Presentation shows how to use Apache Benchmark and JMeter to run load-tests. It also shows how to collect metrics from Google Analytics that are needed to configure your tests.
Exemple Pratique With Selenium WebDriver | Selenium WebDriver Tutorial | Selenium
Selenium WebDriver AVEC JAVA
Selenium est un ensemble d'outils robustes qui prend en charge le développement rapide de l'automatisation des tests pour les applications Web.Selenium a été créé par Jason Huggins en 2004.
WebDriver est un outil pour automatiser les applications Web de test. Il est populairement connu sous le nom de sélénium 2.0.
Simon Stewart a créé WebDriver vers 2006, lorsque les navigateurs et les applications Web devenaient plus puissants et plus restrictifs avec des programmes JavaScript comme Selenium Core
youtube : https://www.youtube.com/channel/UCMhJ-OiC-drUqbBldbRMZvA
This presentation talks about the different ways of getting SQL Monitoring reports, reading them correctly, common issues with SQL Monitoring reports - and plenty of Oracle 12c-specific improvements!
Protecting Java EE Web Apps with Secure HTTP HeadersFrank Kim
This document summarizes techniques for securing Java EE web applications with secure HTTP headers. It discusses cross-site scripting (XSS) and how to prevent it using the HttpOnly and X-XSS-Protection headers. It also covers session hijacking and how to prevent it with the Secure and Strict-Transport-Security headers. Finally, it discusses clickjacking and demonstrates how it works.
This document discusses benchmarking Apache Druid using the Star Schema Benchmark (SSB). It describes ingesting the SSB dataset into Druid, optimizing the data and queries, and running performance tests on the 13 SSB queries using JMeter. The results showed Druid can answer the analytic queries in sub-second latency. Instructions are provided on how others can set up their own Druid benchmark tests to evaluate performance.
Spring Cloud Config provides a centralized way to manage external configuration for distributed systems. The Config Server stores configuration in Git repositories and makes it available via REST APIs to client applications. Clients can bind to the Config Server to initialize their Spring Environment with remote property sources. The default storage backend uses Git, allowing version control and tooling support. The Config Server serves configuration properties and YAML files from Git or HashiCorp Vault. It maps request paths to files in sources by application, profile, and label. Client applications can encrypt/decrypt values.
Schema replication using oracle golden gate 12cuzzal basak
This document provides instructions for configuring asynchronous schema replication between an Oracle source database and target database using Oracle GoldenGate 12c. It outlines the necessary steps which include:
1. Enabling supplemental logging and archivelog mode on both databases.
2. Installing the GoldenGate software and starting the Manager processes on both systems.
3. Configuring the Extract, Data Pump, and Replicate processes to replicate the BASAK schema and tables from the source PDBORCL to the target PRIPDB database.
4. Starting the Extract, Data Pump, and Replicate jobs to begin the replication process and ensure the BASAK schema and tables are synchronized between the source and target databases.
This document discusses Oracle database backup and recovery. It covers the need for backups, different types of backups including full, incremental, physical and logical. It describes user-managed backups and RMAN-managed backups. For recovery, it discusses restoring from backups and applying redo logs to recover the database to a point in time. Flashback recovery is also mentioned.
Este documento describe cómo graficar informes SAR (System Activity Report) utilizando las herramientas KSar y un utilitario en línea. KSar es una aplicación basada en Java que permite generar gráficos a partir de datos SAR almacenados localmente o en otro servidor, mostrando valores como uso de CPU y consumo de RAM. El utilitario en línea permite cargar y visualizar archivos SAR de forma gratuita en un navegador, generando resúmenes e gráficos interactivos de recursos como CPU, carga y RAM.
Lightweight locks (LWLocks) in PostgreSQL provide mutually exclusive access to shared memory structures. They support both shared and exclusive locking modes. The LWLocks framework uses wait queues, semaphores, and spinlocks to efficiently manage acquiring and releasing locks. Dynamic monitoring of LWLock events is possible through special builds that incorporate statistics collection.
Nginx is a popular tool for load balancing and caching. It offers high performance, reliability and flexibility for load balancing through features like upstream modules, health checks, and request distribution methods. It can also improve response times and handle traffic spikes through caching static content and supporting techniques like stale caching.
This document discusses different ways to implement configuration management in a Spring Cloud application using Spring Cloud Config. It describes using the Spring Cloud Config Server with a Git backend to externalize configuration and manage configs across environments. It also covers using a MySQL database instead of Git and implementing the config server functionality within each application to retrieve configs directly from MySQL. While most configs can be externalized, some like datasource URLs may still need to be defined internally for bootstrapping. Security, encryption, and broadcasting config changes to clients would need additional implementation as well.
Oracle goldengate 11g schema replication from standby databaseuzzal basak
GoldenGate can replicate database schemas between an Oracle source and target database. It was configured to replicate the SCOTT schema from a source Oracle 11gR2 database in standby mode to a target Oracle 11gR2 database. The key steps included enabling supplemental logging on the source, setting up the GoldenGate user and processes on both databases, and defining the extract, pump and replicate processes to copy data and DDL changes from the source to the target schema.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Get answers to the real time Oracle Golden gate interview questions!
Here is the link for full article: https://www.support.dbagenesis.com/post/oracle-golden-gate-interview-questions
Mastering Distributed Performance TestingKnoldus Inc.
To delve into the intricacies of optimizing performance and scalability in distributed systems. Learn advanced techniques, tools, and best practices for conducting efficient load testing across diverse environments. Gain valuable insights that will empower you to elevate the performance of your applications under real-world conditions.
Yet another way to run distributed performance/load tests on AWS: Jmeter + Docker + Terraform
Who is Artem?
A Test Automation Engineer at SoftServe with 5 years of IT experience overall and about 3 years dealing with test automation (Web UI, Mobile, API and performance tests).
Schema replication using oracle golden gate 12cuzzal basak
This document provides instructions for configuring asynchronous schema replication between an Oracle source database and target database using Oracle GoldenGate 12c. It outlines the necessary steps which include:
1. Enabling supplemental logging and archivelog mode on both databases.
2. Installing the GoldenGate software and starting the Manager processes on both systems.
3. Configuring the Extract, Data Pump, and Replicate processes to replicate the BASAK schema and tables from the source PDBORCL to the target PRIPDB database.
4. Starting the Extract, Data Pump, and Replicate jobs to begin the replication process and ensure the BASAK schema and tables are synchronized between the source and target databases.
This document discusses Oracle database backup and recovery. It covers the need for backups, different types of backups including full, incremental, physical and logical. It describes user-managed backups and RMAN-managed backups. For recovery, it discusses restoring from backups and applying redo logs to recover the database to a point in time. Flashback recovery is also mentioned.
Este documento describe cómo graficar informes SAR (System Activity Report) utilizando las herramientas KSar y un utilitario en línea. KSar es una aplicación basada en Java que permite generar gráficos a partir de datos SAR almacenados localmente o en otro servidor, mostrando valores como uso de CPU y consumo de RAM. El utilitario en línea permite cargar y visualizar archivos SAR de forma gratuita en un navegador, generando resúmenes e gráficos interactivos de recursos como CPU, carga y RAM.
Lightweight locks (LWLocks) in PostgreSQL provide mutually exclusive access to shared memory structures. They support both shared and exclusive locking modes. The LWLocks framework uses wait queues, semaphores, and spinlocks to efficiently manage acquiring and releasing locks. Dynamic monitoring of LWLock events is possible through special builds that incorporate statistics collection.
Nginx is a popular tool for load balancing and caching. It offers high performance, reliability and flexibility for load balancing through features like upstream modules, health checks, and request distribution methods. It can also improve response times and handle traffic spikes through caching static content and supporting techniques like stale caching.
This document discusses different ways to implement configuration management in a Spring Cloud application using Spring Cloud Config. It describes using the Spring Cloud Config Server with a Git backend to externalize configuration and manage configs across environments. It also covers using a MySQL database instead of Git and implementing the config server functionality within each application to retrieve configs directly from MySQL. While most configs can be externalized, some like datasource URLs may still need to be defined internally for bootstrapping. Security, encryption, and broadcasting config changes to clients would need additional implementation as well.
Oracle goldengate 11g schema replication from standby databaseuzzal basak
GoldenGate can replicate database schemas between an Oracle source and target database. It was configured to replicate the SCOTT schema from a source Oracle 11gR2 database in standby mode to a target Oracle 11gR2 database. The key steps included enabling supplemental logging on the source, setting up the GoldenGate user and processes on both databases, and defining the extract, pump and replicate processes to copy data and DDL changes from the source to the target schema.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Get answers to the real time Oracle Golden gate interview questions!
Here is the link for full article: https://www.support.dbagenesis.com/post/oracle-golden-gate-interview-questions
Mastering Distributed Performance TestingKnoldus Inc.
To delve into the intricacies of optimizing performance and scalability in distributed systems. Learn advanced techniques, tools, and best practices for conducting efficient load testing across diverse environments. Gain valuable insights that will empower you to elevate the performance of your applications under real-world conditions.
Yet another way to run distributed performance/load tests on AWS: Jmeter + Docker + Terraform
Who is Artem?
A Test Automation Engineer at SoftServe with 5 years of IT experience overall and about 3 years dealing with test automation (Web UI, Mobile, API and performance tests).
This document provides an introduction to JMeter, an open source performance testing tool. It discusses what JMeter is used for, its key features, components, strengths and weaknesses. It also provides instructions on setting up and running JMeter, including building a test plan with thread groups, samplers, listeners and other elements. The document demonstrates how to perform tasks like parameterization, session handling, and distributed testing with JMeter.
JMeter is an open source testing tool that can be used to load test functional behavior and measure performance. It was originally designed for testing web applications but has since expanded to other test functions. JMeter loads test web pages and measures performance by working at the protocol level rather than rendering pages like a browser. It can run in GUI mode or non-GUI mode from the command line. Test plans are created using an ordered tree structure and are stored in JMX files. Elements include samplers, timers, listeners, and controllers to organize test steps.
Day5_Apache_JMeter_Test_Execution_RemoteMode_Master_SlaveSravanthi N
1. The document describes the steps to perform remote load testing using JMeter in both GUI and non-GUI modes. It includes steps for setting up the master and slave machines, starting JMeter in server mode, and running tests remotely.
2. It also provides instructions for adding server monitoring to JMeter tests using HTTP requests. This allows monitoring server performance and response times under load.
3. Tips are given for troubleshooting common issues like OutOfMemoryErrors during long tests, and best practices like limiting the use of resource-intensive listeners.
jMeter is an open source load and performance testing tool. It is a 100% Java application that simulates user load on servers and applications. It can test websites, web services, databases, and other application components. jMeter works by recording user actions as test plans that can then be replayed concurrently to simulate multiple users accessing the system. Key components of a jMeter test plan include thread groups, samplers, listeners, and assertions. Listeners and reports provide output on system performance during the load test.
Performance testing involves testing a system to determine how it performs under a particular workload. The document discusses various types of performance testing like load/capacity testing, stress testing, volume testing, endurance testing, and spike testing. It also discusses concepts like bottlenecks, prerequisites for performance testing, popular load testing tools like JMeter, and how to use key JMeter features for performance testing like adding users, HTTP requests, listeners, parameterization, correlation, assertions, and distributed testing.
This document provides an overview of performance testing using JmeterTool. It discusses key concepts like load testing, stress testing, and endurance testing. It describes the goals of performance testing such as assessing production readiness and discovering bottlenecks. The prerequisites for performance testing and typical testing lifecycle are also outlined. Best practices like using ramp up/down periods and repeating tests are recommended. Challenges in performance testing and common fixes to performance issues are also covered. Finally, it provides an introduction to the various elements in Jmeter like samplers, listeners, timers, and controllers that are used to build test plans and simulate load on systems.
The document provides an overview of performance testing and the JMeter load testing tool. It defines performance testing as testing to determine how a system performs under workload. The main types of performance testing are described as load/capacity testing, stress testing, volume testing, endurance/soak testing, and spike testing. Load testing is the simplest form and aims to understand system behavior under expected load. Bottlenecks can be identified through load testing. Stress testing finds a system's capacity limit. Volume testing checks efficiency processing large data amounts. Endurance testing checks withstanding load over long periods. Spike testing observes behavior under sudden load increases. JMeter is introduced as an open source load testing tool that can test various system types and has user
This document provides an overview of using JMeter for load testing. JMeter is a Java application that can load test web applications and other services. It allows generating load from a single machine or distributed across multiple machines. The document discusses how to install JMeter and various ways to create tests, including hand entering requests, reading URLs from files, and recording tests. It also covers adding assertions to tests, ways to generate and monitor load, and provides examples of testing SQL and SMTP services. Resources for learning more about JMeter capabilities and plugins are also listed.
The document provides information about performance testing using Jmeter 2.6. It discusses what performance testing is and the different types including load/capacity testing, stress testing, volume testing, endurance testing, and spike testing. Load testing is described as the simplest form of performance testing to understand system behavior under an expected load. Bottlenecks can be identified through load testing. Stress testing finds a system's capacity limit. Volume testing checks efficiency by processing huge data amounts. Endurance testing checks if a system can withstand load for long periods. Spike testing suddenly increases load to observe behavior. Pre-requisites for performance testing and load testing tools are also mentioned, with JMeter described as an open source tool that can test various server
Load testing is done to determine system limits, verify response times under high load, check stability, and predict future needs. Open source tools like JMeter, Yandex Tank, and Taurus can be used. With JMeter, a test plan is created with thread groups, HTTP requests, and listeners to start load testing. Issues like slow responses or server crashes are identified. Short term fixes include restarting servers or tuning configurations, while long term solutions involve moving to the cloud, using caching, or splitting applications into microservices. Other commercial load testing tools are also available from companies like SOASTA and BlazeMeter.
Tempto is a product test framework that allows developers to write and execute tests for SQL databases running on Hadoop. Individual test requirements such as data generation, HDFS file copy/storage of generated data and schema creation are expressed declaratively and are automatically fulfilled by the framework. Developers can write tests using Java (using a TestNG like paradigm and AssertJ style assertion) or by providing query files with expected results. We will show how we use it for presto product tests.
Benchto is a benchmark framework that provides an easy and manageable way to define, run and analyze macro benchmarks in clustered environment. Understanding behavior of distributed systems is hard and requires good visibility intostate of the cluster and internals of tested system. This project was developed for repeatable benchmarking ofHadoop SQL engines, most importantly Presto.
JMeter is an open-source load testing tool that can test various server types including web servers. It allows performance testing by simulating a heavy load on a system and stress testing to push a system to its limits. Key benefits of JMeter include its ability to test HTTP, database, JMS, mail protocols and more. It also has a full multithreading framework and customizable plugins. Creating a test plan in JMeter involves adding thread groups to simulate users, HTTP request samplers, listeners to view results, and other elements like timers, assertions and post-processors. JMeter also supports recording tests from a browser and distributed testing across multiple machines.
Managing big test environment and running tests with Jenkins, Jenkins Job bui...Timofey Turenko
A short presentation about our experience of using Jenkins and Jenkins Job Builder with Vagrant as a backend tool to manage complex environment (tens of virtual machines for every test run) for database proxy server testing.
This document provides an overview of the JMeter load testing tool, including its history, features, and how to use it. It describes how JMeter can record web application transactions for playback, and explains the various components of a JMeter test plan such as thread groups, samplers, listeners, timers, and more. It also covers installing JMeter and the basics of creating a test plan to load test a web application.
Best Jmeter Interview Questions- Prepared by Working ProfessionalsTesting World
This document provides information about performance testing tools and training. It discusses JMeter, an open source tool for load and performance testing. It provides answers to various questions about JMeter features like thread groups, listeners, controllers, samplers. It also discusses distributed testing, reducing resource usage, and capturing authentication scripts with JMeter. Contact information is provided to purchase lifetime access to video courses on automation and performance testing tools.
The document discusses various topics related to web development including design patterns, version control systems like SVN and GIT, testing tools like Jasmine and QUnit, JavaScript frameworks like Node.js, and setting up a testing environment using JsTestDriver. It provides instructions and code samples for setting up and running tests with JsTestDriver on different platforms. It also covers CSS3 features, media queries, and browser components.
This document provides an overview of how to perform distributed load testing using JMeter. It explains the key terminology used, including master and slave systems. The step-by-step instructions describe how to configure JMeter on the slave systems to run in server mode, and how to configure the master system to control the slaves. It outlines starting the test by selecting remote start or remote start all from the JMeter GUI on the master system. Limitations of the distributed testing approach are also listed.
Presentation from SysOps/DevOps Wrocław MeetUp #8 (28.03.2019). I was sharing my experience of introducing DevOps in projects of various size. We started with agile ways of working with architecture (evolutionary approaches, ADR), talked about introduction of continuous security practices (DevSecOps) and ended with the ways of final business release of our applications.
A Year of Droplr Cloud Architecture Evolution with AWS and ServerlessAntoni Orfin
This document summarizes Droplr's evolution to a serverless cloud architecture on AWS over the past year. Some key points:
- Droplr moved from multiple programming languages and microservices to primarily using Node.js and monolith architecture.
- Monitoring was optimized using services like Pingdom, DataDog, Logz.io and Sentry.
- AWS Lambda was adopted for its cost effectiveness, scalability and reliability. It allowed Droplr to reduce costs by 60% and deploy updates in minutes.
- The current architecture uses Lambda with VPC, CloudFront, API Gateway, SNS and CloudWatch Events. Serverless improved performance and reduced response times.
Future of Cloud Starts with ServerlessAntoni Orfin
Presentation shows how current Cloud looks like and how it will evolve with serverless technologies such as AWS Lambda, API Gateway and CloudFront.
Do we need servers?
Do we need regions?
No.
Droplr Serverless Revolution - How we killed 50 servers in a yearAntoni Orfin
Droplr is transitioning their infrastructure from traditional servers to serverless architectures to reduce complexity, improve maintainability, and increase performance. They have moved many of their services to AWS Lambda, including scheduled jobs, event-driven processes, and HTTP microservices. This has allowed them to simplify their stack, improve scalability, and reduce costs. While serverless architectures require some adjustments, Droplr has seen benefits from their transition and plans to expand their use of serverless and further optimize their infrastructure.
Presentation shows how at Droplr we consider the DevOps role.
It covers topics related with Amazon Web Services, Infrastructure as Code concept (with tools like Terraform and Ansible) and going into Continuous Deployment as the key of making our company the most competitive on the market.
Presentation from the 8th Wrocław's DevOps Meeting which took place on 28.03.2017.
Testowanie poziomu bezpieczeństwa aplikacji internetowychAntoni Orfin
Niniejsza praca ma za zadanie przedstawić zagrożenia związane z bezpieczeństwem aplikacji internetowych.
Omawia najpowszechniejsze rodzaje zagrożeń, przykłady podatności i sposoby ochrony. Pozwoli na zapoznanie się z ogólnymi zasadami, którymi powinny kierować się osoby odpowiedzialne za wytwarzanie systemów webowych. Jest również bazą która pozwoli skuteczniej przeprowadzać audyty bezpieczeństwa.
Projektowanie wysokowydajnych i skalowalnych serwisów WWW - Warstwa danychAntoni Orfin
Część druga prezentacji pochodzącej z warsztatów skupiających się na zagadnieniach projektowania i wytwarzania wysokowydajnych i skalowalnych serwisów webowych.
Prezentacja opisuje problemy związane z warstwą danych:
- Replikacja (master-master, master-slave)
- Partycjonowanie (sharding)
- Wydajne przechowywanie danych (agregacja, denormalizacja)
Projektowanie wysokowydajnych i skalowalnych serwisów WWW - Warstwa aplikacjiAntoni Orfin
Część pierwsza prezentacji pochodzącej z warsztatów skupiających się na zagadnieniach projektowania i wytwarzania wysokowydajnych i skalowalnych serwisów webowych.
Prezentacja opisuje problemy związane z warstwą aplikacji:
- Rodzaje skalowania
- Architektury nastawione na zapewnienie wysokiej wydajności i skalowalności
- Zagadnienia Load-Balancingu
- Metody cache'owanie - n-Tier Cache, Varnish, Redis
- Service Oriented Architecture
This document discusses Elasticsearch and its uses for search. It describes how Elasticsearch can be used for intelligent search engines, autocomplete, geo-search, and search by colors. It then covers the basics of how Elasticsearch works, including its architecture with nodes, shards, and replicas. The document also outlines how to map and index documents, perform searches using queries and filters, and generate analytics through aggregations.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity
This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots.
📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
2. Performance Testing
Types of testing
Stress test1
Load test2
- Run test from low to high load
- Find the peak for the system
„If we reach more than 100 concurrent
users, the system is slowing down”
- Break the system
- Test if it fails and recovers gracefully (recoverability)
3. Performance Testing
Apache Benchmark
Download Apache Utils1
Run test2
$ apt-get install apache2-utils
$ ab -n 100 -c 1 http://localhost/
$ ab -n 100 -c 50 http://localhost/
-n requests Number of requests to perform
-c concurrency Number of multiple requests to make at a time
Concurrency does not mean number of
simultaneous users!
4. Performance Testing
Apache Benchmark
Interpret results3
Server Software: nginx/1.6.2
Server Hostname: localhost
Server Port: 80
Document Path: /
Document Length: 94873 bytes
Concurrency Level: 50
Time taken for tests: 0.094 seconds
Complete requests: 100
Failed requests: 7 (Connect: 0, Receive: 0, Length: 7, Exceptions: 0)
Total transferred: 9503493 bytes
HTML transferred: 9487293 bytes
Requests per second: 1064.54 [#/sec] (mean)
Time per request: 46.969 [ms] (mean)
Time per request: 0.939 [ms] (mean, across all concurrent requests)
Transfer rate: 98797.65 [Kbytes/sec] received
5. Performance Testing
Apache Benchmark
Load testing4
$ ab -n 10000 -c 10 http://localhost/ # c to low (server is not
$ ab -n 10000 -c 100 http://localhost/ # saturated, response
$ ab -n 10000 -c 250 http://localhost/ # times are stable)
$ ab -n 10000 -c 500 http://localhost/
$ ab -n 10000 -c 750 http://localhost/ # c too high (server is
$ ab -n 10000 -c 1000 http://localhost/ # saturated, response)
times are increasing)
HERE J
6. Performance Testing
Apache Benchmark
Cons-
- Tests only one URL at the same time.
- Running load test with various concurrency and
collecting results into nice graphs is irritating
- Can’t run distributed test (with multiple test servers)
Pros-
- Zero-configuration
- Easy to learn and to run first test
- Small CPU/memory footprint
9. Performance Testing
Apache JMeter
Definining parameters4
- Define variables in „User Defined Variables”
- Allow to pass variables via CLI
${__P(host,${host})} - will use value from „host” CLI
argument or from ”User Defined Variables” if not
passed.
$ ./bin/jmeter -t scenario.jmx -Jhost my-host.com
HINT
10. Performance Testing
Apache JMeter
Using CSV file with URLs5
- You can test multiple URLs in a single load-test
- Group results by categorizing URLs
Sample CSV:
Homepage,/
Category,/wallmurals
Category,/prints-and-posters
ProductPage,/wallmurals/cat-425225252
ProductPage,/stickers/dog-12789
11. Performance Testing
Apache JMeter
Defining threads scenario6
- Use „Concurrency Thread Group” (from JMeter
Plugins) to test how your website behaves under
increasing number of threads
12. HINT
Performance Testing
Apache JMeter
Making HTTP Request7
- As a „Name” use variable from CSV file (that will group results)
- You can include also other parameters in CSV (e.g. method,
protocol)
- To avoid network latencies use HEAD HTTP method
- server will return empty responses
- may depend on your application/server’s configuration
13. Performance Testing
Apache JMeter
Moving „Concurrent Threads” into Real Users8
- Find out Average Time on Page (not average session) in your
Google Analytics
- Use „Gaussian Random Timer” to add some randomized delay
after each request
14. Performance Testing
Apache JMeter
Getting statistics from Google Analytics9
Nb of concurrent users:
concurrent_users = (peak_hourly_visits * average_session_duration) / 3600
e.g. 540 * 201 sec / 3600 sec = 30 users
Peak traffic: (peak_hourly_pageviews / 3600)
e.g. 21000 / 3600 = 5,83 req/s
Peak
21.000 pageviews
15. Performance Testing
Apache JMeter
Running test10
$ ./bin/jmeter -t scenario.jmx -n –l results.jtl
Collecting results11
- Always run tests from CLI (non-interaction mode) to avoid
memory/CPU problems
- Use „Graphs Generator” (from JMeter plugins) that
automatically saves graphs after the test.
- …or preview results online: https://sense.blazemeter.com/gui/
16. Performance Testing
Apache JMeter
Cons-
- Difficult to configure (Java & JMeter needs to be
properly tuned-up)
- Sometimes results are not so straightforward to
interpret
Pros-
- Can test multiple URLs in a single load-test
- Can run distributed tests, even in cloud
(e.g. www.blazemeter.com)
- Can be easily integrated into CI (Jenkins plugin)
- Allows to compose complex scenarios, even with Selenium
17. Performance Testing
General hints
- Different machines: Never run tests on the same machine that
application is running
- The same datacenter: To avoid network latencies (ping), it’s better
to run tests from the same datacenter as the target application.
- Rent cloud with hourly pricing: Amazon EC2 „on-demand”
instances are great for short load test:
1. Prepare your test scenario and JMeter installation
2. Rent EC2 instance just for the time that will be needed to finish
the test.
- Watch out for production infrastructure:
1. If you don’t have separate infrastructure to test (cloned
production), run tests during the lowest traffic (e.g. at night).
2. 80% probability that you will take-down the application during
the load test.