fba03133d1
## What changes were proposed in this pull request?
Via some test I found CrossValidator still exists memory issue, it will still occupy `O(n*sizeof(model))` memory for holding models when fitting, if well optimized, it should be `O(parallelism*sizeof(model))`
This is because modelFutures will hold the reference to model object after future is complete (we can use `future.value.get.get` to fetch it), and the `Future.sequence` and the `modelFutures` array holds references to each model future. So all model object are keep referenced. So it will still occupy `O(n*sizeof(model))` memory.
I fix this by merging the `modelFuture` and `foldMetricFuture` together, and use `atomicInteger` to statistic complete fitting tasks and when all done, trigger `trainingDataset.unpersist`.
I ever commented this issue on the old PR [SPARK-19357]
https://github.com/apache/spark/pull/16774#pullrequestreview-53674264
unfortunately, at that time I do not realize that the issue still exists, but now I confirm it and create this PR to fix it.
## Discussion
I give 3 approaches which we can compare, after discussion I realized none of them is ideal, we have to make a trade-off.
**After discussion with jkbradley , choose approach 3**
### Approach 1
~~The approach proposed by MrBago at~~ https://github.com/apache/spark/pull/19904#discussion_r156751569
~~This approach resolve the model objects referenced issue, allow the model objects to be GCed in time. **BUT, in some cases, it still do not resolve the O(N) model memory occupation issue**. Let me use an extreme case to describe it:~~
~~suppose we set `parallelism = 1`, and there're 100 paramMaps. So we have 100 fitting & evaluation tasks. In this approach, because of `parallelism = 1`, the code have to wait 100 fitting tasks complete, **(at this time the memory occupation by models already reach 100 * sizeof(model) )** and then it will unpersist training dataset and then do 100 evaluation tasks.~~
### Approach 2
~~This approach is my PR old version code~~
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.