It's not possible using standard spark library, but you can use Hadoop API for managing filesystem - save output in temporary directory and then move file to the requested path. For example (in pyspark):
df.coalesce(1) \
.write.format("com.databricks.spark.csv") \
.option("header", "true") \
.save("mydata.csv-temp")
from py4j.java_gateway import java_import
java_import(spark._jvm, 'org.apache.hadoop.fs.Path')
fs = spark._jvm.org.apache.hadoop.fs.FileSystem.get(spark._jsc.hadoopConfiguration())
file = fs.globStatus(sc._jvm.Path('mydata.csv-temp/part*'))[0].getPath().getName()
fs.rename(sc._jvm.Path('mydata.csv-temp/' + file), sc._jvm.Path('mydata.csv'))
fs.delete(sc._jvm.Path('mydata.csv-temp'), True)
