in LoadRunner-Vugen, In order to send APIs parallelly I have put them in one script within web_concurrent_start and web_concurrent_end function BUT when I run the script, in results summary it shows 0.001 sec for all of the APIs but the same script if I run disabling web_concurrent_start/end function then APIs report 1-4 seconds. Can anyone please help me here?
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I have an application where user can write custom python function code and request payload. User should be able to test function on click of a button. My requirement is to dynamically deploy this code(can have extra wrapper code if necessary) as Function in my kubernetes cluster using openfaas, send the payload to test and fetch the results.
I need all these steps executed with in 60 sec. I am more worried on cold starts incurred while creating function.
Can anyone suggest on whether this requirement is feasible through openfaas ?
I am not sure whats the best way to implement multiple parallel calls in Jenkins remotely.. Any inputs will be greatly appreciated.
How to get the build number for the multiple parallel calls (2-10 multiple calls) to the Jenkins Server for a parameteried job if it gets triggered remotely. One requirement is there will be no change in the build parameter. The development team is using tool/python program to invoke 50 POST calls in that case how do we track the build number.
Scenario -- I have a freestyle parameterized job with Enable concurrent build if necessary box checked for this job Sequential call for with same build parameters request when initiated remotely, we see build number using https://jenkinsurl/queue/item with filtering out the build number and then https://jenkins url/build/Consoletext -- This works
Scenario 2 -- Same request with no change with parameters when triggered multiple times more than 2 calls we can see the build number /Consoletext for the first call and later ones were unable to track with the build number.
Sorry, I am a beginner and trying to implement multiple parallel calls. My Jenkins job is configured to run a python script on the Jenkins server that will return success along with work id and other responses that the dev Team needs for further processing.
When the team triggers API remotely 50 times, only for the first call we see the build number/full response from the ConsoleText for the rest of the calls we don't see any build number. I don't see any failures also in Jenkins. FYI This is a free-style parameterized job concurrent build option enabled.
I'm using gcloud dataflow job and want individual execution times for all the steps in my dataflow including nested transforms. I'm using a streaming dataflow and the pipeline currently looks like this:
Current dataflow
Can anyone please suggest a solution?
Answer is WallTime. You can access this info by clicking one of the task in your pipeline(even nested).
Elapsed time of a job is the total time takes to complete your dataflow job while wall time is the sum time taken to run each step by the assigned workers. See the below image for more details.
I have a use case where I have a Dataflow job running in streaming mode with an hourly fixed window.
When the pipeline runs for a given window, we calculate some data and write it to a data source. What I want to do next is publish some message to PubSub once the write is complete - how might I go about making sure that the write step is complete before writing to PubSub?
If the pipeline was executed in batch mode I know I could execute it in a blocking fashion as suggested here, but the tricky part is that this constantly running in streaming mode.
Wait.on() transform is designed for this use case. See documentation for usage example.
I have to join data from Google Datastore and Google BigTable to produce some report. I need to execute that operation every minute. Is it possible to accomplish with Google Cloud Dataflow (assuming the processing itself should not take long time and/or can be split in independent parallel jobs)?
Should I have endless loop inside the "main" creating and executing the same pipeline again and again?
If most of time in such scenario is taken by bringing up the VMs, is it possible to instruct the Dataflow to use customer VMs instead?
Thanks,
If you expect that your job is small enough to complete in 60 seconds you could consider using the Datastore and BigTable APIs from within a DoFn in a Streaming job. Your pipeline might look something like:
PCollection<Long> impulse = p.apply(
CountingInput.unbounded().withRate(1, Duration.standardMinutes(1)))
PCollection<A> input1 = impulse.apply(ParDo.of(readFromDatastore));
PCollection<B> input2 = impulse.apply(ParDo.of(readFromBigTable));
...
This produces a single input every minute, forever. Running as a streaming pipeline, the VMs will continue running.
After reading from both APIs you can then window/join as necessary.