Problem with writing RecordIO format, section 'The Amazon Record format' in README.md file probably needs a change - an old import from mllib (should be ml)

### System Information
- Spark/PySpark version: v.2.4.4
- Working in Jupyter Notebook

### Describe the problem
I was trying to write the dataframe object into s3 by following the formatting instructions:

`(...) Writing a DataFrame using the "sagemaker" format serializes a column named "label", expected to contain Doubles, and a column named "features", expected to contain a Sparse or Dense org.apache.mllib.linalg.Vector. If the features column contains a SparseVector, SageMaker Spark sparsely-encodes the Vector into the Amazon Record. If the features column contains a DenseVector, SageMaker Spark densely-encodes the Vector into the Amazon Record. (...)`

and by executing exact the code as follows:
```
myDataFrame.write
.format("sagemaker")
.option("labelColumnName", "myLabelColumn")
.option("featuresColumnName", "myFeaturesColumn")
.save("s3://my-s3-bucket/my-s3-prefix")
```
In my case, I did prepare the dataset with the schema that was described in the section I mentioned before, so my data types were like:
- 'myFeatures':'pyspark.mllib.linalg.VectorUDT'
- 'myLabels':'pyspark.sql.types.DoubleType'

but whenever I was executing the code:
```
df.write
.format("sagemaker")
.option("labelColumnName", "myLabels")
.option("featuresColumnName", "myFeatures")
.save("s3://...")
```

everytime I was receiving such errors:
```
scala.MatchError: (11,[0,3,7,10],[1.0,1.0,1.0,1.0]) (of class org.apache.spark.mllib.linalg.SparseVector)
```

I double-checked on the format of my SparseVector and I was sure that the Vector type that was prepared was explicitly defined and it was a SparseVector from mllib.linalg package

After a few tests and experiments, it turned out that the correct dtype of the feature shouldn't be the SparseVector from mllib.linalg, but from ml.linalg :)

this is the correct/working schema I used and it abled me to write the dataframe into s3 in RecordIO format:
'features': pyspark.ml.linalg.VectorUDT,
'label': pyspark.sql.types.DoubleType,

----

mwe:

```
from pyspark.mllib.linalg import VectorUDT, SparseVector

df = spark.createDataFrame([
(1.0, SparseVector(4, {1: 1.0, 3: 5.5})),
(0.0, SparseVector(4, {1: 1.0, 2: 5.5}))
], ['labels', 'features'])
```

`df.show()`:
```
+------+-------------------+
|labels| features|
+------+-------------------+
| 1.0|(4,[1,3],[1.0,5.5])|
| 0.0|(2,[1,2],[1.0,5.5])|
+------+-------------------+
```
`{s.name: type(s.dataType) for s in df.schema}`:
```
{'features': pyspark.mllib.linalg.VectorUDT,
'labels': pyspark.sql.types.DoubleType}
```
and after executing:
```
df.write.format(
"sagemaker"
).option(
"labelColumnName", "labels"
).option(
"featuresColumnName", "features"
).save(
my_path
)
```
log:
```
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
in ()
6 "featuresColumnName", "features"
7 ).save(
----> 8 my_path
9 )

/usr/lib/spark/python/pyspark/sql/readwriter.py in save(self, path, format, mode, partitionBy, **options)
736 self._jwrite.save()
737 else:
--> 738 self._jwrite.save(path)
739
740 @since(1.4)

/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:

/usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()

/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(

Py4JJavaError: An error occurred while calling o8427.save.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:156)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:83)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:84)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:165)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:74)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 63 in stage 309.0 failed 4 times, most recent failure: Lost task 63.3 in stage 309.0 (TID 15086, ip-10-128-40-43.eu-west-1.compute.internal, executor 19): org.apache.spark.SparkException: Task failed while writing rows.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: scala.MatchError: (2,[1,2],[1.0,5.5]) (of class org.apache.spark.mllib.linalg.SparseVector)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.ProtobufConverter$.rowToProtobuf(ProtobufConverter.scala:69)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:84)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:72)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:245)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:242)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:248)
... 10 more

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
... 32 more
Caused by: org.apache.spark.SparkException: Task failed while writing rows.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Caused by: scala.MatchError: (2,[1,2],[1.0,5.5]) (of class org.apache.spark.mllib.linalg.SparseVector)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.ProtobufConverter$.rowToProtobuf(ProtobufConverter.scala:69)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:84)
at com.amazonaws.services.sagemaker.sparksdk.protobuf.SageMakerProtobufWriter.write(SageMakerProtobufWriter.scala:72)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:245)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:242)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:248)
... 10 more

```

---

and once created with ml.linalg:

```
from pyspark.ml.linalg import VectorUDT, SparseVector

df = spark.createDataFrame([
(1.0, SparseVector(4,[1,3],[1.0,5.5])),
(0.0, SparseVector(2,[1,2],[1.0,5.5]))
], ['labels', 'features'])
df.show()

+------+-------------------+
|labels| features|
+------+-------------------+
| 1.0|(4,[1,3],[1.0,5.5])|
| 0.0|(4,[1,3],[1.0,5.5])|
+------+-------------------+

{s.name: type(s.dataType) for s in df.schema}
->
{'features': pyspark.ml.linalg.VectorUDT,
'labels': pyspark.sql.types.DoubleType}

df.write.format(
"sagemaker"
).option(
"labelColumnName", "labels"
).option(
"featuresColumnName", "features"
).save(
my_path
)
```
everything went ok and the data has been written into s3.

---

my suggestion is to change/update the imports that have been used as an example in that section - it may generate some confusions :)

Open README.md and go to the section titled “The Amazon Record format.” Compare its stated vector import and schema with the working pyspark.ml.linalg example described in the issue. Done means the documentation no longer directs users to the incompatible mllib vector type and accurately reflects the reported working schema.

Written by the indexing model from the issue text.