Jenn,
We do not test with a CI strategy. We have a limited number things that we
do with some parallelization.
1. clone or merge
2. in parallel
a. build product for (window (32, 64), linux (32, 64))
b. run many different types of tests
c. generate report on tests
3. generate consoli
Anyone have any ideas how I can create dynamic pipeline scripts
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Is it possible to pass groovy scripts to a pipeline job
I want to be able to configure custom groovy scripts based on options
selected by my users.
I could have a massive script with many "if" clauses.
It would be nicer to create a groovy script based on selected options.
this api would then be te
I have:
booleanParam(name: 'force_build', value: force_build.toBoolean())
This works
Pipeline with parameter (force_build=True) -> freestsyle job with
parameter (force_build=False)
The value of force_build in the freestyle job is now True
This does not work.
Pipeline job with a pa
Hello,
I was looking for help with a many parallel pipeline.
Here is what I am looking to do
*parallel*(
'linux': {
stage('build_linux') {
build job: 'linux_build', parameters:[]
build job: 'linux_archive', parameters:[]
*parallel*(
s
I assume you have "Block until the remote triggered projects finish their
builds." = False?
- Does it not just set your 'done' variable to true as soon as the
remote build starts, since it does not block or wait.
- Also with this set to False it does not get the log of the remote
bu
*parallel* (
'windows': {
stage('windows tests') {
*parallel* (
*stage*('some_tests_1') {
*parallel* (
some_tests_1, # is a chunk of tests
)
}
#!groovy
import hudson.model.*
stage('Testing') {
parallel (
['windows_remote': {build job: 'remote_trigger_30', parameters:
[string(name: 'branch_name', value: branch_name)]},
'linux_remote': {build job: 'remote_trigger_60', parameters:
[string(name: 'branch_name', value: b
So I was not running in a node block I will do more testing thank you.
Here is a sample of my code:
*#!groovy*
*import hudson.model.**
*stage('many_jobs') {*
*parallel (get_many_jobs(6000))*
*}*
*def get_many_jobs(num_builds) {*
*// The map we'll store the parallel steps in before exec
So I have not run in an ode block I will do more testing thank you.
Here is a sample of my code:
*#!groovy*
*import hudson.model.**
*stage('many_jobs') {*
*parallel (get_many_jobs(6000))*
*}*
*def get_many_jobs(num_builds) {*
*// The map we'll store the parallel steps in before executi
So not sure if this is a Jenkins limitation or pipeline.
The jobs never exceeded available resources.
The garbage collection was stable.
So why did it lock-up?
It did say I used
- 2950 million active threads
- 350 threads
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I need to retry it may be that the jobs never all started
Branch: job_0 - (1ms in block)
Branch: job_9830 - (3min 16sec in block)
Branch: job_9831 - (No timing in block)
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So 3 hr 30 min later the pipeline is locked up
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I am doing testing of our Jenkins setup.
I have a build job that sleeps for 30 sec.
I ran a pipeline that runs 12,000 of these in parallel on 100 agents.
Best possible time is 1 hr.
The builds seem to finish in a reasonable amount of time.
So after build queue is emptied and the pipeline continues
Thank you for your insights.
This reinforces my thoughts after
reading https://jenkins.io/blog/2016/11/21/gc-tuning/
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I am not trying to say anything I just found it curious.
We are doing testing to determine the optimal setup for Jenkins.
We currently use flows calling flows.
We believe we have too many builds in the build queue (3500).
In the process of testing we saw this behavior and I was hoping for insight
So I am doing testing on our current system.
We use flows now.
So I have a flow and a pipeline doing the same task:
- 100 executors
- 2000 builds
- build - echo 'Hello World'
flow time = 2 min 33 sec
pipeline time = 5 min 11 sec
After all the echos are complete the pipeline seems to han
Did you find any useful information I am facing a very similar issue we
have to restart jenkins every few days.
We are not sure if it is the number of executors, flows within flows, or
large number of builds.
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I am beginning the process of changing to pipelines.
We currently use flows to run thousands of jobs(builds).
Say we have 6000 builds to run on 100 Windows virtual machines and 6000
builds to run on 100 Linux virtual machines.
To reduce available virtual machines with no jobs to run we try to kee
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