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Testing |
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Developer Overview |
We have several stand alone examples that you can run in the integration_tests subdirectory/jar.
One set is based off of the mortgage dataset you can download
here
and are in the com.nvidia.spark.rapids.tests.mortgage package.
The other is based off of TPCH. You can use the TPCH dbgen tool to generate data for them. They
are in the com.nvidia.spark.rapids.tests.tpch package. dbgen has various options, one way to
generate the data. Please refer to the documentation that comes with dbgen on how to use it, but
we typically run with the default options and only increase the scale factor dependig on the test.
dbgen -b dists.dss -s 10You can include the test jar rapids-4-spark-integration-tests_2.12-0.1-SNAPSHOT.jar with the
Spark --jars option to get the TPCH tests. To setup for the queries you can run
TpchLikeSpark.setupAllCSV for CSV formatted data or TpchLikeSpark.setupAllParquet
for parquet formatted data. Both of those take the Spark session, and a path to the dbgen
generated data. After that each query has its own object.
So you can make a call like:
import com.nvidia.spark.rapids.tests.tpch._
val pathTodbgenoutput = SPECIFY PATH
TpchLikeSpark.setupAllCSV(spark, pathTodbgenoutput)
Q1Like(spark).count()They generally follow TPCH but are not guaranteed to be the same.
Q1Like(spark) will return a DataFrame that can be executed to run the corresponding query.
Unit tests exist in the tests directory. This is unconventional and is done so we can run the tests on the final shaded version of the plugin. It also helps with how we collect code coverage.
Integration tests are stored in the integration_tests directory.
There are two frameworks used for testing. One is based off of pytest and pyspark in the
src/main/python directory. These tests will run as a part of the build if you have the environment
variable SPARK_HOME set. If you have SPARK_CONF_DIR also set the tests will try to use
whatever cluster you have configured.
To run the tests separate from the build go to the integration_tests directory and submit
run_tests.py through spark-submit. Be sure to include the necessary jars for the RAPIDS
plugin either with spark-submit or with the cluster when it is setup.
The command line arguments to run_tests.py are the same as for
pytest. The only reason we have a separate script
is that spark-submit uses python if the file name ends with .py.
We also have a large number of integration tests that currently run as a part of the unit tests
using scala test. Those are in the src/test/scala sub-directory and depend on the testing
framework from the rapids-4-spark-tests_2.12 test jar.
You can run these tests against a cluster similar to how you can run pytests against an
existing cluster. To do this you need to launch a cluster with the plugin jars on the
classpath. The tests will enable and disable the plugin as they run.
Next you need to copy over some test files to whatever distributed file system you are using.
The test files are everything under ./integration_tests/src/test/resources/ Be sure to note
where you placed them because you will need to tell the tests where they are.
When running these tests you will need to include the test jar, the integration test jar,
scala-test and scalactic. You can find scala-test and scalactic under ~/.m2/repository.
It is recommended that you use spark-shell and the scalatest shell to run each test
individually, so you don't risk running unit tests along with the integration tests.
http://www.scalatest.org/user_guide/using_the_scalatest_shell
spark-shell --jars rapids-4-spark-tests_2.12-0.1-SNAPSHOT-tests.jar,rapids-4-spark-integration-tests_2.12-0.1-SNAPSHOT-tests.jar,scalatest_2.12-3.0.5.jar,scalactic_2.12-3.0.5.jarFirst you import the scalatest_shell and tell the tests where they can find the test files you
just copied over.
import org.scalatest._
com.nvidia.spark.rapids.TestResourceFinder.setPrefix(PATH_TO_TEST_FILES)Next you can start to run the tests.
durations.run(new com.nvidia.spark.rapids.JoinsSuite)
...