Save Spark dataframe as dynamic partitioned table in Hive

Question

I have a sample application working to read from csv files into a dataframe. The dataframe can be stored to a Hive table in parquet format using the method df.sav

Answer 1

This worked for me using python and spark 2.1.0.

Not sure if it's the best way to do this but it works...

# WRITE DATA INTO A HIVE TABLE
import pyspark
from pyspark.sql import SparkSession

spark = SparkSession \
    .builder \
    .master("local[*]") \
    .config("hive.exec.dynamic.partition", "true") \
    .config("hive.exec.dynamic.partition.mode", "nonstrict") \
    .enableHiveSupport() \
    .getOrCreate()

### CREATE HIVE TABLE (with one row)
spark.sql("""
CREATE TABLE IF NOT EXISTS hive_df (col1 INT, col2 STRING, partition_bin INT)
USING HIVE OPTIONS(fileFormat 'PARQUET')
PARTITIONED BY (partition_bin)
LOCATION 'hive_df'
""")
spark.sql("""
INSERT INTO hive_df PARTITION (partition_bin = 0)
VALUES (0, 'init_record')
""")
###

### CREATE NON HIVE TABLE (with one row)
spark.sql("""
CREATE TABLE IF NOT EXISTS non_hive_df (col1 INT, col2 STRING, partition_bin INT)
USING PARQUET
PARTITIONED BY (partition_bin)
LOCATION 'non_hive_df'
""")
spark.sql("""
INSERT INTO non_hive_df PARTITION (partition_bin = 0)
VALUES (0, 'init_record')
""")
###

### ATTEMPT DYNAMIC OVERWRITE WITH EACH TABLE
spark.sql("""
INSERT OVERWRITE TABLE hive_df PARTITION (partition_bin)
VALUES (0, 'new_record', 1)
""")
spark.sql("""
INSERT OVERWRITE TABLE non_hive_df PARTITION (partition_bin)
VALUES (0, 'new_record', 1)
""")

spark.sql("SELECT * FROM hive_df").show() # 2 row dynamic overwrite
spark.sql("SELECT * FROM non_hive_df").show() # 1 row full table overwrite