MongoSpark E11000 error when writing to a MongoDB Replica Set - mongodb

Question

I am using a Spark2 application which uses the following command from com.mongodb.spark.MongoSpark to write a DataFrame to a three-node-MongoDB Replica Set:
//The real command is similar to this one, depending on options
//set to the DataFrame and the DataFrameWriter object about MongoDB configurations,
//such as the writeConcern
var df: DataFrameWriter[Row] = spark.sql(sql).write
.option("uri", theUri)
.option("database", theDatabase)
.option("collection", theCollection)
.option("replaceDocument", "false")
.mode("append")
[...]
MongoSpark.save(df)
The fact is that although I am sure the source data, which comes from a Hive table, has a unique primary key, when Spark application is running I get a duplicate key error:
2019-01-14 13:01:08 ERROR: Job aborted due to stage failure: Task 51 in stage 19.0 failed 8 times,
most recent failure: Lost task 51.7 in stage 19.0 (TID 762, mymachine, executor 21):
com.mongodb.MongoBulkWriteException: Bulk write operation error on server myserver.
Write errors: [BulkWriteError{index=0, code=11000,
message='E11000 duplicate key error collection:
ddbb.tmp_TABLE_190114125615 index: idx_unique dup key: { : "00120345678" }', details={ }}].
at com.mongodb.connection.BulkWriteBatchCombiner.getError(BulkWriteBatchCombiner.java:176)
at com.mongodb.connection.BulkWriteBatchCombiner.throwOnError(BulkWriteBatchCombiner.java:205)
[...]
I have tried setting write concern to "3" or even "majority". Furthermore, the timeout has been set to 4/5 seconds, but sometimes this duplicate key error still appears.
I would like to know how to configurate the load in order not to obtain duplicate entries when writing to the Replica Set.
Any suggestions? Thanks in advance!