Being completely new to webdriver IO, wondering how do I update an existing test cycle (with specific name format) in Jira. I am running the test suite on 3 different browsers and have separate test cycles for these cycles in Jira. After execution, I want the suite to update these cycles with the status and screenshots for each browsers respectively. ANy help is much appreciated.
PS: At the moment I have a function that creates a new test cycle for each execution.
there are no plugins for zephyr scale integration till now, but you can use zephyr api to update your execution results .
I've created a Nodejs lib for creating testruns and reporting results back to Zephyr scale.
Maybe they can help you on your way.
If you have any questions or feedback, let me know!
https://www.npmjs.com/package/#dbouckaert/zephyr-scale-reporter
Example: get all testcases for a project
/**
* This function will get all testcases for a certain project and add them to variables.testCasesArray
* #returns {void}
*/
export const getAllTestcases = async (): Promise<void> => {
await request(variables.url)
.get(`/rest/tests/1.0/project/${variables.projectId}/testcases`)
.auth(variables.username, variables.password)
.expect(200)
.then((res) => {
variables.testCasesArray = res.body.testCases;
});
};
Related
I'm new to the Dart functions framework. My goal is to use this package to create several functions and deploy them to Cloud Run (in combination with Firebase, but I guess that's irrelevant to this question).
I've run the quick starts and I've read all of the contents in the docs.
The quick start mentions just one function at a time (e.g. Hello World, Cloud Events, etc..), like this:
import 'package:functions_framework/functions_framework.dart';
import 'package:shelf/shelf.dart';
#CloudFunction()
Response function(Request request) {
return Response.ok('Hello, World!');
}
But as you can see in the quickstarts only one function is handled in a project at a time. How about me wanting to deploy several functions? Should I:
Write several functions in the same project / file, so that the function framework compiles the 'server.dart` by itself
OR
Create a different functions_framework for each function?
Let me be more specific. Should I do the following (option 1 - which makes more sense to me):
import 'dart:math';
import 'package:functions_framework/functions_framework.dart';
import 'package:shelf/shelf.dart';
#CloudFunction()
Response function(Request request) {
return Response.ok('Hello, World!');
}
#CloudFunction()
Response function2(Request request) {
if (Random().nextBool()) {
return Response.ok('Hello, World!');
} else {
return Response.internalServerError();
}
}
Or should I build a different folder by running a build_runner for each function I need in my project?
Is there a difference and/or a best practice?
Thanks in advance.
EDIT. This question is related to the deployment on Cloud Run itself, and not just testing on my own PC. To test my own functions I did the following:
Run dart run build_runner build, so that it updates the server.dart file correctly (I can see that the framework does a lot behind the scenes and that the _nameToFunctionTarget is basically a router);
Run the server in two different terminals, like this: dart run bin/server.dart --port MYPORT --target MYFUNCTION (where MYPORT and MYFUNCTION are either 8080/8081 or function/function2 respectively).
I guess I'm just confused on how to correctly manage this framework once deployed on Cloud Run.
EDIT 2. I just gave up using Dart as a Serverless language or even a Backend language. There's just too much jargon even for the basic things. Any backend framework is either dead, or maintained by one single enthusiast guy (props to him!). This language has not yet received enough love from the Google Team / the community and at this moment in time is basically not possible to go fullstack on just Dart. It's a dream, but it can't be realized now. Furthermore, Dart hardly lacks a proper SDKs to use Firestore, etc., so Firebase isn't an option. I find it easier to just learn NodeJS and exploit the Firebase support for Firebase Functions written in NodeJS, and I'll wait for more support in there in the future, if there ever will be.
The documentation is a bit sparse right now (and I'm new to it also! I couldn't find any good examples, so here goes...)
You can only have a single function that is served. It should be
named 'function' (the type and name can be overriden, see the
cloudevent example dartfn generate cloudevent)
You 'could' have many of these deployed so that each does a specific thing, such as processing cloudevents above, but most people
want something more REST-like (see next)
You need to attach a Router() so that you can have the single entry point (function) handled by specific logic in your code.
Example for Rest
add to pubspec.yaml (in dependencies:) shelf_router: ^1.1.2
delegate the #CloudFunction to use the Router()
functions.dart
import 'package:functions_framework/functions_framework.dart';
import 'package:shelf/shelf.dart';
import 'package:shelf_router/shelf_router.dart';
Router app = Router()
..get('/health', (Request request) {
return Response.ok('healthy');
})
..get('/user/<user>', (Request request, String user) {
// fetch the user... (probably return as json)
return Response.ok('hello $user');
})
..post('/user', (Request request) {
// convert request body to json and persist... (probably return as json)
return Response.ok('saved the user');
});
#CloudFunction()
Future<Response> function(Request request) => app.call(request);
This question already has answers here:
Karate Tests Stuck on Running Forever
(2 answers)
Closed 1 year ago.
Sometimes when I run a test on jenkins with logical OR tag selection (karate.options="--tags #vld,#command-composition") the tests are executed unitl the end but will never step out of
Results results = Runner.parallel(getClass(),5 , karateOutputPath);
This is definetly a problem concerning karate AND jenkins because when executed on a local machine, the problem never occured.
Also when the tags are selected separatelly (eg. "--tags #vld" or "--tags #command-composition") the problem does not occure. Even on jenkins.
This is my parallel runner class used for executing the tests in parallel. I added some logging debug outputs in order to clarify where the execution is running into an infinite loop.
The execution is not stepping out of test execution:
Results results = Runner.parallel(getClass(),5 , karateOutputPath);
and therefore everything after (including)
logger.debug("DEBUG-LOG: AFTER TEST - BEFORE REPORT GENERATION");
is never executed.
#KarateOptions(tags = {"~#ignore"})
public class ParallelRunner {
private static final Logger logger = LoggerFactory.getLogger("com.intuit.karate");
#Test
public void testParallel() throws IOException {
// RESET LAST USED ICCID
Helper.resetLastIccid();
String outputPath = "build";
String karateOutputPath = outputPath + "/surefire-reports";
logger.debug("DEBUG-LOG: BEFORE TEST");
Results results = Runner.parallel(getClass(),5 , karateOutputPath);
logger.debug("DEBUG-LOG: AFTER TEST - BEFORE REPORT GENERATION");
generateReport(karateOutputPath);
logger.debug("DEBUG-LOG: AFTER GEN REPORT");
assertTrue("scenarios failed", results.getFailCount() == 0);
}
private static void generateReport(String karateOutputPath) {
logger.debug("DEBUG-LOG: IN GEN REPORT");
Collection<File> jsonFiles = FileUtils.listFiles(new File(karateOutputPath), new String[] {"json"}, true);
List<String> jsonPaths = new ArrayList(jsonFiles.size());
jsonFiles.forEach(file -> jsonPaths.add(file.getAbsolutePath()));
Configuration config = new Configuration(new File("build"), "MH Conan Mobile Systemtest");
ReportBuilder reportBuilder = new ReportBuilder(jsonPaths, config);
reportBuilder.generateReports();
}
}
I also played arround with the number of used threads and parallel execution==false. Nothing seems to help.
I also compared the surefire-reports output folder between a local run and a jenkins build. All surefire reports for the executed features are there but on jenkins the results-json.txt and timeline.html is missing.
So, is there anyone out there who has also experienced this kind of behaviour? Or has someone further ideas how to debug or at least narrow down this problem somehow?
Help is very much appreciated! :)
Karate-Version: 0.9.2 (latest)
With two features who respectively have the #feature1 and #feature2 tags, when running
mvn clean install -Dkarate.options="--tags #feature1,#feature2"
Both features are played. That's not an AND tag selection but an OR tag selection.
With
mvn clean install -Dkarate.options="--tags #feature1 --tags #feature2"
None of the two features are played. That's an AND selection.
Appart from that, have you tried creating a minimal karate project, with just a couple of features that are just doing a few simple steps each, and to launch it on Jenkins?
using scio version 0.4.7, I have a streaming job that's listening to a pubsub topic, I'm using event processing here with 'timestamp' attribute present on the message properties in RFC3339
val rtEvents: SCollection[RTEvent] = sc.pubsubTopic(args("topic"), timestampAttribute = "timestamp").map(jsonToObject)
val windowedEvents = rtEvents.withFixedWindows(Duration.standardMinutes(1L),
options = WindowOptions(trigger = Repeatedly.forever(AfterWatermark.pastEndOfWindow()),
accumulationMode = DISCARDING_FIRED_PANES,
allowedLateness = Duration.standardSeconds(1L)
)
)
I use windowedEvents for further aggregation and calculations in the pipeline
doSomeAggregation(windowedEvents)
def doSomeAggregation(events: SCollection[RTEvent]): SCollection[(String, Map[String, Int])] =
events.map(e => (e.properties.key, (e.properties.category, e.id)))
.groupByKey
.map { case (key, tuple: Iterable[(String, String)]) =>
val countPerCategory: Map[String, Int] = tuple.groupBy(_._1)
.mapValues(_.toList.distinct.size)
//some other http post and logging here
(key, countPerCategory)
}
sc.close().waitUntilFinish()
If i run the job with the following autoscaling parameters on google dataflow
--workerMachineType=n1-standard-8 --autoscalingAlgorithm=THROUGHPUT_BASED
--maxNumWorkers=4
the job runs and the fixed windows fire correctly if there is only one worker running. As soon as the job autoscales up to more > 1 worker, the fixed windows stop firing and initial pubsub step's system lag and wall time keeps growing, while data watermark does not move forward.
Is there something wrong with my trigger setup? Has anyone else experienced this on dataflow runner or other runners?
Any help is greatly appreciated. I'm inclined to drop scio and revert to back to apache-beam java sdk if I can't solve this.
I managed to resolve the issue. In my current setup the workers were unable to communicate with each other. The job silently fails without any timeout errors (something beam should probably propagate up as an error).
If you're using dataflow as your runner, make sure the firewall defined for dataflow on your project is defined for 'default' network.
If the dataflow firewall is defined for your network, you will need to pass additional runtime parameter into your job
--workerMachineType=n1-standard-8 --autoscalingAlgorithm=THROUGHPUT_BASED
--maxNumWorkers=4 --network='your-network'
Can we use graph database neo4j with react js? If not so is there any alternate option for including graph database in react JS?
Easily, all you need is neo4j-driver: https://www.npmjs.com/package/neo4j-driver
Here is the most simplistic usage:
neo4j.js
//import { v1 as neo4j } from 'neo4j-driver'
const neo4j = require('neo4j-driver').v1
const driver = neo4j.driver('bolt://localhost', neo4j.auth.basic('username', 'password'))
const session = driver.session()
session
.run(`
MATCH (n:Node)
RETURN n AS someName
`)
.then((results) => {
results.records.forEach((record) => console.log(record.get('someName')))
session.close()
driver.close()
})
It is best practice to close the session always after you get the data. It is inexpensive and lightweight.
It is best practice to only close the driver session once your program is done (like Mongo DB). You will see extreme errors if you close the driver at a bad time, which is incredibly important to note if you are beginner. You will see errors like 'connection to server closed', etc. In async code, for example, if you run a query and close the driver before the results are parsed, you will have a bad time.
You can see in my example that I close the driver after, but only to illustrate proper cleanup. If you run this code in a standalone JS file to test, you will see node.js hangs after the query and you need to press CTRL + C to exit. Adding driver.close() fixes that. Normally, the driver is not closed until the program exits/crashes, which is never in a Backend API, and not until the user logs out in the Frontend.
Knowing this now, you are off to a great start.
Remember, session.close() immediately every time, and be careful with the driver.close().
You could put this code in a React component or action creator easily and render the data.
You will find it no different than hooking up and working with Axios.
You can run statements in a transaction also, which is beneficial for writelocking affected nodes. You should research that thoroughly first, but transaction flow is like this:
const session = driver.session()
const tx = session.beginTransaction()
tx
.run(query)
.then(// same as normal)
.catch(// errors)
// the difference is you can chain multiple transactions:
const tx1 = await tx.run().then()
// use results
const tx2 = await tx.run().then()
// then, once you are ready to commit the changes:
if (results.good !== true) {
tx.rollback()
session.close()
throw error
}
await tx.commit()
session.close()
const finalResults = { tx1, tx2 }
return finalResults
// in my experience, you have to await tx.commit
// in async/await syntax conditions, otherwise it may not commit properly
// that operation is not instant
tl;dr;
Yes, you can!
You are mixing two different technologies together. Neo4j is graph database and React.js is framework for front-end.
You can connect to Neo4j from JavaScript - http://neo4j.com/developer/javascript/
Interesting topic. I am using the driver in a React App and recently experienced some issues. I am closing the session every time a lifecycle hook completes like in your example. When there where more intensive queries I would see a timeout error. Going back to my setup decided to experiment by closing the driver in some more expensive queries and it looks like (still need more testing) the crashes are gone.
If you are deploying a real-world application I would urge you to think about Authentication and Authorization when using a DB-React setup only as you would have to store username/password of the neo4j server in the client. I am looking into options of having the Neo4J server issuing a token and receiving it for Authorization but the best practice is for sure to have a Node.js server in the middle with something like Passport to handle Authentication.
So, all in all, maybe the best scenario is to only use the driver in Node and have the browser always communicating with the Node server using axios...
The product I work on comprises 3/4 seperate (non-dependant) TFS builds.
I would like to create a single TFS build which queues the other 3/4 builds from within the ProcessTemplate AND, critically, pass process parameters to them. This build would wait for them all to complete and return an overall success/failure of the build.
So my questions are:
Can this be achieved by any existing 'standard' Workflow activities (my manager has had bad experiences with custom workflow activities)?
If not, I am able to 'shell out' to powershell. Can I achieve what I want from within Powershell (accessing the API)?
Maybe using TFSBuild.exe? But I can't find a way of passing the custom process parameters I need.
Any assistance or guidance would be appreciated.
UPDATE
The following powershell script will execute the build, but I'm still at a loss to be able to pass my custom process parameters :-(
function Get-BuildServer
{
param($serverName = $(throw 'please specify a TFS server name'))
[void][System.Reflection.Assembly]::LoadWithPartialName("Microsoft.TeamFoundation.Client")
[void][System.Reflection.Assembly]::LoadWithPartialName ("Microsoft.TeamFoundation.Build.Client")
$tfs = [Microsoft.TeamFoundation.Client.TeamFoundationServerFactory]::GetServer($serverName)
return $tfs.GetService([Microsoft.TeamFoundation.Build.Client.IBuildServer])
}
$buildserver = Get-BuildServer "http://tfsserver:8080/tfs/My%20Project%20Collection"
$teamProject = "ESI"
$buildDefinition = "iPrl_BuildMaster"
$definition = $buildserver.GetBuildDefinition($teamProject, $buildDefinition)
$request = $definition.CreateBuildRequest()
$buildserver.QueueBuild($request, "None")
Now after googling, I have found the following C# code to update the verbosity and, assuming it's the same for my custom process parameters, I need to convert this to work with the above powershell script. Any ideas?
IDictionary<String, Object> paramValues = WorkflowHelpers.DeserializeProcessParameters(processParameters);
paramValues[ProcessParameterMetadata.StandardParameterNames.Verbosity] = buildVerbosity;
return WorkflowHelpers.SerializeProcessParameters(paramValues);