Hi All: i have a question about repartition api and sparksql partition. I have an table which partition key is day ``` ./bin/spark-sql -e "CREATE TABLE t_original_partitioned_spark (cust_id int, loss double) PARTITIONED BY (day STRING) location 'hdfs://localhost:9000/t_original_partitioned_spark'" ``` insert serveral data and now there are 2 partitions as two days ( 2019-05-30 and 2019-05-20) ``` sqlContext.sql("insert into t_original_partitioned_spark values (30,'0.3','2019-05-30')) sqlContext.sql("insert into t_original_partitioned_spark values (20,'0.2','2019-05-20')) ``` now i want to repartition the data to 1 partition as in actual case there maybe too much partitions ,i want to make fewer partitions. I call repartition api and overwrite the the table. i hope now there is 1 partition but actually there are two partitions when query by "show partitions default.t_original_partitioned_spark" ``` val df = sqlContext.sql("select * from t_original_partitioned_spark") df1=df.repartition(1) df1.write.mode(org.apache.spark.sql.SaveMode.Overwrite).format("seq").insertInto(s"default.t_original_partitioned_spark") ``` my question is the actually partition number is decided by the num of repartition($num) or the hive table partitions if i use both of them? Best Regards Kelly Zhang