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# AppVeyor Guides
Currently, SparkR on Windows is being tested with [AppVeyor](https://ci.appveyor.com). This page describes how to set up AppVeyor with Spark, how to run the build, check the status and stop the build via this tool. There is the documentation for AppVeyor [here](https://www.appveyor.com/docs). Please refer this for full details.
### Setting up AppVeyor
#### Sign up AppVeyor.
- Go to https://ci.appveyor.com, and then click "SIGN UP FOR FREE".
- As Apache Spark is one of open source projects, click "FREE - for open-source projects".
- Click "GitHub".
#### After signing up, go to profile to link GitHub and AppVeyor.
- Click your account and then click "Profile".
- Enable the link with GitHub via clicking "Link GitHub account".
- Click "Authorize application" in GitHub site.
#### Add a project, Spark to enable the builds.
- Go to the PROJECTS menu.
- Click "NEW PROJECT" to add Spark.
- Since we will use GitHub here, click the "GITHUB" button and then click "Authorize GitHub" so that AppVeyor can access the GitHub logs (e.g. commits).
- Click "Authorize application" from GitHub (the above step will pop up this page).
- Come back to https://ci.appveyor.com/projects/new and then adds "spark".
#### Check if any event supposed to run the build actually triggers the build.
- Click "PROJECTS" menu.
- Click Spark project.
### Checking the status, restarting and stopping the build
- Click "PROJECTS" menu.
- Locate "spark" and click it.
- Here, we can check the status of current build. Also, "HISTORY" shows the past build history.
- If the build is stopped, "RE-BUILD COMMIT" button appears. Click this button to restart the build.
- If the build is running, "CANCEL BUILD" button appears. Click this button to cancel the current build.
### Specifying the branch for building and setting the build schedule
Note: It seems the configurations in UI and `appveyor.yml` are mutually exclusive according to the [documentation](https://www.appveyor.com/docs/build-configuration/#configuring-build).
- Click the settings button on the right.
- Set the default branch to build as above.
- Specify the branch in order to exclude the builds in other branches.
- Set the Crontab expression to regularly start the build. AppVeyor uses Crontab expression, [atifaziz/NCrontab](https://github.com/atifaziz/NCrontab/wiki/Crontab-Expression). Please refer the examples [here](https://github.com/atifaziz/NCrontab/wiki/Crontab-Examples).
### Filtering commits and Pull Requests
Currently, AppVeyor is only used for SparkR. So, the build is only triggered when R codes are changed.
This is specified in `.appveyor.yml` as below:
```
only_commits:
files:
- R/
```
Please refer https://www.appveyor.com/docs/how-to/filtering-commits for more details.
### Checking the full log of the build
Currently, the console in AppVeyor does not print full details. This can be manually checked. For example, AppVeyor shows the failed tests as below in console
```
Failed -------------------------------------------------------------------------
1. Error: union on two RDDs (@test_binary_function.R#38) -----------------------
1: textFile(sc, fileName) at C:/projects/spark/R/lib/SparkR/tests/testthat/test_binary_function.R:38
2: callJMethod(sc, "textFile", path, getMinPartitions(sc, minPartitions))
3: invokeJava(isStatic = FALSE, objId$id, methodName, ...)
4: stop(readString(conn))
```
After downloading the log by clicking the log button as below:
the details can be checked as below (e.g. exceptions)
```
Failed -------------------------------------------------------------------------
1. Error: spark.lda with text input (@test_mllib.R#655) ------------------------
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:/projects/spark/R/lib/SparkR/tests/testthat/data/mllib/sample_lda_data.txt;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:376)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:365)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
...
1: read.text("data/mllib/sample_lda_data.txt") at C:/projects/spark/R/lib/SparkR/tests/testthat/test_mllib.R:655
2: dispatchFunc("read.text(path)", x, ...)
3: f(x, ...)
4: callJMethod(read, "text", paths)
5: invokeJava(isStatic = FALSE, objId$id, methodName, ...)
6: stop(readString(conn))
```