Jmeter introduction
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Apache JMeter is an open source testing tool that allows users to load test and performance test web applications. It is a 100% pure Java application that can be used to test load, functionality, performance, regression, and more. JMeter sends requests to the target server, collects statistics on the server, and generates test reports in different formats.
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This document introduces JMeter, an open source load testing tool. It discusses performance testing concepts like load testing and stress testing. It then provides an overview of JMeter's basic elements like test plans, thread groups, samplers, assertions, listeners, timers and config elements. It also mentions some useful JMeter plugins and provides an example test plan configuration.
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Performance testing involves determining how a system performs under a specific workload. Load testing is a type of performance testing that evaluates a system as the number of concurrent users increases to the expected load. JMeter is an open source load testing tool that can be used to simulate many different types of network traffic. It provides various elements like samplers, listeners, timers and controllers that allow building test plans to generate and measure load.
Performancetestingjmeter 131210111657-phpapp02
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.
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Here are the top 20 JMeter interview questions and answers:
1.What is Apache JMeter?
Apache JMeter is an open-source tool used for load testing, performance testing, and functional testing of web applications. It can simulate heavy loads on servers, test their performance, and analyze the results.
Soft Test Ireland - Introduction to Jakarta Jmeter - Philip Bannon
This document provides an introduction to functional, performance, and non-functional testing with JMeter. It discusses what JMeter is, how it works, its main components, and how to perform tests. It also provides demonstrations of creating functional and performance tests with JMeter.
Learning j meter
Apache JMeter is a free and open-source desktop application that can be used to load test and measure performance both on static and dynamic resources. It is written in 100% Java, so it can run on many platforms, and has a graphical user interface for building test plans. JMeter tests can be run in distributed mode across many machines for large load tests. This document provides instructions on installing JMeter, recording a test script using Badboy, importing the script into JMeter, configuring threads and samplers, running tests, and viewing results.
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This document provides instructions for creating a load balancer and virtual private cloud (VPC) on MuleSoft's Anypoint Platform. It describes how to create a VPC with a specified name, region, and CIDR block. It then explains how to create a load balancer within that VPC, associating it with an SSL certificate and setting firewall rules. It also discusses how to add mapping rules and update an existing load balancer's configuration via the API.
Anypoint connectors
Anypoint Connectors allow Mule to communicate with external systems by receiving or sending messages. There are two types of connectors: endpoint-based connectors follow request-response patterns and are named after protocols like FTP; operation-based connectors perform specific third-party API operations. Connectors can act as message sources, processors, or destinations. Some require global configuration for connection details shared across multiple uses of the same connector type.
Batch processing
Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
Msmq connectivity
The document provides steps to configure connectivity between Mule and MSMQ (Microsoft Message Queuing) by installing and configuring the MSMQ connector in Mule. It discusses that the MSMQ connector allows sending and receiving data from MSMQ queues via the Anypoint Gateway for Windows. It then outlines the steps to download and install the Windows Gateway Services, .NET 4.0, MSMQ services, and the MSMQ connector in Mule. Finally, it mentions running a sample application which involves setting up a demo queue in MSMQ and configuring a Mule project.
Scopes in mule
Scopes in mule allow you to group message processors together. Certain scopes like Poll, Message Enricher, and Until Successful allow only one embedded message processor. Scopes can execute synchronously, halting the parent flow until the child scope completes, or asynchronously where the parent flow continues independently. The Cache Scope stores and reuses frequently called data, and the Composite Source handles messages from multiple sources. The Foreach splits messages into parts for processing while the Poll periodically retrieves messages. The Transactional scope executes a series of steps atomically, while the Until Successful retries an embedded processor until it succeeds or hits retry limits.
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This document describes several string matching operations in DataWeave including matches, startsWith, endsWith, find, match, and similar. It provides examples of the operations by showing DataWeave code that defines the operation, applies it to sample data, and outputs the results as JSON.
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This document demonstrates how to perform basic math operations like addition, subtraction, multiplication, division, finding maximum and minimum values, rounding, and square root functions using DataWeave. It provides code examples for each operation, showing how to transform and output the results in JSON or XML format.
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Operators in mule dataweave
This document discusses common operations in Mule Dataweave including map, pluck, filter, and remove. Map applies a function to each element of an array and returns a new array. Pluck maps an object to an array by applying a function to each key-value pair. Filter returns an array or object containing only elements that pass the criteria in the provided function. Remove returns an array with specified indexes removed.
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DataWeave is Mule's tool for transforming data between different formats. The Transform Message component uses a DataWeave script to transform the incoming Mule message. DataWeave scripts contain a header section defining directives and an output structure, and a body section describing the transformations using the DataWeave syntax. DataWeave allows mapping between input and output fields visually or by writing code to aggregate, normalize, group, join, partition, pivot and filter data.
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Introduction to testing mule
Unit testing validates individual units of code, like Mule flows, to check their correctness in isolation. Integration testing validates how units of code work together by testing entire applications or groups of interconnected flows. The document provides guidance on unit and integration testing for Mule applications, including writing modular, short flows; using environments for integration tests; and parameterizing code with placeholders.
Choice flow control
The choice flow control routes messages based on evaluating expressions against message properties or payload. It provides conditional logic similar to if/then/else statements, directing messages to the first routing option whose configuration evaluates to true, or to a default option if none match. Configuring it requires determining the evaluation criteria, routing options, processing for each, and default routing.
Message enricher example
The Mule Message Enricher allows the current message to be enriched by invoking an enrichment resource, which can be any message processor. The enrichment resource runs independently and modifies a copy of the message rather than the original message. The enricher supports enriching flow variables and message headers. It will leave the original message untouched if the enrichment resource does not return a result.
Mule exception strategies
Mule applications have two types of exceptions: system exceptions and messaging exceptions. System exceptions occur at the application level while no message is involved. Messaging exceptions occur when processing a message through a Mule flow. There are several exception strategies in Mule including catch, rollback, reference, and choice strategies. Catch strategies consume messages while rollback strategies roll messages back for reprocessing. Choice strategies handle exceptions based on message content.
Anypoint connector basics
The Anypoint Connector DevKit is a tool that makes building reusable connectors easier. It uses annotations and code generation to handle boilerplate code needed to integrate connectors with Mule runtime and Anypoint Studio. Connectors allow communication between external resources and Mule applications, transforming data between the two. DevKit abstracts this communication and generates user interfaces, simplifying connector usage. Well-built connectors make app development simpler by handling tasks like pagination and session handling.
Mule global elements
Global elements in Mule allow you to configure elements once and then reference them multiple times from flows, rather than repeatedly writing the same code. They provide flexibility by allowing elements to use a global element's configuration exactly or with modifications, and they are reusable by configuring an element once and sharing it. Global elements also offer efficiency by changing a configuration in one place and having it apply to all elements that reference it.
Mule message structure and varibles scopes
The document discusses Mule message structure and variable scopes. A Mule message contains a header and payload. It is embedded in a Mule message object that can include variables, attachments, and exceptions. Properties have inbound and outbound scopes, with inbound being immutable metadata from the source and outbound being mutable metadata set during a flow. Variables have flow, session, and record scopes and are temporary pieces of information about a message that are not passed through the entire flow. Session variables apply across all flows in an application.
How to create an api in mule
To create an API in Mule, first create a Mule project called "Sample API". Then create a RAML definition file called "test.raml" to define the API. Write the RAML and generate flows from it in Studio. This will create the flows automatically. Run the project as a Mule application. The API can then be accessed at http://localhost:8081/api/console/ and the GET method will return a response.
Log4j is a reliable, fast and flexible
Log4j is a popular Java logging framework that provides reliable and fast logging. It follows a layered architecture with core objects like loggers, appenders, and layouts. Loggers capture log information, appenders publish it to destinations like files or databases, and layouts format the information. The log4j.properties configuration file defines the logging level, appenders, and their layout patterns. For example, it can set the root logger level to DEBUG and direct DEBUG logs to a file appender that writes to a log file using a %m%n pattern.
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Jmeter introduction
- 1. Apache jMeter is an Open Source testing software. It is 100% pure Java application for load and performance testing. jMeter is designed especially for load, functional, performance, regression, etc.
- 2. We you should have a basic understanding of Java programming language, text editor and execution of programs, etc., because you are going to use jMeter to handle all levels of Java project testing (regression, functional, load, performance, etc.,), So it will be good if you have knowledge of software development and software testing processes.
- 3. 1.Open source 2.Friendly GUI 3.Platform Independent Visualize test results Highly flexible Easy Installation Support multi protocol Record & Playback
- 4. JMeter Send Request to Target Server Get Statistics information of target server Generate test report in different format
- 5. 1.Jmeter is pure java application, it requires JMV6 or Higher 2.First install JDK and set the path as required 3.Download Jmeter from http://jmeter.apache.org/download_jmeter.cg i 4.Apache-jmeter2.9 chose either zip or tgz 5.Installation of Jmeter is easy and simple
- 7. /bin: Contains JMeter script file for starting JMeter /docs: JMeter documentation files /extras: ant related extra files /lib/: Contains the required Java library for JMeter /lib/ext: contains the core jar files for JMeter and the protocols /lib/junit: JUnit library used for JMeter /printable_docs:
- 8. You can start JMeter in 3 modes GUI Mode Server Mode Command Line Mode
- 11. Server mode is used for distributed testing. This testing works as client-server model. In this model, JMeter runs on server computer in server mode. On client computer, JMeter runs in GUI mode. To start the server mode, you run the bat file binjmeter-server.bat as below figure
- 12. Using JMeter in Linux is the same as in Window; you simply run the following shell script. Run the script file jmeter (This file has no extension)- run JMeter (in GUI mode by default). Run the script file jmeter-server - start JMeter in server mode (calls JMeter script with appropriate parameters) jmeter.sh - very basic JMeter script with no JVM options specified. mirror-server.sh - runs the JMeter Mirror Server in non-GUI mode shutdown.sh - Run the Shutdown client to stop a non- GUI instance gracefully stoptest.sh - Run the Shutdown client to stop a non- GUI instance abruptly
- 13. JMS supports two types of messaging − Point-to-Point messaging − Queue messaging is generally used for transactions where the sender expects a response. Messaging systems are quite different from normal HTTP requests. In HTTP, a single user sends a request and gets a response. Topic messaging − Topic messages are commonly known as pub/sub messaging. Topic messaging is generally used in cases where a message is published by a producer and consumed by multiple subscribers.