, and includes a demo and Q&A section. It provides an in-depth look at debugging and resolving issues with WebSphere Application Server.
This document discusses monitoring Java Virtual Machine (JVM) performance. It covers monitoring garbage collection, the JIT compiler, class loading, and Java applications. It provides details on metrics, commands, and tools to monitor these aspects of the JVM and Java applications, including JConsole, VisualVM, and JMX. Monitoring is important for tuning performance and troubleshooting issues.
Quick overview on Visual Studio 2012 Profiler & Profiling tools : the importance of the profiling methods (sampling, instrumentation, memory, concurrency, … ), how to run a profiling session, how to profile unit test/load test, how to use API and a few samples
The document describes the history and development of the "little Jenkinsfile" used to automate testing of the AdoptOpenJDK, OpenJ9, and Eclipse OMR projects. It outlines the principles of keeping the Jenkinsfile simple and learn from past lessons. Key aspects include testing across multiple platforms and versions, using open source tools when possible, and whole team involvement. The ecosystem now includes over 250,000 tests running on multiple servers.
Struts 2 is an open source MVC framework based on Java EE standards. It uses a request-response pipeline where interceptors pre-process and post-process requests. The core components are interceptors, actions, and results. Interceptors provide functionality like validation while actions contain application logic and results define the response. Values are stored and accessed from a value stack using OGNL.
This slides show
1. How to obtain code coverage information for Java code
2. What kind of code coverage it is possible to get
3. Is 100% block coverage feasible, is it useful
4. How the code coverage could be used for more than discovering a percentage of uncovered code
Performance Test Driven Development with Oracle Coherencearagozin
This presentation discusses test driven development with Oracle Coherence. It outlines the philosophy of PTDD and challenges of testing Coherence, including the need for a cluster and sensitivity to network issues. It discusses automating tests using tools like NanoCloud for managing nodes and executing tests remotely. Different types of tests are described like microbenchmarks, performance regression tests, and bottleneck analysis. Common pitfalls of performance testing like fixed users vs fixed request rates are also covered.
Did you know your JDK contains visual monitoring tools with features like: Profiler, Visual GC, Heap dump browsing and much more?
VisualVM, BTrace and jhat are just some examples for useful performance diagnostics tools hidden in your JDK.
Join us for this session to see what tools are out there and how you can use them to identify and solve performance related issues.
Embracing Jakarta EE 10 not only enable you to write better structured applications, it also enables you to write more testable applications. Correctly leveraging CDI enables integration testing where you can simulate a database error and verify that JMS messages are lost due to a RuntimeException. This session will focus on strategies and techniques for testing message handing, transaction management, security, and data model integrity. You will see how you can check message delivery and error handling, session failover, database versioning, and two-phase commits with JMS and databases. All too often assumptions are made that are proven wrong in production. This session will show you how to take your testing to the next level.
This document provides an overview of performance testing and the Apache JMeter tool. It discusses what performance testing is, why it is important, and common types of performance tests. It then describes Apache JMeter, why it is useful for performance testing, how it works, and its basic elements like thread groups, timers, listeners, and samplers. The document demonstrates how to install JMeter, create a simple test plan, get results and reports. It also covers JMeter plugins, especially the WebDriver sampler for testing web applications using real browsers.
Symbiosis of test automation tools. Several tools (graphwalker/selenide/allure/sikuli/browsermob proxy) overview and practical appliance in your Selenium automation project will be presented
This document discusses migrating applications from Java 8 to later versions of Java such as Java 11 and Java 12. It outlines changes in the Java timeline including features added in versions 9 through 13. Major changes include modules, removed packages and methods, and behavior changes. The document recommends using scanning tools to analyze applications for migration issues and references additional resources on the Java migration.
Java Performance and Using Java Flight RecorderIsuru Perera
Slides used for an internal training. Explains why throughput and latency are important when measuring performance. How Java Flight Recording can be used to analyze performance issues.
The Java Virtual Machine (JVM) is an abstract computing machine that executes Java bytecode. It has several core components including a class loader, memory areas like the heap and stack, and an execution engine. The execution engine initially interprets bytecode instructions but can optimize performance by just-in-time compiling frequently used bytecode into native machine code. The JVM provides a layer of abstraction between Java applications and the underlying hardware or operating system.
This document provides an overview of implementing a keyword-driven test automation framework in HP QuickTest Professional (QTP). It describes the necessary test settings in QTP including associating framework files, object repositories, recovery scenarios, and environment variables. It also explains how to call the framework and use keywords in a test data table to execute test steps. Test results can then be viewed and analyzed after a keyword-driven test run completes.
The document describes NexOS's test automation framework, which takes a hybrid approach combining test script modularity, library-driven testing, and data-driven testing. It utilizes VBScript library files for different application partitions and generic functions. Test data is stored in Excel files and imported into QTP. Results are recorded in an Access database and Quality Center. Scripts follow guidelines for components and are executed from Quality Center or manually from the test lab.
This document discusses setting up a Selenium project with Maven and Jenkins for continuous integration. It describes installing and configuring Maven, creating a Maven project structure for Selenium, and setting up Jenkins to automatically run test cases and generate XSLT reports on the test results. Key steps include downloading and configuring Maven, creating a Maven project with dependencies in the pom.xml, and configuring Jenkins with the project details to enable continuous integration and reporting.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
OrientDB - The 2nd generation of (multi-model) NoSQLRoberto Franchini
This document provides an overview of OrientDB, a multi-model database that combines features of document, graph, and other databases. It discusses data modeling and schema, querying and traversing graph data, full-text and spatial search, deployment scenarios, and APIs. Examples show creating classes and properties, inserting and querying graph data, and live reactive queries in OrientDB.
The document discusses Java SE 7 adoption rates over time, the Java SE 7 and 8 roadmap, new features in Java SE 8 like lambda expressions and streams, and the Java Mission Control toolset. It also summarizes Java FX, Java EE 7 themes, and the future of Java including Project Jigsaw.
Performance testing of a system will be done using YourKit Java profiler and Jmeter load testing tools. YourKit is a Java profiler that can analyze CPU usage, threads, memory, garbage collection, exceptions, and detect deadlocks. It helps find performance and scalability issues early in development. The presentation will cover the functionality of YourKit and how to integrate it into Eclipse. Requirements for using YourKit like downloading and ensuring client and agent versions match will also be discussed.
This document provides an overview and agenda for the "Busy Java Developer's Guide to WebSphere Debugging & Troubleshooting" presentation. The presentation covers various WebSphere Application Server components, troubleshooting tools like IBM Support Assistant, JVM troubleshooting tools, problem determination tools, common problem scenarios, how customers run into trouble, and includes a demo and Q&A section. It provides an in-depth look at debugging and resolving issues with WebSphere Application Server.
This document discusses monitoring Java Virtual Machine (JVM) performance. It covers monitoring garbage collection, the JIT compiler, class loading, and Java applications. It provides details on metrics, commands, and tools to monitor these aspects of the JVM and Java applications, including JConsole, VisualVM, and JMX. Monitoring is important for tuning performance and troubleshooting issues.
Quick overview on Visual Studio 2012 Profiler & Profiling tools : the importance of the profiling methods (sampling, instrumentation, memory, concurrency, … ), how to run a profiling session, how to profile unit test/load test, how to use API and a few samples
The document describes the history and development of the "little Jenkinsfile" used to automate testing of the AdoptOpenJDK, OpenJ9, and Eclipse OMR projects. It outlines the principles of keeping the Jenkinsfile simple and learn from past lessons. Key aspects include testing across multiple platforms and versions, using open source tools when possible, and whole team involvement. The ecosystem now includes over 250,000 tests running on multiple servers.
Struts 2 is an open source MVC framework based on Java EE standards. It uses a request-response pipeline where interceptors pre-process and post-process requests. The core components are interceptors, actions, and results. Interceptors provide functionality like validation while actions contain application logic and results define the response. Values are stored and accessed from a value stack using OGNL.
This slides show
1. How to obtain code coverage information for Java code
2. What kind of code coverage it is possible to get
3. Is 100% block coverage feasible, is it useful
4. How the code coverage could be used for more than discovering a percentage of uncovered code
Performance Test Driven Development with Oracle Coherencearagozin
This presentation discusses test driven development with Oracle Coherence. It outlines the philosophy of PTDD and challenges of testing Coherence, including the need for a cluster and sensitivity to network issues. It discusses automating tests using tools like NanoCloud for managing nodes and executing tests remotely. Different types of tests are described like microbenchmarks, performance regression tests, and bottleneck analysis. Common pitfalls of performance testing like fixed users vs fixed request rates are also covered.
Did you know your JDK contains visual monitoring tools with features like: Profiler, Visual GC, Heap dump browsing and much more?
VisualVM, BTrace and jhat are just some examples for useful performance diagnostics tools hidden in your JDK.
Join us for this session to see what tools are out there and how you can use them to identify and solve performance related issues.
Embracing Jakarta EE 10 not only enable you to write better structured applications, it also enables you to write more testable applications. Correctly leveraging CDI enables integration testing where you can simulate a database error and verify that JMS messages are lost due to a RuntimeException. This session will focus on strategies and techniques for testing message handing, transaction management, security, and data model integrity. You will see how you can check message delivery and error handling, session failover, database versioning, and two-phase commits with JMS and databases. All too often assumptions are made that are proven wrong in production. This session will show you how to take your testing to the next level.
This document provides an overview of performance testing and the Apache JMeter tool. It discusses what performance testing is, why it is important, and common types of performance tests. It then describes Apache JMeter, why it is useful for performance testing, how it works, and its basic elements like thread groups, timers, listeners, and samplers. The document demonstrates how to install JMeter, create a simple test plan, get results and reports. It also covers JMeter plugins, especially the WebDriver sampler for testing web applications using real browsers.
Symbiosis of test automation tools. Several tools (graphwalker/selenide/allure/sikuli/browsermob proxy) overview and practical appliance in your Selenium automation project will be presented
This document discusses migrating applications from Java 8 to later versions of Java such as Java 11 and Java 12. It outlines changes in the Java timeline including features added in versions 9 through 13. Major changes include modules, removed packages and methods, and behavior changes. The document recommends using scanning tools to analyze applications for migration issues and references additional resources on the Java migration.
Java Performance and Using Java Flight RecorderIsuru Perera
Slides used for an internal training. Explains why throughput and latency are important when measuring performance. How Java Flight Recording can be used to analyze performance issues.
Software Profiling: Understanding Java Performance and how to profile in JavaIsuru Perera
Guest lecture at University of Colombo School of Computing on 27th May 2017
Covers following topics:
Software Profiling
Measuring Performance
Java Garbage Collection
Sampling vs Instrumentation
Java Profilers. Java Flight Recorder
Java Just-in-Time (JIT) compilation
Flame Graphs
Linux Profiling
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at RuntimeNETWAYS
If you want to trace or extract specific data from a Java application with OpenTelemetry, you usually have to modify the application’s code. However, this is often not possible, especially with bought-in software. We would like to show, how the open source inspectIT Ocelot Java agent can be used to dynamically inject OpenTelemetry code at runtime for extracting specific application and business data – and all this without having to adapt the application itself.
Q con shanghai2013-罗婷-performance methodologyMichael Zhang
This document summarizes a presentation on performance methodology at Salesforce given at QCon Beijing 2014. It discusses:
- The importance of performance for user experience, decreasing costs, and serving more customers.
- Salesforce's dedicated performance team is organized by areas like UI, mobile, platforms, and infrastructure.
- Key performance metrics include response time, throughput, CPU/memory utilization, and database metrics.
- Performance is tested proactively via feature and regression tests, and passively via production analysis. Automated testing uses tools like JMeter and internal frameworks. Profilers like Yourkit and HeapAudit help identify causes.
The document discusses the evolution of a SPARQL benchmarking framework from version 1.x to 2.x. Version 1.x had several limitations, such as only supporting SPARQL queries and using a hardcoded methodology. Version 2.x addressed these limitations by supporting different types of operations, separating the test methodology, and making the framework more customizable and extensible. Examples are given of how the framework is used internally and how others can further customize it for their needs.
This document discusses Java performance and contains sections on Java Persistence and EJB performance. It provides details on the Java Persistence API and its reference implementation, monitoring and tuning the EJB container including the thread pool and transaction isolation levels. It also outlines best practices for Enterprise Java Beans and Java Persistence including using appropriate fetch types, bulk updates and inheritance strategies.
This document discusses benchmarking multitiered Java applications. It covers challenges in benchmarking these applications, considerations for enterprise benchmarking including defining the system under test and performance metrics. It also discusses application server monitoring and profiling enterprise applications.
This document discusses strategies for optimizing web services performance. It covers topics like XML performance, using the appropriate XML parsing API, validating XML efficiently, optimizing external entity resolution, partial XML processing, web service benchmarking, factors affecting performance like message size and schema complexity, and best practices such as using MTOM, custom providers, and Fast Infoset.
This document discusses strategies for improving web application performance in Java. It covers monitoring and tuning web containers, including optimizing thread pool parameters, connection queues, and request handling. The document also provides best practices for Java servlets, JSPs, caching content, managing sessions, leveraging HTTP server file caches, and analyzing access logs to enhance performance.
This document discusses benchmarking Java applications. It covers challenges like warmup time, garbage collection, and Java time APIs. It also discusses designing experiments through clearly stating questions and hypotheses testing. Statistical methods for benchmarking are presented, including averaging, standard deviation, confidence intervals, and hypothesis testing.
The document describes the step-by-step methodology for tuning the Java Virtual Machine (JVM). It discusses determining system requirements and performance goals, choosing a JVM deployment model and runtime configuration, tuning garbage collection fundamentals, analyzing memory footprint and tuning for latency/responsiveness. Specific techniques covered include sizing the young and old generations, tuning survivor space size, CMS initiation occupancy and pause time.
This document discusses strategies for improving Java application performance, including reducing system CPU usage, addressing lock contention, optimizing volatile usage, resizing data structures efficiently, increasing parallelism, and other tips. Specific strategies covered in more depth include using Java NIO for non-blocking operations to reduce system CPU, identifying and fixing lock contention issues, replacing unnecessary volatile usage, ensuring data structures resize appropriately, keeping threads busy by addressing idle threads, and various other performance tuning techniques.
This document provides an overview of the Java Virtual Machine (JVM). It discusses the key components of the HotSpot JVM including the architecture, runtime environment, class loading process, bytecode interpretation, exception handling, synchronization, thread management, and Java Native Interface. The runtime environment is responsible for command line parsing, the JVM lifecycle such as loading, linking and initialization of classes, bytecode interpretation, and thread management. The document also describes class loading in the JVM, bytecode verification, and class data sharing. It provides details on synchronization approaches like object monitor mapping and biased locks. Finally, it discusses thread management aspects including JVM internal threads, safe points, and thread lifecycles.
This document discusses strategies for monitoring operating system performance on Java applications. It covers monitoring CPU utilization, memory usage, disk I/O, network I/O, and more using tools like Windows Performance Monitor, vmstat, mpstat, and sar. The document provides guidance on measuring performance on Windows, Linux, Solaris and SPARC systems. It is part of a larger document on Java performance that also discusses profiling Java applications, tuning the JVM, and benchmarking.
This document outlines an introduction to Java performance, including 12 chapters on strategies, approaches and methodologies for Java performance. It discusses forces at play in performance, top-down versus bottom-up approaches, choosing the right platform and CPU architecture, and evaluating system performance. It also provides an overview of operating system and JVM monitoring, Java application profiling tips, tuning the JVM, benchmarking Java applications, and analyzing web and web service performance.
This document compares containers and virtual machines for application deployment architecture. It outlines the architectural differences between containers running applications directly on the host operating system compared to virtual machines running isolated operating systems. A hybrid approach is discussed that uses both containers and virtual machines. Finally, it summarizes that virtual machines are better suited for legacy applications while containers are better for new web applications, and that a combined approach can utilize the advantages of both technologies.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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5. JAVA Application Profiling
5
SNo Capability
1 Native Level
2 Running/Paused/Blocked Thread
3 Wait vs Read Time differentiation
4 Monitor or Lock Information
Performance Analyser Features
6. JAVA Application Profiling
6
SNo Type Description
1 Method Execution time of method
2 Memory Number, size and method of object allocation
Profile Types
7. JAVA Application Profiling
7
SNo Type Description
1 Profiler Tool that shows behavior of jvm java & native code
2 Profile File that contains information collected by Profiler
3 Overhead Time spent by the profiler collecting the profile information
4 Call Tree Listing of methods in a call stack
5 Filter Artifact that narrows scope of collected profile information
Terminology
8. JAVA Application Profiling
8
SNo Type Description
1 Experiment Artifact produced by collecting a profile of an application
2 collect Command line tool used to collect an experiment or profile by profiling and tracing function usage
3 Analyser GUI used to view collected experiment
4 er_print Command line tool used to view collected experiment
5 Inclusive time Time taken to execute a method and submethods
6 Exclusive time Time taken to execute a method excluding submethods
7 Attributed time Time attributed to a method
8 Caller Calee A relationship of a method either being called by some
method (a caller), or a method being called by some other method (a callee)
9 System CPU Elapsed execution time within operating system kernel
10 User CPU Elapsed execution time outside operating system kernel
Oracle Performance Analyser Terminology
9. JAVA Application Profiling
9
SNo Type Description
1 Instrumentation insertion of counters, timers into java byte code to be profiled
2 Heap The memory pool used by JVM for objects allocated with new keyword
3 Garbage
collection
Operation responsible for cleanup of unused java objects from Heap
4 Memory leak Unused object that cannot be collected due to one or more objects holding references
5 Self time The amount of time needed to execute the instructions in a method excluding submethods
6 Hot spot Method with large Self Time
7 Root method Method selected for performance profiling
NetBeans Profiler Terms
10. JAVA Application Profiling
10
Solaris Studio Performance Analyer
● Both Java & native code (Java,C,C++,Fortran)
● Method Profiling
● Monitor/Lock Profiling
11. JAVA Application Profiling
11
Solaris Studio Performance Analyer
1. Category - User CPU, System CPU, Lock contention
2. Within each Category - Inclusive Time & Exclusive Time
14. JAVA Application Profiling
14
Solaris Studio Performance Analyer
Collect Options
-o <experimentFile> Create experiment file name with specified name
-d <experimentDirectoryPath> Put Experiment file in specified directory
-p <option> (on| lo| hi) Frquency of sample collection (default 10ms)
-A <option> (on|off|copy) Application artifact archived or copied
-y <signal> Toggle data collection based on specifeid signal
-h <cpu counter> (Solaris) Specify cpu counter (ex cache miss,TLB miss etc.
20. JAVA Application Profiling
20
Solaris Studio Performance Analyer
View Collected Experiment
Analyzer GUI
SNo Icon Description
1 Open experiment File
2 Combine an experiment’s data to an existing experiment
3 Drop an experiment’s results from the experiments already
loaded
4 Collect an experiment
5 Print the listing of data currently displayed in the Analyzer
21. JAVA Application Profiling
21
Solaris Studio Performance Analyer
View Collected Experiment
Analyzer GUI
SNo Icon Description
6 Create a new Analyzer window with the same experiment file loaded in both windows
7 Close and exit the Analyzer GUI program
8 Modify the category of information to display
9 Filter the data presented
10 Show and/or hide APIs or methods
11 Switches to alternative viewing modes: User, Expert, Machine
22. JAVA Application Profiling
22
Solaris Studio Performance Analyer
View Collected Experiment
Analyzer GUI
SNo TabName Description
1 Summary Shows all the recorded metrics for the selected object, both as values and
percentages, and information on the selected object
2 Event Shows the available data for the selected Java method, source code line, or program
counter, including the event type, method name, LWP Id, threadId, and CPU Id
30. JAVA Application Profiling
30
Solaris Studio Performance Analyzer
View Collected Experiment
Analyzer GUI
Call Stack Fragment
Fragment Specific Metric
Next we will talk about java application profiling
Intrusive to responsiveness & throughput of application
Reactive - in response to production issue
Planned - specific performance requirements are known
Example of application profilers and the supported platforms
List of Performance Analyser features
Types of Profile
Description of profler terminology
Descrioption of oracle analyser terminology
Description of netbean profiler terminology
can be used to profile C, C++, and Fortran based applications
As a java profiler most useful as most useful as a method profiler and Java monitor/lock profiler
Data that is displayed in Performance Analyser
This slide shows the two distinct steps to profiling.
The first is collecting an experiment using the Performance Analyzer’s collect command
The second step, analysis, is viewing the collected experiment and analyzing its results with either the Performance Analyzer’s Analyzer GUI tool or er_print
Execution of the collect command results in generation of test.1er file in directory where this command is run
List of options to collect comand
This is an example of Delay profile data collection based on specified signal (application warmup)
two ways to view the collected data in the experiment file. One way is using the GUI, called the Analyzer, and the second way is using a command line tool called
er_print
The Analyzer can be launched with the name of the experiment to load. Assuming an experiment named test.1.er, the Analyzer loads the test.1.er experiment auto-
matically by executing the displayed command
View of the Open experiment dialog box
Default analyzer View
Analyzer Tool bar icon description
Analyzer Tool bar icon description
There are two tabs on the right panel of the default Analyzer GUI view, a Summary tab and Event tab. Information found on the Summary and Event tabs on the right panel are described above
Useful Tabs → Functions, Call Tree, Callers-Callees, Source, and Disassembly
The default view for the Functions tab lists inclusive and exclusive User CPU columns for Java methods in the Java application and orders the list of methods in decreasing order of exclusive User CPU time
The Call Tree tab shows the hierarchy of calls where an application spends its time. This view offers the ability to quickly recognize at a high level and
in what use case(s) an application spends most of its time
The time and percent shown at each node in the Call Tree is the cumulative time spent both in the method represented by the node and everything it calls
Identify method with highest percentage execution time among all the methods that consitute the total execution time
(As shown in this slide the render method has highest contribution to total time to execute of the paint method)
In this slide notice that the sum of attributed metrics for the callees and the selected method add up to the inclusive metric for the center method
In this slide notice that the sum of attributed metrics for the callers add up to the inclusive metric for the center method
In this slide notice that the add method can be used to create custom call stack fragment
In this slide notice that attributed metric for center method is updated once caller is added to the call stac/k fragment
This slide shows the use of the functions tab to identify hot methods
Complementary to using the Call Tree, is to analyze the experiment using the Functions tab and identifying the hottest methods in the applica-
tion. This approach focuses more on improving the implementation of a specific method.
(As shown in this slide Blit.Blit method is identified as a hot method)
This slide shows a description of analyzer tabs. Functions and Call Tree are the most useful are highlighted in green.
Description of analyser tabs continued
This slide shows difference between User Expert and Machine modes of view
Filter timeline to narrow investigation to specific time interval
This slide shows the usage of the er_print command.
The er_print command line utility prints an ASCII text version of the various displays found in the Performance Ana-
lyzer GUI such as method lists and callers-callees of User CPU, System CPU, and User Lock consumers
Er_Print command Syntax
---------------------------------------
The text between [ and ] means the arguments are optional. Text demarcated by | means any of the options can be used between the | characters. And text between
< and > means a name of a script or file that you have created is required, or an er_print command is required. When the -script option is not used, er_print
reads commands from the command line
This slide shows example usage of the er_print command to print the top 10 hottest methods sorted by exclusive user cpu time.
The limit command tells er_print to limit the number of methods printed with the functions command to the number
of methods given as an argument to the limit command
The csingle command prints the callers-callees of the method name passed as an argument to the csingle command
This slide shows example usage of the filter outfile and viewmode commands.
Filters command = limit the samples to specific interval, multiple intervals separated by comma
Outfile command = specify name of output file
Viewmode command = change to user,expert or machine mode
This slide lists the available metrics in analyzer
Next we will talk about the features of the netbeans profiler
Low Overhead : You can control the profiler’s performance impact on your application. Based on your selections, the performance impact ranges from extensive to none
CPU performance profiling: Time spent in every method of your application or just in selected methods can be reported
Memory profiling: You can check for excessive object allocations
Memory leak detection:The profiler’s statistical reports make it easy todetect object instances that are leaking
This slide shows the usage of the monitor tab in netbeans
Menu -->Attach Profiler-->Open Dialog Option View → Default Monitor allows thread and lock contention monitor with minimal overhead
This slide describes the default monitor view
Default monitor view displays live thread state and jvm memory information in telemetry overview panel as shown in this slide
This slide describes the steps to obtain a cpu profile
From Menu -->Attach Profiler-->Choose CPU option on the left and ensure default quick sample radio button is selected
This slide shows the location of the the Live Results View of the Profiler
This slide shows the function hotspots ordered in descending order of self time consumed
This slide shoes feature in netbeans profiler to take snapshot stack state
This slide shows the Call Tree View of the Snapshots View
Clicking on the the combined tab on the lower right shows both call tree and hot spot function as shown in this slide
This slide shows that by clicking a method in the Hot Spot list will find that method’s entry in the Call Tree making it easy to see the relationship between a method’s root and the hot spot
Next we look at the memory tab.
From Menu -->Attach Profiler-->Choose Memory option on the left and check the record full object lifecycle and record stack trace checkboxes.
This slide describes the avg age and generations tab of net beans profiler.
Avg. Age. Average age of the Live Objects. The age of each object is the num-
ber of garbage collections that it has survived. The sum of the ages divided by
the number of Live Objects is the Avg. Age.
Generations. Calculated using the Live Objects. The Generations value is the
number of different ages for the Live Objects.
An increasing value of generations is a sign of a memory leak
This slide show allocation call tree for an object
Click on Take Snapshot and Right Click Object to see the allocation stack trace ( Detailed breakup of the method which allocated object on heap)
This slide shows example of offline heap dump analysis in netbean profiler.
A binary heap dump is a snapshot of all the objects in the Java HotSpot VM at the time the heap dump is taken
Click Profile -> Load Heap Dump for offline analysis of Memory Dump file useful to troubleshoot OOMemory Error
Heap dump can be generated with jmap command
In this slide, the highlighted numbers show that 16.4% of total heap allocation is attributed to char[]