Optimized way to use spark as db to hdfs etl

[Rohit Verma]

I am using spark to read from database and write in hdfs as parquet file. Here 
is code snippet.

private long etlFunction(SparkSession spark){
spark.sqlContext().setConf("spark.sql.parquet.compression.codec", “SNAPPY");
Properties properties = new Properties();
properties.put("driver”,”oracle.jdbc.driver");
properties.put("fetchSize”,”5000");
Dataset<Row> dataset = spark.read().jdbc(jdbcUrl, query, properties);
dataset.write.format(“parquet”).save(“pdfs-path”);
return dataset.count();
}

When I look at spark ui, during write I have stats of records written, visible 
in sql tab under query plan.

While the count itself is a heavy task.

Can someone suggest best way to get count in most optimized way.

Thanks all..