Comments (10)
Hi @king1402 ,
right now we do not support stopping of H2O services. If you are interested you can make PR to deal with this issue.
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thanks very much
could you tell me What PR means
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Ahh, sorry, PR means pull requests. It is common way how to contribute into open-source projects.
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Looking at the h2o code, it will be difficult, as h2o use a lot of static variables
Any suggestions on a workaround for this?
it is very difficult to write unit tests for sparkling water since usually each suite will create it's own spark context, but the h2o context can only be created once
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Hi @lev112
yup, we are working on that and iterating H2O's API - and we appreciate any feedback!
Regarding your questions - there are two kinds of tests:
-
sharing SparkContext and H2OContext: for example here: https://github.com/h2oai/sparkling-water/blob/master/core/src/test/scala/org/apache/spark/rdd/H2OSchemaRDDTest.scala
-
each test needs a fresh H2OContext. In this case you have to implement test suite per test and make sure that your testing framework forks a new JVM for each test suite: see https://github.com/h2oai/sparkling-water/blob/master/examples/src/integTest/scala/water/sparkling/itest/local/RegularExamplesSuite.scala
Let me know if it works for you!
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hi @mmalohlava
thanks for your response.
for 1, I'm sharing context between tests in a test class, but there are a few different test class that are used,
for 2, I'm running the tests in intellij, and as far as I can tell, forking tests is not supported
looking forward for any changes in that area
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Hi @lev112
for 1., you should use one context per test class right now
for 2, i normally setup configuration in the following way:
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We are iterating H2O code to support stopping H2O inside a single JVM, right now H2O can't be fully stopped in a single JVM
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Any update on this problem
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No update so far
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Related Issues (20)
- Upgrade H2O to 3.42.0.1
- Fix release process ignoring Nexus upload errors
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- CVE exposed via `ai.h2o:[email protected] › no.priv.garshol.duke:[email protected] ` HOT 2
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- Upgrade H2O to 3.42.0.3
- Upgrade Spark 3.4 docker base img to Spark 3.4.1 HOT 1
- Upgrade H2O to 3.42.0.4
- Fix Spark 3.4 Docker image building
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- Upgrade H2O to 3.44.0.1
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