pyspark - getting Latest partition from Hive partitioned column logic

Question

I am new to pySpark. I am trying get the latest partition (date partition) of a hive table using PySpark-dataframes and done like below. But I am sure there is a better way to do it using dataframe functions (not by writing SQL). Could you please share inputs on better ways.

This solution is scanning through entire data on Hive table to get it.

df_1 = sqlContext.table("dbname.tablename");

df_1_dates = df_1.select('partitioned_date_column').distinct().orderBy(df_1['partitioned_date_column'].desc())

lat_date_dict=df_1_dates.first().asDict()

lat_dt=lat_date_dict['partitioned_date_column']

Answer 1

I agree with @philantrovert what has mentioned in the comment. You can use below approach for partition pruning to filter to limit the number of partitions scanned for your hive table.

>>> spark.sql("""show partitions test_dev_db.newpartitiontable""").show();
+--------------------+
|           partition|
+--------------------+
|tran_date=2009-01-01|
|tran_date=2009-02-01|
|tran_date=2009-03-01|
|tran_date=2009-04-01|
|tran_date=2009-05-01|
|tran_date=2009-06-01|
|tran_date=2009-07-01|
|tran_date=2009-08-01|
|tran_date=2009-09-01|
|tran_date=2009-10-01|
|tran_date=2009-11-01|
|tran_date=2009-12-01|
+--------------------+

>>> max_date=spark.sql("""show partitions test_dev_db.newpartitiontable""").rdd.flatMap(lambda x:x).map(lambda x : x.replace("tran_date=","")).max()
>>> print max_date
2009-12-01
>>> query = "select city,state,country from test_dev_db.newpartitiontable where tran_date ='{}'".format(max_date)

>>> spark.sql(query).show();
+--------------------+----------------+--------------+
|                city|           state|       country|
+--------------------+----------------+--------------+
|         Southampton|         England|United Kingdom|
|W Lebanon        ...|              NH| United States|
|               Comox|British Columbia|        Canada|
|           Gasperich|      Luxembourg|    Luxembourg|
+--------------------+----------------+--------------+

>>> spark.sql(query).explain(True)
== Parsed Logical Plan ==
'Project ['city, 'state, 'country]
+- 'Filter ('tran_date = 2009-12-01)
   +- 'UnresolvedRelation `test_dev_db`.`newpartitiontable`

== Analyzed Logical Plan ==
city: string, state: string, country: string
Project [city#9, state#10, country#11]
+- Filter (tran_date#12 = 2009-12-01)
   +- SubqueryAlias newpartitiontable
      +- Relation[city#9,state#10,country#11,tran_date#12] orc

== Optimized Logical Plan ==
Project [city#9, state#10, country#11]
+- Filter (isnotnull(tran_date#12) && (tran_date#12 = 2009-12-01))
   +- Relation[city#9,state#10,country#11,tran_date#12] orc

== Physical Plan ==
*(1) Project [city#9, state#10, country#11]
+- *(1) FileScan orc test_dev_db.newpartitiontable[city#9,state#10,country#11,tran_date#12] Batched: true, Format: ORC, Location: PrunedInMemoryFileIndex[hdfs://xxx.host.com:8020/user/xxx/dev/hadoop/database/test_dev..., PartitionCount: 1, PartitionFilters: [isnotnull(tran_date#12), (tran_date#12 = 2009-12-01)], PushedFilters: [], ReadSchema: struct<city:string,state:string,country:string>

you can see in above plan that PartitionCount: 1 it has scanned only one partition from 12 available partitions.