仅覆盖分区 spark 数据集中的某些分区

Question

How can we overwrite a partitioned dataset, but only the partitions we are going to change? For example, recomputing last week daily job, and only overwriting last week of data.

Default Spark behaviour is to overwrite the whole table, even if only some partitions are going to be written.

Answer 1

Since Spark 2.3.0 this is an option when overwriting a table. To overwrite it, you need to set the new spark.sql.sources.partitionOverwriteMode setting to dynamic , the dataset needs to be partitioned, and the write mode overwrite . Example in scala :

spark.conf.set(
  "spark.sql.sources.partitionOverwriteMode", "dynamic"
)
data.write.mode("overwrite").insertInto("partitioned_table")

I recommend doing a repartition based on your partition column before writing, so you won't end up with 400 files per folder.

Before Spark 2.3.0, the best solution would be to launch SQL statements to delete those partitions and then write them with mode append.

Answer 2

Just FYI, for PySpark users make sure to set overwrite=True in the insertInto otherwise the mode would be changed to append

from the source code :

def insertInto(self, tableName, overwrite=False):
    self._jwrite.mode(
        "overwrite" if overwrite else "append"
    ).insertInto(tableName)

this how to use it:

spark.conf.set("spark.sql.sources.partitionOverwriteMode","DYNAMIC")
data.write.insertInto("partitioned_table", overwrite=True)

or in the SQL version works fine.

INSERT OVERWRITE TABLE [db_name.]table_name [PARTITION part_spec] select_statement

for doc look at here