XGBoostModel training failed on large dataset, works on subset of dataset

Hey all,

Hoping I can get some help with this. I have a dataset with about 27 million rows and 300 columns, and I cannot figure out how to get training to work. It always fails with this error:

```
ml.dmlc.xgboost4j.java.XGBoostError: XGBoostModel training failed
at ml.dmlc.xgboost4j.scala.spark.XGBoost$.postTrackerReturnProcessing(XGBoost.scala:697)
at ml.dmlc.xgboost4j.scala.spark.XGBoost$.trainDistributed(XGBoost.scala:572)
at ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier.train(XGBoostClassifier.scala:190)
at ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier.train(XGBoostClassifier.scala:40)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
```

I don't see anything else useful anywhere in the logs. I'm using XGBoost 1.1.0, Spark 2.3.2, and running the code in a Zeppelin notebook.

I'm using a simple Spark ML pipeline to create the vector for training. I am aware of the compatibility issues with VectorAssembler [documented here](https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html), but my dataset has very few zeros and no nulls. Here is the pipeline I'm using to assemble the vectors:

`val featureIndexers = Array("company", "business")
.map(c => new StringIndexer().setHandleInvalid("keep").setInputCol(c).setOutputCol(c + "_index").fit(trainingData))

val oneHot = new OneHotEncoderEstimator()
.setInputCols(Array("company_index", "business_index"))
.setOutputCols(Array("company_ohe", "business_ohe"))

val vectorAssembler = new VectorAssembler()
.setInputCols(colsFeatures.toArray)
.setOutputCol("features")`

And here is the XGBoostClassifier:

```
val xgb = new XGBoostClassifier()
.setNumWorkers(40) // just a few less than the total number available
.setNthread(4) // same as spark.task.cpus
.setTreeMethod("hist")
.setObjective("binary:logistic")
.setMaximizeEvaluationMetrics(true)
.setSeed(1)
.setTrainTestRatio(0.9)
.setSubsample(0.9)
.setEta(0.01)
.setLabelCol("label")
.setFeaturesCol("features")
.setNumRound(50000)
.setNumEarlyStoppingRounds(45)
.setMaxDepth(3)
```

I intentionally set the max depth extremely low to ensure that I wasn't running into memory issues with depth.

When I call fit, I receive the error I posted above. I've attached a screenshot of what the Yarn UI looks like right before the job fails. [Here is the line being referenced in the UI.](https://github.com/dmlc/xgboost/blob/15419d3fd9c39ed17cf5ad8418a629cfbc04caee/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/DataUtils.scala#L162) I'm not sure why it's referring to the SparseVector line. I've tried to do explicit conversions to a dense vector within a UDF just to confirm, and I still received the problem.

The size of the cluster or executors does not seem to affect the outcome, but using only a subset of my data does. It works if I only take 1 million rows, for example. This implies that it's a data size issue, but I have not yet been able to resolve it.

I've tried various combinations of Spark configuration parameters without success, mainly around these three:

```
spark.task.cpus 4
spark.executor.cores 8
spark.executor.instances 47
```

I'm at a loss of how to address this. If there are any suggestions, please let me know.